FINAL REPORT Evaluation of Alternatives in Obtaining Structural Elevation Data Part I — Assessment of Elevation Strategies Part II — Providing Structural Elevation Data Prepared by Dewberry & Davis LLC for Federal Emergency Management Agency Contract EMW-2002-CO-0267 January 31, 2005 PURPOSE Insurance agents and write-your-own (WYO) companies have long affirmed that the requirement for Elevation Certificates (ECs) is a major impediment in selling flood insurance. In 2000, FEMA held a forum for parties interested in developing an eRating system for flood insurance policies. The purpose of the meeting was to exchange ideas on the best strategy to achieve FEMA’s goals for an eRating system and to discuss alternatives for overcoming the difficulties and high cost of implementing such a system. FEMA had not developed a requirement for an eRating system but simply sought input from industry, other government agencies, and academia on the best strategy to develop such a system. After careful consideration of the issues raised at the eRating forum, FEMA decided not to pursue developing an eRating system for flood insurance but rather, concluded that it must (1) examine whether it is appropriate, feasible, and legally possible for the government to provide elevation information on individual structures for use in rating structures, and (2) determine if it is technically feasible and cost effective to do so. The purpose of this study is to determine if it is appropriate, feasible, and legally possible for FEMA to obtain the elevation data on individual structures and to make this elevation information available to properly rate the structures for flood risks and flood insurance premiums so that ECs costing hundreds of dollars each would not be needed in most cases for insurance rating. The study examines the legal issues involved in collecting and making elevation information available and assesses five approaches for obtaining structure elevation information. The cost effectiveness of these various approaches is evaluated and a recommendation is provided for implementation of an elevation registry. This study will allow FEMA to determine if providing individual structure elevation data best serves the National Flood Insurance Program’s (NFIP) needs. Initially, the Dewberry team submitted a report on legal issues, prepared by FEMA Law Associates and the EOP Foundation, which summarized research and analysis on legal issues relevant to a determination by the Federal Emergency Management Agency (FEMA) of whether it can develop an elevation registry ("registry") of structural elevation data for National Flood Insurance Program (NFIP) purposes. The Dewberry team sought to identify and evaluate the significance of potential legal obstacles to developing this registry primarily in three areas: (1) the Privacy Act of 1974 and other privacy issues; (2) potential exposure to liability for inaccurate elevation information; and (3) potential ownership rights that third parties may have to elevation data. As indicated in APPENDIX A of this report, we identified no legal issues that would preclude FEMA from establishing and maintaining a registry and making it available to insurance companies and agents writing NFIP policies, or even to the general public. Creation of the proposed registry is an activity well within the authority granted by the National Flood Insurance Act. An elevation registry as described in the Statement of Work, and in subsequent meetings with FEMA, would not violate federal or state privacy law or policy or significantly expand the liability exposure of participants in the NFIP. Part I of this Final Report summarizes Dewberry's research and analysis of five (5) elevation strategies/alternatives considered for acquiring data for an elevation registry, explains how data were gathered and evaluated for utility in populating this registry, and assesses whether it is technically feasible and appropriate to utilize any or all of the five elevation strategies proposed by Dewberry to develop an elevation registry of structures. The five elevation strategies evaluated in Part I of this Final Report are as follows: ? Strategy A: Maximize use of existing Elevation Certificates ? Strategy B: Maximize use of airborne remote sensing (photogrammetry, LIDAR and IFSAR) ? Strategy C: Evaluate use of mobile photogrammetric vans ? Strategy D: Maximize cost-effectiveness of future Elevation Certificates ? Strategy E: Leverage alternative data sources for an elevation The purpose of Part I of this Final Report is to document the accuracy standards and the evaluation of each strategy, and to develop conclusions and recommendations regarding the utility of these strategies for populating an elevation registry. The purpose of Part II of this Final Report is to document the process for arriving at the recommended strategy or strategies for obtaining structure elevation data. TABLE OF CONTENTS PURPOSE ii PART I — ASSESSMENT OF ELEVATION STRATEGIES BACKGROUND 1 ACCURACY OF ELEVATION CERTIFICATES 4 LESSONS LEARNED IN NATIONWIDE SURVEYS 7 STRATEGY ASSESSMENTS 12 Strategy A — Maximize use of existing Elevation Certificates 14 Sources of Elevation Certificates 15 A.1 Insurance Services Office (ISO) 15 A.2 LOMA 2000 Database 16 A.3 Dewberry and URS 18 A.4 FEMA Regions and State NFIP Coordinators 21 A.5 Local Communities 22 A.6 U.S. Army Corps of Engineers 23 A.7 Policies in Force Database 24 A.8 Limitations of existing Elevation Certificates 25 A.9 Tools for Georeferencing existing Elevation Certificates 27 A.10 Conversion of paper Elevation Certificates to digital format 31 Strategy B — Maximize use of Airborne Remote Sensing 32 B.1 Photogrammetry 32 Conventional (Vertical) Photogrammetry 32 Conventional Photogrammetry with Measured Offsets 33 Oblique Photogrammetry — Pictometry 34 Photogrammetry Conclusions 38 B.2 Light Detection and Ranging (LIDAR) 39 LIDAR Data of Charlotte/Mecklenburg County, NC 42 LIDAR Data of Prince George's County, MD 44 LIDAR Data of Harris County, TX 44 LIDAR Data of Beaufort County, SC 45 LIDAR Conclusions 46 B.3 Interferometric Synthetic Aperture Radar (IFSAR) 47 IFSAR Data of Jefferson County, CO 49 IFSAR Conclusions 49 Strategy C — Evaluate use of Mobile Photogrammetric Vans 50 C.1 VISAT™ Photogrammetric Van 50 C.2 SideSwipe™ Vehicle Mounted Side Scan LIDAR 54 C.3 Mobile Remote Sensing Van Conclusions 55 Strategy D — Maximize Cost-Effectiveness of Future Elevation Certificates 56 D.1 Future Web-Based Elevation Certificates 56 D.2 Legal Considerations for Web-Based Elevation Certificates 56 Strategy E — Leverage Alternative Data Sources for an Elevation Registry 57 E.1 U.S. Census Bureau 57 E.2 U.S. Postal Service 58 E.3 U.S. Army Corps of Engineers 58 E.4 Community GIS Data 58 E.5 National Parcelmap Data Portal 59 E.6 CitySets 59 E.7 Home Owner Support 60 E.8 Flood Zone Determination Companies 61 E.9 Insurance Industry 63 E.10 NEMIS Database 63 Summary of Technology Capabilities 64 COST-EFFECTIVENESS (CE) ANALYSES 66 Methods for Populating an Elevation Registry 66 Base Scenario 73 CE Model Sensitivity to EC Total Value 81 CE Model Sensitivity to Elevation Registry Cost Parameters 82 CE Model Sensitivity to Accuracy/Digitization Cost of Existing ECs 83 CE Model Sensitivity to Accuracy of Future Ground-Surveyed ECs 85 CE Model Sensitivity to Accuracy/Cost of Photogrammetric ECs 86 CE Model Sensitivity to Accuracy/Cost of Pictometry ECs 88 CE Model Sensitivity to Accuracy/Cost of LIDAR ECs 90 CE Model Sensitivity to Accuracy/Cost of IFSAR ECs 93 CE Model Sensitivity to Accuracy/Cost of Photogrammetric Van ECs 94 STRATEGY SUMMARIES 96 Strategy A — Existing Elevation Certificates 99 Strategy B — Airborne Remote Sensing 100 Photogrammetry 100 Pictometry 101 LIDAR 102 IFSAR 103 Strategy C — Vehicular Remote Sensing 104 Strategy D — Future Web-Based Elevation Certificates 105 Strategy E — Leverage Alternative Data Sources 106 STRATEGY RECOMMENDATIONS 108 Strategy A — Maximizing the use of existing ECs 108 Strategy B — Maximizing the use of existing LIDAR or photogrammetric data 111 Strategy C — Utilizing data from mobile photogrammetric vans 113 Strategy D — Web entry of future ECs 114 Strategy E — Leveraging alternative data sources 114 PART II — PROVIDING STRUCTURAL ELEVATION DATA Purpose 117 Summary of Part I Requirements for eRating 117 Summary of Part I Legal Findings 117 Summary of Part I Technical Findings 119 Elevation Registry 120 Web Services 122 Populating the Registry 123 Registry Maintenance and Updates 124 Registry Use by Insurance Agents 127 Judgment Ratings 127 Registry Use by Others 130 FEMA Implementation Costs 132 Cost Recovery 133 Community Implementation Costs 134 Incentives 134 Advantages and Disadvantages 135 Summary 135 LIST OF TABLES Table 1 — Elevation Certificate Information 14 Table 2 — Summary of ISO Elevation Certificate Holdings 16 Table 3 — Relevant Data from LOMA 2000 Database 17 Table 4 — Dewberry's Elevation Certificates 19 Table 5 — URS' Elevation Certificates 20 Table 6 — Example File from Policies in Force Database 24 Table 7 — Same House with Four Different Elevation Certificates 26 Table 8 — Comparison of Geocoding Services 30 Table 9 — Pictometry Accuracy Comparisons 38 Table 10 — Mecklenburg County LIDAR Accuracy Comparison 43 Table 11 — Prince George's County LIDAR Accuracy Comparison 44 Table 12 — Beaufort County LIDAR Accuracy Comparison 45 Table 13 — Jefferson County IFSAR Accuracy Comparison 49 Table 14 — DataQuick Data Availability 62 Table 15 — Technology Suitability Matrix 64 Table 16 — Summary of Twenty Alternative Methods 66 Table 17 — Cost-Effectiveness Model Base Spreadsheet 76 Table 18 — CE Model Sensitivity to EC Total Value 81 Table 19 — CE Model Sensitivity to Elevation Registry Cost Parameters 82 Table 20 — CE Model Sensitivity to Accuracy/Digitization Cost of Existing ECs 83 Table 21 — CE Model Sensitivity to Accuracy of Future Ground-Surveyed ECs 85 Table 22 — CE Model Sensitivity to Accuracy/Cost of Photogrammetry ECs 86 Table 23 — CE Model Sensitivity to Accuracy/Cost of Pictometry ECs 89 Table 24 — CE Model Sensitivity Accuracy/Cost of LIDAR ECs 90 Table 25 — CE Model Sensitivity to Accuracy/Cost of IFSAR ECs 93 Table 26 — CE Model Sensitivity to Accuracy/Cost of Photogrammetric Van ECs 94 Table 27 — Summary of Elevation Alternatives 96 Table 28 — Methods Ranked by Overall Value of EC Records 97 Table 29 — Methods Ranked by Overall Cost of EC Records 98 Table 30 — Theoretical Flood Insurance Premium Increases 129 LIST OF FIGURES Figure 1 — Example of Poor Absolute Accuracy 11 Figure 2 — Photogrammetric Spot Heights 34 Figure 3 — Sample Pictometry Oblique Photo 36 Figure 4 — Sample 4-View Pictometry Images 37 Figure 5 — LIDAR Surfaces Before and After Post-Processing 40 Figure 6 — Examples of Building Footprints and Buffer, Centroids, and Parcel Polygons 41 Figure 7 — Comparison of ORI and Orthophoto Images 48 Figure 8 — VISAT Van 50 Figure 9 — VISAT Camera Array Configuration 51 Figure 10 — VISAT Navigation Route and Display 52 Figure 11 — VISAT Van Camera Comparison 53 APPENDICES APPENDIX A — Report of Legal Findings 137 APPENDIX B — Geospatial Accuracy Standards 234 APPENDIX C — Elevation Surveys 240 APPENDIX D — ISO Elevation Certificate Data 244 APPENDIX E — Elevation Certificate holdings of Dewberry and URS 275 APPENDIX F — Comparison of Commercial Geocoding Services 283 APPENDIX G — Photogrammetry Accuracy Analyses 287 APPENDIX H — Pictometry Explanation 289 APPENDIX I — Pictometry Accuracy Analyses 292 APPENDIX J — LIDAR Automated Data Extraction Report 297 APPENDIX K — LIDAR Accuracy Analyses 380 APPENDIX L — IFSAR Accuracy Analyses 404 APPENDIX M — VISAT Accuracy Analyses 406 APPENDIX N — SideSwipe Vehicle Mounted Side Scan LIDAR 408 APPENDIX O — Legal Comments on Web-Based Elevation Certificates 414 APPENDIX P — Elevation Registry System Description 418 APPENDIX Q — CE Spreadsheets for Base Scenario 421 APPENDIX R — Proposed Data Dictionary 424 APPENDIX S — Web-Based Registry 427 APPENDIX T — Community Rating System 437 PART I — ASSESSMENT OF ELEVATION STRATEGIES BACKGROUND The National Flood Insurance Act of 1968 created the National Flood Insurance Program (NFIP). The NFIP is a cooperative venture involving the federal government, state and local governments and the private insurance industry. The federal government sets insurance rates, provides the necessary risk studies to communities, and establishes floodplain management criteria guiding construction in the floodplain. Communities must adopt and enforce floodplain management standards for new and substantially improved structures. Flood insurance is only available in those communities that enact and enforce these measures. Private insurance companies, under an arrangement known as the Write Your Own (WYO) program, sell and service federal flood insurance policies in their own name and withhold part of the premium for their efforts. The government also sells flood insurance directly through its servicing contractor and retains the risk for all flood insurance policies. Within the Department of Homeland Security (DHS), the Mitigation Division, a component of FEMA, administers the NFIP. The regulations governing the NFIP appear in Title 44 of the Code of Federal Regulations and in manuals, procedures and other documents. The provision of insurance, the regulation of the floodplain, and the enforcement of the mandatory purchase requirements depend on three things: ? Flood hazard identification and risk assessment -- certain key information about the nature and extent of the flood risk in a given area ? Floodplain management -- the elevation of the structure ? Insurance rating -- structural characteristics such as the number of floors and occupancy type Flood Hazard Identification and Risk Assessment: FEMA provides flood-zone information in the form of a Flood Insurance Study (FIS) and Flood Insurance Rate Map (FIRM). The FIRM shows the flood risk in a given jurisdiction and serves as the guiding document for communities in the regulation of floodplain construction and for lenders in enforcing the mandatory purchase requirements. The primary flood risk characteristics shown on the FIRMs are the Special Flood Hazard Areas (SFHAs) or areas inundated by a one-percent annual probability flood and the elevation relative to the mean sea level to which the floodwaters will rise. (A discussion of accuracy standards is in APPENDIX B, and a discussion of mean sea level and vertical datums is contained in APPENDIX C). Whereas Base Flood Elevations (BFEs) are also shown on FIRMs, communities and surveyors usually get this information from a community's flood insurance study profile. The FIRM also serves insurance companies and agents as the source of needed risk information for writing and rating applications for flood insurance under the NFIP. Agents must have the flood zone and BFE to rate a flood insurance policy. For most Post-FIRM structures within the SFHA, the agent gets the flood zone and BFE from the Elevation Certificate (EC). In other cases, the agent must get the flood-zone and BFE from the community's FIRM and FIS. Locating a property on the paper copy of the FIRM has historically been a problem for agents, a problem that has not diminished substantially over the years. Flood zone determination companies and some WYO companies have digitized much of the information on the FIRMs and now provide this information to some agents. Zone information is far from universally available from the WYO companies, and agents may not want to pay the fee that flood zone determination companies charge for the service. The primary clients of the flood zone determination companies are federally regulated lenders who need the information to comply with the mandatory flood insurance purchase requirements of the National Flood Insurance Reform Act of 1994. Lenders are able to pass along the fee for the service to borrowers as part of a mortgage loan's closing costs. However, insurance agents may hesitate to do the same with their customers because charging for this service could jeopardize their competitive position. In 2002, FEMA made all effective maps available in raster scan version through the Map Service Center. Digital map files, flood insurance studies and flood profiles are now available on FEMA's website and on CD. This greatly improves accessibility for agents by eliminating the need to maintain paper copies of the maps and providing data which an agent or others can use to calculate the BFE. Floodplain Management: FEMA cannot provide flood insurance unless a community adopts and enforces a floodplain management ordinance that meets or exceeds the minimum requirements of the NFIP. Elevation information is needed to guide floodplain construction and to rate insurance applications. The community must ensure that the lowest floor elevation of a new structure, built in the SFHA after the date of the current effective FIRM, is at or above the BFE shown on the FIRM. To encourage community participation in the NFIP and the purchase of flood insurance, Congress subsidized the insurance premiums for buildings constructed before the issuance of a FIRM or before 1975, whichever is later. The NFIP does not require an EC to rate these buildings though, in the case of better-situated Pre-FIRM properties, actuarial rates may be lower. The NFIP, as adopted and enforced by each participating community, requires the community to obtain the elevation of the lowest floor, which includes the basement, of all new and substantially improved buildings, and to maintain a record of such information [44 CFR 60.3(b)(5) and 60.3(e)(2)]. The local floodplain administrator must determine which level is the "as built" lowest floor to verify whether the building complies with the community's floodplain management regulations. "As built" means that construction of the building is complete and the building is ready for occupancy. For new construction and substantially improved building in the floodplain, there are a series of surveys and inspections to verify that construction takes place according to plan and that it meets floodplain management requirements. FEMA's EC is an important tool that communities can use to document the level of flood protection for various building components and the "as built" lowest floor elevation. All communities must obtain and retain elevation information but they are not required to document that information on a FEMA EC. Communities participating in the NFIP's Community Rating System (CRS), which account for 66 percent of the policies, must obtain and retain this information on a FEMA EC (currently FEMA Form 81-31, July 2000). Communities often archive the elevation data they maintain and hence it is not readily accessible. The passage of time may have compromised the reliability of older elevation information. Insurance Rating: All applicants for flood insurance on Post-FIRM structures within the SFHA must provide the lowest floor elevation on a property in the form of an EC completed by a licensed engineer or surveyor. For these buildings, flood insurance rates take into account a number of different factors including the flood risk zone shown on the FIRM, the elevation of the lowest floor above or below the BFE, the type of building, the number of floors, and the existence of a basement or enclosure. The NFIP uses elevation information to determine rates for flood insurance coverage. The EC shows the structure's elevation relative to the mean sea level. Insurance agents writing a flood insurance policy use this information to determine a structure's lowest floor elevation and calculate the difference between the BFE and the lowest floor elevation to determine the proper rate for insurance coverage. Approximately 40 percent of the policies sold require an EC. The cost for the certificate is usually more than $300. The insurance agent obtains the relevant structural characteristics from the insured. For example, the property owner can supply information about the number of floors, occupancy type, date of construction, etc. to the agent. The NFIP's Flood Insurance Manual provides detailed guidance for the agent's use in rating flood insurance policies, which the agent uses to determine the type of building. Building types include those with no basement, an unfinished basement, a finished basement, mobile homes on foundations, and elevated buildings. The agent also classifies the building's occupancy type as single- family dwelling, two to four family dwelling, other residential building, or a non- residential building. The agent determines if the structure is used for commercial or residential purposes, records the value of the structure, its owners and, as appropriate, the elevation ACCURACY OF ELEVATION CERTIFICATES This section explains why it is important for structural elevation data to be accurate, especially for structures in or near the SFHA. Flood insurance premiums, for a post-FIRM structure (built after publication of a FIRM) and showing the structure's location to be within a SFHA, are largely based on the difference in elevations between the BFE and the top of the bottom floor of the structure. If the structure's top of bottom floor elevation is above the BFE, insurance premiums are much lower than when the top of bottom floor is below the BFE. For example, using NFIP flood insurance premiums as of May 1, 2004, for a post-FIRM building in the SFHA of a non-CRS community, annual premiums shown below are for $150,000 in building coverage and $75,000 in contents coverage for a one-story building with no basement and a $500 deductible: ? When the top of bottom floor is 2 ft above the BFE: $418 ? When the top of bottom floor is 1 ft above the BFE: $595 ? When the top of bottom floor equals the BFE: $892 ? When the top of bottom floor is 1 ft below the BFE: $3,201 ? When the top of bottom floor is 2 ft below the BFE: $4,040 From this example, it is obvious that elevation errors could have a major effect on actuarial rates charged for post-FIRM buildings, whereas the "subsidized" rate charged for pre-FIRM buildings (constructed prior to publication of a FIRM) would be $1,471, regardless of the top of bottom floor elevation. If the building is outside the SFHA in areas of low or moderate flood risk (shown as B, C, or X zones on a FIRM) and with no significant history of flooding, its premium for a Preferred Risk Policy (PRP) would be $264 (with $60,000 in contents coverage) regardless of the top of bottom floor elevation. Higher accuracy top of bottom floor elevation data also reduces risks in actuarial ratings. For this study FEMA assumes that ground-surveyed ECs are accurate to 0.5 ft at the 95% confidence level. For example, if an EC surveyor certifies a top of bottom floor elevation to be 1 ft higher than the BFE, FEMA can assume (with 95% confidence) that the actual top of bottom floor elevation is between 0.5 ft and 1.5 ft above the BFE, the structure has a relatively low risk of flooding and insurance premiums would be at a relatively low rate ($595 in the above example). However, if a less-accurate "approximate" aerial survey process were used to determine the top of bottom floor elevation, accurate to 2 ft at the 95% confidence level for example, then a top of bottom floor elevation determined to be 1 ft higher than the BFE might actually be between 1 ft below the BFE and 3 ft above the BFE at the 95% confidence level. Then, FEMA would have less confidence that the structure has a low risk of flooding and would need to charge a higher "judgment rating" premium (somewhere between $595 and $3,201) to account for increased risk of flooding, even though the top of bottom floor elevation is probably on the "safer side" of the BFE. Executive Order 12906 requires all Federal agencies collecting or producing geospatial data to comply with standards adopted through the Federal Geographic Data Committee (FGDC) which requires accuracy to be reported in ground distances at the 95% confidence level. As stated in FGDC-STD-007.1- 1998, Geospatial Positioning Accuracy Standards, Part 1: Reporting Methodology, the Federal Geographic Data Committee (FGDC) defines geospatial positioning accuracy in terms of local accuracy and network accuracy, described in APPENDIX B. To understand local accuracy, one needs to understand the concept of relative accuracy, as applied to a 95% confidence level. The most common ground surveys are referenced to local survey monuments or benchmarks, including temporary benchmarks such as elevation reference marks (ERMs), often selected for their proximity or convenient location rather than for their accuracy or stability. To understand network accuracy, one needs to understand the concept of absolute accuracy, as applied to a 95% confidence level. Such control surveys are referenced to a rigorous geodetic control network of survey monuments that are both accurate and stable. FGDC-STD-007.1-1998 states: "Geodetic control surveys are usually performed to establish a basic control network (framework) from which supplemental surveying and mapping work, covered in other parts of this document, are performed. Geodetic network surveys are distinguished by use of redundant, interconnected, permanently monumented control points that comprise the framework for the National Spatial Reference System (NSRS) or are often incorporated into the NSRS. These surveys must be performed to far more rigorous accuracy and quality assurance standards than control surveys for general engineering, construction, or topographic mapping. Geodetic network surveys included in the NSRS must be performed to meet automated data recording, submission, project review, and least squares adjustment requirements established by the National Geodetic Survey (NGS). The lead agency is the Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, NGS; the responsible FGDC unit is the Federal Geodetic Control Subcommittee (FGCS)." In addition to understanding distinctions between the various accuracy standards explained in APPENDIX B, it is also important to understand distinctions between ellipsoid heights (from GPS surveys that follow the rules of geometry) and orthometric heights (from differential leveling surveys that follow the rules of gravity). The term "elevation" is normally meant to infer orthometric heights. These distinctions, and other factors necessary in the generation of "accurate" elevation data, are discussed in APPENDIX C. As referenced in Table B.4 of APPENDIX B, accuracies in this study will refer to vertical errors at the 95% confidence level and equivalent contour interval (CI), as follows: ? ECs, whether surveyed by GPS or conventional means, are assumed to be accurate to 0.5 ft at the 95% confidence level. This means that 5% of the ECs will have errors larger than 0.5 ft when compared against a standard of higher accuracy such as geodetic surveys that satisfy NGS 2- cm or 5-cm standards. ? If alternative ECs are equivalent to 1' contours, as with the highest accuracy LIDAR surveys, this means that 95% of EC elevations checked should be accurate within 0.6 ft when compared against a standard of higher accuracy. ? If alternative ECs are equivalent to 2' contours (as with the most common LIDAR or high accuracy photogrammetric surveys), this means that 95% of EC elevations checked should be accurate within 1.2 ft when compared against a standard of higher accuracy. ? If alternative ECs are equivalent to 5' contours (as with mid-accuracy photogrammetric surveys), this means that 95% of EC elevations checked should be accurate within 3.0 ft when compared against a standard of higher accuracy. ? If alternative ECs are equivalent to 10' contours (as with IFSAR or USGS DEMs produced from 10' contours), this means that 95% of EC elevations checked should be accurate within 6.0 ft when compared against a standard of higher accuracy. A "standard of higher accuracy" is assumed to be at least three times more accurate than the product being evaluated, e.g., geodetic surveys accurate to 5- cm (?2") at the 95% confidence level are suitable for checking the accuracy of another product to determine if it is accurate to 6" at the 95% confidence level. As explained later in this report, FEMA considers elevations to have zero value for an elevation registry when elevation errors are 4 ft or worse at the 95% confidence level. LESSONS LEARNED IN NATIONWIDE SURVEYS This section describes early GPS survey projects that provided lessons learned for subsequent surveys performed by Dewberry, URS, and G&O. Occasionally, Dewberry has been asked to review ECs produced by other firms that were unaware of these lessons learned. Regardless, the major problem remains today for most EC surveys nationwide — that local surveys are still performed relative to the most convenient and accessible benchmarks, regardless of accuracy and stability, rather then using more rigorous (and expensive) procedures to guarantee some reasonable level of network accuracy. For decades, Dewberry surveyed ECs with conventional survey procedures; it has been using combinations of GPS and conventional survey procedures since 1993. Dewberry has also been hired to determine the most likely reasons for errors in elevation surveys performed by others, once a client determined that errors had occurred. This section summarizes lessons learned during the past decade. The following studies provided valuable input into shaping Dewberry’s current GPS elevation survey procedures. ? In 1993-1994, Dewberry performed GPS surveys of thousands of homes flooded in Georgia, Alabama, Florida, and Texas. For those surveys, FEMA only needed GPS to determine the latitude and longitude of the flooded homes. At that time, GPS elevation survey procedures had not yet been published. For elevations, FEMA asked only for "windshield survey estimates" of the depth of interior flooding as well as the area of each building's footprint, plus costs to repair (estimated by a Certified Flood Adjustor looking only at external conditions without leaving the car). ? In early 1995, Dewberry was tasked by FEMA to survey 1,300 structures in Louisville and Jefferson County, KY, to demonstrate the viability of generating low cost and expedient ECs and to provide homeowners with credible, personalized flood risk information on which to determine their need for flood insurance. This project, which became known as the "GPS shootout," compared the capabilities of a relatively unsophisticated GPS Backpack solution with those of a highly sophisticated GPS TruckMAP solution, both using "stand-off" survey procedures that did not require surveyors to walk on private property. ? In 1995, Dewberry, with support of ISO/CRS specialists, performed No- Cert GPS surveys of 1,468 structures. Dewberry developed Standards and Specifications for GPS "No Cert" Reference Level Surveys to be conducted in eight states (NJ, NC, SC, FL, LA, TX, CA, and WA), using procedures validated by the National Geodetic Survey (NGS). Demonstrations were conducted to validate the accuracy of the procedures to be used. ? In 1996, Charlotte and Mecklenburg County, NC, concluded that the mass production of accurate elevation surveys was the key to proactive floodplain management in their community. They hired Dewberry to survey approximately 2,190 floodprone buildings throughout the county and to develop a GIS database designed for proactive floodplain management. This database has since grown to over 3,000 structures, and all structural elevation data is freely available to the public on-line. ? ECs and databases were similarly prepared for Boone, NC; Roanoke, VA; and Prince George's County, MD. ? In 1998, as part of a Price-Waterhouse study of the economic effects of actuarially based premiums, Dewberry surveyed 7,628 pre-FIRM houses in 23 communities nationwide. By then, GPS elevation survey procedures were established and published by NGS for achieving 5-cm vertical accuracy. Local and temporary bench marks are not always accurate. When asking the Director of Public Works (DPW) in Albany, Georgia to recommend a survey monument to be used as the GPS base station, he asked, "Do you want a high one or a low one?" Dewberry replied that we wanted the most accurate one. The DPW replied that he didn't know which monument was actually the most accurate, but he knew that if one (high) monument was used as the reference station, elevations would be about one foot higher than if a different (low) monument was used. Because Dewberry didn't need accurate elevations for this particular project, this discrepancy did not need to be resolved. This was Dewberry's introduction to the fact that local surveyors often know which monuments are high or low, but may not know which one is more accurate; furthermore, it could be perfectly legal for a surveyor to choose a high monument, when surveying an EC, and this could artificially cause a house to appear to be less floodprone. In Jefferson County, KY, the County Surveyor provided a list of temporary benchmarks (TBMs) in the vicinity of the Southern Ditch which posed a threat of flooding to the majority of the 1,300 houses surveyed. These TBM descriptions were typical of FEMA's Elevation Reference Marks (ERMs), e.g., railroad spikes in power poles and trees (that grow), and chisel marks on bridge abutments, for example. While recognizing that none of these were suitable for accurate vertical surveys, Dewberry checked two telephone poles with railroad spikes for which elevations were provided. One had two, and the other had three railroad spikes at different elevations on the referenced power poles, visible from different directions. Depending on the surveyor's care and angle of approach, he/she could have selected a railroad spike several feet higher or lower than the one intended. Also, none of the benchmarks on this list referenced the vertical datum used. Unfortunately, this is typical of local benchmarks used for surveys of many ECs, where surveyors traditionally select the most convenient benchmarks, rather than those of higher accuracy at greater distances which are more expensive to survey. Surveyors may not be able to detect the presence of basements from the street. Of the 62 most difficult houses surveyed in Jefferson County, KY, the lowest floor elevations surveyed independently by two different methods all agreed within 1 inch, except for one house (3140 Sunny Lane) where two significantly different survey reference points were selected in large part because of the desire to minimize intrusion on private property. A neighbor had indicated to one survey team that the house next door had no basement under the northern half of the split-level home; this team classified the house as building diagram number 3 and surveyed the ground level entrance door to the southern half of the house. The other team detected the presence of basement windows in the northern section, classified the house as building diagram number 4, and surveyed the bottom of siding of the northern section with an offset of 8 feet to the basement floor. The elevation difference was over 5 feet between these two split levels. Failure to detect the presence of basements could cause lowest floor elevations to be in error by a full story (9 feet when including floor joists and flooring materials). Because of the high visibility failure at 3140 Sunny Lane in Jefferson County, KY, all surveys conducted by Dewberry since 1995 have included brief intrusions onto private property, if only to detect the presence or absence of basement windows and/or walk-out basement doors. (Uncertainty in the height of floor joists is the main reason why surveyors prefer to survey the top of the foundation and then subtract 8 feet -- the standard height of construction forms used to pour concrete foundations -- to determine the elevation of basement floors.) ERMs shown on FIRMs may not be reliable or may not be recoverable. To achieve high absolute accuracy from GPS surveys, it is important to first validate the accuracy of all monuments to be used as GPS base stations. Dewberry recommends that four of the best monuments surrounding a project area be checked relative to each other and validated, prior to actual surveys of structures. If the relative elevations of these four monuments are consistent within 1 inch, then any of them could be used as a GPS base station without causing significant errors in surveys derived therefrom. Dewberry attempted to survey the elevation of ERM 45, on Jefferson County's FIRM panel 170, which is nearest to the majority of the houses surveyed during this project, but this ERM could not be located after 2 hours of searching. ERMs from FIRM panels were found to be unreliable in all 8 states included in the No-Cert GPS Survey. They often were obsolete, having been destroyed because of construction or buried under concrete years ago. Most ERMs could not be recovered, and when they were recovered, they were often found to be inaccurate. For example, RM61 in Carteret County, NC, was documented as 10.41 ft on the FIRM, when its elevation was actually 5.78 ft per the NGS Data Sheet and confirmed by Dewberry's surveys as having an elevation of 5.78 ft. This was a significant error of nearly 5 feet. Other ERMs typically had errors of 6 to 18 inches. Note that FEMA no longer shows ERMs on its FIRMs but rather, on newer FIRMs, includes only NGS benchmarks of First or Second Order Vertical and a stability classification ranking of A, B, or C as defined by NGS. Local vertical monuments also may be shown on the FIRM with the appropriate designations. Local monuments shall be placed on the FIRM only if the community has requested that they be included, and if the monuments meet the NGS inclusion criteria. Additional information on qualifying criteria is as follows: ? They must be surveyed per NGS-58 guidelines for Secondary Base 5- centimeter monuments relative to existing NSRS monuments. ? They must have stability classifications of A, B, or C. ? Global Positioning System (GPS) files and station descriptions must have been previously submitted and accepted by the NGS for inclusion in the NSRS. Survey monuments must be verified before beginning a survey project. Before starting the surveys in Louisville and Jefferson County, KY, the elevations of four control points, recommended for use by the County Surveyor, were checked for accuracy. Three of the four were consistent within 1 inch, but control point BF26- 01 was found to be in error by 30 feet; its published elevation was 494.97 ft. but its correct elevation was 464.97 ft. During the No-Cert GPS Survey study, Dewberry's GPS teams invariably arrived in a community and had to "start from scratch" using NGS monuments to determine survey control. Local benchmarks were generally found to be inaccurate, also with errors of 6 to 18 inches, even in areas where subsidence was not a problem. In Charlotte-Mecklenburg County, NC, Dewberry's surveyors spent the first two weeks attempting to sort out the discrepancies found between the various survey monuments and benchmarks required as GPS base stations. Many discrepancies were over 12 inches, whereas Dewberry's standard was one inch. Dewberry required all surveys to be performed relative to accurate, reliable and stable benchmarks documented in the National Spatial Reference System and internally consistent within one inch; but it took two weeks to resolve control discrepancies throughout the county. To avoid multi-path errors, GPS elevations must be validated. To prevent potential GPS multipath errors, NGS-58 states that single elevation points should be surveyed twice, on successive days with distinctly different satellite geometry. Alternatively, pairs of inter-visible points can be surveyed the same day, using conventional survey procedures to survey a "backsight" and validate the elevation differences between the two points; if they agree within a few centimeters, then no multipath errors occurred. Subsidence can be a problem. In Louisiana and Texas, subsidence was a problem in several communities. In some cases, several days were spent by 2- person survey crews trying to resolve 12 inch discrepancies in elevations of control points that should have been accurate to a fraction of an inch, but which were apparently sinking at different rates as a result of subsidence. Accurately located structures must be compared to accurately located floodplain boundaries to make in/out determinations. The left side of Figure 1 shows a segment of FIRM panel 25 of 50 in Omaha. The SFHA to the east includes two tree-lined streets that closely parallel the creek. The pre-FIRM houses on these two streets are clearly in the SFHA. However, the right side of the figure shows that an automated determination would plot the houses outside the SFHA -- not because they were actually outside the SFHA, but because the entire paper FIRM, from which the Q3 Flood Data was produced, lacks absolute horizontal accuracy. The GPS points with absolute accuracy (network accuracy) could not be accurately registered to the less accurate base map that has relative accuracy (local accuracy) only. Similar problems occur with elevation surveys where ECs may have good local accuracy relative to the nearest benchmarks, but lack good network accuracy relative to the geodetic datum which should form the vertical basis for all Flood Insurance Studies. Figure 1 — Example of Poor Absolute Accuracy STRATEGY ASSESSMENTS FEMA’s intent in creating the elevation registry is to expedite and simplify the rating and issuance of flood insurance policies by insurance agents, WYO companies, and the FEMA contractors issuing FEMA flood insurance policies directly and, when possible, avoid the need for new ECs to be surveyed. If FEMA decides to establish an elevation registry, it would probably be a subset of the NextGen data warehouse, with firewalls to prevent Privacy Act violations. The data will be available to WYO companies and agents in a format capable of linking to their existing computer systems. Further, for purposes of rating and writing policies, FEMA intends that agents and companies be able to rely on elevation data in the registry, and policies properly written and rated consistent with elevation data in the registry will be deemed correct until the registry information is changed. The registry, at minimum, will provide to insurance agents and companies improved and simplified access to a key element of evaluating flood risk: elevation of the structure as compared to the BFE as determined in that area. As noted in Dewberry's Report on Legal Issues at APPENDIX A, Registry data will likely also be available and accessible to homeowners, potential homeowners, communities, lenders, and any private companies requesting access to this data. While the registry is not designed for this purpose, homeowners or prospective homeowners might seek to use the data to evaluate flood risk of their homes, or of properties prior to purchase. Communities might use this data in studies of flood prone areas or as part of a building permit process. For communities that maintain their own ECs online, the registry might simply provide a link to the community web site. FEMA should consider granting CRS credits for providing this information to the public. From the Dewberry team's legal analysis at APPENDIX A, we understand that elevation information required for use in determining premiums for an actuarially sound flood insurance program need not be as accurate as information required for evaluating the true flood risk of individual structures. An actuarially sound program can average out modest positive and negative errors in elevations of individual buildings, whereas those same errors could hide true flood risk for the owner of a particular structure. Whereas FEMA can accept some uncertainty in approximate, uncertified elevations of existing structures for insurance rating purposes and use judgment rating procedures to increase flood insurance premiums accordingly, communities and home owners need elevation data with absolute accuracy, providing certified assurance that new construction does not result in floodprone structures intended to be built at higher elevations. Communities would be advised not to rely on an elevation registry of approximate top of bottom floor elevations for evaluating a permit or for determining compliance of an existing structure that is being substantially improved or was substantially damaged. A new EC would have to be obtained to determine the "as built" information on the structure. In the case of new construction, an EC would not even exist. Elevation information used for floodplain management purposes must be as accurate as possible for any proposed construction in the floodplain. This elevation information includes the BFE, any topographic information, and the proposed building elevations of all new and substantially improved structures that are provided to the community as part of the application for a development permit. It also includes “as built” elevation information the community must obtain once the structure is completed before it can issue a certificate of occupancy or compliance. Information in a registry cannot properly be used as a substitute for “as built” information because it is generally not available at the time the building is completed and may not be of the required level of accuracy. To ensure that potential users of the registry are aware of its limitations, the registry should include a prominent notice stating that it may be used in lieu of ECs in rating or writing flood insurance policies but that the approximate elevation information may not be sufficiently accurate for other purposes, particularly in determining whether to purchase a structure in the flood plain or to permit new construction or renovation in the floodplain. Relying on elevation data that is costly to obtain led FEMA to examine whether it is legally possible, appropriate and feasible to obtain and make available the elevation information necessary to rate a flood insurance policy. FEMA's goal is to make elevation information more accessible to foster the development of an eRating system that supports the actuarial rating of a flood insurance policy. For this study, FEMA originally identified two strategies to obtain elevation information to eRate flood insurance policies, which FEMA wanted thoroughly examined. (1) The first strategy called for a means to efficiently gather into a single, accessible database all available ECs for structures in the floodplain and to continually update this database as additional or better structure elevation information becomes available; this strategy is designed to capture the elevation data needed to rate a flood insurance policy in a single database. (2) The second strategy called for exploring new mapping technologies and approaches, combined with other property data, to gather elevation data. For example, Light Detection and Ranging (LIDAR) and Interferometric Synthetic Aperture Radar (IFSAR) can provide information on the lowest adjacent grade near a structure from which it is possible to determine the ground elevation and estimate the structure's lowest floor elevation, using foundation types or some other parameter, measured from that ground elevation. From FEMA's two strategies, Dewberry proposed five strategies to be evaluated for populating an elevation registry once it was determined that there were no major legal impediments for doing so. These strategies are evaluated in the following sections. Strategy A — Maximize use of existing Elevation Certificates Strategy A is based on gathering all available ECs for flood prone structures and capturing the elevation information, needed to rate a flood insurance policy, into an accessible elevation registry that would be maintained and updated as new information becomes available. Owner names would be deleted from the elevation registry because of Privacy Act considerations. Since ECs are required to rate a policy when the structure meets certain conditions (e.g., date of construction, flood hazard zone, etc.) and to obtain a LOMA or LOMR-F, EC information is vital for the elevation registry. Strategy A will be applicable to structures for which ECs have been developed and where they are most readily available, preferably in electronic format. Strategy A alone will not result in a structural elevation database for all structures in and near the SFHA. In most communities, ECs have only been produced where required for selected structures. Several previous studies have identified the potential need to evaluate existing ECs for completeness and accuracy. The other strategies assessed in this study will shed light on this issue through the comparison of ECs with other data sources. ECs include structure-specific information about the elevation of various features of that structure. Depending on the type of structure, as depicted in standard building diagrams, the information in Table 1 is currently required by FEMA and by insurance agents to rate policies for flood risks. Table 1 — Elevation Certificate Information General Information Elevation Information Address Base Flood Elevation (BFE) Flood zone Lowest adjacent grade (LAG) Building use* Highest adjacent grade (HAG)* Building diagram number Top of bottom floor (TBF) Latitude/longitude (optional)* Top of next higher floor (TNHF)* Source of latitude/longitude* Bottom of lowest horizontal structural member (LHSM) Horizontal datum* Top of slab of attached garage* Source of elevation information Lowest elevation of machinery and/or equipment servicing the building* Vertical datum * Note: Older ECs may not contain these items. ECs also typically include other information such as FIRM panel number and date. ECs that are already in a computer database format will be the least costly to convert to an elevation registry. Ideally, all pertinent elevation information has been captured from the ECs and the data would be able to be imported directly into the registry. Sources of Elevation Certificates In order to populate an elevation registry, the logical starting point is to determine where existing elevation data exists, to determine the data format(s), to assess the data suitability to the needs of the registry, and to assess the ease with which the data could be obtained and imported. Seven potential data sources were evaluated: (1) databases of the Insurance Services Office, Inc. (ISO), (2) FEMA's LOMA 2000 database, (3) ECs available to Dewberry and URS Corporation, (4) ECs available to FEMA Regions and state NFIP coordinators, (5) ECs available at selected local communities where large numbers of digital or hardcopy ECs are available, (6) ECs available from the U.S. Army Corps of Engineers, and (7) FEMA's Policies in Force database. In order to document the existence of ECs that might be available from various sources, Dewberry developed an Existing Data Review Form to use as an aid when contacting agencies that have ECs. The form was designed to be used to document the findings of the available ECs and/or elevation databases in the various FEMA regions and states. With few exceptions, Dewberry learned that such elevation information is only available at the community level, except for data already being collected by ISO. For the most part, Dewberry did not contact individual communities as part of this study. The exception was that we contacted communities whose ECs we needed to augment with additional data to determine if ancillary data could be made available for geocoding addresses. A.1 Insurance Services Office, Inc. (ISO) Under this contract ISO was tasked to inventory the ECs and/or elevation databases that they have access to. This includes the ECs submitted annually by Community Rating System (CRS) communities. All CRS communities are required to maintain ECs for all buildings built in the SFHA after the date of their application to the CRS. The community must make copies of the ECs available to all inquirers, and FEMA publishes a listing of the phone number of the point of contact for the ECs for each CRS community. Additional CRS credits are given for maintenance of ECs for older buildings and for maintenance of ECs in an electronic format. FEMA estimates that 63% of the flood insurance policies are in CRS communities. Approximately 25% of the CRS communities receive credits for maintaining their ECs in a computer format. They submit these data annually to ISO which collects them on behalf of FEMA. Additionally, ISO participated in a 1995 study on the retrieval and conversion of ECs to a database format. At the time, significant difficulties were encountered in creating a database from information documented on more than 7 different FEMA EC forms and provided in hardcopy format of varying quality, completeness, and legibility. However, there remain a significant number of ECs in this database. ISO reported the EC holdings in Table 2. Table 2 — Summary of ISO Elevation Certificate Holdings Source Format Number of CRS communities Number of Elevation Certificates 1995 conversion project Access database 315 33,865 CRS program Diskettes 90 17,751 (Non-CRS) 50 1,367 Total 404 52,983 Dewberry subsequently asked ISO to provide existing ECs and/or elevation databases for Pinellas County, FL; Beaufort County, SC; Jefferson County, CO; and Harris County, TX to support Strategies B and C. Several issues were found with the data that are noted later in this chapter. A complete listing of ISO’s data sets can be found in APPENDIX D. A.2 LOMA 2000 Database LOMA 2000 is a software application used by all of FEMA’s Mapping Coordination Contractors (MCCs). LOMA 2000 automates the writing of Letters of Map Amendment (LOMAs) and Letters of Map Revision based on Fill (LOMR- Fs) and their attachments. Approximately 20,000 LOMAs and LOMR-Fs are processed annually by the MCCs. For most, if not all, LOMAs and LOMR-Fs, an EC and additional information, e.g., lowest elevation on the parcel is required. Owners typically request that their house be administratively removed from the SFHA because the lowest grade adjacent to the structure is higher than the BFE on the FIRM. LOMA 2000 has been in use since 1999 and contains approximately 163,000 records with EC information. Historic Letters of Map Change (LOMCs) have been entered into LOMA 2000; however, pertinent information is missing in the database for these older records. The LOMA 2000 data dictionary includes the elements, listed in Table 3, that could be relevant to an elevation registry, as well as many other elements that are non-relevant. It should be noted that the lowest floor elevation (LFE) often is not the same as the top of bottom floor elevation because the LFE may include the lowest insurable elevation, to include crawl space, floor of attached garage, or lowest elevation of machinery. Table 3 — Relevant Data from LOMA 2000 Database General Information Horizontal Location Data Elevation Data Community code Latitude BFE State code Longitude BFE source Street address Lat/Long source LAG (elevation) City Lat/Long datum LAG source Zip code Old zone Lowest floor (elevation) County New zone Lowest floor source Lot Elevation datum Block Section Panel Panel date LOMA LOMR_F CLOMA CLOMR_F Dewberry alone currently has approximately 130,000 addresses in the LOMA 2000 database. Depending on the age of the ECs, they may or may not include latitude and longitude. Approximately 80,000 records in Dewberry's LOMA 2000 database include latitude and longitude, Baker has approximately 12,000 files, with 4,200 geocoded. PBS&J has approximately 21,000 records, with 7,400 geocoded. All of the MCCs use commercial geocoding packages to estimate the latitude and longitude of LOMA 2000 addresses if they have not been provided on the EC. Only 124 entries in LOMA 2000 list latitude and longitude as having been derived by GPS survey. The elevation data in the LOMA 2000 database was obtained from many different documents including various editions of the FEMA Elevation Certificate. Therefore, the data will only be as good as the knowledge of the persons completing and interpreting the form before entering it in the database. For insurance rating, FEMA considers data in LOMA 2000 to be less reliable than data on an EC submitted with a LOMA application. Whereas the ECs submitted with a LOMA application may be highly accurate, the elevation data in the LOMA 2000 database is believed to be less accurate. Moreover, the latitude and longitude errors that may exist within LOMA 2000 from the use of commercial geocoding packages may be several hundred feet in any direction, as documented in A.9 below. Of note, the FEMA DFIRM Database design team considered adding point locations of LOMCs as a layer in the DFIRM Database, using LOMA 2000 as the source. However, because of the known geocoding variances, it was decided that a relational table listing LOMC cases by panel was a more prudent option than including approximate structure locations. The LAG elevations in the LOMA 2000 database do have value for the elevation registry. A.3 Dewberry and URS Dewberry and URS each searched their archives to determine the availability of existing EC data that they had produced. The complete results are at APPENDIX E. A summary is provided below. Dewberry Dewberry has produced thousands of GPS ECs as part of the 1995 No Cert study; the 1999 Study of the Economic Effects of Charging Actuarially Based Premium Rates for Pre-FIRM Structures; for pro-active communities such as Charlotte-Mecklenburg, NC; and as part of post-disaster surveys of damaged structures. As indicated in Table 4, Dewberry was able to assemble a combined Access database of 16,381 GPS ECs. The Dewberry ECs included a mix of residential, commercial, and public structures; were collected by GPS survey; include latitude/longitude, LAG, and BFE; include structure details such as building diagram number or building description; and most contain elevations for top of bottom floor or top of reference floor. Some of the earliest surveys, including the No Cert surveys in 1995 and post- flood surveys in 1993-94, were not retrievable in digital format and/or lacked suitable information for an elevation registry. The hardcopy deliverables were provided to FEMA. The 1993-94 post-flood surveys in Georgia, Alabama, Florida, and Texas did not include any actual elevations, but depths of interior flooding to the nearest whole foot, based on "windshield surveys" from the car. Dewberry also obtained structure information from the City of Austin, TX, to be used for a demonstration by FIA at the 2000 National Flood Conference of the feasibility of automating flood policy writing. The data provided by the City of Austin included addresses, structure type, latitude/longitude and first floor elevation for 863 structures in and near the floodplain of Waller Creek. The city has collected this type of information for an EC database. Additionally, the city maintains an address centroid database of 272,127 addresses for the entire city (not including first floor elevation). Table 4 — Dewberry's Elevation Certificates Source Geographic Area Date Structure Category Format Number of Elevation Certificates HMTAP West Virginia 1996 Damaged structures Database 1,129 Actuarial Study Throughout U.S. 1997 Pre-FIRM structures in SFHA Database 8,083 HMTAP North Topsail, NC 1997 Damaged structures Database 2,046 Charlotte- Mecklenburg County, NC Mecklenburg County, NC 1997 All structures in SFHA Database 2,197 (Add’l ECs done by county since) HMTAP Horry County, SC 2000 Damaged structures Database 207 HMTAP Coastal counties of Maryland 2000 Damaged structures Database 84 Boone, NC Boone, NC 2001 Selected structures in SFHA Database 378 Project Impact Roanoke Valley Alleghany Regional Commission Roanoke, Roanoke County, Vinton, & Salem, VA 2001 Selected structures in SFHA Database 1,495 (Add’l ECs done by PDC since) Prince George’s County, MD Prince George’s County, MD 2002 & 2003 Selected structures in SFHA Database 762 Subtotal Database 16, 381 No-Cert GPS Survey Study Florida, New Jersey, North Carolina, & South Carolina 1995 Post-FIRM structures in SFHA Hardcopy 1,368 Post-Flood Surveys Georgia, Alabama, Florida, and Texas 1993- 1994 Damaged structures Hardcopy (not true ECs) 7,963 No absolute elevations Total 25,712 URS Under various HMTAP task orders managed by URS, contractors such as Dewberry, Greenhorne & O'Mara (G&O), GRI (now Baker), and SKW have surveyed thousands of additional ECs, as summarized in Table 5. The data in these ECs were collected using conventional and GPS surveys. All are available in hardcopy format and some may be available in digital format. These surveys normally include the pertinent EC items. Table 5 — URS' Elevation Certificates Source Geographic Area Date Structure Category Format Number of Elevation Certificates HMTAP TO012 Sonoma County, CA Russian River 1995 Damaged structures Hardcopy 450 HMTAP TO032 Lexington, VA & surrounding counties 1995 Damaged structures Hardcopy 750 HMTAP TO048 Wyoming, Bedford, & Lycoming Counties, PA 1996 Damaged structures Hardcopy 1,050 HMTAP TO079 Hoisington, KS 2001 Damaged structures Hardcopy 20 HMTAP TO081 Wyoming County, WV 1996 Damaged structures Hardcopy (see Dewberry’s listing for database) 175 HMTAP TO082 West Virginia 1996 Damaged structures Hardcopy (see Dewberry’s listing for database) 1,060 HMTAP TO113 North Topsail, NC 1996 Damaged structures Hardcopy (see Dewberry’s listing for database) 1,000 HMTAP TO122 Shenandoah County, VA 1996 Damaged structures Hardcopy 46 HMTAP TO126 Danville & South Boston, VA 1996 Damaged structures Hardcopy 14 HMTAP TO129 Barbour & Harrison Counties, WV 1996 Damaged structures Hardcopy 172 HMTAP TO130 West Virginia 1996 Damaged structures Hardcopy 240 HMTAP TO131 Hampshire County, WV 1996 Damaged structures Hardcopy 80 HMTAP TO139 Page & Warren Counties, WV 1997 Damaged structures Hardcopy 17 HMTAP TO142 West Virginia 1997 Damaged structures Hardcopy 330 HMTAP TO144 Shenandoah & Rockingham Counties, VA 1997 Damaged structures Hardcopy 10 HMTAP TO373 Horry County, SC 2000 Damaged structures Hardcopy (see Dewberry’s listing for database) 220 TOTAL 5,634 Additional information about the URS data sets can be found in APPENDIX E. A.4 FEMA Regions and State NFIP Coordinators For this study, Regional engineers and State NFIP Coordinators nationwide were telephoned by Dewberry, URS and G&O to determine the availability of ECs at the regions and states. Without exception, the regions and state NFIP coordinators indicated that individual community NFIP coordinators would need to be queried to determine what was available at community level, because the regions and state coordinators do not maintain ECs. It was beyond the scope of this study to contact all individual community NFIP coordinators, but some state coordinators provided information about communities known to have large numbers of ECs. A few of these communities were contacted as noted below. A.5 Local Communities Several local communities believed to have large holdings of ECs were contacted regarding available data. Some of them have ECs online for public outreach; to avoid duplication of effort, FEMA's elevation registry should (as a minimum) provide a link to such sites. Several communities with larger numbers of electronic ECs are noted below. Additionally, a few local communities provided other GIS data or support that enabled Dewberry to make use of the ECs for use in evaluating Strategies B through D. These are also noted below. Monterey County, CA. Monterey County maintains a database containing the information from Sections A-F of the new (2000) EC form. The database is sent to FEMA during every verification cycle for the Community Rating System (contained on CD). The builder collects most of the data; however, some data are verified by the County. Currently the database contains 383 records. This is consistent with ISO’s EC database records for this community (374). The community is also receiving full CRS credit for maintaining ECs in a computer format. Sacramento County, CA. Sacramento County maintains a database that contains all the EC data for structures built in the floodplain within the last few years. The database is directly linked to an EC template that can be printed. Approximately 90% of the data was collected by county surveyors specifically for this purpose. Additional data is kept for local flooding regulatory elevations such as high-water marks, etc. Currently the database contains approximately 200 records out of approximately 3000 structures. ISO reports that this community is not receiving full credit for maintaining ECs in a computer format and reports 0 database records for this community. Santa Barbara County, CA. Santa Barbara County keeps hardcopies of ECs filed by parcel. In addition, the County maintains an internally developed Access database that duplicates all of the EC information. All structures (pre- and post- FIRM) are kept in the database, which numbers approximately 1000 entries. It is required that all elevation data be obtained by a licensed land surveyor, and are tied into USGS benchmarks. ISO reports that this community is not receiving any CRS credit for maintaining ECs in a computer format and reports 0 database records for this community. Maricopa County, AZ. Maricopa County currently maintains a database of all EC data. This database is linked to their geographic information system (GIS) using ArcView. A database query can be used to complete and printout ECs for any parcel. They are in the process of linking this information to their internet site so that it will be available to the public. All of their ECs are in the database. The number of structures currently numbers 662. ISO reports 932 database records for this community. The community is also receiving full CRS credit for maintaining ECs in a computer format. Simi Valley, CA. The City of Simi Valley maintains EC data for all residential structures in the form of a database that contains all of the same information. Elevation data for larger structures (commercial, industrial, etc.) are kept on file in hardcopy form. ISO reports that this community is receiving full CRS credit for maintaining ECs in a computer format and reports 13 database records for this community. Charlotte and Mecklenburg County, NC. The Charlotte-Mecklenburg (NC) Storm Water Services provided over 3,000 ECs used by Dewberry for evaluation of low- resolution LIDAR data produced of Mecklenburg County as part of the North Carolina Floodplain Mapping Program. A GIS database with building footprints, plus the raw LIDAR dataset, was also provided by the community. Note that Dewberry also lists 2,197 EC records for this community; the remaining ECs were surveyed by other firms. This is one of those communities that maintain ECs online for public information and outreach. Prince George's County, MD. Prince George's County, MD provided ECs used by Dewberry for evaluation of mid-resolution LIDAR data and oblique Pictometry images. Note that Dewberry also lists 136 EC records for this community. Beaufort County, SC. Beaufort County, SC provided GIS data used by Dewberry for evaluation of high-resolution LIDAR data also provided by the county. Jefferson County, CO. The Jefferson County Planning and Zoning Department provided Dewberry with 25 ECs as well as accurate geographic coordinates for each structure. Pinellas County, FL. The GIS Coordinator for Pinellas County, FL provided a GIS file (Pinellas_co_parcels_roads.dxf) used by Dewberry to georeference ECs. A.6 U.S. Army Corps of Engineers (USACE) The Philadelphia District of USACE used contractors to survey thousands of floodprone houses for the Susquehanna River Flood Warning and Response System (FWRS) in Pennsylvania. They used a quasi-photogrammetric method whereby photogrammetric spot heights were established of the terrain surrounding the corners of each house visible in stereo, and then surveyors were hired to measure the vertical offset up or down from these spot heights in order to determine the top of bottom floor elevation, top of next higher floor elevation, and lowest adjacent grade elevation of each house. The specifications for photogrammetric spot heights called for vertical accuracy of 0.5 ft (6 inches) at the 90% confidence level, with spot height accuracies equivalent to 2' contours. They have approximately 1,200 structures with street addresses and elevation data, plus an additional 1,400 structures with elevation data and only partial or incomplete addresses. A. 7 Policies in Force Database FEMA’s Policies in Force database contains some 3 million records, 80,000 of which have elevation data. The Policy database includes the following information as shown in the example from CSC's BureauNet (sometimes called FIANet) in Table 6 below. Note that latitude and longitude are not available for many records. Table 6 — Example File from Policies in Force Database Company No: 23779 Pol Nbr: 5400518089 Pol Status: Expired more than 94 days Pol Eff Dt: 08/12/2001 Pol Exp Dt: 08/12/2002 Org Nb Dt : 08/12/2000 End Eff Dt: 08/12/2001 Org Con Dt: 01/01/1996 As of Date: 01/31/2003 Community : 370246 CRS Class : 0 Probation : 0 First Name: RICHARD Last Name : EDDINS Address 1 : Address 2 : 204 DULCIMER LN City : ZEBULON State: NC Zip Code : 27597 2876 Addr Key : NC2145LN2043425 WYO Rate Data Program : Regular Rate Meth : Manual Rollover : New Policy Exp Const : 0 Condo Ind : Non-Condo Condo Unit: 1 Prem Pay I: Bldg Basic: Occupancy : Single Family Building : One Floor Bsmt/Encl : None Bldg Addtl: Post Firm : Y Flood Zone: AE Loc Cont : Cont Basic: Crse Const: N State Own : N Dis Assist: 0 Cont Addtl: Pol Term : 1 Small Bus : N Ins To Val: ICC Prem : 0 Comm Prob : 0 Premium : 209 Pol Fee : 30 NFIP Expc : 50 Deduct Pct: Bldg Covg : 900 Cont Covg : 50 Rep ICCCov: 200 NFIP ICC $: 6 Bldg Deduc: 500 Cont Deduc: 500 Base Flood: 232.5 Low Floor : 244.4 Elev Diff : 12 Diagram # : 8 Low Adj Gr: 235.3 Fld Proof : N Obstruct : 10 Elev Cert : 3 Post V Crt: N Longitude : .000000 Latitude : .000000 GEO Result: N GEO Census: A.8 Limitations of Existing Elevation Certificates As noted above, ISO provided Dewberry with a spreadsheet of EC data for CRS communities within four counties: Pinellas County, FL; Beaufort County, SC; Jefferson County, CO; and Harris County, TX. In working with these data and through evaluation of other available ECs, several limitations have been identified. These are issues that will make the creation of a consistent, up to date, and accurate elevation registry challenging. Data are not centralized. Most ECs are maintained at the local level. This means that obtaining the information that would be needed for an elevation registry will require significant effort to identify and obtain. Additionally, most communities that maintain their ECs in a hard copy format reported that substantial effort would be required to collect and submit ECs. Most ECs are not digital. Most communities do not maintain their ECs in an electronic format. Those databases that do exist tend to contain only newer structures and/or newer LOMAs and LOMR-Fs. The older records that are stored on paper in scattered locations may be much more difficult and potentially cost-prohibitive to retrieve. A 1995 study by ISO on the retrieval and conversion of ECs to a database format resulted in over 30,000 records that were provided to FEMA and to the CRS communities that originally supplied the information. At the time, significant difficulties were encountered in creating a database from information documented on seven different FEMA EC forms and provided in hardcopy format of varying quality, completeness, and legibility. These issues would still exist with ECs not currently in a digital format. Many EC database records are missing information or appear to contain questionable information. Related to the issue of older EC forms noted above is the fact that certain pertinent pieces of information for an elevation registry may not have been included in older forms. Highest Adjacent Grade (HAG) is an example of an item currently required but not included on older EC forms. Many of the ECs that were retrieved for use in evaluating Strategies B and C lacked relevant elevation data. Pinellas County, Florida (1,524 records) Of the 1,524 Pinellas County records, 1,361 (89.3%) had lowest floor elevations in A-zones, 27 (1.8%) had lowest floor elevations in V-zones, 136 (8.9%) had no lowest floor elevations, and 581 (38.1%) had no LAG information. Of the 1,524 records, 1,306 had elevations less than 25 feet (most were between 5 and 15 feet); but 55 had elevations over 100 feet, with two over 500 feet and one over 800 feet. Thus, 55 (4.2%) of the 1,306 elevations were probably in error, especially since there were no elevations between 25 feet and 100 feet. Of these 55 erroneous elevations, 17 listed NGVD as the vertical datum, whereas the remaining 38 had the datum field blank in the database. Thus, of the 1,524 records in Pinellas County, 191 records (12.5%) either had no lowest floor elevation data or the elevations were grossly in error, and 581 (38.1%) had no LAG elevations. There were about 400 records that were duplicates for fewer than 200 homes, normally resurveyed on different dates, with different elevations. In one interesting example (see Table 7), the lowest floor elevations for the same house vary between 1.4 and 11 feet, LAG elevations vary between 6.5 and 10.4 feet, and BFEs vary between 10 and 12 feet. The lowest floor elevation change from 11 feet to 1.4 feet may also be caused by illegal construction below an elevated structure. Table 7 — Same House with Four Different Elevation Certificates House Number Street Prefix Street Name Street Suffix EC Date Zone BFE LAG Lowest Floor A-zone Lowest Floor V- zone 4200 S 54th Ave 7/11/91 A12 10 blank 10 blank 4200 S 54th Ave 2/19/92 A12 11 10.4 11 blank 4200 S 54th Ave 11/25/92 A12 10 6.5 blank blank 4200 S 54th Ave 3/05/93 V15 12 blank blank 1.4 Based on prior experience, it is common to survey LAG and lowest floor elevations that differ by a foot or more when surveyors base their surveys on different elevation reference marks (ERMs) that are unstable, inaccurate, and not accurately surveyed with GPS relative to the National Spatial Reference System (NSRS) maintained by the National Geodetic Survey (NGS). Furthermore, as demonstrated in the above example, elevations are sometimes in error by 8-9 feet because of confusion by surveyors as to which floor is the lowest, a reason why changes were made to FEMA's new EC Form 81-31 in 2000. Beaufort County, South Carolina (448 records) Of the 448 records of Beaufort County, 122 (27.2%) had no lowest floor elevation, and 126 (28.1%) had no LAG elevation. Only 46 of 448 (10.3%) of the records had street addresses and lowest floor elevations for the same records. Others had street names that duplicated other records, but no house numbers to distinguish one building from the other; but they did have lot numbers. Jefferson County, Colorado (10 records) Of the ten records of Jefferson County, only three (30%) had lowest floor elevations, and none had LAGs. Harris County, Texas (22 records). Of the 22 Harris County records, 13 had LAG elevations and 21 had lowest floor elevations, but one of these elevations was (erroneously) 5,429 ft, whereas all other elevations in the county's dataset were less than 25 feet. ISO, the source of the EC database information cited above, noted that "gaps" in the data exist and "vary greatly by community because in most cases the community is not only the source of the data but also the checker of data quality." ISO also provided additional rationale: ? “Communities transfer the EC information from hard copy to a data set, not ISO. If information is missing it is because the community failed to enter it. ? "ISO only randomly quality checks hard copy ECs. We do not compare the hardcopy and the data sets, as this is the communities’ responsibility. ? "No EC information is typically found for properties in the un-numbered A zones or AO zones, C or X zones because the FIRMs do not show elevation data. Gaps in EC data can subsequently result. ? "EC data quality also varies greatly by state." No latitude/longitude. As expected, none of the street addresses found in the ISO database were georeferenced, i.e., none had latitude/longitude, UTM coordinates or State Plane northings/eastings. This means that an alternative means for determining geospatial coordinates would need to be identified so that the EC data could be used and maintained in an elevation registry. See section A.8 below for a further discussion of geocoding options. Non-standard addresses. Of the 1,524 Pinellas County EC records, 371 (24.3%) had no street addresses; most of these had some form of lot number, but some listed only a name, e.g., "Tooke, O.J. UNREC" in the address column. Most of the 1,153 addressed records did have different columns for the property's house number, house suffix, street prefix, street name, street suffix, and apartment number, but many of the records had the street suffix merged with the street name. Pinellas County’s database was designed in a way that made it very difficult to link the county's street addresses with those from the ECs. The Pinellas County database had all address information merged in a single column, and many of the addresses were very complex and not suited for "normal" address matching. After reviewing the EC data from these four counties, Dewberry concluded that quality control review changes would be needed in the way community data are entered into a database such as that developed for the CRS program before it could be reliably used to populate an elevation registry. A.9 Tools for Georeferencing existing Elevation Certificates Latitude and longitude are required to georeference/geocode a structure in a GIS, but such geographic coordinates are an optional entry on FEMA Form 81- 31, July, 2000. As a result, a very small percentage of ECs include this optional entry, except when Global Positioning System (GPS) procedures are used for the survey. Even when GPS procedures are used for the surveys, the latitude and longitude are not always provided since the entry is optional. Without geographic coordinates, or comparable UTM or State Plane coordinates, street addresses alone are not adequate to accurately determine the location of a structure in a GIS. For this study, without accurate geocoding, existing ECs could not be used as "ground truth" to validate the accuracy of the alternative elevation strategies discussed below. For a potential elevation registry, the lack of accurate geocoding would impact the accuracy of revisions to the elevation registry when new DFIRMs or other changes would normally dictate the need for maintenance and updating of registry records. GPS Surveys. The most accurate and direct way to establish latitude/longitude for an EC is to use GPS procedures which automatically yield geographic coordinates of all points surveyed. When using differential GPS procedures with survey-grade receivers, GPS is capable of producing centimeter-level accuracy; however, when using a single mapping-grade receiver, GPS produces positions with error on the order of 10 meters horizontally and 20 meters vertically. In surveying buildings, GPS antennas cannot be placed immediately adjacent to a building because the building itself would block many of the GPS satellite signals, and visible satellites would suffer from multipath errors -- causing errors in x/y/z coordinates so surveyed. For these reasons, differential GPS procedures are used to survey temporary benchmarks in front of each building to be surveyed (often PK nails driven into the street pavement), followed by conventional surveys from the PK nails to the survey reference points being surveyed on or near the building, e.g., bottom of front door, top of foundation, LAG or HAG point. Such high-accuracy GPS/conventional survey procedures were used for the ECs used as "ground truth" in Charlotte-Mecklenburg County, NC and Prince George's County, MD. For these two counties, each street address was directly linked to the surveyed latitude/longitude of the front door of each building. Digital Orthophotos. An accurate but indirect way to establish latitude/longitude for an EC is to utilize digital orthophotos, combined with some other means for identifying which rooftop image on the orthophoto goes with each street address. In Harris County, TX, georeferencing of existing ECs was performed by the Harris County Flood Control District (HCFCD) which had a GIS database that linked street addresses to a vector polygon bounding each parcel/lot to which a street address is referenced. For each street address for which an EC was provided in Houston, Mike Walters (713-684-4173) at HCFCD established the parcel/lot polygon from the HCFCD's GIS database; overlaid each parcel/lot polygon on top of the city's digital orthophotos, manually selected the rooftop within that parcel/lot, and then selected the latitude/longitude of the rooftop centroid. When performed correctly, this is a perfectly acceptable way to geocode the centroid of a building from its street address. As demonstrated in a Dewberry study for FEMA Region 5 in 1998 and this current study, documented below, it is relatively easy to determine the latitude and longitude of buildings from various forms of digital orthophotos commonly used and available nationwide, but it is very difficult to identify the correct street addresses for those buildings from commonly-used geocoding software programs. GIS Polygons. A slightly less accurate way to georeference an EC, based on its street address, is to utilize tax parcel polygons from the community's GIS, but without refinement by digital orthophotos. The tax assessor and city planner are among the officials who typically utilize such a GIS that digitizes the parcel/lot perimeter boundary lines, as with the HCFCD database above. Such polygons are typically digitized by using Computer Aided Design and Drafting (CADD) coordinate geometry (COGO) procedures to enter the boundary survey information (line distances and angles) for each boundary line segment bounding a tax lot or parcel. However, rather than overlaying the parcel/lot polygon over a digital orthophoto to manually select the location of the building centroid, the GIS itself is used to automatically place a centroid at the center of the lot or some alternative means to estimate the location of the main building on the lot. Resulting geocoding errors are insignificant on small lots, but could be significant on large lots. Several states, notably Maryland (http://www.op.state.md.us/data/mdview.htm) and New York (http://www.nysgis.state.ny.us/inventories/orps.htm) provide statewide parcel data to the public (for a fee), as do numerous local and county entities. However, if New York is representative, some entities may begin restricting access to this type of information due to increased security concerns. Commercial Geocoding Services. A number of commercial companies offer georeferencing software and/or services to perform either or both of the following: (a) geocoding -- providing latitude/longitude values for a known list of street addresses, but excluding P.O. box addresses or rural route addresses, and (b) reverse geocoding -- providing street addresses for a known list of geographic coordinates. For establishment of an elevation registry, FEMA has need for both geocoding and reverse geocoding. As discussed above, the geocoding of ECs with known street addresses would be required for records derived from existing ECs. However, an elevation registry could also be populated by any of the aerial remote sensing techniques described in this study, for which geographic coordinates (latitude/longitude) are known but street addresses are unknown when surveyed from the air; this would require reverse geocoding. Commercial geocoding solutions typically rely on street centerlines with address ranges or zip-code points that serve known address ranges. Linear interpolation of the target address between the low and high address ranges is used to identify where along the block and on which side of the block (odd or even) the address falls. If the address ranges of the street centerlines are larger than the real addresses, as is most often the case, a “clustering” effect can happen, with all of the addresses landing at the “low” address end of the block. Standard offsets or setbacks of the house from the street are usually included and can sometimes be varied. If an address cannot be found, a default location at a zip-code centroid is sometimes returned. Accuracy of geocoding relies on the spatial accuracy and currency of the street centerlines and the accuracy and completeness of the street names and address ranges used. Rural route addresses, post office boxes, and lot and block numbers are addressing systems that do not lend themselves to geocoding. Four leading commercial georeferencing services were evaluated by Dewberry to test the geocoding of a small sample set of 53 ECs in the City of Houston, TX. One of these four services readily admitted that procedures were approximate and could not distinguish between neighboring houses or houses across the street from each other. Table 8 summarizes some of the differences between the other three geocoding services for the same addresses; the names of these services will remain anonymous as results may vary widely in different communities, and none was clearly superior to the others. The complete results are at APPENDIX F with geocoded coordinates compared with "ground truth" coordinates provided by Harris County derived from parcels and rooftops identified on digital orthophotos (described above). All of the geocoding services delivered approximate positions, but yielded positioning errors of several hundred feet. Thus, none of the commercial services evaluated can be relied upon to distinguish between neighboring houses or houses across the street. Still, as with the LOMA 2000 database, such crude geopositioning is better than no geopositioning. Table 8 — Comparison of Geocoding Services Geocoding Service A B C Address matching 50 of 53 52 of 53 53 of 53 ?N average (Northing) 129.80 ft 152.97 ft 178.79 ft ?E average (Easting) 362.97 ft 206.37 ft 212.68 ft ?N maximum (Northing) 1055.66 ft 1127.10 ft 894.98 ft ?E maximum (Easting) 7684.08 ft 1469.53 ft 767.13 ft ?N 95th percentile 360.13 ft 509.85 ft 428.63 ft ?E 95th percentile 449.52 ft 600.65 ft 533.35 ft Horizontal errors at the 95% confidence level * 575.99 ft 787.86 ft 684.24 ft * Because of systematic formulas that interpolate street addresses, there is no reason to assume that geocoding errors follow a normal distribution, therefore, the 95th percentile method is warranted and the horizontal (radial) error at the 95% confidence level is assumed to equal the square root of [(?N 95th percentile)2 + (?E 95th percentile)2]. A.10 Conversion of paper Elevation Certificates to digital format As noted previously, most communities do not maintain their ECs in an electronic format. These records tend to be stored in scattered locations and may be cost- prohibitive to retrieve. A 1995 study by ISO on the retrieval and conversion of ECs to a database format resulted in over 30,000 records that were provided to FEMA and to the CRS communities that originally supplied the information. At the time, significant difficulties were encountered in creating a database from information documented on more than 7 different FEMA EC forms and provided in hardcopy format of varying quality, completeness, and legibility. Scanning and Optical Character Recognition (OCR) software was tested by ISO on the conversion project and abandoned due to the poor quality of the scans. Dewberry contacted S.A.I.D. Inc. regarding the feasibility and cost of scanning paper ECs and digitizing the pertinent data into a database. S.A.I.D. Inc. was contacted because they provide similar scanning services for FEMA’s Engineering Study Data Packages and have provided data entry for other services. S.A.I.D. estimated $5 per EC for double-entry digitization (whereby two different personnel enter the data which is then compared for quality control purposes to detect differences/errors) and 300 dpi PDF files for each EC form. The assumptions used for this cost estimate are as follows: ? The ECs would be 2-sided forms or 2 pages. ? There would be a minimum of 50,000 ECs to be digitized. ? Within those 50,000 EC forms, there will be up to 7 types of EC forms. These forms will have varying amounts of data, similar in nature to the current FEMA Form 81-31. ? Up to 50% of the EC forms may be handwritten. ? The output will be a PDF multi-page image (2 pages) and an ASCII data file, including the image file name, to satisfy the format of the data dictionary for the elevation registry. ? The size of the 300 dpi PDF file for each EC form would be approximately 138 Kb. Strategy B — Maximize use of Airborne Remote Sensing B.1 Photogrammetry Conventional (Vertical) Photogrammetry. Photogrammetry is that branch of surveying that deduces the physical three-dimensional measurements of objects from measurements on stereo photographs that photograph an area from two or more different perspectives. The 3rd dimension (elevation) is normally mapped as contours of equal elevation, or as spot heights for which the z-value (elevation) of each point is carefully measured. Spot heights are normally mapped at tops of mountains, bottoms of depressions, centers of road intersections, tops of dams or dikes, or other locations where there is a need for an accurate elevation value; but spot heights can also be mapped at LAG or HAG points (if these points are visible on both of the stereo photographs) or at the four corners of a building, for example. Normally, vertical stereo photography is flown of entire communities with numerous adjacent/parallel flight lines. The area imaged with each photograph overlaps the adjoining photo (before and after) in the same flight line by about 60% and has 10-20% sidelap with photos from adjoining flight lines. With 60% forward overlap, all of the terrain area can be seen on at least two successive photos, and up to 30% of the terrain area can be seen on three successive photos. The camera's focal length and the aircraft's flying height dictate the accuracy of elevation data surveyed photogrammetrically. When elevation data are acquired, mapping cameras with a standard 6" focal length are normally used, and flying heights are varied to satisfy requirements for a specified contour interval to be mapped. Subsequently, when mapped to National Map Accuracy Standards, spot heights measured from this stereo photography will have 90% of the elevations accurate to one-fourth of the contour interval or less, with no spot height elevation errors larger than one-half the contour interval. For example, to produce a map with 2 ft contours, it is common to acquire the aerial photography from an altitude of 4,000 ft above the mean elevation of the terrain being mapped; then, at least 90% of the spot height elevations should be accurate to 0.5 ft, and the remaining 10% of the spot height elevations should be accurate to 1.0 ft. Whereas the National Map Accuracy Standard expresses accuracies at the 90% confidence level, the new National Standard for Spatial Data Accuracy requires accuracies to be expressed at the 95% confidence level, as used throughout this report. The three-dimensional (3-D) coordinates (latitude, longitude, and elevation) of any point can be surveyed photogrammetrically if the point can be seen on two or more stereo photos. Some ground points cannot be seen in stereo when tall trees or buildings block the view to the ground from one or more perspectives. For the purpose of this study, vertical aerial photography (aimed straight down) can accurately survey rooftops and many points on the ground including LAGs and HAGs, but basement windows cannot normally be seen in stereo because one photo might see a basement window, but the second photo will look straight down on the house, and the third photo will see the opposite side of the house -- thus no stereo images of the same basement window feature. Conventional Photogrammetry with Measured Offsets. As described at www.nap.usace.army.mil/GIS/fwrs.htm, the Philadelphia District of the U.S. Army Corps of Engineers (USACE) executed the structure inventory portion of the Susquehanna River Flood Warning and Response System (FWRS) in 2000. A part of the FWRS involved the Corps using a multi-technology method to survey the top of bottom floor elevations of thousands of floodprone houses along the Susquehanna River in Pennsylvania. The District hired a photogrammetric firm (BAE/ADR) to establish photogrammetric spot heights on the ground adjacent to the corners of each house (as many corners as could be seen in stereo). The Corps also hired a survey firm to measure the offset distances up/down from one of the surveyed spot heights (per structure) to indirectly compute the top of bottom floor elevation and other elevations relative to the spot heights. The specifications for the photogrammetry were that the spot heights should satisfy National Map Accuracy Standard for 2 ft photogrammetric contours. Figure 2 (left) illustrates how spot heights (shown here as red dots) might initially appear when photogrammetric x/y/z coordinates are provided as spot heights. At this point, there is no basis for reference. Normally only two or three spot heights can be established on the ground adjacent to the corners of any building because, from one or two directions, the building itself blocks the view of one of the photographs needed to make stereo measurements on the ground. With only georeferenced points, it is difficult for the land surveyor to determine which house, and which corner of which house those coordinates (dots) pertain to. Figure 2 (right) illustrates how those same spot heights might be plotted on top of a digital orthophoto or other base map so that the surveyor can determine the spot height, for each building, most appropriate for use for measurement of vertical offsets. Not all surveyors have GIS tools for overlaying such "dots" on top of orthophotos or other base maps. Similarly, for home owners, they too would not be able to easily determine which "dots" pertained to their house. However, most communities have GIS specialists who could easily perform such tasks on a community-wide basis. Furthermore, home owners may actually be able to recognize the pattern of dots relative to streets and homes in the area. Figure 2 — Photogrammetric Spot Heights To verify the accuracy of this method, Dewberry hired a survey firm to use GPS and conventional survey procedures to directly survey the top of bottom floor elevations of 40 of the same houses previously mapped by the Corps of Engineers. The comparisons of the expedient photogrammetric method vs. ground survey method are at APPENDIX G for the 40 houses selected at random. Assuming the GPS surveys were correct with all errors attributed to the photogrammetric surveys and/or offset measurements, the vertical accuracy of the top of bottom floor elevations by this method was 1.19 ft at the 95% confidence level. This exactly satisfied the mapping standard for 2 ft contour interval accuracy and proves that this is a viable method for obtaining accurate structural elevation data. A total of 31 of the 40 top of bottom floor elevations (77.5%) were accurate within 0.5 ft. Oblique Photogrammetry - Pictometry. Because Pictometry's oblique imagery will be new to most readers of this report, the company-provided information is included at APPENDIX H to explain the products and how they are acquired and used. Figure 3 shows a sample Pictometry oblique image, photographed from an elevation of approximately 2,000 feet above the mean terrain. Figure 4 provides samples of Pictometry imagery, zoomed-in from four different perspectives. When features are not in the shadows, the oblique views enable the GIS analyst to see and measure the bottom of doors, tops of foundations, presence of walk-out basements, or basement windows, for example. In fact, this is the only airborne remote sensing technology that is able to "see" such features needed to determine the elevation of the lowest floor. All other airborne remote sensing technologies are able to survey the rooftop and the LAG and HAG, for example, but only estimates or infers the lowest floor or top of bottom floor elevation. However, as can be seen from the four views at Figure 4, because of shadows and shrubbery, it is nearly impossible on this particular residence to see basement windows or flood vents from any of the four views. Dewberry learned during this study that Pictometry's elevations are actually relative rather than absolute. For example, Pictometry can accurately measure the distance up from the ground (e.g., LAG point) to the top of foundation, and then subtract 8 feet to compute the top of bottom floor elevation of the basement, but if it doesn't know the absolute elevation of the ground, then the absolute elevation of the lowest floor will also be in error by the error in the ground elevation. The next three subsections describe Pictometry projects with different techniques for obtaining digital elevation data from which relative height differences were determined and tested by Dewberry. 2-View Pictometry with USGS DEM. Pictometry had flown over 100 counties/communities by this method, but none where Dewberry had georeferenced ECs to serve as ground truth. However, Pictometry had flown over Prince George's County, MD, while flying Montgomery County, MD, Washington D.C., and Arlington County, VA and had acquired imagery of Prince George's County from two directions instead of four. Pictometry provided measurements of 29 buildings, using 2-view images, where Dewberry had georeferenced ECs to serve as ground truth. Of these 29, three houses could not be accurately surveyed for various reasons and their top of bottom floor elevations were left blank; they were obscured by trees or neighboring buildings on the two sides of the houses where photos were available and they did not have photos of the front and rear of these houses. Several other houses were measured with questionable accuracy because the analyst could not clearly see whether or not these houses had basement windows. Furthermore, Pictometry used USGS DEMs to determine the elevation of the ground, from which offset measurements were made to determine top of bottom floor elevations. See APPENDIX I. Of the houses measured, the average absolute error in the top of bottom floor elevation was 2.61 ft; the error at the 95% confidence level was 6.34 ft; and the largest error was -10.75 ft, caused by identifying a basement where none actually existed. While unimpressive in and of itself, Dewberry still considered this potentially encouraging for three reasons: (1) the 2.61 ft average elevation error was strongly influenced by several houses where the wrong assessment had been made regarding the presence or absence of basement windows, and the Pictometry analyst can annotate the database with a confidence level indicator, rating houses where he/she is highly confident that the house has a basement or doesn't have a basement, or various degrees of diminished confidence; (2) confidence levels should be increased if the normal 4-view images were available to view each building from all four sides rather than from only two sides as in PG County; and (3) because USGS DEMs (with potentially large elevation errors) had been used as the reference elevation for each building. Figure 3 — Sample Pictometry Oblique Photo Figure 4 — Sample 4-View Pictometry Images 2-View Pictometry with LIDAR DEM. Dewberry subsequently provided Pictometry with a LIDAR dataset of Prince George's County, MD, and these same 29 buildings were re-measured. This time, the analyst provided top of bottom floor elevations for all 29 homes. The average (absolute) error in the top of bottom floor elevation was 2.53 ft; the top of bottom floor error at the 95% confidence level was 4.66 ft; and the largest top of bottom floor error was 5.63 ft, again influenced by the same factors (1) and (2) above, but without major errors from factor (3). See APPENDIX I 4-View Pictometry with Spot Heights Finally, to eliminate factors (1) and (2), Dewberry decided to survey additional houses in Arlington County, VA for comparison with the 4-view Pictometry images previously available. Since LIDAR data was not available for this county, Dewberry provided Pictometry with the surveyed spot height elevations at three corners of 27 houses. This is essentially the most accurate DEM that could be provided, allowing Dewberry to isolate errors from Pictometry's measurement process from errors in the DEM. Each of these houses had dense tree cover on at least two sides. For these 27 houses, the average error in the top of bottom floor elevation was 1.59 ft; the error at the 95% confidence level was 5.01 ft; and the maximum error was 5.85 ft. See APPENDIX I This time, many houses again had misidentified basements. Pictometry Accuracy Summary Table 9 summarizes the accuracies achieved in three different evaluations of Pictometry datasets compared with ECs. Table 9 — Pictometry Accuracy Comparisons Pictometry Dataset Evaluated LAG errors 95% Conf. Average absolute LAG errors HAG errors 95% Conf. Average absolute HAG errors TBF errors 95% Conf. Average absolute TBF errors Prince George's County, MD w/USGS DEM 3.96 ft 1.65 ft 3.61 ft 1.54 ft 6.34 ft 2.61 ft * Prince George's County, MD w/ LIDAR 3.99 ft 1.62 ft 3.83 ft 1.70 ft 4.66 ft 2.53 ft ** Arlington County, VA, w/surveyed spot heights N/A N/A N/A N/A 5.01 ft 1.59 ft * Initially, only 26 of 29 homes were surveyed for top of bottom floor (TBF) elevations; three were not surveyed because existence of basements could not be determined ** Subsequently, all 29 homes were surveyed for top of bottom floor elevations. The same Pictometry images were used. LIDAR results would have been better if the analyst had not guessed on the questionable basements. Photogrammetry Conclusions The major photogrammetry conclusions are as follows: ? Conventional vertical photography can survey LAG and HAG elevations as well as spot heights of the terrain at multiple corners of a structure; but this technology cannot directly survey the lowest floor elevations because the bottom of front door or other survey "target points" are not normally visible on aerial photographs looking straight down at rooftops. ? When conventional photogrammetric spot heights are combined with on- site tape measurements from the ground to bottom of front door or top of foundation, for example, then the top of bottom floor elevations could be computed with errors comparable to the elevations interpolated from topographic contours, i.e., 90% of top of bottom floor elevations accurate within ½ the contour interval and the remaining 10% accurate within the full contour interval. However, errors in measuring the offset distances could also be a factor. For the 40 Susquehanna structures, the vertical error at the 95% confidence level was 1.19 ft, equivalent to 2' contours, and 31 of 40 lowest floor elevations were accurate within 6 inches. However, it must be recognized that discrepancies were not necessarily due to errors in the photogrammetry but partly due to two different surveyors measuring vertical offsets by different methods, or selecting different points on the houses on which to base the lowest floor measurements. ? Oblique aerial photography, from Pictometry for example, cannot directly survey LAG and HAG elevations or spot heights, but can indirectly survey top of bottom floor elevations relative to elevations of surrounding terrain. However, in all three tests performed, there difficulties in detection of basements, causing top of bottom floor elevations to have average errors between 1.59 and 2.61 ft, and top of bottom floor errors at the 95% confidence level between 4.66 ft and 6.34 ft. For these reasons, Dewberry concludes that Pictometry imagery can not be reliably used to determine top of bottom floor elevations but, instead, has its best value for other applications, such as providing a "birds' eye" view of the property so an insurance agent or others can see the house to be insured, and/or to provide a means to check for unauthorized construction. The exception is in some areas where there are no basements, such as in many Florida counties; then Pictometry can provide more information without fear that a wrong assessment is made regarding the presence or absence of basements. B.2 Light Detection and Ranging (LIDAR) LIDAR collects thousands of spot heights every second of flight, currently with up to 100,000 laser pulses per second. LIDAR is most commonly flown of entire counties or communities to establish the elevation layer of their GIS. Each LIDAR pulse can receive multiple returns, yielding elevations (actually x/y/z coordinates) of features mapped. The first return for each pulse provides the elevation of the first thing hit by the pulse, to include treetops and rooftops. Some of the light from each laser pulse penetrates through or between the trees and hopefully hits the ground for use in establishing a bare-earth digital terrain model (DTM). With dense vegetation, LIDAR pulses might never penetrate the vegetation to reach the ground. Vegetated features are "soft" where there is a difference between the elevation of the first and last return. Other features, including bare earth, sand, concrete, rock, short grass, and building rooftops are "hard" where the elevations from the first and last returns are the same, i.e., where there is no LIDAR penetration of the feature. To generate a bare-earth DTM, the LIDAR data is post-processed by computer algorithms to "remove" buildings and vegetation. Figure 5 provides an example of LIDAR processing. The left image shows LIDAR last-return elevations prior to post-processing. The center image shows the bare-earth DTM after post- processing for removal of buildings and vegetation and interpolation to fill in the missing spaces where elevation points were deliberately removed. The right image shows where there is no longer elevation data. There is no data in the black areas either because there were no LIDAR returns in the first place (in water) or because those returns were deliberately deleted during post-processing because they mapped trees, rooftops or other elevated features above the bare- earth DTM that was needed. Note the bridge decks that were deliberately "cut out" for hydro-enforcement of the streams and canal for hydraulic modeling purposes. The reverse of the right image would show where LIDAR elevation points remain after removal of trees and buildings. As with the right image, a surveyor or home owner could probably navigate to specific streets just by recognizing the LIDAR dot pattern for the streets and buildings when using the LIDAR's bare-earth point file. Figure 5 — LIDAR Surfaces Before and After Post-Processing When LIDAR data is used to automatically determine the LAG and HAG of structures, or to estimate their lowest floor elevations, it is preferred to have a GIS file of structure footprints, as maintained by many communities. Such footprints are most commonly mapped photogrammetrically or with rooflines digitized from digital orthophotos. Figure 6 shows an example of building footprints (top left), and those same footprints with a surrounding buffer (top right), shown in red, that can be automatically generated by a GIS with any buffer width desired. Some community GIS files have these footprints georeferenced so that the street addresses are known for each building. Other community GIS files do not link their footprints to street addresses. For analyses of the various technology sub-options for both LIDAR and IFSAR, the base scenario assumption is that footprints are linked to street addresses; but separate calculations are also performed with the assumption that street addresses are not linked to footprints. With bare-earth DTMs, there is no need for a buffer zone because each footprint can directly "cut" the DTM to establish the LAG and HAG. Figure 6 — Examples of Building Footprints and Buffer, Centroids, and Parcel Polygons Some communities do not have footprints, but they do have building centroids linked to street addresses. The center left image at Figure 6 shows examples of such centroid points, and the center right image shows those centroids superimposed on top of digital orthophotos. Still other communities may have neither footprints nor centroids but instead have parcel polygons that are linked to street addresses as shown at the bottom left image at Figure 6 and superimposed on top of digital orthophotos at the bottom right image. The superimposition of footprints, centroids, or parcel polygons on top of digital orthophotos provides graphic orientation but not street addresses. Such addresses must be established in the GIS database. Of all these options, footprints are preferred because they can be overlaid on top of a bare-earth LIDAR TIN to "cookie cut" the LIDAR data to determine the LAG and HAG. In this report, the "with footprint" process will be abbreviated as the "w/FP" method. With either centroids or parcel polygons, there is less certainty in the computation of LAG and HAG elevations from LIDAR data because the location of LAG and HAG points must be estimated; however, when either centroids or parcel polygons are overlaid on top of digital orthophotos, it is a simple GIS task to generate footprints around the perimeters of visible rooftops. When footprint files are not available, Dewberry uses Computational Consulting Service, Inc. (CCS) which has developed sophisticated algorithms to process the raw LIDAR "point cloud" dataset in order to detect building locations and search for the LAG and HAG. This "no footprint" process will be abbreviated as the "NoFP" method. When LIDAR data is widely spaced (e.g., 4-5 meter post spacing), CCS computer algorithms have a much harder time detecting building locations than when the post spacing is narrower. With 4-5 meter point spacing, it is possible to have only one LIDAR pulse hit a rooftop, making it impossible to determine the shape of the roof for estimating the shapes of the building footprints and their buffers. With wide post spacing, it is even possible that no LIDAR pulse hits a rooftop. One purpose of this research project is to determine how well narrower post spacings perform in helping these computer algorithms to estimate building footprints so that buffers can be accurately established. As described below, CCS processed four different LIDAR datasets and prepared the LIDAR Automated Data Extraction Report at APPENDIX J that describes procedures for extraction of buildings from LIDAR data, determination of main parameters of buildings from LIDAR data, and determination of additional parameters of buildings using 3D models created of those buildings. LIDAR data of Charlotte/Mecklenburg County, NC. Raw LIDAR data of Mecklenburg County was provided by the Charlotte-Mecklenburg Storm Water Services. The LIDAR data was flown by EarthData in 2003 and had nominal post spacing of approximately 16 feet. This is considered to be a low resolution dataset because each house might have only one LIDAR pulse hit an entire rooftop, and some houses might even have no pulse hit an entire rooftop if the width of the house, for example, is less than 5 meters (16 feet). Building footprint files were also provided by the County for some of the buildings. Dewberry chose a test area that included 2617 buildings with footprints. CCS' "NoFP" processing was performed which automatically identified 2270 of those 2617 buildings for which one-to-one GIS relationships were identified, missing 347 (13.3%) of the buildings; Dewberry considered this a success because of the low resolution dataset that might have no LIDAR pulse, or perhaps only one or two pulses hit many of the rooftops. Some of the one-to- one "misses" were actually cases where there were one-to-many or many-to-one relationships because of rows of townhouses, for example, with different rooftop elevations for different units, but for which multiple units may have only a single footprint. Dewberry then determined that 217 of these 2160 buildings also had ECs for use as "ground truth" elevations, so these 217 buildings became the basis for comparison of CCS' automated "NoFP" method to be used when there are no footprints, and Dewberry's automated "w/FP" method to be used when there are footprints. Of these 217 ECs, 215 had LAG elevations, and 108 had HAG elevations (HAG elevations were not required on earlier versions of FEMA form 81-31). The spreadsheet that computes the overall statistics for LAG and HAG elevations, comparing CCS' "NoFP" method (without footprints) with Dewberry's "w/FP" method (with footprints) is at APPENDIX K — LIDAR Accuracy Analysis (Mecklenburg County, NC). The results are summarized in Table 10. Table 10 — Mecklenburg County LIDAR Accuracy Comparison Mecklenburg County, NC, LAG/HAG from LIDAR with 16 ft nominal post spacing "NoFP" LAG "w/FP" LAG "NoFP" HAG "w/FP" HAG LIDAR average post spacing 16 ft Number of Houses 215 108 Standard Deviation 1.91 ft 1.13 ft 1.84 ft 1.15 ft Average (absolute) Error 1.22 ft 0.87 ft 1.11 ft 0.71 ft Minimum Elevation Error -9.39 ft -4.64 ft -5.75 ft -5.75 ft Maximum Elevation Error 9.47 ft 3.63 ft 9.44 ft 4.05 ft 95th Percentile Error 3.79 ft 2.82 ft 3.57 ft 2.09 ft 90th Percentile Error 2.55 ft 1.98 ft 2.28 ft 1.61 ft 85th Percentile Error 1.95 ft 1.43 ft 1.71 ft 1.16 ft ? For CCS' "NoFP" method (no footprints), the average LAG elevation error was 1.22 ft, but the LAG elevation error at the 95% confidence level was 3.79 ft. Similarly, the average HAG elevation error was 1.11 ft, but the HAG elevation error at the 95% confidence level was 3.57 ft. The spreadsheet at APPENDIX K shows some larger outlier errors that indicate potential systematic errors when the two methods both yield poor results. ? For Dewberry's "w/FP" method (with footprints), the average LAG elevation error was 0.87 ft, but the LAG elevation error at the 95% confidence level was 2.82 ft. Similarly, the average HAG elevation error was 0.71 ft, but the HAG elevation error at the 95% confidence level was 2.09 ft. ? For systematic errors, it is possible that the ECs or footprints include errors in horizontal position, that the LAG/HAG elevations on the ECs may include errors, or that Dewberry's attempts to establish one-to-one GIS relationships between ECs, building footprints, and LIDAR data failed for some records — all potentially causing the wrong elevations to be compared. ? It is interesting to note that errors at the 95% confidence level are nearly twice as large as errors at the 85% confidence level. LIDAR data of Prince George's County, MD. LIDAR data of Prince George's County was acquired by Waggoner Engineering, Inc. in 2000 and had nominal post spacing of approximately 8 feet. This is considered to be a medium resolution dataset because each house should have several LIDAR pulses hit individual rooftops. See APPENDIX K — LIDAR Accuracy Analysis (Prince George's County, MD). The results, summarized at Table 11, are considerably better than Table 10, demonstrating the benefits of narrower post spacing for this purpose. By using 8 ft spacing instead of 16 ft, the LAG elevation errors at the 95% confidence level were reduced from 3.79 ft to 1.68 ft when using CCS' "NoFP" method (no footprints), and they were reduced from 2.82 ft to 2.02 ft when using Dewberry's "w/FP" method (with footprints). Table 11 — Prince George's County LIDAR Accuracy Comparison Pr. George's County, MD, LAG/HAG from LIDAR with 8 ft nominal post spacing "NoFP" LAG "w/FP" LAG "NoFP" HAG "w/FP" HAG LIDAR average post spacing 8 ft 8 ft 8 ft 8 ft Number of Houses 579 579 579 579 Standard Deviation 0.57 ft 0.61 ft 1.71 ft 0.60 ft Average (absolute) Error 0.53 ft 0.80 ft 0.77 ft 0.65 ft Minimum Elevation Error -5.15 ft -4.28 ft -1.76 ft -3.11 ft Maximum Elevation Error 1.91 ft 2.51 ft 29.69 ft 4.91 ft 95th Percentile Error 1.68 ft 2.02 ft 2.40 ft 1.82 ft 90th Percentile Error 1.11 ft 1.54 ft 1.34 ft 1.44 ft 85th Percentile Error 0.82 ft 1.19 ft 0.91 ft 0.95 ft LIDAR data of Harris County, TX. Raw LIDAR data of Harris County was provided by TerraPoint and had nominal post spacing of approximately 5 feet. This is a high resolution dataset that became available for evaluation during the progress of the study. However, this dataset was flown with an older sensor not optimized for foliage penetration, and there were considerable difficulties with the old ECs that lacked geographic coordinates. When the ECs were geocoded, they appeared to be far out of registration with the LIDAR data, causing CCS and Dewberry to be unsure of the validity of comparing the LIDAR data with EC data that appeared to be questionable at best and erroneous at worst. Furthermore, compounding this issue is the fact that Houston suffers from severe subsidence, and there was a distinct possibility that the land subsided significantly between the time when the ECs were surveyed (up to 20 years ago) and recent years when the LIDAR was flown. For these reasons, Dewberry abandoned any attempts to evaluate the Harris County LIDAR dataset. Fortunately, an alternative high resolution LIDAR dataset of Beaufort County, SC was already available. LIDAR data of Beaufort County, SC. LIDAR data of Beaufort County was provided by the county's GIS coordinator. The LIDAR data was flown by Laser Mapping Specialists, Inc. (LMSI) and had nominal post spacing of approximately 4 feet. This is the highest resolution dataset evaluated in this study; each house should have many LIDAR pulses hit individual rooftops. See APPENDIX K — LIDAR Accuracy Analysis (Beaufort County, SC). Table 12 — Beaufort County LIDAR Accuracy Comparison Beaufort County, SC, LAG/HAG/TBF from LIDAR with 4 ft nominal post spacing "NoFP" LAG Elevations "w/FP" LAG Elevations "NoFP" HAG Elevations "w/FP" HAG Elevations "NoFP" TBF Elevations Average post spacing 4 ft 4 ft 4 ft 4 ft 4 ft Number of Houses 27 38 27 38 27 Standard Deviation 0.43 ft 0.28 ft 0.39 ft 0.39 ft 0.78 ft Average (abs) Error 0.42 ft 0.28 ft 0.37 ft 0.95 ft 2.93 ft Minimum Error -1.37 ft -0.65 ft -0.25 ft 0.23 ft -0.19 ft Maximum Error 0.27 ft -0.55 ft 1.19 ft 1.73 ft 3.63 ft 95th Percentile Error 1.09 ft 0.59 ft 0.97 ft 1.60 ft 3.58 ft 90th Percentile Error 0.91 ft 0.54 ft 0.84 ft 1.50 ft 3.50 ft 85th Percentile Error 0.77 ft 0.51 ft 0.77 ft 1.40 ft 3.44 ft The results summarized at Table 12 are considerably better than Table 11 for all of the statistics shown in these two tables, again demonstrating the benefits of narrower post spacing. By using 4 ft spacing instead of 8 ft, the LAG elevation errors at the 95% confidence level decrease from 1.68 ft to 1.09 ft when using the "NoFP" method and from 2.02 ft to 0.59 ft when using the "w/FP" method. Similarly, the HAG elevation errors at the 95% confidence level decrease from 2.40 ft to 0.97 ft when using the "NoFP" method, and decrease from 1.82 ft to 1.60 ft when using the "w/FP" method. CCS's estimation of top of bottom floor elevations, using "NoFP" methodology, yielded errors of 3.58 ft at the 95% confidence level. This was the only dataset that yielded top of bottom floor elevations that could even be considered for the registry, and these results are this good in large part because the test houses in Beaufort County had no basements. If these houses had basements, the "NoFP" top of bottom floor elevation accuracies would probably have been poorer. Overall, for estimation of LAG elevations, CCS' "NoFP" method yielded errors of approximately 1.09 ft at the 95% confidence level. Similarly, Dewberry's "w/FP" method yielded LAG elevation errors of 0.59 ft at the 95% confidence level. This equals the accuracy expected of data equivalent to 1 ft contours, although most LIDAR datasets are compiled to meet 2 ft contour interval standards. Throughout the remainder of this study, LIDAR data will be evaluated as though equivalent to 2 ft contours — in spite of the fact that this particular LIDAR dataset in Beaufort County is considerably more accurate than 2 ft. LIDAR Conclusions. At the 2004 International LIDAR Mapping Forum (ILMF), several presentations pointed out the fact that LIDAR firms in Europe and Japan routinely collect much higher resolution LIDAR data than in North America, with some countries now collecting data at extremely high resolution, i.e., up to 28 points per square meter, whereas in the U.S. there is normally one LIDAR point for several square meters. The reason for this difference is that LIDAR data is most commonly used for engineering design applications in Europe and Japan and is collected with helicopter-based sensors, whereas LIDAR data is most commonly used for mapping applications in North America and is collected with fixed wing aircraft designed for flying longer distances at higher altitudes. Regardless of the type of aircraft used, LIDAR systems now being sold in the U.S. have extremely high pulse repetition rates, now up to 100,000 pulses per second. This alone will cause high resolution LIDAR datasets to become the norm rather than the exception in North America. The results achieved in Beaufort County, SC make it reasonable for Dewberry to proceed with this study assuming that LAG and HAG elevations, when footprints are available to "cookie cut" the LIDAR TIN surface, can be derived with accuracies comparable to 2 foot contours, i.e., accurate to 1.20 ft or less at the 95% confidence level. When proceeding on this assumption, LIDAR datasets should have independent confirmation of the overall accuracy of the data. In all cases, the availability of building footprints makes it possible to obtain the most accurate LAG/HAG elevations from existing LIDAR datasets. B.3 Interferometric Synthetic Aperture Radar (IFSAR) For a quick reference to Interferometric Synthetic Aperture Radar (IFSAR), the reader is referred to Intermap's web site at www.intermaptechnologies.com, Product Handbook. IFSAR products have traditionally consisted of ortho-rectified radar images (ORI) and digital surface models (DSMs). An ORI is a grayscale image of the earth's surface that has been corrected to remove geometrical distortions that are a normal part of the imaging process. Although they are similar to black and white aerial photographs, ORIs differ because, instead of being made of visible light, the radar pulses the ground with "flashes" of radio waves which then return from imaged features to the antennas to give distance and intensity measurements. The key feature of ORI imagery is that it provides a means of viewing the earth's surface in a way that accentuates features far more than is possible with aerial photography. The radar looks to the side of the aircraft and casts "shadows" that enable the user to visually perceive the elevation information in the image. See Figure 7 for comparison of ORI imagery with traditional digital orthophotos. IFSAR DSMs are derived from the return signals received by the two radar antennas on the aircraft. The signals bounce off the first surface they strike, making the DSM a representation of any object large enough to be resolved. This includes buildings, vegetation and roads, as well as natural terrain features. DSMs map the top reflective surfaces, i.e., treetops and rooftops. IFSAR DSMs can be further processed to produce digital terrain models (DTMs) of the bare-earth terrain with buildings and trees removed. However, Intermap's Product Handbook, referenced above, identifies limitations of IFSAR DTMs, especially DTMs in built-up areas: ? Layover and foreshortening which tend to make objects (including buildings) look shorter than they really are. ? Shadowing which causes no returns on the back sides of buildings. ? Signal saturation where too much light is returned and image detail is lost -- most often a problem over urban areas because of the strong return from buildings. ? Multipath, where the radar signals bounce off of buildings and other objects before hitting the ground, making the ground appear lower than it really is. (Note: this affects LIDAR also). ? Edge effects, sometimes called "blooming," near buildings and forests where interpolation between true ground and elevated points creates intermediate elevations in transition zones up to 25 meters away from the elevated edge. ? Slope effects that degrade accuracy. The impact depends on the magnitude of the slope, whether the slope is positive or negative, aspect angle, and where it lies in the radar swath (look angle) Figure 7 — Comparison of ORI and Orthophoto Images IFSAR data of Jefferson County, CO. Intermap Technologies Inc. provided a sample IFSAR dataset of Jefferson County, CO that included both a DSM file and a DTM file for which buildings and trees were removed. In open areas, the DTM was expected to be approximately equivalent to 10 ft contours. Dewberry compared the DTM file (converted to TIN format) with 21 ECs surveyed in Jefferson County, CO specifically for this evaluation. The quality control survey firm was tasked to select approximately half the houses with trees, and half the houses relatively free of trees, and to provide geographic coordinates of the front doors of each house. For this evaluation, Dewberry drew circles around these geographic coordinates with radii of both 20 ft and 30 ft and then "cut" the TIN surface to determine the LAG and HAG values around these circles. The spreadsheet at APPENDIX L is summarized in Table 13. The LAG and HAG errors were consistent for either the 20' radius or 30' radius. Of the 21 houses, LAG/HAG errors were between 0' and 5' for nine houses, between 5' and 10' for five houses, between 10' and 15' for five houses, between 15' and 20' for one house, and approximately 25' for one house. The spreadsheet at APPENDIX L indicates whether each house had trees, some trees, or no trees. This data would indicate that the LAG/HAG elevations in this dataset are unsuitable for eRating purposes. Any or all of the possible causes bulletized on the prior page could have contributed to these errors. Table 13 — Jefferson County IFSAR Accuracy Comparison Comparison with EC surveys in Denver area Within 20' Radius Within 30' Radius LAG HAG LAG HAG Number of Houses 21 21 21 21 Standard Deviation (ft) 6.99 8.10 7.00 8.16 Average ± Error (ft) +7.22 +6.70 +6.73 +7.17 Average (absolute) Error (ft) 8.15 8.44 7.81 8.85 Minimum Elevation Error (ft) -7.63 -8.65 -7.76 -8.51 Maximum Elevation Error (ft) +24.10 +26.56 +23.37 +27.31 95th Percentile Error (ft) 15.11 16.84 14.98 17.27 90th Percentile Error (ft) 15.08 15.79 14.45 16.38 85th Percentile Error (ft) 14.15 13.10 13.56 13.30 IFSAR Conclusions. After the above research was completed, Dewberry was informed that FEMA considered LAG and HAG elevations with errors equal to or larger than 4' at the 95% confidence level to have no value for populating an elevation registry. This negated the need for additional IFSAR research for this study. IFSAR elevations could still have value for a FEMA database used for other natural disasters, e.g., wildfire modeling, but not for eRating of flood insurance. IFSAR remains a viable alternative for other applications, however. IFSAR was not designed or intended to provide accurate elevations around buildings, but remains the lowest cost alternative for providing elevations for broad areas of terrain that is relatively free of trees and other obstructions Strategy C — Evaluate use of Mobile Photogrammetric Vans C.1 VISAT™ Photogrammetric Van Dewberry contracted with Sanborn Mapping to evaluate the viability of data collected using Sanborn’s VISAT (Video Inertial SATellite) technology from which to extract the necessary information to complete FEMA ECs. The objective of the study was to conduct a “prototype test” of van-based data collection systems configured with GPS and Inertial Navigation System technology to the EC data collection process. The VISAT van is shown at Figure 8. This study utilizes data collected by Sanborn Mapping in Pinellas County, FL. A neighborhood area of seven streets was selected as the study area. The data extracted from the VISAT imagery included: ? Image and description of each house ? Latitude and longitude of the front door ? Top of bottom floor ? Elevation of lowest adjacent grade (LAG) as visible in the image ? Elevation of highest adjacent grade (HAG) as visible in the image ? Building address ? FEMA building type as best determined from the image Pinellas County VISAT Data The majority of the Pinellas County data set was acquired in the fall of 1998. The County had originally contracted with Sanborn to collect images at a 5-meter interval in both directions on all County-maintained roadways and alleys. Pertinent data to be extracted from the VISAT imagery and incorporated into the County’s GIS included: ? Edges of roads and sidewalks ? Sidewalk width ? Storm manholes and catch basins ? Signs, including type and text ? Fire hydrants ? Guard rails The project entailed approximately four months of field acquisition and an additional six months of data extraction. The yield was over 2,200 miles of collected imagery and well in excess of 30,000 point-features and 3,000 linear features extracted. VISAT System Overview The heart of Sanborn’s VISAT technology consists of a rigid, inertial frame that carries an array of b/w digital cameras, a geodetic grade GPS receiver (or more precisely, the antenna), and an inertial measurement unit (IMU). The GPS and IMU data are used to observe the position and orientation of each camera as images are collected. The camera array is designed to provide stereoscopic coverage of up to a 220-degree swath in front of the vehicle. Once acquired and georeferenced, the images are then used to extract three- dimensional coordinates and attributes using the same colinearity principles that apply to aerial photogrammetry. The VISAT Station software is used to navigate or “drive” spatially through the images and collect features and attributes into an SQL database. The accuracy target for features extracted for Pinellas County was 40cm horizontal and 20cm vertical, RMSE, approximately equivalent to the vertical accuracy expected from 2 ft contours. Accuracy was assured by extracting known control targets within each mission and extracting the same features from multiple missions. Figure 9 illustrates the camera array configuration for the Pinellas County project (viewed from above the vehicle). This configuration was designed with the data extraction requirements and the technical limitations of storing images every five meters in mind. As shown also in Figure 8, cameras 1 and 2 point forward, camera 3 points forward and to the left, camera 4 points forward and to the right, and camera 6 points to the right (used for this project). Figure 9 — VISAT Camera Array Configuration Structure Elevation Study For the purpose of this study, a neighborhood in Pinellas County was selected based primarily on the visibility of the buildings and addresses. Figure 10 provides an overview of the VISAT vehicle’s route through the neighborhood. The small X’s represent the measurements that were made on the candidate buildings. Each building was selected based on the visibility of the front door, adjacent grade, and address, and viewed with camera 6. Figure 10 — VISAT Navigation Route and Display Limitations to extracting the necessary data included: ? Camera configuration. With reference to Figure 9, camera number 6 was used almost exclusively for all observations. In order to observe the same feature in a stereo pair, it was necessary to see the feature from camera 6 from two different image capture “epochs” (i.e. an epoch refers to an instance of image capture as the vehicle moves forward). Because images were captured only at an interval of 5 meters, each building was only visible in a maximum of two or three capture epochs. Unlike when capturing features in front of the vehicle, this camera configuration severely limits the available visible angles from which to observe features to the side of the vehicle. ? Vegetation. Owing to the location, many buildings are obscured with dense tropical vegetation in the front yards and along the streets. Attempting to see around the vegetation was limited by camera configuration. ? Image resolution. Several ideal houses were not selected for the study because the address was unreadable. This is directly a function of image resolution. As a result of these limitations, only about 20% of the buildings were suitable for data extraction. It was necessary to navigate several blocks of the neighborhood, as depicted in Figure 10, in order to locate and survey 25 candidate buildings. The other obvious limiting factors of this approach include the inability to see or access the rear, and sometimes the sides, of the buildings and to positively identify the FEMA building type. If the VISAT technology were to be deployed specifically for data capture to support FEMA ECs, an alternate camera configuration would aid capture rate and accuracy tremendously. Sanborn's suggestion would be to extend the inertial frame longitudinally along the vehicle facilitating the placement of a side- looking camera at both the front and the rear, i.e., rotating the camera rack by 90 degrees in order to take stereo photos to the side with cameras 1 and 2. This will allow a stereo observation at every capture epoch and provide additional viewing angles to see around vegetation and obstructions. In addition, the VISAT technology has been improved since the Pinellas County project. Specifically, the original b/w cameras have been replaced with higher resolution color cameras. Figure 11 illustrates the dramatic difference in image quality. The higher resolution will facilitate higher accuracy observations, more positive feature identification, and the ability to observe the street address in all cases. Figure 11 — VISAT Van Camera Comparison VISAT Van Current Image COHU Analog Camera 640 x 240 (interpolated to 640 x 480) 256 Level of Gray VISAT Van Proposed New Camera Sony Progressive Scan Digital Camera 1280 x 960 256 Colors (YUV 4:2:2) APPENDIX M provides the accuracy assessment statistics, comparing the VISAT-derived elevations of 27 houses in Pinellas County with GPS-surveyed elevations. The VISAT-derived elevations were tested to have vertical accuracy of approximately 1.5 ft at the 95% confidence level. The checkpoint surveys indicated top of bottom floor elevations were accurate to 1.54 feet at the 95% confidence level; LAG elevations were accurate to 1.34 feet at the 95% confidence level; and HAG elevations were accurate to 1.59 feet at the 95% confidence level. However, each of these houses had a concrete pad in the back yard for the air conditioner that was not seen from the street and/or could not have been mapped in stereo. Although these pad elevations were only slightly lower than the top of bottom floor, they could not have been mapped even if they were significantly lower. For visible features, their surveyed accuracies were quite acceptable; however, the inability to see (in stereo) the majority of the target points to be surveyed presents the major challenge for this technology. An alternate camera configuration coupled with the improved camera technology will overcome some of the limitations encountered during this study. Pricing Following are Sanborn's pricing scenarios for the VISAT technology. ? Commission a VISAT campaign……………...…$5000.00 mobilization fee ……………………………………………..$100.00 per mile of image capture note: it is usually recommended that all streets be driven in both directions ? Extraction per house from data………………….$20.00 - 40.00 per building note: price assumes a minimum of 100 houses per image campaign C.2 SideSwipe™ Vehicle Mounted Side Scan LIDAR LIDAR vans are just now becoming available commercially. Mosaic Mapping's SideSwipe™ is described at APPENDIX N. Such LIDAR vans operate with a low power laser scanner and also take digital images. LIDAR vans drive streets at normal traffic speed, normally scanning to only the right side of the van. The LIDAR point density on the ground is very high resolution, sometimes enabling street signs to be read from LIDAR intensity images. The only such commercial system is currently operating overseas and data is not yet available for evaluation by Dewberry. Because LIDAR requires only a single laser pulse to measure any point (as opposed to two different views required with photogrammetric vans), LIDAR vans should be better able to map the elevations on front porches, decks, patios, etc. when viewed from only a single angle. Dewberry presumes that the imagery would normally enable the correct identification of basement windows; however, the LIDAR van would be unable to detect walk-out basement doors visible only from the back yard. Without a field test, Dewberry is unable to determine how well a LIDAR van could determine street addresses, or accurately determine the lowest floor elevations, LAGs and HAGs, or elevation of lowest machinery. It is probably the best technology for determining the elevation of lowest horizontal structural members in V-zones. Dewberry is confident that this technology should have fewer limitations than the VISAT van described above, but benefits can only be estimated at this time. C.3 Mobile Remote Sensing Van Conclusions Assuming houses are not far above or far below street level, either a LIDAR van or a photogrammetric van has the capability to survey accurate elevations of features that can be seen from the street, but neither can map features (or identify features such as walk-out basement doors or air conditioner pads) that are not visible from the street. The photogrammetric van has an additional disadvantage in that photogrammetry requires any feature mapped to be visible in stereo (from two different perspectives). The VISAT dataset of Pinellas County was already available, and it had not been configured for mapping features to the side of the van. Turning the camera rack sideways so that cameras 1 and 2 (Figure 9) would take simultaneous stereo images to the side (rather than forward) would help to alleviate this limitation. Also, Sanborn already has plans to upgrade the VISAT cameras so they have a wider field of view. Strategy D — Maximize Cost-Effectiveness of Future ECs D.1 Future Web-Based Elevation Certificates To maximize the cost-effectiveness of future ECs, Dewberry recommends that FEMA develop a web-based system for surveyors to use when producing new ECs, building upon current functionality of the NFIP/CRS Elevation Certificate software. APPENDIX P explains Dewberry's concepts for web-based ECs in greater detail. This web site would have eight major features/advantages: 1. It would prompt surveyors to assist them in entering correct data into various fields on the ECs. 2. It would include some quality control tools to automatically "flag" incorrect entries that need to be corrected by the surveyor. 3. It would ensure that all mandatory data items are completed (to include latitude and longitude which ought to be mandatory rather than optional). 4. It would be designed to eliminate or minimize the kinds of quality control problems evident in existing databases mentioned above. 5. It would or could validate the name and license number of the professional surveyor, engineer or architect authorized by law to certify elevation information. (This could get tricky if a newly-registered professional is not yet in the database used by FEMA to query the lists of authorized personnel in each state.) 6. It would prepare a .pdf file of the final EC to be printed by the surveyor for his/her seal and signature for submission of a hardcopy EC to the person who paid for the survey. 7. It would automatically populate the elevation registry with information from the appropriate data fields on the EC, but deliberately exclude the name of the owner because of Privacy Act considerations. 8. This web site could also be used by communities to digitize and enter their existing hardcopy ECs into the elevation registry. D.2 Legal Considerations for Web-Based Elevation Certificates In determining the legality of establishing a web-based procedure that (1) helps the surveyor prepare the EC correctly and (2) automatically enters the data into the elevation registry, Dewberry again consulted with FEMA Law Associates, PLLC, and received the legal opinion at APPENDIX O which concludes that such a web site would be legal and helpful. FEMA Law Associates recommended that the foreword to the web site should state that the site will help the surveyor prepare the EC correctly, and that it will automatically enter the data into the registry, but delete the name of the owner in the registry. The legal opinion concluded with the statement: "FEMA would not be well served by trying to hide the fact that elevation information required for insurance under the NFIP is in the public domain." Strategy E — Leverage Alternative Data Sources E.1 U.S. Census Bureau For this study, Dewberry coordinated with the Census Bureau and orchestrated an April 2003 meeting between FEMA and the Census Bureau. The purpose of the meeting was to determine if there were ways for FEMA and Census to collaborate to solve common problems. FEMA specifically hoped to be able to collaborate on the Master Address File (MAF) and the MAF TIGER Accuracy Improvement Project (MTAIP) which Census is currently conducting with five goals as follows: 1. To achieve a 7.6-meter or better horizontal positioning accuracy for Census' TIGER road centerlines to a CE95 criterion (radial accuracy at the 95% confidence level, where 7.6 meters is the radius of a circle of uncertainty such that the true or theoretical location of points being measured fall within that circle 95% of the time.) 2. To add National Hydrography Dataset (NHD) data from USGS (specifically, reach codes) utilizing the NHD for alignment purposes when the NHD data exceeds the positional accuracy of available local GIS hydrography data. 3. To update and geocode addresses, initially using a GPS van with stereo cameras plus a laser range finder to obtain x/y coordinates of the front door of buildings if possible, and also to get street centerlines with an accuracy of ±3 meters. The pilot will focus on rural areas where they do not have normal street addresses. 4. To update the MAF. 5. To maintain the currency of TIGER data for growth areas between 2001 and 2010. Census' use of a GPS van with stereo cameras (goal 3 above) is similar to the concept presented above for Sanborn's VISAT GPS van, but presumably with the stereo cameras pointed sideways. As of early 2004, Census had cancelled its GPS van initiative as being too expensive. During the Census/FEMA meeting, there was complete agreement that the two agencies both needed to solve common problems and they wished to collaborate. Specifically, both agencies needed up-to-date geocoded addresses. However, the MAF is covered by Title 13 of the United States Code, and the Census Bureau cannot share the MAF with other government agencies. For example, the U.S. Postal Service, which also needs geocoded addresses, has been specifically excluded from receiving Census MAF data, but the USPS is required to give its data to Census. The same is true of FEMA. FEMA can give its data to Census to update the MAF, but Census cannot give its MAF data to FEMA or anyone else. This Title 13 restriction effectively thwarted FEMA's attempts to collaborate on the MTAIP. E.2 U.S. Postal Service Dewberry next coordinated with the U.S. Postal Service (USPS) Address Management Office which provided Dewberry with sample USPS files of zip+4 address ranges of Charlotte, NC. However, these files included geographic coordinates only for the start and end of mail routes, without geocoding any of the addresses along those routes. Dewberry concluded that such files would be of minimal benefit in establishing an elevation registry. E.3 U.S. Army Corps of Engineers As indicated above, the Philadelphia District has a sizeable structure database in its Susquehanna River Flood Warning and Response System (FWRS). For this study, Dewberry did not attempt to determine if other Engineer Districts have anything comparable that could be used to help populate an elevation registry. Should FEMA decide to establish a registry, the Corps' Director of Civil Works would be invaluable in official solicitation of cooperation and input from the Corps' various districts, divisions, and R&D laboratories that may have elevation data on structures surveyed in the past, or surveys planned in the future. E.4 Community GIS Data Many communities have GIS data that would be beneficial to FEMA in attempting to identify the most cost-effective way to generate thousands of elevation records for individual buildings, to include the following: ? Photogrammetric base maps and/or aerial triangulated aerial photography that could be used cost effectively to generate spot heights or LAG/HAG elevations. ? Digital orthophotos that could be used to overlay spot heights for surveyors to reference in measuring elevation offsets between spot heights and top of bottom floor elevations, elevation of lowest machinery, or elevation of lowest horizontal structural member. ? Georeferenced building footprint files that could be used to "cookie cut" LIDAR or IFSAR data to automatically compute LAG/HAG elevations. ? Georeferenced centroids for buildings or lots that could be used to geocode ECs that are currently not geocoded. ? Georeferenced parcel polygon files, as provided by Harris County, TX for this study, that could be used to geocode current ECs when those polygons are overlaid on top of digital orthophotos. ? Tax/appraisal records that indicate whether or not addressed structures have basements. Each of these datasets has potential value to FEMA as will be subsequently apparent when performing the cost-effectiveness analyses in the Conclusion of Part I of this report. E.5 National Parcelmap Data Portal (NPDP) As explained at www.geodata.gov, the NPDP is a compilation of digital parcel maps of U.S. counties normalized by Boundary Solutions, Inc. at 1:12,000 scale (the same scale as digital orthophoto quarter quads) and available commercially as standardized ArcView shape files linked to street addresses, assessed values and other information. The NPDP consists of nearly 60 million parcels in approximately 200 major metropolitan areas nationwide, including over 400 jurisdictions. www.boundarysolutions.com/ORDER.html provides a list of these areas. Since Boundary Solutions purchases the data from the various jurisdictions for resale to users such as the insurance, hazard disclosure, infrastructure and real estate information industries, these communities are obviously among the thousands of communities nationwide believed to have digitized parcel maps. The significance for this study is that all Strategy B technologies (photogrammetry, Pictometry, LIDAR and IFSAR) will work in these jurisdictions by linking the dominant rooftop located within each parcel to its street address. (Note: rooftops are seen with photogrammetry, Pictometry, LIDAR, and IFSAR, as well as digital orthophotos now available nationwide.) Presumably, the jurisdictions listed would not need to purchase the data in the NPDP because these jurisdictions already have comparable or even newer data for their own use which they periodically update and sell to Boundary Solutions for normalization and resale to the public. The NPDP may be of interest to FEMA for additional reasons beyond the elevation registry. In the event that a natural or manmade disaster strikes one of these metropolitan areas, the parcel polygons could be overlaid on top of current digital orthophotos or even high-resolution satellite imagery showing the damaged areas. With common ESRI GIS software, polygons defining the limits of areas totally destroyed, 75% destroyed, 50% destroyed, 25% destroyed, etc. could be quickly established. A spatial intersection of these polygons with the NPDP parcel polygon layer would return highly accurate listings of addresses, floor area, facility use, and assessed values of buildings on each parcel so FEMA could make timely and accurate damage assessments for the entire metropolitan area. Furthermore, since HAZUS is designed to work with parcel level data if it's available, the elevation registry with NPDP data would be valuable to HAZUS. E.6 CitySets With some similarities to the NPDP, CitySets is a detailed geographic dataset that focuses on downtown areas of major cities in the U.S. and includes digital orthophotography, three dimensional buildings (and not just building footprints), point address locations (including all addresses within large buildings), number of stories, construction materials and other attributes used by the insurance industry and those involved with risk management and disaster response. CitySets was developed by Sanborn Mapping for Risk Management Solutions (RMS), a software provider for the insurance industry. Whereas CitySets may be of marginal stand-alone value to the NFIP at this time, it could be used by the NFIP in the event that other FEMA programs choose to use CitySets in another FEMA database to support emergency response command centers, as currently used in Washington, D.C. and other major cities in the U.S. CitySets' major current value to the NFIP would be in providing a complete listing of addresses with geocoded building footprints for major cities. E.7 Home Owner Support Several of the technical alternatives, discussed below, would become more cost effective if costs could be avoided for paying surveyors to collect street addresses, count and measure the area of flood vents, and measure the vertical offset distances from the LAG to the top of bottom floor, garage floor, elevation of lowest machinery, and/or elevation of lowest horizontal structural member in V- zones. For those communities that have accurate LAG elevations linked to street addresses, whether the LAGs came from LIDAR or photogrammetry, the insurance agents themselves could be allowed to enter selected data into the elevation registry, based on photographs, measurements and other information provided by home owners. Homeowner photographs could be provided showing all sides of the structure. The agent could identify the correct FEMA building diagram number and verify the number and size of flood vents —if a yardstick is photographed at close range next to the vents. If the location of the LAG point could be reasonably determined, then the homeowner could also provide a photograph showing a yardstick or tape measure being used to measure offset distances. The most common distances would be the offset distance from the LAG point to the top of foundation (from which 8 feet is subtracted to derive the elevation of the basement floor), to the bottom of a walk-out basement door, or to the bottom of the front door, for example. Surveyors themselves rarely survey inside of buildings. Instead, they normally compute the elevation of a basement floor as Top of Foundation minus 8 feet, or Bottom of Front Door minus 9 feet to allow one additional foot for floor joists and flooring materials. Tape measures were used in 2000 for measurement of vertical offsets for the Susquehanna River Flood Warning and Response System that was tested to have vertical accuracy of 1.19 ft at the 95% confidence level. Dewberry believes that homeowners could make such vertical offset measurements and collect other necessary data, provided they had access to a web site or instruction pamphlet provided by the insurance agent that explained correct procedures to be used. They would also need some way to identify which spot heights or LAG points pertain to their home; this problem was explained previously with Figure 2. However, Dewberry does recognize that insurance agents may have practical reasons for resisting homeowner input in the rating process and may oppose the additional effort required. E.8 Flood Zone Determination (FZD) Companies The mandatory purchase requirements of the NFIP have created a requirement for all Federally-insured mortgage lenders to locate a subject property on a FIRM panel and make a determination as to whether flood insurance should be required as a condition for the issuing of a mortgage. Virtually all lending institutions have found it more effective to outsource for this service. This has given rise to the Flood Zone Determination (FZD) industry. FZD companies provide flood zone determination services to mortgage lending institutions, Write- Your-Own insurance companies and their agents, commercial insurers, real estate appraisers, and appraisal form providers. FZD services are typically provided through an electronic data interchange (EDI) with lenders. Through these interfaces mortgage lenders and other FZD clients provide real property information and in return receive completed FEMA Standard Determination Forms that include FIRM panel, zone and other data. In some instances, the FZD companies may also provide Census tract information to assist lenders in meeting other Federal regulations. While the majority of FZD services are provided in bulk to mortgage lenders, FZD companies do provide services to any client for single determinations. The application of internet-based technologies is becoming an increasingly important aspect of this business. The flood zone determinations are often but not always accompanied by a guarantee. Most FZD companies also offer a Life of Loan (LoL) service. Through the LoL service, these companies continuously track the flood zone status of properties and notify their customers whenever a change occurs. Most large FZD companies maintain databases of information regarding property address, tax assessor parcel number, and flood zone. The content and comprehensiveness of these databases varies among companies. First American and Transamerica In 2003, First American Corporation announced its acquisition of Transamerica Finance Corporation’s Flood and Tax companies. First American Flood Data Services claims to be the largest flood zone determination provider in the country. They combine GIS technology, manual map reading, and a library of over 450,000 tax, plat, and flood maps to deliver their services. Transamerica Flood Hazard Certification (TFHC), as part of its automated delivery system, has developed a database of 111,000,000 properties. The U.S. Census Bureau estimates that there are 118,000,000 structures in the U.S., so the TFHC database represents 94% of the structures in the U.S. The database includes property address results for all 50 States, Puerto Rico, the Virgin Islands, and other U.S. Territories. TFHC originally built its database by collecting comprehensive tabular data taken directly from the rolls of tax assessors and/or tax collectors. They combine this with spatial data using their own mapping library, digital flood maps, aerial photos, and GIS technology to determine the latitude/longitude of a property. TFHC passes records through its “triple-geocoder” to clean, standardize, and geocode addresses. Duplicate addresses are identified. The TFHC database includes the following information that would be pertinent to the development of an elevation registry: Latitude and longitude (mostly geocoded, not surveyed) Address Parcel number Determination code FIRM panel number FIRM effective data Community name Flood zone The TFHC database would not provide all of the needed components of the elevation registry but might provide an initial source of information to identify the “universe of floodprone structures” for which EC records should be sought. DataQuick DataQuick is a MacDonald Detweiler Company that claims to be the nation’s leading provider of real property and land data. They maintain data on approximately 83 million properties in 880 jurisdictions in 36 states, primarily in the more populous areas of the country. The list at Table 14 summarizes the areas where DataQuick data are available. Table 14 — DataQuick Data Availability State Available Data State Available Data AL 3 counties NV 34 counties and 1 city AK 1 county NJ All 21 counties AZ 15 counties NM 2 counties CA 58 counties NY 16 counties CO 14 counties NC 15 counties CT 169 communities OH 23 counties FL 59 counties OK 3 counties GA 10 counties OR 11 counties HI 4 counties PA 14 counties IL 19 counties SC 13 counties IA 1 county TN 95 counties MD 23 counties and 1 city TX 13 counties MA 351 communities UT 4 counties MI 5 counties VT 2 communities MN 2 counties VA 15 counties and 3 cities MO 4 counties and 1 city WA 11 counties MT 3 counties WI 4 counties NE 4 counties WY 1 county No data are available from DataQuick in the following states: Arkansas, Delaware, the District of Columbia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Maine, Mississippi, New Hampshire, North Dakota, Puerto Rico, Rhode Island, South Dakota, Virgin Islands, and West Virginia. The total number of policies in force in these states as of September 2002 was 601,398 including over 367,000 policies in Louisiana. Texas is another state with a relatively high policy count (450,663) that may also be underserved by the DataQuick holdings. Only 13 counties are available, whereas 75 counties met the population and policies in force criteria for Q3 Flood Data production. E. 9 Insurance Industry Insurance companies also have collected large inventories of ECs. However, issues of obtaining and assembling data from disparate sources coupled with industry concerns over release of potentially business-sensitive data would make this a challenging source of EC data, especially since insurance companies may be reluctant to release information on their book of business. E.10 NEMIS Database In FY 1999, FEMA deployed the National Emergency Management Information System (NEMIS) which serves as the information technology standard for the agency's Presidential disaster operations. During the transition to NEMIS, the data in the Automated Disaster Assistance Management System (ADAMS), the predecessor system, was transferred to the newer system. NEMIS automates Federal disaster programs including incident activities, preliminary damage assessment, declaration processing, human services, infrastructure support, mitigation, and associated administrative and financial processing. During FY 2002, NEMIS supported more than 197 disasters, 42 of which were Presidential declarations. Currently, NEMIS contains information on over 400,000 structures, including a structure’s address, type (basement, no basement), and UTM coordinates (latitude and longitude). Given the complexity of NEMIS, it would be more cost effective to restrict the elevation registry to only import NEMIS data and display it with other elevation registry data, and not export data to NEMIS nor require damage inspectors to enter data into the elevation registry directly. Summary of Technology Capabilities Table 15 summarizes the suitability of the various technologies evaluated for detecting/surveying information needed to populate an elevation registry. For airborne remote sensing (traditional photogrammetry, Pictometry, LIDAR or IFSAR), and even for the photogrammetric or LIDAR van, their inability to always see basement windows or walk-out basement doors (not visible from the street) can be offset by on-site measurements of vertical offsets, and simultaneously determine the street address, building diagram number, number and size of vents, etc. Such on-site measurements could be performed in minutes with a simple tape measure or steel tape, rather than requiring precise survey measurements. Table 15 — Technology Suitability Matrix Elevation Certificate items or elevation registry Items vs. ability to detect/survey by various methods El ev ati on Ce rti fic at es Aer ial Ph oto gra m me try Pi ct o me try Ai rb or ne LI D A R Ai rb or ne IF S A R Ph ot og ra m m etr ic Va n H o m e O w ne r Dr iv e- by Re co nn ai ss an ce O n- sit e M ea su re m en ts An cill ar y Co m m un ity Da ta Street Address A N N N N M A M A A City A A A A A A A A A A State A A A A A A A A A A Zip Code A S S S S M A M A A Property Description P N N N N P M P P A Building Use A S M N N A A A A A Latitude/Longitude P A A A A A N P P A Horizontal Datum P A A A A A N P P A Source P A A A A A N P P A B1. NFIP Community Name/Number A A A A A A N A A A B2. County Name A A A A A A A A A A B3. State A A A A A A A A A A B4. Map and Panel Number A M M M M A N A A A B5. Suffix A M M M M A N A A A B6. FIRM Index Date A A A A A A N A A A B7. FIRM Panel Date A M M M M A N A A A B8. Flood Zone(s) A M M M M A N A A A B9. BFE A M M M M A N A A A B10. Source of BFE A A A A A A N A A A B11. Elevation Datum A A A A A A N A A A B12. CBRS P P P P P P N P P A C1. Building elevations based on A A A A A A A N N A Elev accuracy @ 95% confidence level A A M M S A N N N N C2. Building Diagram Number A N M N N M A M A A C3a. Elevation, top of bottom floor A N M N N S O N O S C3b. Elevation, top of next higher floor A N M N N S S N N S C3c. Bottom, lowest horiz structural member A N N N N S O N O S C3d. Elevation, attached garage (top of slab) A M M S S M O N O S C3e. Lowest elevation of machinery A N N N N S O N O S C3f. Lowest adjacent grade (LAG) A A M M S S N N N S C3g. Highest adjacent grade (HAG) A A M M S S N N N S C3h. Number of flood vents <1' above grade A N N N N M M M A N C3i. Area of flood vents A N S N N S A S A N Certifier's Name A M M M S A N N N N License Number A M M M S A N N N N Ability to detect basements A N M N N S A S A A Was structure previously flooded inside? N N N N N N S N N A Depth of prior interior flooding N N N N N N S N N A Street addresses are always mandatory. Other bold items are most critical for an elevation registry A = Can be determined - All of the time M = Most of the time (>50%) S = Some of the time (<50%) N = None of the time, or almost never O = Can measure vertical offsets P = Possible if/when required COST-EFFECTIVENESS (CE) ANALYSES Methods for Populating an Elevation Registry This section performs cost-effectiveness (CE) analyses of twenty (20) alternative methods for populating the elevation registry. These are 20 alternatives to the traditional survey method (Method 0) employed by those communities who hire surveyors to mass produce elevation surveys of all existing structures in or near floodplains community-wide. Table 16 — Summary of Twenty Alternative Methods The Base Method 0 assumes a community hires a survey firm to mass produce ECs digitally for batch entry into the registry (cost = $300 each). Methods 1 through 20 are lower cost alternatives. Existing ECs are digital for Method 1 but hardcopy for Methods 2 through 4. Future ECs (Methods 5 and 6) are digital and individually entered into the registry. Methods 7 through 20 all use remote sensing data, and all batch-entered into the registry, except for Methods 9, 16 and 18 where home owners enter records individually into the registry. Provided by Community or Surveyors H o m e o w n e r o n - s i t e m e a s u r e m e n t s B a t c h w e b e n t r y i n t o r e g i s t r y I n d i v i d u a l w e b e n t r y i n t o r e g i s t r y E C s w i t h o u t L a t i t u d e / L o n g i t u d e E C s w i t h L a t i t u d e / L o n g i t u d e P h o t o g r a m m e t r y f o r 2 ' c o n t o u r s P h o t o g r a m m e t r y f o r 5 ' c o n t o u r s L I D A R D T M s e q u i v a l e n t t o 2 ' c o n t o u r s I F S A R D S M s e q u i v a l e n t t o 1 0 ' c o n t o u r s P i c t o m e t r y B u i l d i n g F o o t p r i n t s w i t h A d d r e s s e s C o m m u n i t y o n - s i t e m e a s u r e m e n t s 0. Surveyed ECs accurate lat/long, batch entry X X 1. Digital ECs, no/inacc. lat/long, batch entry X X 2. Hardcopy ECs, no/inacc. lat/long, batch entry X X 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry X X 4. Hardcopy ECs accurate lat/long, 1 web entry X X 5. Future ECs, no/inacc. lat/long, 1 web entry X X 6. Future ECs, accurate lat/long, 1 web entry X X 7. Photogrammetry (2' CI), no offsets X X 8. Photogrammetry (2' CI), surveyor offsets X X X 9. Photogr. (2' CI) with footprints, owner offsets X X X 10 Photogrammetry (5' CI), no offsets X X 11. Photogrammetry (5' CI), surveyor offsets X X X 12. Pictometry with LIDAR DTM X X X 13. LIDAR (2' CI), no footprints, no offsets X X 14. LIDAR (2' CI) with footprints, no offsets X X X 15. LIDAR (2' CI), no footprints, surveyor offsets X X X 16. LIDAR (2' CI), no footprints, owner offsets X X X 17. LIDAR (2' CI) with footprints, surveyor offsets X X X X 18. LIDAR (2' CI) with footprints, owner offsets X X X X 19. IFSAR (10' CI) w/footprints, surveyor offsets X X X X 20. Photogrammetric Van (VISAT) X X Color Legend for Table 16 Strategy A — Existing ECs methods are in blue Strategy B — Airborne remote sensing methods are in green Strategy C — Photogrammetric van method is in orange Strategy D — Future EC methods are in yellow Strategy E — Community-provided alternative data methods are in violet * Strategy E — Home-owner provided alternative data methods are in pink * * Alternative data (Strategy E) to augment airborne remote sensing data (Strategy B) is in one of two categories: (1) "Reverse geocoding" whereby latitude and longitude of airborne data are linked to street addresses by use of addressed building footprints, centroids or parcel polygons (2) On-site measurements from LAG to determine top of bottom floor, lowest horizontal structural member and other elevations and to count vents and take vent measurements With Method 0, communities hire survey contractors: (1) to perform GPS elevation surveys of temporary bench marks (TBMs) consistent with NOS NGS- 58 guidelines for GPS elevation surveys, or alternative GPS survey standard, (2) to extend conventional surveys from the TBMs to survey points on or adjacent to houses in order to determine elevations of the top of bottom floor in A-zones, bottom of lowest horizontal structural member (LHSM) in V-zones, lowest adjacent grade (LAG), highest adjacent grade (HAG), garage floor, and lowest machinery, (3) to provide accurate geographic coordinates and street addresses for each structure, (4) to determine the correct building diagram number, and (5) to determine the number, size and location of flood vents. Minor additional costs would be incurred to enter all appropriate data into the elevation registry, excluding the names of the home owners. As explained below with assumptions, Method 0 is assumed to cost $300 per structure on average; the other 20 lower cost methods are then compared with Method 0 for their cost effectiveness, with lower values given to methods that yield less-accurate or incomplete records. The 20 alternative methods, summarized in Table 16 and further explained below, are not the only alternatives, but they do comprise major alternatives evaluated during this research project, avoiding dozens of additional combinations that depend upon whether or not a community has digital orthophotos, building footprints, centroids, and/or tax parcels, and whether or not the footprints, centroids, or parcels are linked to street addresses. Each of these 20 methods has strengths and/or limitations in their ability to provide information required by the registry, evaluated in six categories: (1) ability to provide accurate geographic coordinates (latitude and longitude) and street addresses for individual records, (2) ability to provide information needed to identify the correct FEMA building diagram number, (3) ability to provide accurate top of bottom floor and LHSM elevations, (4) ability to provide accurate LAG and HAG elevations, (5) ability to provide accurate elevations of garage floor and lowest machinery, and (6) ability to identify and measure flood vents. Using traffic light symbology, when all six of these items can be provided with reasonable accuracy by the method being evaluated, the value to the registry will be color-coded green — of high value. When a key item such as top of bottom floor or LHSM elevation cannot be provided by the method being evaluated, the record value will be color-coded amber — of reduced value. When several key items, such as street address and top of bottom floor elevation cannot be provided by the method being evaluated, the record value will be color-coded red — of little or marginal value to the registry. A cost-effectiveness (CE) rating system is used, compared with 100% full value. Methods that are between 0% and 33.3% are color-coded red; methods that are between 33.3% and 66.7% are color-coded amber; and methods that are between 66.7% and 100% are color- coded green. The following twenty (20) alternative methods were evaluated during this study. Methodology used to quantify the relative high-, mid-, and low-value of EC components is explained in the next section of this report. 1. Method 1 utilizes individual existing digital ECs or digital elevation records already available in databases, but which do not include accurate latitude/longitude information and therefore are not full value. These existing digital records would be quality controlled and reformatted for entry into the elevation registry. This method would include files from ISO, the LOMA 2000 database, the Policies in Force database (BureauNet), and potentially files from map determination companies or other sources. Because each of these files are in different formats, some computer programming may be required to reformat the records, and some addresses may need to be manually re-entered into the registry in the recommended format. However, Dewberry still estimates that such records can be reformatted for entry into the registry at a unit cost of $5 per structure. [Some of the existing digital ECs (Dewberry's and the U.S. Army Corps of Engineers') also include accurate latitude and longitude; they would therefore be full value and have even a higher EC ratio than those shown for Method 1 which assumes no latitude/longitude or inaccurate coordinates because of automated geocoding.] Method 1 is part of Strategy A to maximize the use of existing EC data. 2. Method 2 utilizes large batches of existing hardcopy ECs, normally maintained by communities. Because these commonly lack latitude and longitude, they are not full value. They would be batch digitized and batch processed into the registry. Method 2 is also part of Strategy A. 3. Method 3 utilizes individual existing hardcopy ECs or small quantities of ECs. Because these commonly lack latitude and longitude, they are not full value. These would be individually digitized and entered into the registry. Method 3 is also part of Strategy A. 4. Method 4 is the same as Method 3 except that these are individual or small quantities of ECs that are 100% of full value because they include accurate geographic coordinates (normally from GPS surveys). There are no known large quantities of such certificates beyond those already known to be in digital format. Method 4 is also part of Strategy A. 5. Method 5 utilizes individual future ECs that are not full value because they lack latitude/longitude values. With a web-based registry, it is intended for these ECs to be entered into the registry by the Land Surveyor or other professional licensed to perform such surveys, when validated by FEMA (as described later in this report). Method 5 is part of Strategy D to make the best use of future ECs produced by others, while minimizing costs to FEMA for surveying the new ECs. 6. Method 6 is the same as Method 5 except that these ECs are 100% of full value because they include accurate latitude/longitude values (normally from GPS surveys). Method 6 is also part of Strategy D. Methods 1 through 6 are color-coded green in all scenarios evaluated below as these six methods always yield high value records. 7. Method 7 utilizes stereo photogrammetric data already available in some communities for prior generation of 2' contours. This photogrammetric data would now be further used to stereo-compile photogrammetric spot heights and LAG/HAG elevations for large groups of structures batch processed into the registry. Without leveraging additional data sources, Method 7 alone would yield relatively low value records because: (1) the geographic coordinates are not linked to street addresses so the basic address would be missing from each record, (2) the building diagram numbers would be unknown, (3) there would be no vertical offset measurements up/down from the LAG to determine the elevation of the top of bottom floor, LHSM or other elevations, and (4) there would be no identification or measurement of flood vents. Method 7 is part of Strategy B to maximize the use of existing airborne remote sensing data, in this case, existing photogrammetric data previously determined to be suitable for prior generation of 2' contours. 8. Method 8 supplements Method 7 with vertical offset measurements and other data acquired on-site by a surveyor or other qualified person (e.g., a GIS technician) so as to complete high value records. This method assumes that the surveyor or GIS technician has a GPS receiver and/or GIS software necessary to link photogrammetric spot heights with street address and geographic coordinates of each structure surveyed, or can overlay the spot heights on top of digital orthophotos (as shown in Figure 2) so as to identify structures for address matching. Method 8 is a combination of Strategy B and Strategy E which leverages alternative data sources; in this case, the alternative data source would be community- wide surveyor-measured offsets (or offsets measured by non-surveyors) and other data collected on-site at each structure, but without employing traditional surveying equipment to perform conventional or GPS surveys. 9. Method 9 is the same as Method 8, with two exceptions: (1) the homeowner himself or herself provides photographs and vertical measurements to the insurance agent who completes individual registry records rather than batch processed records as in Method 8; and (2) the community has street-addressed building footprints (see Figure 6) or alternative method for identifying the addresses that go with the photogrammetric spot heights. Without address matching, the homeowner will not know which spot heights pertain to his/her house and this method would be ineffective -- as with Method 16 described below. Method 8 is also a combination of Strategy B and Strategy E; but in this case, there are two alternative data sources, i.e., owner-provided data and measurements and the community-provided building footprints linked to street addresses. 10. Method 10 is the same as Method 7 except that the photogrammetric spot heights are equivalent to 5' contours instead of 2' contours. Because of the poorer accuracy, Method 10 yields lower value records than Method 7. Method 10 is part of Strategy B to maximize the use of existing airborne remote sensing data -- in this case, existing photogrammetric data suitable for generation of 5' contours. 11. Method 11 supplements Method 10 with vertical offset measurements and other data acquired on-site by a surveyor or other qualified person, as used in Method 8 above, so as to yield mid-value records. Method 11 is a combination of Strategy B and Strategy E which leverages alternative data sources; in this case, the alternative data source would be community- wide surveyor-measured offsets (or offsets measured by non-surveyors) and other data collected on-site at each structure, but without employing traditional surveying equipment to perform conventional or GPS surveys. 12. Method 12 utilizes oblique Pictometry imagery already available in a few communities, but now further used to identify building diagram numbers, identify full or walk-out basements, identify flood vents, and estimate vertical offset measurements relative to LIDAR digital terrain models (DTMs) equivalent to 2' contours. These records would be of mid value because the scale of the Pictometry imagery is such that basements may often be misidentified and because rigorous aerial triangulation is not performed for each image to enable direct measurements of points viewed in stereo. Method 11 is part of Strategy B to maximize the use of existing airborne remote sensing data, in this case, existing Pictometry imagery available in 100+ communities nationwide, but rapidly growing in popularity. 13. Method 13 utilizes high resolution raw LIDAR "point cloud" data, already available in some communities, typically used by communities to generate bare-earth DTMs equivalent to 2' contours. Without building footprints, a LIDAR specialty firm (such as Computation Consulting Services, CCS) would be needed to automatically extract building centroids and/or footprints and to compute LAG and HAG elevations using a "NoFP" method. Although LAG and HAG elevations can be derived with good accuracy, Method 13 would yield relatively low value records batch processed into the registry because: (1) the geographic coordinates of LIDAR points would not be linked to street addresses so the basic address would be missing from each record, (2) the building diagram numbers would be unknown, (3) the top of bottom floor and other elevations would be inaccurately estimated, (4) there would be no identification or measurement of flood vents, and (5) the automatic process for extracting buildings from raw LIDAR data would typically be less than 90% successful (depending largely on the point spacing of the raw LIDAR data). Method 13 is part of Strategy B to maximize the use of existing airborne remote sensing data, in this case, existing raw LIDAR "point cloud" data. 14. Method 14 utilizes the bare-earth DTM equivalent to 2' contours (as in Method 13), but provided in Triangulated Irregular Network (TIN) format and supplemented with addressed footprints provided by the community. LIDAR specialists at Dewberry or other LIDAR processing firm would use a "w/FP" method to "cookie cut" the LAG and HAG elevations from the TIN with street address also known for each LAG/HAG value. This method would yield mid-value records batch processed into the registry. Method 14 is a combination of Strategy B (existing LIDAR-derived bare-earth DTM data) and Strategy E (community-provided building footprints linked to street addresses). 15. Method 15 supplements Method 13 with vertical offset measurements and other data acquired on-site by a surveyor or other qualified person, yielding relatively high quality records batch processed into the registry. This method assumes that the surveyor or GIS technician would have a GPS receiver and/or GIS software necessary to link LIDAR data with street address and geographic coordinates of each structure surveyed, or could overlay LIDAR-derived building centroids on top of digital orthophotos (as shown in Figure 6) so as to perform address matching. Method 15 is a combination of Strategy B (existing raw LIDAR "point cloud" data) and Strategy E (community-wide surveyor measured offsets and other data collected on-site). 16. Method 16 (color-coded magenta) is the same as Method 15 except that the homeowner would provide vertical offset measurements and photos to the insurance agent to complete a single record individually entered into the registry. However, a typical home owner does not have access to a GPS receiver or GIS software necessary to overlay LIDAR data on digital orthophotos or link LIDAR building centroid data with street addresses and geographic coordinates. Therefore, this method would be ineffective (color-coded magenta) unless the homeowner, insurance agent or community has GIS software to overlay LIDAR building centroid points or parcel polygons on top of digital orthophotos so that the someone can identify which LIDAR centroid and LAG/HAG elevation data pertain to his or her street address. Only with such added support would this method provide a high value record. Method 16 is a combination of Strategy B (existing raw LIDAR "point cloud" data) and Strategy E (homeowner measured offsets and other data collected on-site — plus community- provided support necessary to link LIDAR-derived building centroids or footprints and LAG/HAG elevations to street addresses). 17. Method 17 supplements Method 14 with vertical offset measurements and other data acquired on-site by a surveyor or other qualified person, yielding high quality records batch processed into the registry. Because addressed footprint files would be provided, this method does not assume that the surveyor or GIS technician has a GPS receiver and/or GIS software needed for address matching. Because addressed footprints are provided by this method, each street address would already be linked to the geographic coordinates of the LIDAR-derived LAG/HAG elevations. Method 17 is a combination of Strategy B (bare-earth LIDAR TIN) and Strategy E (community-provided building footprints linked to street addresses, and community-wide surveyor measured offsets and other data collected on-site). 18. Method 18 is the same as Method 17 except that the homeowner would provide vertical offset measurements and photos to the insurance agent to complete a single record individually entered into the registry. Because addressed footprints would be provided by this method, each street address would already be linked to the geographic coordinates of the LIDAR-derived LAG/HAG elevations. Method 18 is also a combination of Strategy B (bare-earth LIDAR TIN) and Strategy E (owner-provided data and measurements and community-provided building footprints linked to street addresses). 19. Method 19 utilizes IFSAR data, already available in some regions, and typically used by states/counties to generate broad area coverage of digital surface models (DSMs) of top surfaces, as opposed to DTMs of bare-earth surfaces -- combined with addressed building footprints as well as vertical offset measurements and other data acquired on-site by a surveyor or other qualified person. Because addressed footprint files would be provided, this method does not assume that the surveyor or GIS technician has a GPS receiver and/or GIS software needed for address matching. Because addressed footprints would be provided by this method, each street address is already linked to the geographic coordinates of the IFSAR-derived LAG/HAG elevations. Method 19 is a combination of Strategy B (IFSAR DSM) and Strategy E (surveyor- provided data and measurements and community-provided building footprints linked to street addresses). These records are of low value primarily because of the inherent accuracy of the IFSAR data, typically equivalent to 10' contours. The CE ratio can be improved by using the home owner, instead of a surveyor, to measure the vertical offsets and collect other data, but the value of the records are still low. 20. Method 20 utilizes photogrammetric van technology, such as Sanborn's VISAT, to acquire stereo images of structures as the van drives streets at normal traffic speed, recording the six required positioning and orientation parameters required for each image necessary to make accurate 3-D measurements of ground features measured in stereo. This technology is comparable to aerial photogrammetry except that the cameras are on the ground, pointing sideways instead of downward. Method 20 is part of Strategy C to evaluate the use of mobile photogrammetric vans. This method also yields low value records. In summary, the following methods pertain only to individual structures or perhaps to small batches of structures processed individually into the registry: 3, 4, 5, 6, 9, 16 and 18. More importantly, the following methods pertain to community-wide initiatives that could be batch processed into the registry: 1, 2, 7, 8, 10, 11, 12, 13, 14, 15, 17, 19 and 20. Base Scenario In order to perform cost-effectiveness assessments of the 20 different methods for populating the registry, it is desirable to assess both the total value of each accurate and complete EC entered into an elevation registry, as well as the relative value of the most important elevation registry items highlighted (bold) in Table 15, considering that some EC items may be less accurate or less complete and therefore have lesser value to the registry. Dewberry's best estimate of the correct value of each variable is reflected in the Base Spreadsheet at Table 17 where EC elevation values become worthless when elevation errors are 4 feet or worse at the 95% confidence level, based on input from FEMA. To estimate the total value of surveyed ECs in the registry (Method 0), Dewberry considered its own costs for performing such surveys as well as cost proposals and input received from many other sources, including cost quotes from survey firms nationwide. The unit costs varied from a low of $150 to a high of $2300 per structure. There are many variables that cause large variations in the unit cost of EC surveys. Costs are lowest under the following conditions: (a) thousands of structures to be surveyed in the project area, (b) all structures are relatively close together in high density housing areas, (c) all structures are near to accurate, stable and GPS-able survey monuments that can easily be located by surveyors (d) survey monuments do not need to be validated prior to use, (e) structures are relatively simple (few split foyers and split levels), (f) few basements or crawl spaces, (g) communities notify owners of authorized survey activity on/near their property, (h) no strict deadline to be met, (i) surveys are performed on all structures within broad areas (e.g., entire SFHA for a community), (j) surveys are performed by local survey firms that do not incur travel/lodging/per diem expenses, and (k) survey specifications are relatively lax, allowing the surveyor to use the least expensive means to accomplish a survey that satisfies the scope of work. Costs are higher under the following conditions: (a) dozens or hundreds of structures are surveyed, rather than thousands of structures, (b) structures are dispersed and/or isolated, (c) the most desirable survey monuments are far from the survey project area, (d) surveyors need to first identify and recover suitable survey monuments and then validate their accuracy relative to other survey monuments in the community, (e) structures are complex with many levels, or difficult to classify building diagrams, e.g., split foyers, (f) complex basements or crawl spaces, or basements that do not have standard 8' foundation walls, (g) surveyors have to notify home owners and/or seek prior permission to survey, (h) tight schedules and strict deadlines that require accelerated planning and diversion of resources from other projects, (i) surveys are based on assigned address lists that require individual location, (j) surveys are performed by out-of- state specialists who incur travel, lodging and per diem expenses, and (k) strict conformance with NOAA Technical Manual NOS NGS-58 is mandated by the scope of work, whereby all GPS surveys must be performed twice, on two different days with distinctly different satellite geometry. The lowest cost estimate ($150 per structure) came from Charlotte-Mecklenburg Storm Water Services for whom Dewberry had surveyed over 2,000 ECs in 1996. Costs are lower there in 2004 because significant additional costs were incurred in 1996 when two weeks were spent in finding six survey control monuments throughout the county that, when surveyed relative to each other, all agreed within 1 inch. Two weeks of survey team expenses were incurred before surveys were ever started on the first EC in 1996. Because of significant discrepancies found between the county's survey monuments in 1996, many thousands of dollars were spent in identifying FA0318, FA0357, FA2462, FA2594, FA4563, and FA1406 as six monuments that could be used throughout the county, so that EC surveys from any of these monuments would yield similar results within 1 inch, regardless of which monument was used. If Dewberry had not gone through this additional expense in 1996, surveys from other monuments might yield structure elevations that differed by 6 inches or more because they would be surveyed relative to monuments that were inaccurate or inconsistent with other local monuments. Now, in 2004, surveyors do not need to repeat this initial validation process, so new ECs are less expensive than when starting afresh with new surveys that conform to NOS NGS-58. Dewberry's best judgment is that the value of each EC accurately surveyed should be $300 nationwide when local surveyors are used on large projects. When Dewberry's surveyors perform single surveys locally, the cost is approximately $600 when only one house is surveyed. When they are surveyed locally for batch processing, the cost is about $300 when the control points first need to be validated prior to use, as is necessary for the majority of elevation surveys. When Dewberry hires local surveyors to survey up to a hundred certificates in their own community, the unit cost is approximately $300. For this reason, the base scenario assumes traditional ECs may be mass produced community-wide for $300 each, as indicated at Table 17. Communities will find that their actual costs may be higher or lower than $300, and for this reason sensitivity analyses are performed for unit costs that vary between $100 and $600 (see Table 18). Table 17 — Cost-Effectiveness Model Base Spreadsheet A B C D E F G H I J Cost-Effectiveness (CE) Ratio and Relative Value of Different Methods for Generating Data for the Elevation Registry Assumes Value of Elevations Degrade to Zero as Elevation Errors become 4' or larger at the 95% confidence level A d d r e s s G e o c o d i n g t o g e t L a t i t u d e / L o n g i t u d e a n d B F E F E M A B u i l d i n g D i a g r a m N o . T o p o f B o t t o m F l o o r ( T B F ) i n A - z o n e , L o w e s t H o r i z S t r M e m b e r i n V - z o n e L A G a n d H A G E l e v a t i o n s O t h e r E l e v a t i o n s ( G a r a g e a n d L o w e s t M a c h i n e r y ) N u m b e r a n d A r e a o f F l o o d V e n t s T o t a l P e r c e n t V a l u e o f E C Us in g av ail ab le da ta, ad dit io na l co st pe r str uc tu re ad de d to re gi str y C E R a ti o c o m p a r e d w it h $ 3 0 0 v al u e o f E C s s u r v e y e d a n d e n t e r e d i n r e g is tr y w h e n m a s s - p r o d u c e d c o m m u n it y - w i d e Maximum Possible Percentage Points 5 5 55 25 5 5 100 N/A N/A 0. Surveyed ECs accurate lat/long, batch entry 5.0 5.0 55.0 25.0 5.0 5.0 100 $300 1.00 1. Digital ECs, no/inacc. lat/long, batch entry 0.0 5.0 55.0 25.0 5.0 5.0 95.0 $2.50 114.0 2. Hardcopy ECs, no/inacc. lat/long, batch entry 0.0 5.0 55.0 25.0 5.0 5.0 95.0 $7.50 38,00 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry 0.0 5.0 55.0 25.0 5.0 5.0 95.0 $15.00 19.00 4. Hardcopy ECs accurate lat/long, 1 web entry 5.0 5.0 55.0 25.0 5.0 5.0 100 $15.00 20.00 5. Future ECs, no/inacc. lat/long, 1 web entry 0.0 5.0 55.0 25.0 5.0 5.0 95.0 $10.00 28.50 6. Future ECs, accurate lat/long, 1 web entry 5.0 5.0 55.0 25.0 5.0 5.0 100 $10.00 30.00 7. Photogrammetry (2' CI), no offsets 0.0 0.0 9.5 17.2 0.9 0.0 27.5 $12.50 6.60 8. Photogrammetry (2' CI), surveyor offsets 5.0 5.0 47.3 21.5 4.3 5.0 88.1 $62.50 4.23 9. Photogr.(2' CI) with footprints, owner offsets 5.0 5.0 47.3 21.5 4.3 5.0 88.1 $20.00 13.22 10 Photogrammetry (5' CI), no offsets 0.0 0.0 3.9 7.0 0.4 0.0 11.2 $12.50 2.69 11. Photogrammetry (5' CI), surveyor offsets 5.0 5.0 19.3 8.8 1.8 5.0 44.8 $62.50 2.15 12. Pictometry with LIDAR DTM (2' CI) 0.0 4.0 18.9 17.2 1.7 0.0 41.8 $52.50 2.39 13. LIDAR (2' CI), no footprints, no offsets 0.0 0.0 8.0 14.6 0.7 0.0 23.4 $12.50 5.61 14. LIDAR (2' CI) with footprints, no offsets 5.0 0.0 11.8 21.5 1.1 0.0 39.4 $7.50 15.76 15. LIDAR (2' CI), no footprints, surveyor offsets 5.0 5.0 40.2 18.3 3.7 5.0 77.1 $62.50 3.70 16. LIDAR (2' CI), no footprints, owner offsets 0.0 5.0 40.2 18.3 3.7 5.0 72.1 $20.00 10.82 17. LIDAR (2' CI) with footprints, surveyor offsets 5.0 5.0 47.3 21.5 4.3 5.0 88.1 $57.50 4.60 18. LIDAR (2' CI) with footprints, owner offsets 5.0 5.0 47.3 21.5 4.3 5.0 88.1 $15.00 17.62 19. IFSAR (10' CI) w/footprints, surveyor offsets 5.0 5.0 0.0 0.0 0.0 5.0 15.0 $57.50 0.78 20. Photogrammetric Van (VISAT) 5.0 4.0 8.8 4.0 0.8 1.0 23.6 $37.50 1.89 *Note: no "owner" method works without footprints, centroids, or parcels linked to street addresses Elevation Accuracy Multipliers that degrade maximum value when less than best accuracy If 95% of elevations accurate within 6" 1.00 Assumed accuracy of conventional/GPS ECs If 95% of elevations accurate within 1' 0.90 If 95% of elevations accurate within 1.2' (2' CI) 0.86 Accuracy of 2' CI photogrammetry & LIDAR If 95% of elevations accurate within 1.5' 0.80 Accuracy of VISAT photogrammetric van If 95% of elevations accurate within 2' 0.70 If 95% of elevations accurate within 3' (5' CI) 0.35 Accuracy of 5' CI photogrammetry If 95% of elevations accurate within 4' 0.00 If worse than 4' at 95% confidence level 0.00 Accuracy of 10' CI IFSAR Column A of Table 17 lists Method 0 and the 20 different technology combinations considered as alternatives. They will be further explained below in conjunction with various parameters that define the mathematical cost- effectiveness model. Webster's dictionary defines "parameter" as follows: "In mathematics, a quantity or constant whose value varies with the circumstances of its application; any constant, with variable values, used as a reference for determining other variables." The eight bottom rows of Table 17 show the elevation accuracy parameters (multipliers) that degrade the maximum value of all elevation entries when less than the best (6") accuracy. These accuracy multipliers were provided by FEMA. This assumes the maximum multiplier of 1.0 for traditional ground-surveyed ECs assumed to have a vertical accuracy of 6" or better at the 95% confidence level. This also assumes the worthless multiplier of 0.0 when elevations are accurate to 4 ft at the 95% confidence level; this equates to approximate 6.7' contours. Column B lists the value, for each technology, of being able to provide accurate geocoding (latitude and longitude) of street addresses, as opposed to map coordinates or automated geocoding which has been demonstrated to have errors of hundreds of feet, implying that the coordinates could be incorrectly attributed to a neighboring house or another house across the street. Inaccurate coordinates will provide incorrect information when estimating BFEs or updating flood risk with new flood information or when identifying addresses to receive flood warnings. A base value of 5% was assigned to this parameter. In the Base Scenario spreadsheet shown in color at APPENDIX Q, column B entries in pink are those with no building footprints, i.e., there would be difficulty linking the remote sensing data to the correct street addresses if the data could not be overlaid on top of digital orthophotos or base maps; and those entries in yellow would have difficulty doing so if the building footprints are not linked to street addresses. Column C lists the value, for each technology, of being able to determine the correct FEMA building diagram number. This is needed for rating flood insurance policies. A base value of 5% was assigned to this parameter. Column D lists the value, for each technology, of being able to accurately determine the top of bottom floor in A-zones as well the elevation of the lowest horizontal structural member (LHSM) in V-zones. A base value of 55% was assigned to the top of bottom floor/LHSM parameter as FEMA officials considered these elevations to provide more than half the total value of an EC record for insurance rating purposes. Column E lists the value, for each technology, of being able to accurately determine the LAG and HAG, recognizing that the LAG is clearly more important than the HAG. A base value of 25% was assigned to the LAG/HAG parameter because the LAG is vital for mandatory purchase requirements. Column F lists the value, for each technology, of being able to accurately determine the elevation of the garage and lowest machinery. A base value of 5% was assigned to this parameter. Column G lists the value, for each technology, of being able to determine the number and area of flood vents within 1 foot of grade. A base value of 5% was assigned to this parameter. Column H lists the total percentage point value of an EC produced by each of the 20 technology combinations, as a percentage of the maximum value ($300) for an accurate and complete EC. Using "traffic light" color coding scheme, Column H entries in green represent "go" or high-value records from that technique (values between 66.7% and 100%); entries in amber represent "caution" or mid- value records from that technique (values between 33.3% and 66.7%); and entries in red represent "stop" or low-value records from that technique (values between 0% and 33.3%). Method 16 is color-coded magenta because, without addressed footprints, no homeowner option will work unless there is another alternative for linking remote sensing data to street addresses, such as addressed centroids or footprints. With none of these, the homeowner cannot determine which LAG/HAG elevations pertain to his/her address. Column I uses various cost models to compute the additional costs necessary to produce the EC record entered into the elevation registry, assuming that the remote sensing data (aero-triangulated aerial photography, Pictometry images, raw "point cloud" and/or bare-earth LIDAR data, IFSAR data, or VISAT data) has already been acquired and paid for by the county/community. Also, when building footprints, centroids or tax parcels are available from community GISs, it is assumed that these costs have already been paid for. Column J computes the Cost-Effectiveness (CE) ratio of the $300 value divided by the additional costs in column I, beyond costs already borne by the community to acquire remote sensing data and addressed footprints when available. All CE ratios larger than 1.00 indicate a good CE ratio, i.e., better than 1:1 (more than $1 in value for each dollar spent), and all ratios smaller than 1.00 indicate a poor CE ratio, i.e., poorer than 1:1 (less than $1 value for each dollar spent). The following are the remaining CE parameters in the full spreadsheet at APPENDIX Q that will be varied, below, in sensitivity analyses over their most probable range of uncertainty: ? Spreadsheet parameter K3 (the value in cell K3 of the spreadsheet) is the reduced accuracy and value of top of bottom floor and LHSM (column D) and other elevations (column F) by not knowing the vertical offset between these elevations and the LAG or HAG (column E). Base value = 0.25. ? Spreadsheet parameter K5 is the reduced accuracy of determining the LAG elevations automatically for LIDAR and IFSAR by not having a file of building footprints. Base value = 0.85. ? Spreadsheet parameter K7 is the uncertainty in being able to link LAG values with the correct street address, as well as uncertainty in a technology's ability to see all sides of a building to determine the correct building diagram number. Base value = 0.80 for Pictometry and VISAT. ? Spreadsheet parameter K9 is the reduced confidence in Pictometry's ability to see and accurately measure elevation offsets relative to surrounding DTMs. Base value = 0.50. ? Spreadsheet parameter K11 is the uncertainty in VISAT being able to see past shrubbery to measure vertical offsets, e.g., top of bottom floor and other elevations, relative to the LAG. Base value = 0.20 based on Pinellas County data. ? Spreadsheet parameter K13 is the unit cost for image identification and measurement of VISAT images to determine addresses and other items required for ECs. Base cost = $35.00, based on Sanborn cost quote. ? Spreadsheet parameter K15 is the unit cost to digitize existing ECs for database entry (bulk processing) into the registry. Base cost = $5.00, based on cost quote from a Dewberry subcontractor for double entry of most entries (with automatic comparison of two files to identify and correct errors), plus a scanned image of the original EC. ? Spreadsheet parameter K17 is the unit cost for batch entries of EC data by all methods other than individual web-based entries of new ECs into the registry. Base cost = $2.50. This value was computed based on $1.5 million estimated cost for developing a web-based registry for batch entry of ECs, plus an estimated $1 each for costs incurred in obtaining an estimated one million ECs from CRS communities that currently hold them. This $1 unit cost for acquisition of ECs is in addition to the estimated cost of $5 for digitizing each hardcopy EC. ? Spreadsheet parameter K18 is the unit cost for individual web-based entry of a single EC into the registry. Base cost = $10.00. This value was computed based on $2.5 million estimated cost for developing a web- based registry for entry of individual ECs, amortized over 5 years assuming 50,000 web-based entries per year. ? Spreadsheet parameter K19 is the unit cost for generation of two or three photogrammetric spot heights per building, assuming that the aero- triangulated digital imagery is already available within a community. Normally, two or three ground elevations at corners of a building are visible in stereo whereas the building itself normally blocks the stereo view of at least one corner spot height per building. Base cost = $10.00, based on cost estimates from BAE/ADR which provided this service for the Susquehanna River Flood Warning and Response System for the Philadelphia District, U.S. Army Corps of Engineers (USACE). ? Spreadsheet parameter K21 is the unit cost for a survey firm to measure all the vertical offsets, relative to LAG elevations or spot heights, count and measure the area of flood vents, take a digital photo of the building, determine its building diagram number, and verify its address. Base cost = $50 based on Dewberry assumption that 2004 costs would be higher than the USACE's actual unit costs ($85) in 2000, when utilizing an out-of- state surveyor, further increased by appreciated costs between 2000 and 2004, but reduced significantly with the use of a local surveyor, GIS technician, or even a GIS-enabled summer intern hired for this project. ? Spreadsheet parameter K23 is the assumed $300 value of each accurate and complete EC entered into the registry. ? Spreadsheet parameter K25 is the unit cost for Pictometry to load and review 4-view images, to identify EC items to be measured, and to determine elevations relative to the best available DTM. Base cost = $50.00, based on cost quote from Pictometry. ? Spreadsheet parameter K27 is the countywide cost for CCS to use "NoFP" methods to process LIDAR last-return "point cloud" data to automatically extract buildings, determine LAGs and HAGs, and estimate top of bottom floor elevations. Base cost = $10,000 per countywide LIDAR dataset with average area of 500 square miles. There is little difference in cost for small areas or large areas since the process is automated. ? Spreadsheet parameter K29 is the countywide cost for Dewberry to use "w/FP" methods to process LIDAR bare-earth Triangulated Irregular Network (TIN) data to determine LAG elevations when building footprint data is made available to Dewberry in addition to the LIDAR bare earth TIN data. Base cost = $5,000 per countywide LIDAR bare-earth TIN dataset with average area of 500 square miles. ? Spreadsheet parameter K31, the average number of buildings to be measured from a countywide LIDAR or IFSAR dataset. Base value = 1,000. CE Model Sensitivity to EC Total Value Table 18 varies the value of ECs in the elevation registry between $100 and $600 each. Table 18 — CE Model Sensitivity to EC Total Value Cost-Effectiveness Ratios, Varying the Value of EC Records in Registry $100 $200 $300 Base $400 $500 $600 1. Digital ECs, no/inacc. lat/long, batch entry 38.00 76.00 114.0 152.0 190.0 228.0 2. Hardcopy ECs, no/inacc. lat/long, batch entry 12.67 25.33 38.00 50.67 63.33 76.00 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry 6.33 12.67 19.00 25.33 31.67 38.00 4. Hardcopy ECs accurate lat/long, 1 web entry 6.67 13.33 20.00 26.67 33.33 40.00 5. Future ECs, no/inacc. lat/long, 1 web entry 9.50 19.00 28.50 38.00 47.50 57.00 6. Future ECs, accurate lat/long, 1 web entry 10.00 20.00 30.00 40.00 50.00 60.00 7. Photogrammetry (2' CI), no offsets 2.20 4.40 6.60 8.81 11.01 13.21 8. Photogrammetry (2' CI), surveyor offsets 1.41 2.82 4.23 5.64 7.05 8.46 9. Photogr.(2' CI) with footprints, owner offsets 4.41 8.81 13.22 17.62 22.03 26.43 10 Photogrammetry (5' CI), no offsets 0.90 1.79 2.69 3.58 4.48 5.38 11. Photogrammetry (5' CI), surveyor offsets 0.72 1.43 2.15 2.86 3.58 4.30 12. Pictometry with LIDAR DTM 0.80 1.59 2.39 3.19 3.98 4.78 13. LIDAR (2' CI), no footprints, no offsets 1.87 3.74 5.61 7.49 9.36 11.23 14. LIDAR (2' CI) with footprints, no offsets 5.25 10.51 15.76 21.01 26.27 31.52 15. LIDAR (2' CI), no footprints, surveyor offsets 1.23 2.47 3.70 4.94 6.17 7.40 16. LIDAR (2' CI), no footprints, owner offsets * 3.61 7.21 10.82 14.43 18.03 21.64 17. LIDAR (2' CI) with footprints, surveyor offsets 1.53 3.06 4.60 6.13 7.66 9.19 18. LIDAR (2' CI) with footprints, owner offsets 5.87 11.75 17.62 23.49 29.37 35.24 19. IFSAR (10' CI) w/footprints, surveyor offsets 0.26 0.52 0.78 1.04 1.30 1.57 20. Photogrammetric Van (VISAT) 0.63 1.26 1.89 2.52 3.15 3.78 Conclusions from Table 18 There is a direct linear relationship between the estimated value of an EC in the registry and the variations in CE ratios. The higher the value placed on an EC, the higher the CE ratio for alternative methods for producing elevation data for the registry. This is logical and intuitive. However, because of this linear relationship, Table 18 will not be used below for comparison of best case and worst case scenarios for the various technologies because the $100 value per EC record would artificially force the worst CE ratio and/or the $600 value per EC record would artificially force the best CE ratio for all 20 methods evaluated. CE Model Sensitivity to Elevation Registry Cost Parameters Table 19 varies the assumed cost for individual and batch entry of data into the elevation registry over the maximum range of uncertainty. Table 19 — CE Model Sensitivity to Elevation Registry Cost Parameters Cost-Effectiveness Ratios, Varying the Unit Costs for Entry of Individual and/or Large Batches of Elevation Records into an Elevation Registry Unit cost of individual web entry into elevation registry Unit cost of large batch entry into elevation registry $ 5 $ 7 . 5 0 $ 1 0 b a s e v a l u e $ 1 5 $ 2 0 $ 1 $ 1 . 7 5 $ 2 . 5 0 B a s e V a l u e $ 3 . 7 5 $ 5 1. Digital ECs, no/inacc. lat/long, batch entry 285 163 114 76 57 2. Hardcopy ECs, no/inacc. lat/long, batch entry 47 42 38 33 29 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry 29 23 19 14 11 4. Hardcopy ECs accurate lat/long, 1 web entry 30 24 20 15 12 5. Future ECs, no/inacc. lat/long, 1 web entry 57 38 28 19 14 6. Future ECs, accurate lat/long, 1 web entry 60 40 30 20 15 7. Photogrammetry (2' CI), no offsets 7.5 7.0 6.6 6.0 5.5 8. Photogrammetry (2' CI), surveyor offsets 4.3 4.3 4.2 4.2 4.1 9. Photogr. (2' CI) with footprints, owner offsets 18 15 13 11 8.8 10 Photogrammetry (5' CI), no offsets 3.1 2.9 2.7 2.4 2.2 11. Photogrammetry (5' CI), surveyor offsets 2.2 2.2 2.2 2.1 2.1 12. Pictometry with LIDAR DTM (2' CI) 2.5 2.4 2.4 2.3 2.3 13. LIDAR (2' CI), no footprints, no offsets 6.4 6.0 5.6 5.1 4.7 14. LIDAR (2' CI) with footprints, no offsets 20 18 16 14 12 15. LIDAR (2' CI), no footprints, surveyor offsets 3.8 3.8 3.7 3.6 3.6 16. LIDAR (2' CI), no footprints, owner offsets * 14 12 11 8.7 7.2 17. LIDAR (2' CI) with footprints, surveyor offsets 4.7 4.7 4.6 4.5 4.4 18. LIDAR (2' CI) with footprints, owner offsets 26 21 18 13 11 19. IFSAR (10' CI) w/footprints, surveyor offsets 0.8 0.8 0.8 0.8 0.8 20. Photogrammetric Van (VISAT) 2.0 1.9 1.9 1.8 1.8 Conclusions from Table 19 The CE ratios have large variations for those methods where the cost of registry entry is the only cost for the method (as with Methods 1, 5 and 6). The CE ratios have small variations for those methods where the cost of registry entry is a small percentage of the total cost for the method (as with Methods 8, 11, 12, 15, 17, 19 and 20 where the cost of surveyor offsets, Pictometry or VISAT measurements comprise the major cost). All variations are logical and intuitive. CE Model Sensitivity to Accuracy/Digitization Cost of Existing ECs Strategy A pertains to Methods 1, 2, 3 and 4. Table 20 shows the sensitivity of the CE model to variations in the accuracy and digitization cost of existing ECs as accuracy and digitization cost parameters are varied by ±50%. Table 20 — CE Model Sensitivity to Accuracy/Digitization Cost of Existing ECs A B C D E F Cost-Effectiveness Ratios for Existing (old) Elevation Certificates, entered in an elevation registry, Varying Accuracy and Cost Parameters by ± 50% B a s e S c e n a ri o If 50 % p o or er ve rti ca l ac cu ra cy th an in ba se sc en ar io If 50 % be tt er ve rti ca l ac cu ra cy th an in ba se sc en ar io If u ni t co st s fo r di gi tiz ati o n of ol d E C s ar e 50 % le ss th an in ba se sc en ar io If u ni t co st s fo r di gi tiz ati o n of ol d E C s ar e 50 % m or e th an in ba se sc en ar io 1. Digital ECs, no/inacc. lat/long, batch entry 114 109 119 114 114 2. Hardcopy ECs, no/inacc. lat/long, batch entry 38.0 36.3 39.7 57.0 28.5 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry 19.0 18.2 19.9 22.8 16.3 4. Hardcopy ECs accurate lat/long, 1 web entry 20.0 19.2 20.9 24.0 17.1 Conclusions from Table 17, Table 19 and Table 20 A variation of ±50% in the assumed vertical accuracy of existing ECs has minimal effect (<5%) on their CE ratios (see Table 20, columns C and D). A variation of ±50% in the assumed cost of digitizing old ECs has a moderate effect (up to 20%) on CE ratios for single web entries (Methods 3 and 4) and larger effect (up to 50%) on CE ratios for batch entries (see Table 20, columns E and F) for Method 2. There is no effect on Method 1 where records are already digitized. Method 1 for existing digital EC records with no latitude/longitude or automated geocoding, using batch entry procedures: ? These ECs have a high value of 95%, but deducted 5% in value because they do not include accurate latitude and longitude needed for update of the registry as BFEs change, etc. ? Best Case Scenario: CE ratio = 285 when the unit cost of large batch entry into the elevation registry is $1 instead of $2.50 (see Table 19, line 1, $1 column). ? Worst Case Scenario: CE ratio = 57 when the unit cost of large batch entry into the elevation registry is $5 instead of $2.50 (see Table 19, line 1, $5 column). Method 2: For existing hardcopy ECs with no latitude/longitude or automated geocoding, using batch entry procedures: ? These ECs have a high value of 95%, but deducted 5% in value because they do not include accurate latitude and longitude needed for update of the registry as BFEs change, etc. ? Best Case Scenario: CE ratio = 57 when the unit cost for digitizing hardcopy ECs is assumed to be $2.50 instead of $5.00 (see Table 20, line 2, column E). ? Worst Case Scenario: CE ratio = 28.5 when the unit cost for digitizing hardcopy ECs is assumed to cost $7.50 instead of $5.00 (see Table 20, line 2, column F). Method 3: For existing hardcopy ECs with no latitude/longitude or automated geocoding, using single web entry procedures: ? These ECs have a high value of 95%, but deducted 5% in value because they do not include accurate latitude and longitude needed for update of the registry. ? Best Case Scenario: CE ratio = 29 when the unit cost of individual web entry into the elevation registry is $5 instead of $10 (see Table 19, line 3, $5 column). ? Worst Case Scenario: CE ratio = 11 when the unit cost of individual web entry into the elevation registry is $20 instead of $10 (see Table 19, line 3, $20 column). Method 4: For existing hardcopy ECs with accurate latitude/longitude, using single web entry procedures: ? These ECs have the maximum value of 100% because they include accurate latitude/longitude needed for update of the registry records. ? Best Case Scenario: CE ratio = 30 when the unit cost of individual web entry into the elevation registry is $5 instead of $10 (see Table 19, line 4, $5 column). ? Worst Case Scenario: CE ratio = 12 when the unit cost of individual web entry into the elevation registry is $20 instead of $10 (see Table 19, line 4, $20 column). For existing ECs, the CE computer model is most sensitive to the unit cost of entering records into the elevation registry; it is least sensitive to variations in the assumed vertical accuracy of existing ECs. CE Model Sensitivity to Accuracy of Future Ground-Surveyed ECs Strategy D pertains to Methods 5 and 6. Table 21 shows the sensitivity of the CE model to variations in the accuracy of future ground-surveyed ECs as accuracy parameters are varied by ±50%. Table 21 — CE Model Sensitivity to Accuracy of Future Ground-Surveyed ECs A B C D Cost-Effectiveness Ratios for Future (new) Elevation Certificates, entered in an elevation registry, Varying Accuracy Parameters by ± 50% B a s e S c e n a ri o If 50 % po or er ve rti ca l ac cu ra cy th an in ba se sc en ari o If 50 % be tte r ve rti ca l ac cu ra cy th an in ba se sc en ari o 5. Future ECs, no/inacc. lat/long, 1 web entry 28.5 27.2 29.8 6. Future ECs, accurate lat/long, 1 web entry 30.0 28.7 31.3 Conclusions from Table 17, Table 19 and Table 21 Variations of ±50% in the assumed vertical accuracy of future new ECs has minimal effect (<5%) on their CE ratios (see Table 21, columns C and D). Method 5: Future ECs, no latitude/longitude or georeferenced only, single web entry: ? These ECs have a high value of 95%, but deducted 5% in value because they do not include accurate latitude and longitude needed for update of the registry. ? Best Case Scenario: CE ratio = 57 when the unit cost of individual web entry into the elevation registry is assumed to be $5 instead of $10 assumed in the base scenario (see Table 19, line 5, $5 column). ? Worst Case Scenario: CE ratio = 14 when the unit cost of individual web entry into the elevation registry is assumed to be $20 instead of $10 assumed in the base scenario (see Table 19, line 5, $20 column). Method 6: Future ECs, accurate latitude/longitude, single web entry: ? These ECs have the maximum value of 100% because they include accurate latitude/longitude needed for update of the registry records. ? Best Case Scenario: CE ratio = 60 when the unit cost of individual web entry into the elevation registry is assumed to be $5 instead of $10 assumed in the base scenario (see Table 19, line 6, $5 column). ? Worst Case Scenario: CE ratio = 15 when the unit cost of individual web entry into the elevation registry is assumed to be $20 instead of $10 assumed in the base scenario (see Table 19, line 6, $20 column). CE Model Sensitivity to Accuracy/Cost of Photogrammetry ECs Table 22 shows the sensitivity of the CE model to variations in the accuracy and cost of photogrammetric ECs as accuracy and cost parameters are varied by ±50%. Table 22 — CE Model Sensitivity to Accuracy/Cost of Photogrammetry ECs A B C D E F G H Cost-Effectiveness Ratios for Photogrammetric Elevation Records, entered in an Elevation Registry, Varying Accuracy and Cost Parameters by ± 50% B as e S ce n ar io If p h ot o gr a m m et ri c sp ot he ig ht s 50 % p o or er ve rti ca l ac cu ra cy th an in ba se sc en ar io If p h ot o gr a m m et ri c sp ot he ig ht s 50 % be tt er ve rti ca l ac cu ra cy th an in ba se sc en ar io If p h ot o gr a m m et ri c sp ot he ig ht s co st 50 % le ss th an in ba se sc en ar io If p h ot o gr a m m et ri c sp ot he ig ht s co st 50 % m or e th an in ba se sc en ar io If su rv ey or m ea su re d of fs et s co st 50 % le ss th an in ba se sc en ar io If su rv ey or m ea su re d of fs et s co st 50 % m or e th an in ba se sc en ar io 7. Photogrammetry (2' CI), no offsets 6.6 7.5 5.7 11.0 4.7 6.6 6.6 8. Photogrammetry (2' CI), surveyor offsets 4.2 4.7 3.7 4.6 3.9 7.1 3.0 9. Photogr.(2' CI) with footprints, owner offsets 13.2 14.8 11.7 17.6 10.6 13.2 13.2 10 Photogrammetry (5' CI), no offsets 2.7 6.1 0.0 4.5 1.9 2.7 2.7 11. Photogrammetry (5' CI), surveyor offsets 2.2 4.0 0.7 2.3 2.0 3.6 1.5 Conclusions from Table 17, Table 19 and Table 22 Variations of ±50% in the assumed vertical accuracy of photogrammetric spot heights has a relatively small effect (between 11% and 16%) on the CE ratios of the three 2' CI photogrammetric methods but a major effect (several hundred percent) on the CE ratios of the two 5' CI photogrammetric methods (see Table 22, columns C and D). Variations of ±50% in the assumed cost of photogrammetric spot heights has a major effect (up to 67%) on the CE ratios of Methods 7 and 10 where photogrammetric spot heights are the major cost, but a small effect (7% to 10%) on the CE ratios of Methods 8 and 11 where surveyor offsets comprise the major cost (see Table 22, columns E and F). Variations of ±50% in the assumed cost of surveyor offset measurements has no effect on Methods 7, 9 and 10 that don't use surveyor offsets, but a major effect (up to 69%) on the CE ratios of options 8 and 11 because the cost of offset measurements is the major cost driver for these methods (see Table 22, columns G and H). Method 7: For photogrammetry (2' contours) with no surveyor offsets or data: ? These ECs have a poor value of 27.5% because there is no presumed way to link photogrammetric spot heights with street addresses, and there is no way to determine the correct building diagram number, measure accurate vertical offsets to determine top of bottom floor or LHSM elevations, or measure vents. ? Best Case Scenario: CE ratio = 11 when cost of photogrammetric spot heights are 50% less than in base scenario (see Table 22, line 7, column E). ? Worst Case Scenario: CE ratio = 4.7 when cost of photogrammetric spot heights are 50% more than in base scenario (see Table 22, line 7, column F). Method 8: For photogrammetry (2' contours) with surveyor offsets and data: ? These ECs have a high value of 88.1% because of the high accuracy spot heights and because the surveyor can perform accurate geocoding, determine the correct building diagram number, measure accurate vertical offsets to determine top of bottom floor and LHSM elevations, and measure vents. ? Best Case Scenario: CE ratio = 7.1 when costs of surveyor measured offsets are 50% less than in base scenario (see Table 22, line 8, column G). ? Worst Case Scenario: CE ratio = 3.0 when costs of surveyor measured offsets are 50% more than in base scenario (see Table 22, line 8, column H). Method 9: For photogrammetry (2' contours) with owner-provided offsets and data: ? These ECs have a high value of 88.1% because of the high accuracy spot heights and because the home owner can help to identify his/her home on an orthophoto for accurate geocoding, can provide photos to help the insurance agent determine the correct building diagram number, and can measure vents and vertical offsets needed to determine top of bottom floor and LHSM elevations. ? Best Case Scenario: CE ratio = 18 when the unit cost of individual web entry into the elevation registry is $5 instead of $10 (see Table 19, line 9, $5 column). ? Worst Case Scenario: CE ratio = 8.8 when the unit cost of individual web entry into the elevation registry is $20 instead of $10 (see Table 19, line 9, $20 column). Method 10: For photogrammetry (5' contours) with no surveyor offsets or data: ? These ECs have a poor value of 11.2% because the spot heights have considerably poorer accuracy than those from 2' contours, because there is no presumed way to link photogrammetric spot heights with street addresses, and because there is no way to determine the correct building diagram number, measure accurate vertical offsets to determine top of bottom floor or LHSM elevations, or measure vents. ? Best Case Scenario: CE ratio = 6.1 when the photogrammetric spot height accuracies are 50% better than in the base scenario (see Table 22, line 10, column C). ? Worst Case Scenario: CE ratio = 0.0 when the photogrammetric spot height accuracies are 50% poorer than in the base scenario (see Table 22, line 10, column D). Method 11: For photogrammetry (5' contours) with surveyor offsets and data: ? These ECs have a mid value of 44.8% because the spot heights have considerably poorer accuracy than those from 2' contours, providing poor elevation accuracy for LAG, HAG, top of bottom floor, LHSM, etc. ? Best Case Scenario: CE ratio = 4.0 when photogrammetric spot height accuracies are 50% better than in the base scenario (see Table 22, line 11, column C). ? Worst Case Scenario: CE ratio = 0.7 when the photogrammetric spot height accuracies are 50% poorer than in the base scenario (see Table 22, line 11, column D). CE Model Sensitivity to Accuracy/Cost of Pictometry ECs Table 23 shows the sensitivity of the CE model to variations in the accuracy and cost of Pictometry ECs as accuracy and cost parameters are varied by ±50%. Table 23 — CE Model Sensitivity to Accuracy/Cost of Pictometry ECs A B C D E F Cost-Effectiveness Ratios for Pictometry Elevation Records, entered in an Elevation Registry, Varying Accuracy and Cost Parameters by ± 50% B a s e S c e n a ri o If Pi ct o m et ry rel ati ve el ev ati on ac cu ra cy is 50 % po or er th an in ba se sc en ari o If Pi ct o m et ry rel ati ve el ev ati on ac cu ra cy is 50 % be tte r th an in ba se sc en ari o If Pi ct o m et ry un it m ea su re m en t co st s ar e 50 % le ss th an in ba se sc en ari o If Pi ct o m et ry un it m ea su re m en t co st s ar e 50 % hi gh er th an in ba se sc en ari o 12. Pictometry with LIDAR DTM (2' CI) 2.4 1.8 3.0 4.6 1.6 Conclusions from Table 17, Table 19 and Table 23 A variation of ±50% in the assumed vertical accuracy of Pictometry's relative elevations has a moderate effect (up to 33%) on the CE ratio of the Pictometry method (see Table 23, columns C and D). A variation of ±50% in the assumed unit costs for Pictometry measurements has a major effect (up to 92%) on the CE ratio of the Pictometry option (see Table 23, columns E). Method 12: For Pictometry with LIDAR DTM, 2' CI: ? These ECs have a mid value of 41.8% because Pictometry's relative elevations sometimes misidentify basements, because without a separate georeferenced address file there is no presumed way to link Pictometry images of houses with their street addresses for geocoding, and because there is no way to measure vents. However, Pictometry's greatest value is in "seeing" each house in perspective and identification of illegal construction — factors not considered in assessing the value of ECs. ? Best Case Scenario: CE ratio = 4.6 when the unit cost of Pictometry measurements are 50% less than in base scenario (see Table 23, line 12, column E). ? Worst Case Scenario: CE ratio = 1.6 when the unit cost of Pictometry measurements are 50% more than in base scenario (see Table 23, line 12, column F). CE Model Sensitivity to Accuracy/Cost of LIDAR ECs Table 24 shows the sensitivity of the CE model to variations in the accuracy and cost of LIDAR ECs as accuracy and cost parameters are varied by ±50%. Table 24 — CE Model Sensitivity Accuracy/Cost of LIDAR ECs A B C D E F G H Cost-Effectiveness Ratios for LIDAR Elevation Records, entered in an Elevation Registry, Varying Accuracy and Cost Parameters by ± 50% B a s e S c e n a r i o If LI D A R el ev ati on s ha ve 50 % po or er ve rti ca l ac cu ra cy th an in ba se sc en ari o If LI D A R el ev ati on s ha ve 50 % be tte r ve rti ca l ac cu ra cy th an in ba se sc en ari o If LI D A R pr oc es si ng un it co st s ar e 50 % le ss th an in ba se sc en ari o If LI D A R pr oc es si ng un it co st s ar e 50 % m or e th an in ba se sc en ari o If 1, 50 0 ho m es ar e au to m ati ca lly pr oc es se d in st ea d of th e 1, 00 0 in ba se sc en ari o If 50 0 ho m es ar e au to m ati ca lly pr oc es se d in st ea d of th e 1, 00 0 in ba se sc en ari o 13. LIDAR (2' CI), no footprints, no offsets 5.6 4.8 6.4 9.4 4.0 7.7 3.1 14. LIDAR (2' CI) with footprints, no offsets 15.8 13.8 17.7 23.6 11.8 20.3 9.5 15. LIDAR (2' CI), no footprints, surveyor offsets 3.7 3.3 4.1 4.0 3.4 3.9 3.2 16. LIDAR (2' CI), no footprints, owner offsets * 10.8 9.5 12.1 14.4 8.7 13.0 7.2 17. LIDAR (2' CI) with footprints, surveyor offsets 4.6 4.1 5.1 4.8 4.4 4.7 4.2 18. LIDAR (2' CI) with footprints, owner offsets 17.6 15.6 19.7 21.1 15.1 19.8 13.2 Conclusions from Table 17, Table 19 and Table 24 Variations of ±50% in the assumed vertical accuracy of LIDAR elevations has a relatively minor effect (up to 16%) on the CE ratios of the six LIDAR options (see Table 24, columns C and D). Column D represents accuracy actually tested in Beaufort County, SC. Variations of ±50% in the assumed cost of LIDAR processing has a major effect (33% to 679%) on the CE ratios of the two LIDAR options (Methods 13, 14) with no offsets, but lesser effects (4% to 33%) on the four LIDAR options with offsets (see Table 24, columns E and F). Variations of ±50% in the number of homes with LIDAR automatically processed per community has a small effect (5% to 15%) on the CE ratios of the two LIDAR methods (15 and 17) with surveyor offsets; a large effect (37% to 80%) on the CE ratios of the two LIDAR methods (13 and 14) with no offsets; and a small to moderate effect (12% to 50%) on the CE ratios of the two LIDAR methods (16 and 18) with owner-provided offsets (see Table 24, columns G and H). Method 13: For LIDAR, no footprints, no surveyed offsets: ? These ECs have a low value of 23.4% because there is no presumed way to link LIDAR mass points with street addresses for geocoding, and there is no way to determine the correct building diagram number, measure accurate vertical offsets for determination of top of bottom floor or LHSM elevations, or measure flood vents. ? Best Case Scenario: CE ratio = 9.4 when LIDAR processing unit costs are 50% less than assumed in the base scenario (see Table 24, line 13, column E). ? Worst Case Scenario: CE ratio = 3.1 when only 500 homes are automatically processed per county instead of 1,000 assumed in the base scenario (see Table 24, line 13, column H). . Method 14: For LIDAR with footprints, no surveyed offsets: ? These ECs have a mid value of 39.4% because there is no way to determine the correct building diagram number, measure accurate vertical offsets needed for top of bottom floor and LHSM elevations, and measure flood vents. ? Best Case Scenario: CE ratio = 23.6 when LIDAR processing unit costs are 50% less than assumed in the base scenario (see Table 24, line 14, column E). ? Worst Case Scenario: CE ratio = 9.5 when only 500 homes are automatically processed per county instead of 1,000 assumed in the base scenario (see Table 24, line 14, column H). Method 15: For LIDAR, no footprints, surveyor offsets: ? These ECs have a relatively high value of 77.1% because of the relatively high accuracy LIDAR mass points with buildings automatically extracted, and because the surveyor can perform accurate geocoding, determine the correct building diagram number, measure accurate vertical offsets, and measure flood vents. ? Best Case Scenario: CE ratio = 4.1 when LIDAR vertical accuracy is 50% better than assumed in the base scenario (see Table 24, line 15, column D). ? Worst Case Scenario: CE ratio = 3.2 when only 500 homes are automatically processed per county instead of 1,000 assumed in the base scenario (see Table 24, line 15, column H). ? This CE cost model is remarkably stable, with maximum and minimum values fluctuating only between 3.2 and 4.1 for all variables. Method 16: For LIDAR, no footprints, owner-provided offsets: ? These ECs are color-coded magenta (not do-able) because the homeowner has no good way to link the street address with the LIDAR data for geocoding and determination of BFE. The various surveyor methods (e.g., Method 15) are presumed to have GIS tools to overlay LIDAR points on digital orthophotos to assist in identification of houses, but a homeowner or Insurance Agent (e.g., Method 16) is not presumed to have this capability. This method can only be considered if there is some alternative means for the home owner to know which LIDAR LAG/HAG elevations pertain to his/her house; only then can best and worst case scenarios be considered: ? Best Case Scenario: CE ratio = 14.4 when the LIDAR processing unit costs are 50% less than assumed in the base scenario (see Table 24, line 16, column E). ? Worst Case Scenario: CE ratio = 7.2 when the unit cost of individual web entry into the elevation registry is $20 instead of $10 assumed in the base scenario (see Table 19, line 16, $20 column) and when only 500 homes are automatically processed per county instead of 1,000 assumed in the base scenario (see Table 24, line 16, column H). Method 17: For LIDAR with footprints, surveyor offsets: ? These ECs have a high value of 88.1% because of the high accuracy LIDAR mass points and because the surveyor can perform accurate geocoding, determine the correct building diagram number, measure accurate vertical offsets for accurate determination of top of bottom floor and LHSM elevations, and measure flood vents. ? Best Case Scenario: CE ratio = 5.1 when LIDAR elevations have 50% better vertical accuracy than in base scenario (see Table 24, line 17, column D). The best case scenario is a real possibility since LIDAR datasets sometimes are tested at accuracies equivalent to 1' contours rather than the normal 2' contours. ? Worst Case Scenario: CE ratio = 4.1 when LIDAR elevations have 50% poorer vertical accuracy than in base scenario (see Table 24, line 17, column C). ? This CE cost model is remarkably stable, with maximum and minimum values fluctuating only between 4.1 and 5.1 for all variables. Technical methods are stable as parameters are varied when the methods yield geocoded positions as well as accurate top of bottom floor, LHSM and LAG elevations. Methods are less stable when they do some tasks well and other tasks poorly. Method 18: For LIDAR with footprints, owner-provided offsets: ? These ECs have a high value of 88.1% because of the high accuracy LIDAR mass points which are addressed because of the footprints, and because the home owner can provide photos to help the insurance agent determine the correct building diagram number, and can measure flood vents and vertical offsets needed to determine top of bottom floor or LHSM elevation. ? Best Case Scenario: CE ratio = 26 when unit cost of individual web entry into the elevation registry is $5 instead of $10 assumed in the base scenario (see Table 19, line 18, $5 column). ? Worst Case Scenario: CE ratio = 11 when unit cost of individual web entry into the elevation registry is $20 instead of the assumed $10 (see Table 19, line 18, $20 column). CE Model Sensitivity to Accuracy/Cost of IFSAR ECs Table 25 shows the sensitivity of the CE model to variations in the accuracy and cost of IFSAR ECs as accuracy and cost parameters are varied by ±50%. Table 25 — CE Model Sensitivity to Accuracy/Cost of IFSAR ECs A B C D E F G H Cost-Effectiveness Ratios for IFSAR Elevation Records, entered in an Elevation Registry, Varying Accuracy and Cost Parameters by ± 50% B a s e S c e n a ri o If IF S A R el ev ati on s ha ve 50 % po or er ve rti ca l ac cu ra cy th an in ba se sc en ari o If IF S A R el ev ati on s ha ve 50 % be tte r ve rti ca l ac cu ra cy th an in ba se sc en ari o If IF S A R pr oc es si ng un it co st s ar e 50 % le ss th an in ba se sc en ari o If IF S A R pr oc es si ng un it co st s ar e 50 % m or e th an in ba se sc en ari o If 1, 50 0 ho m es ar e au to m ati ca lly pr oc es se d in st ea d of th e 1, 00 0 in ba se sc en ari o If 50 0 ho m es ar e au to m ati ca lly pr oc es se d in st ea d of th e 1, 00 0 in ba se sc en ari o 19. IFSAR (10' CI) w/footprints, surveyor offsets 0.78 0.78 2.33 0.82 0.75 0.81 0.72 Conclusions from Table 17, Table 19 and Table 25 A variation of ±50% in the assumed vertical accuracy of IFSAR elevations has a huge effect (up to 300%) on the CE ratios of the IFSAR option (see Table 25, line 19, column D) because improving the vertical accuracy from the equivalent of 10' contours to the equivalent of 5' contours drastically changes the value of the EC elevation data (LAG/HAG and derived top of bottom floor or LHSM elevations). A variation of ±50% in the assumed costs of IFSAR processing has an insignificant effect on the CE ratio of the IFSAR method (see Table 25, line 19, columns E and F). A variation of ±50% in the number of homes with IFSAR automatically processed per community has a minor effect on the CE ratios of the IFSAR method (see Table 25, line 19, columns G and H). Method 19: IFSAR with footprints, surveyor offsets: ? These ECs have a low value of 15.0% because of poor accuracy of the IFSAR data which is assumed to be equivalent to 10' contours in the base scenario, having no value to the elevations of LAG, HAG, top of bottom floor, LHSM, or other elevations for purposes of eRating. ? Best Case Scenario: CE ratio = 2.33 when the vertical accuracy of the IFSAR data is 50% better than assumed in the base scenario, i.e., equivalent to 5' contours instead of 10' contours (see Table 25, line 19, column D). ? Worst Case Scenario: CE ratio = 0.72 when only 500 houses are automatically processed per county rather than 1,000 houses assumed in the base scenario (see Table 25, line 19, column H). CE Model Sensitivity to Accuracy/Cost of Photogrammetric Van ECs Strategy C pertains to Method 20 only. Table 26 shows the sensitivity of the CE model to variations in the accuracy and cost of photogrammetric van ECs as accuracy and cost parameters are varied by ±50%. Table 26 — CE Model Sensitivity to Accuracy/Cost of Photogrammetric Van ECs A B C D E F Cost-Effectiveness Ratios for Photogrammetric Van Elevation Records, entered in an Elevation Registry, Varying Accuracy and Cost Parameters by ± 50% B a s e S c e n a ri o If ph ot og ra m m et ric va n el ev ati on s ar e 50 % po or er th an in ba se sc en ari o If ph ot og ra m m et ric va n el ev ati on s ar e 50 % be tte r th an in ba se sc en ari o If VI S A T' s un it m ea su re m en t co st s ar e 50 % le ss th an in ba se sc en ari o If VI S A T' s un it m ea su re m en t co st s ar e 50 % hi gh er th an in ba se sc en ari o 20. Photogrammetric Van (VISAT) 1.89 1.63 2.09 3.54 1.29 Conclusions from Table 17, Table 19 and Table 26. A variation of ±50% in the assumed vertical accuracy of VISAT elevations has a small effect (10% to 16%) on the CE ratio of the VISAT option (see Table 26, line 20, columns C and D). A variation of ±50% in the assumed costs of VISAT processing has a major effect (46% to 87%) on the CE ratio of the VISAT option (see Table 26, line 20, columns E and F). Line 20: VISAT Photogrammetric Van: ? These ECs have a low value of 23.6% because of the VISAT's inability (demonstrated in Pinellas County, FL) to see (in stereo) the majority of the target points to be surveyed. It is possible that improved camera configuration could solve a major part of this problem, or that other geographic areas may have considerably less vegetation that blocks the view of features to be surveyed. When target points were visible in stereo, they were surveyed with vertical accuracy of 1.5' at the 95% confidence level, which is good. ? Best Case Scenario: CE ratio = 3.54 when the VISAT unit measurement costs are 50% less than assumed in the base scenario (see Table 26, line 20, column E). ? Worst Case Scenario: CE ratio = 1.29 when the VISAT unit measurement costs are 50% more than assumed in the base scenario (see Table 26, line 20, column F). STRATEGY SUMMARIES This section explains all strategies and methods summarized in Table 27 and synopsizes the advantages, disadvantages, costs and conclusions for each. Using "stop light" analogy, green represents high value data, amber represents mid value data, and red represents relatively low value data. However, lower value methods with high CE ratios provide better return for each dollar invested, even though they don't necessarily produce EC records of the highest quality. Table 27 — Summary of Elevation Alternatives Strategy Method Major Limitations % Value Unit Cost CE Ratio A 1. Digital ECs, no/inacc. lat/long, batch entry No latitude & longitude 95.0 $2.50 114.0 A 2. Hardcopy ECs, no/inacc. lat/long, batch entry No latitude & longitude 95.0 $7.50 38.00 A 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry No latitude & longitude 95.0 $15.00 19.00 A 4. Hardcopy ECs accurate lat/long, 1 web entry None 100 $15.00 20.00 D 5. Future ECs, no/inacc. lat/long, 1 web entry No latitude & longitude 95.0 $10.00 28.50 D 6. Future ECs, accurate lat/long, 1 web entry None 100 $10.00 30.00 B 7. Photogrammetry (2' CI), no offsets Slightly less accurate + no TBF elev, address, bldg diag, vents 27.5 $12.50 6.60 B & E 8. Photogrammetry (2' CI), surveyor offsets Slightly less accurate than ground surveys 88.1 $62.50 4.23 B & E 9. Photogr.(2' CI) with footprints, owner offsets Slightly less accurate than ground surveys 88.1 $20.00 13.22 B 10 Photogrammetry (5' CI), no offsets Considerably less accurate + no TBF elev, address, bldg diag, vents 11.2 $12.50 2.69 B & E 11. Photogrammetry (5' CI), surveyor offsets Considerably less accurate than ground surveys 44.8 $62.50 2.15 B 12. Pictometry with LIDAR DTM No address, some misidentified basements 41.8 $52.50 2.39 B 13. LIDAR (2' CI), no footprints, no offsets Somewhat less accurate + no TBF elev, address, bldg diag, vents 23.4 $12.50 5.61 B & E 14. LIDAR (2' CI) with footprints, no offsets Slightly less accurate than ground svy + no TBF elev, bldg diag, vents 39.4 $7.50 15.76 B & E 15. LIDAR (2' CI), no footprints, surveyor offsets Somewhat less accurate than ground surveys 77.1 $62.50 3.70 B & E 16. LIDAR (2' CI), no footprints, owner offsets/data * Somewhat less accurate plus errors or difficulty in owner georeferencing 72.1 $20.00 10.82 B & E 17. LIDAR (2' CI) with footprints, surveyor offsets Slightly less accurate than ground surveys 88.1 $57.50 4.60 B & E 18. LIDAR (2' CI) with footprints, owner offsets/data Slightly less accurate than ground surveys 88.1 $15.00 17.62 B & E 19. IFSAR (10' CI) with footprints, surveyor offsets Accuracy has zero value to registry 15.0 $57.50 0.78 C 20. Photogrammetric Van (VISAT) Can't measure many points because foliage blocks stereo view 23.6 $37.50 1.89 Table 28 summarizes the 20 methods ranked by their total value and by their range of possible CE ratios. Some of the lower-ranked methods (amber and red) have better CE ratios because they cost relatively little to produce elevation data of some usable but lesser value, e.g., method 14 which costs only $7.50 per house can generate records with 39.4% of the value of a $300 EC while providing data potentially usable for LOMA determinations, and a framework for future addition of on-site measurements to complete high value records. Table 28 — Methods Ranked by Overall Value of EC Records Method Major Limitations % Value Best CE Ratio Worst CE Ratio 6. Future ECs, accurate lat/long, 1 web entry None 100 60 15 4. Hardcopy ECs accurate lat/long, 1 web entry None 100 30 12 1. Digital ECs, no/inacc. lat/long, batch entry No latitude & longitude 95.0 285 57 2. Hardcopy ECs, no/inacc. lat/long, batch entry No latitude & longitude 95.0 57 28.5 5. Future ECs, no/inacc. lat/long, 1 web entry No latitude & longitude 95.0 57 14 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry No latitude & longitude 95.0 29 11 18. LIDAR (2' CI) with footprints, owner offsets/data Slightly less accurate than ground surveys 88.1 26 11 9. Photogr.(2' CI) with footprints, owner offsets Slightly less accurate than ground surveys 88.1 18 8.8 8. Photogrammetry (2' CI), surveyor offsets Slightly less accurate than ground surveys 88.1 7.1 3.0 17. LIDAR (2' CI) with footprints, surveyor offsets Slightly less accurate than ground surveys 88.1 5.1 4.1 15. LIDAR (2' CI), no footprints, surveyor offsets Somewhat less accurate than ground surveys 77.1 4.1 3.2 16. LIDAR (2' CI), no footprints, owner offsets/data * Somewhat less accurate plus errors or difficulty in owner georeferencing 72.1 14.4 7.2 11. Photogrammetry (5' CI), surveyor offsets Considerably less accurate than ground surveys 44.8 4.0 0.7 12. Pictometry with LIDAR DTM No address, some misidentified basements 41.8 4.6 1.6 14. LIDAR (2' CI) with footprints, no offsets Slightly less accurate than ground svy + no TBF elev, bldg diag, vents 39.4 23.6 9.5 7. Photogrammetry (2' CI), no offsets Slightly less accurate + no TBF elev, address, bldg diag, vents 27.5 11.0 4.7 20. Photogrammetric Van (VISAT) Can't measure many points because foliage blocks stereo view 23.6 3.5 1.3 13. LIDAR (2' CI), no footprints, no offsets Somewhat less accurate + no TBF elev, address, bldg diag, vents 23.4 9.4 3.1 19. IFSAR (10' CI) with footprints, surveyor offsets Accuracy has zero value to registry 15.0 2.3 0.7 10 Photogrammetry (5' CI), no offsets Considerably less accurate + no TBF elev, address, bldg diag, vents 11.2 6.1 0.0 Table 29 summarizes the various methods ranked by their total cost. Method 1 refers to existing digital elevation records, assumed to be of high value (green), that can be incorporated into the registry most cost effectively ($2.50), either with or without accurate latitude/longitude. Method 2 is the next low-cost option that yields high value records. Method 14, which costs only $7.50 per house, provides geocoded addresses and accurate LAG elevations, but not top of bottom floor elevations. Low value records (red) plus Method 12 (amber) do not provide street addresses but latitude/longitude coordinates only — making these methods generally unusable in a GIS until the community can provide reverse- geocoding. All of the higher cost methods involve surveyor-provided offset measurements and supporting data batch processed community-wide. Table 29 — Methods Ranked by Overall Cost of EC Records Method % Value Web Entry Costs R.S. Data Process Costs Offset Measure -ment Costs Digiti- zation Costs Total Unit Costs 1. Digital ECs, no/inacc. lat/long, batch entry 95.0 $2.50 $2.50 2. Hardcopy ECs, no/inacc. lat/long, batch entry 95.0 $2.50 $5 $7.50 14. LIDAR (2' CI) with footprints, no offsets 39.4 $2.50 $5 $7.50 6. Future ECs, accurate lat/long, 1 web entry 100 $10 $10 5. Future ECs, no/inacc. lat/long, 1 web entry 95.0 $10 $10 7. Photogrammetry (2' CI), no offsets 27.5 $2.50 $10 $12.50 13. LIDAR (2' CI), no footprints, no offsets 23.4 $2.50 $10 $12.50 10 Photogrammetry (5' CI), no offsets 11.2 $2.50 $10 $12.50 4. Hardcopy ECs accurate lat/long, 1 web entry 100 $10 $5 $15 3. Hardcopy ECs, no/inacc. lat/long, 1 web entry 95.0 $10 $5 $15 18. LIDAR (2' CI) with footprints, owner offsets/data 88.1 $10 $5 $15 9. Photogr. (2' CI) with footprints, owner offsets 88.1 $10 $10 $20 16. LIDAR (2' CI), no footprints, owner offsets/data * 72.1 $10 $10 $20 20. Photogrammetric Van (VISAT) 23.6 $2.50 $35 $37.50 12. Pictometry with LIDAR DTM 41.8 $2.50 $50 $52.50 17. LIDAR (2' CI) with footprints, surveyor offsets 88.1 $2.50 $5 $50 $57.50 19. IFSAR (10' CI) with footprints, surveyor offsets 15.0 $2.50 $5 $50 $57.50 8. Photogrammetry (2' CI), surveyor offsets 88.1 $2.50 $10 $50 $62.50 15. LIDAR (2' CI), no footprints, surveyor offsets 77.1 $2.50 $10 $50 $62.50 11. Photogrammetry (5' CI), surveyor offsets 44.8 $2.50 $10 $50 $62.50 Strategy A — Existing Elevation Certificates Strategy A pertains to all existing ECs, including those already in digital format plus hardcopy ECs that require digitization, quality control and entry into the registry (methods 1, 2, 3 and 4). Existing ECs are high value records, between 95% and 100% of maximum value, depending on the accuracy of latitude and longitude coordinates. They are assumed to have the highest elevation accuracy of ±0.5 ft at the 95% confidence level. Advantages: Ground-surveyed ECs provide the maximum benefits in all Table 17 categories with high total values and CE ratios. GPS ECs are better than conventional ECs because GPS provides accurate latitude and longitude of the surveyed buildings needed for a GIS-based registry and for revisions to BFEs and other updates. Ground surveys, combining GPS and conventional survey procedures, provide the most accurate and complete way to generate EC data. Disadvantage: Some existing ECs have missing data and questionable accuracy, and they generally lack latitude and longitude. As shown in Table 8, geocoding services often have positioning errors of several hundred feet. Still, these issues are less significant that any of the other airborne remote sensing methods and alternative datasets evaluated herein. Costs: Compared with the assumed $300 value of each EC when surveyed community-wide, the costs for entering existing ECs into the registry are minimal and include: (a) $5 each for digitization of ECs when mass produced, (b) $2.50 each for batch entry into the registry, and (c) $10 each for entry of individual ECs into the registry. When community ECs are mass processed (Method 2), hardcopy ECs can be digitized for as little as $5 each on average with an additional $2.50 each for quality control and processing into the registry. Digitization, quality control and entry of individual ECs into the registry (Methods 3 and 4) cost $15 per record when assuming $10 each for development of the web-based registry. Conclusion: Land survey methods used for existing ECs can achieve elevation accuracy of ± 0.5 ft at the 95% confidence level for the lowest floor, LAG and HAG. Existing EC data already in digital format (Method 1) can most efficiently be converted into the elevation registry data format for an estimated average cost of $2.50 per record. Existing hardcopy ECs held by communities, ISO and others (Method 2) could also be digitized, quality controlled and entered into the registry for an estimated average cost of $7.50 per record. When alternative elevation records are already in digital format (e.g., Policies in Force, LOMA 2000, Corps of Engineers), they can be quality controlled and reformatted for the registry for an estimated $2.50 each, but such alternative records typically have issues pertaining to accuracy and completeness. Methods 1 and 2 are the most obvious for initial implementation of the registry. Strategy B — Airborne Remote Sensing — Photogrammetry Photogrammetry methods 7, 8, 9, 10 and 11 assume that a community has aerial photography and aerial triangulation (AT) data suitable for generating digital elevation data equivalent to either 2' or 5' contours, two of the most common contour intervals, which can be provided for additional photogrammetric mapping of footprints and spot heights used for LAG and HAG elevations. Advantages: This research project has demonstrated that photogrammetric spot heights can be generated with predictable accuracy (related to the supported contour interval) at adjacent grades next to the corners of houses, while also providing latitude/longitude. Spot heights can be used to mass produce accurate LAG/HAG elevations, but not top of bottom floor elevations needed for ECs. Disadvantages: From photogrammetry alone, street addresses are unknown and require on-site address determinations. Similarly, top of bottom floor and lowest horizontal structural member (LHSM) elevations cannot be measured or accurately estimated because the map compiler cannot see inside buildings to map unseen features. Supplementary measurements and data needed to complete ECs must be provided by contracted surveyors or others (e.g., summer GIS interns) for batch entry into the registry, or individually provided by homeowners who measure vertical offsets and provide other required information to their insurance agent for completing individual records in the registry. Costs: When mass produced, it costs an estimated $10 per house for building footprints and spot heights, plus an additional $50 per house for a surveyor to visit each house to link the street address to the latitude/longitude, to make vertical offset measurements for top of bottom floor/LHSM elevations, etc., to determine the building diagram number, and to record vent data. It costs an additional $2.50 per house for batch entry of the data into the registry, for a total cost of $62.50 per house for Method 8 which yields high quality data (green) and Method 11 which yields mid quality data (amber) . For methods 7 and 10, the on-site data is not acquired and the unit costs are only $12.50 per house, but the quality is poor (red in Table 29) as top of bottom floor/LHSM elevations, latitude and longitude, building diagram numbers and vent information are missing. Method 9 uses the homeowner, working in cooperation with the insurance agent, to substitute for the surveyor, and then the ECs are processed individually into the registry at an estimated total cost of $20 per house for high quality records. Conclusion: When a community already has 2' photogrammetric contours, photogrammetric spot heights combined with on-site offset measurements can achieve elevation accuracy of ±1.2 ft at the 95% confidence level for the lowest floor, LAG and HAG. Of the photogrammetric methods 7 through 11 considered for this study, Method 8 stands out as the most practical, costing an estimated average of $62.50 per record, $50 of which is the estimated unit cost for on-site offset measurements. Strategy B — Airborne Remote Sensing — Pictometry Pictometry Method 12 assumes that a community already has Pictometry imagery that can be further utilized to support elevation registry requirements. Advantages. This research project has demonstrated that Pictometry oblique images are the only airborne remote sensing tools that can: (1) determine the existence or absence of basement windows and vents much of the time, (2) see and measure the vertical offsets between LAG and top of bottom floor elevations, and (3) determine the building diagram numbers. These oblique images are also useful in identification of unauthorized construction. Disadvantages. As with other airborne remote sensing technologies, street addresses cannot be determined from Pictometry images. However, the major disadvantage is that Pictometry elevations are not absolute, but relative to LAG/HAG elevations extracted from the best available digital terrain models (DTMs). This research project demonstrated that elevations may be accurate or inaccurate, depending on the DTMs used and the presence or absence of trees and shrubbery that obscure views of basement windows, vents, etc. If accurate DTMs are not available, Pictometry defaults to the use of USGS DEMs which normally lack accuracy needed for registry entries. Also, Pictometry images cannot see beneath buildings to measure offsets to LHSM elevations in V-zones. Costs. When Pictometry imagery is already available, analyses and measurements of the imagery cost approximately $50 per house, but volume discounts would apply. Batch entry of EC data into the registry costs an additional $2.50 for a total cost of $52.50 per house by this method. Conclusions. We conclude that this method is unreliable for its intended eRating purpose. Pictometry datasets evaluated in Prince George's County, MD, and Arlington County, VA had mixed results that were not particularly impressive because of the relatively high percentage of structures for which the presence or absence of basements was misinterpreted, causing large errors in top of bottom floor elevations. Since the LAG/HAG elevations already came from LIDAR or other sources, the Pictometry imagery provided marginal additional benefits for generating data needed for ECs. Therefore, even though Pictometry is the only airborne remote sensing method that can detect basement windows and vents, and the only airborne remote sensing method that can see and measure vertical offsets between LAG and top of bottom floor elevations, Dewberry concludes that the major advantage of this technology is to provide the insurance agent or others with a "birds eye" view of the structure from all sides to help in "seeing" the building being insured, and also to help in identification of unauthorized construction. The error rate is simply too high to accept Pictometry interpretations as authoritative regarding the presence or absence of basements. Strategy B — Airborne Remote Sensing — LIDAR LIDAR methods 13, 14, 15, 16, 17 and 18 assume that a community already has LIDAR data suitable for generating digital elevation data equivalent to 2' contours or better and this data can be processed by LIDAR specialists to determine LAG and HAG elevations when LAG/HAG points are visible from the air. Advantages. This project has demonstrated that LIDAR data can be used to generate LAG and HAG elevations with ? 1' vertical accuracy that improves with the availability of building footprints, narrower post spacing and other variables. Although LAG and HAG elevations may be accurate enough to be used for LOMA's, estimated top of bottom floor elevations are unreliable. Disadvantages. From LIDAR alone, street addresses are unknown and require on-site address determinations. Similarly, top of bottom floor/LHSM elevations cannot be measured directly because the LIDAR cannot map inside buildings; top of bottom floor elevations can be estimated, but with a high error rate that Dewberry considers to be unacceptable. Utilizing Strategy E, supplementary measurements and data needed to complete ECs must be provided by contracted surveyors or others (e.g., summer GIS interns) for batch entry into the registry, or individually provided by homeowners who measure vertical offsets and provide street addresses, photos, vent and other required information to their insurance agents for completing individual records in the registry. Costs. When counties already have LIDAR raw "point cloud" data and bare- earth DTM datasets in TIN format, entire small- to mid-sized counties can be post-processed to determine accurate LAG and HAG elevations of all buildings for a total cost of approximately $5,000 per county if building footprint files are available and linked to street addresses, or $10,000 per county if there are no footprints — regardless of the number of buildings to be processed in the county. Because these processes are automated, costs do not increase appreciably for larger numbers of houses to be processed. However, it still costs an estimated $50 per structure for surveyors to measure offsets and collect the ancillary information, unless done so by individual homeowners working with their Insurance Agents. With various assumptions, methods 17 and 15 cost $57.50 to $62.50 on average per structure for high quality data; method 18 costs $15 for high quality data; and methods 14 and 13 cost $7.50 to $12.50 per structure for mid to low quality data, with quality largely depending on the availability of building footprints linked to street addresses. Method 16 is ineffective. Conclusions. When a community already has LIDAR data equivalent to 2' contours or better, LIDAR Method 17 (with on-site offset measurements) can achieve elevation accuracy of ±1.2 ft or less at the 95% confidence level for the lowest floor, LAG and HAG at an average estimated cost of $57.50 per record. Method 14 (without offset measurements) can achieve elevation accuracy of ±1.2 ft for the LAG and HAG only, suitable for mass LOMA determinations. Strategy B — Airborne Remote Sensing — IFSAR IFSAR method 19 assumes that a county or state has IFSAR data suitable for generating digital elevation data equivalent to 10' contours and this data can be further processed by IFSAR specialists to determine LAG and HAG elevations. Advantages. The major advantage of IFSAR is that it is the least expensive way to collect elevation data of large areas, often entire states rather than individual counties or communities. IFSAR provides accurate latitude and longitude information plus ortho-rectified radar images that can be interpreted by radar analysts. Where there is little or no vegetation, IFSAR data can provide bare- earth elevations comparable to 10-foot contours. Disadvantages. Of all methods evaluated for this study, IFSAR is the least accurate way to collect LAG/HAG elevations. IFSAR collects digital surface models (DSMs) of top surfaces, trees and rooftops; procedures for generating bare-earth elevations are not yet reliable. New IFSAR technology, designed to provide better penetration of vegetation, has not yet been proven to provide accurate bare-earth elevations near buildings and other vertical surfaces. IFSAR also needs on-site measurements and data collection to complete other information required for ECs. As shown in Figure 7 (bottom), IFSAR imagery is noisy and more difficult to interpret than film or digital images commonly used. Costs. When counties already have IFSAR datasets and footprints, entire counties can be post-processed to determine LAGs of all buildings for a total cost of approximately $5,000, regardless of the number of buildings involved. If assuming 1000 ECs are produced per average community, the unit cost comes to $5 per structure. If footprints are not available, a radar image analyst will need to manually interpret the images in order to estimate building footprints, around which LAG and HAG elevations are extracted. It costs an estimated $50 per structure to measure offsets and collect other ancillary information needed, unless done so by individual homeowners working with their Insurance Agents. With various other assumptions, Method 19 costs an estimated $57.50 per house to mass produce low-quality records. Until IFSAR is better able to penetrate vegetation to acquire accurate bare-earth DTMs adjacent to buildings, there is no IFSAR option that produces high-accuracy elevation records. Conclusions. We conclude that this method is unreliable for its intended eRating purpose. IFSAR methods combined with on-site offset measurements can achieve elevation accuracy of ±6 ft at the 95% confidence level for the lowest floor, LAG and HAG. FEMA's criteria determined that methods having elevation errors larger than 4 ft were of no value for eRating purposes. Strategy C — Vehicular Remote Sensing Method 20 assumes that imagery from VISAT or other photogrammetric van is available for a community. Photogrammetric Van Advantages. For communities that already have imagery from VISAT or other photogrammetric vans, accurate ECs can be produced therefrom provided shrubbery does not block the needed views of target points to be surveyed in stereo. Elevations tested in Pinellas County, FL were accurate to 1.5 ft at the 95% confidence level. When LAG and HAG elevations are available from another source (e.g., photogrammetry, LIDAR, IFSAR) and address numbers are visible, photogrammetric vans could be used to collect addresses, building diagram numbers, vertical offset measurements for top of bottom floor and other elevations, and vent information, but not elevation of lowest machinery if air conditioner pads, for example, are in the back yard where they can't be seen from the street. Photogrammetric Van Disadvantages. The sample tests in Pinellas County concluded that the bottom of front door and other target points could only be seen in stereo for about 20% of the houses; the remaining 80% were unsuitable for data extraction because of foliage that blocked stereo views of features to be surveyed. Latitude and longitude measurements could be made on all houses, but not the critical elevations. Also, imagery from the street cannot see in back yards to detect the possible existence of walk-out basements. Photogrammetric Van Costs. Estimated $35 per building extracted plus $2.50 per building for batch entry of records into the registry. Photogrammetric Van Conclusion. Because landscaping blocks stereo views, photogrammetric vans are not a reliable alternative for generating complete ECs. With reconfiguration of the cameras, this method could become a cost-effective alternative to hiring a surveyor to measure vertical offsets and provide other ancillary information needed to complete ECs, but only in areas that do not have dense vegetation surrounding the front and side views of homes. LIDAR Van Advantages. LIDAR vans, recently introduced, may be better than photogrammetric vans at measuring elevations of features such as front or side porches, decks and patios, from which top of bottom floor elevations are derived because only a single line of sight is needed to survey target points with LIDAR. LAG and HAG elevations should also be relatively simple to measure. LIDAR Van Disadvantages. LIDAR van technology is totally new and untested on applications such as ECs. The sensor is unable to see into back yards to detect the possible existence of walk-out basements. LIDAR Van Conclusions. Unreliable at this time. Strategy D — Future Web-Based Elevation Certificates Strategy D pertains to future ECs presumed to be entered into the elevation registry using a new web-based system that would help surveyors correctly prepare ECs for conventional printing, but then remove the owner's name (Privacy Act consideration) and record the remaining information in the registry. See Methods 5 and 6. Advantages: The web registry helps the surveyor to correctly complete ECs. It automatically enters all data, except for owner's name, into the registry without any further need for digitization. Records are of high accuracy and quality, generated at no cost to FEMA beyond the cost of developing the registry. Disadvantages: The cost of developing and maintaining a user-friendly web- based registry is the only disadvantage. Obviously, the system must be extremely user-friendly to the surveyor, or else the system will be by-passed in favor of completing a conventional paper EC form. Costs: Dewberry assumed a maximum cost of $4 million over a 5-year period, to include Oracle and/or other license fees, to develop the web-based registry. Assuming this system is used to generate 50,000 ECs per year for 5 years, the amortized cost at $10 per individual web entry would be $2.5 million. Assuming this system is also used for batch entry of 1,000,000 ECs currently held by CRS communities, the amortized cost at $1.50 each would be $1.5 million. This same $4 million is assumed to also provide functionality for batch entry of other large databases into the registry, including databases from Policies in Force, LOMA 2000, ISO, Dewberry, and the Corps of Engineers. For batch entry of records, Dewberry assumed the cost would be $2.50 per record; for the known 162,360 digital EC records that already exist, this could subtract $405,900 from the cost basis presumed to apply to individual EC functionality. In actuality, Dewberry does not know if $4 million is a good cost estimate for such a system, but believes $4 million to be at the high end of all cost options. For this reason, Dewberry conservatively pro-rated the cost at $10 per individual EC entered, and $2.50 each for batch entries. Conclusions: Conventional or GPS survey methods used for future ECs can achieve elevation accuracy of ±0.5 ft at the 95% confidence level for the lowest floor, LAG and HAG. Web-enabled entries of new ECs into the registry are key to the updating and maintenance of the registry. Assuming the pro-rated cost at $10 per EC, the cost-effectiveness ratio is on the order of 30:1 when full valued ECs are produced using GPS. FEMA should remove the word "optional" next to the latitude/longitude entry on the EC form 81-31 and encourage surveyors to use GPS technology which, when properly utilized, is both more accurate and also provides accurate geographic coordinates needed for future updates of BFEs and other registry information pertaining to revised flood risks. Strategy E — Leverage Alternative Data Sources For Strategies A and D, GPS ECs provide the highest quality records, with conventional ECs being nearly as good except for the lack of geographic coordinates; but ground-surveyed ECs are the most expensive to produce. For Strategy B, assuming the airborne remote sensing costs have already been paid for, the additional costs are minimal for provision of geographic coordinates plus LAG and HAG elevations, but "ancillary information" including street addresses, building diagram numbers, top of bottom floor and other elevations, and vent information are not provided from airborne remote sensing. Commercial georeferencing services were determined to lack the accuracy necessary for geocoding or reverse-geocoding of structures for the registry. Although various Federal, state, and local agencies, as well as commercial organizations such as map determination companies and real estate firms, may possess some of the ancillary data needed, the alternative data sources for this study focused on the following: (1) community building footprint files or building centroids, preferably linked to street addresses, (2) community-provided vertical offset measurements, photos, and other on-site information provided by a surveyor, GIS technician, summer intern or other person hired to make simple measurements community-wide, and (3) measurements and information on individual structures provided by homeowners to their insurance agents. Item (1) is already available in many communities; item (2) could be provided by communities if motivated with proper incentives to do so; item (3) is a newly- proposed alternative that would enable insurance agents to enter ancillary data when presented with measurements, "yardstick photos" and other appropriate information from homeowners seeking flood insurance, but this proposal is expected to encounter resistance because of implementation issues. Although there are other sources of ancillary information, these are reasonable standard alternatives that could be used nationwide. Advantages. For all study methods that utilize footprint files (Methods 9, 14, 17, 18 and 19) the total value of the ECs and their CE ratios increase significantly when footprints are available; in many communities, footprint files are already available or could be produced at relatively low cost, even if digitized off of digital orthophotos. For all study methods that utilize surveyor-provided or equivalent on-site ancillary data (Methods 8, 11, 15, 17 and 19), the total value of the ECs and their CE ratios again increase significantly, especially when simple vertical offset measurements (from a yardstick or steel tape) are provided between LAG and HAG and top of bottom floor or LHSM elevations. For all study methods that utilize owner-provided ancillary data (Methods 9, 16 and 18), the value stays the same as when a surveyor takes the measurements, but the CE ratios increase dramatically because the $50 per structure cost is saved by not having to hire a surveyor or someone else to take simple measurements. The homeowner options only pertain to individual houses where the homeowner and insurance agent see an individual need, whereas the surveyor options pertains to surveys performed community-wide and address community-wide needs. Disadvantages. One disadvantage of using community-based alternative data sources is in the need to establish incentives that are successful in motivating communities to take multiple steps necessary to acquire all data needed for batch entry of EC data into the registry. Not only would they need to provide their photogrammetry or LIDAR data to FEMA for use in establishing geographic coordinates and LAG/HAG elevations, but they would also need to take additional steps to provide building footprints, vertical offset measurements and other on-site information necessary for mass production of EC data. The second disadvantage is that the process is complicated; extraction of geographic coordinates and LAG/HAG elevations from LIDAR data is highly technical and should be performed by specialized FEMA or Regional contractors, whereas the ancillary information is less technical and could be added to the community's EC database by either the community or by FEMA contractors. Costs. The cost of digitizing building footprints (either from stereo photogrammetry or from digital orthophotos) is very low (perhaps $1 per house) but the complicated part is linking these footprints to the correct street addresses. Such addressing can be done relatively inexpensively where the community already has georeferenced tax parcels in a GIS database; communities without a GIS would consider this option only when it becomes GIS-enabled. The cost of hiring a surveyor to take vertical offset measurements community-wide and to collect other on-site ancillary information is estimated at $50 per house, although summer GIS interns or others could undoubtedly do a satisfactory job at lower cost. Presumably, homeowners could do this at no additional cost to the government, but this only pertains to individual structures, not community-wide. Conclusions. When a community already has elevation data from photogrammetry or LIDAR equivalent to 2' contours, on-site offset measurements can convert LAG and HAG elevations into lowest floor elevations, all of which are accurate to ±1.2 ft at the 95% confidence level. Although new GPS-surveyed ECs are preferred (Strategy D), aerial survey data (Strategy B), supplemented with additional information from the community, is the next best alternative for acquisition of community-wide structural elevation data. Although it is doubtful that FEMA will ever be able to offer adequate CRS credits to cover community costs of collecting and maintaining elevation data, several communities have already funded the acquisition of the highest accuracy (and more expensive) ECs using GPS ground surveys throughout community flood plains. Other communities that feel they cannot afford such ground surveys, but have already borne the cost of photogrammetric or LIDAR surveys, may find that CRS credits will at least help to offset a portion of the overall costs of acquiring the additional information necessary for development of structural elevation data community-wide STRATEGY RECOMMENDATIONS All five of the technical strategies have advantages and disadvantages for populating an elevation registry. There is no "one size fits all" solution. Flexibility is required in order to maximize the advantages and minimize the disadvantages of the various strategies, recognizing that capabilities and limitations vary by individual community, depending on the availability of existing survey data, accurate photogrammetric and/or LIDAR data, GIS-based building footprints linked to street addresses, or alternatives such as building centroids or tax parcels linked to street addresses. Strategy A: Maximizing the use of existing ECs. Recommendation A.1: Assemble as many ECs in the registry's database format as possible. Known existing sources include the following: ? ISO ECs. ISO has over 50,000 ECs of 352 CRS communities entered into the database explained in section A.1 of the Part I Report; these have been received to date from communities and have been transmitted via the Elevation Certificates in Computer Format element over the years and/or entered by ISO through a special project in 1995. This database already has each address separated into the required multiple data fields as required for automated address matching. ISO's database includes the most important data fields required by the registry. However, some programming will be required to eliminate records that have neither Top of Bottom Floor elevation nor Lowest Adjacent Grade (LAG) elevation. ? Dewberry and URS Digital and Hardcopy ECs. These two firms have approximately 19,560 ECs, of which 16,381 are already in a database format suitable for importing into the elevation registry. Additional EC records should come available as a result of Dewberry's ongoing surveys. ? Susquehanna River Flood Warning and Response System. The Philadelphia District of USACE has the street address and elevation of the basement, top of next higher floor, and lowest adjacent grade for approximately 1,200 structures, plus elevation data with partial/incomplete address information for approximately 1,400 additional houses, with elevations produced from photogrammetric spot heights supplemented with vertical offset measurements. At least 1,200 of these structures should be suitable for the elevation registry. The Philadelphia District has already indicated its willingness to share this data with FEMA. These were tested by Dewberry to be accurate to 1.2 ft at the 95% confidence level, i.e., equivalent to 2 foot contour interval accuracy. ? Other Corps of Engineers Data. The Philadelphia District is probably not the only Corps district or laboratory that collects structural elevation data. If and when a registry is implemented, FEMA should seek cooperation from the Corps' Director of Civil Works and officially solicit input from the Corps' various districts, divisions, and R&D laboratories that may have elevation data on structures surveyed in the past, or surveys planned in the future. ? Community ECs. ISO estimates that there are 4.5 million flood policies in three approximately equal categories: (1) 1.5 million outside the SFHAs, normally having no ECs, (2) 1.5 million pre-FIRM, normally having no ECs, and (3) 1.5 million post-FIRM, normally having ECs that should be archived by local communities. Of the 1.5 million ECs, ISO could potentially obtain as many as 1 million of them from the 1000 CRS communities that have two-thirds of the total policies — assuming that these communities have these ECs — but incentives will be required to reach out to get them. (Under the CRS, communities are only required to maintain those ECs from the date of application to the CRS. This number presumes these communities have all the post-FIRM and pre-FIRM ECs available and can provide them. Since many CRS communities do not receive full or partial credit for post-FIRM or pre-FIRM ECs, it is doubtful that 1 million such ECs actually exist. Furthermore, as with ECs collected by ISO, many of these would probably be missing various pieces of data. ISO commented: "The reason we don't see more credit [for post-FIRM and pre-FIRM ECs] is that these ECs are not available or are not fully completed.") Regardless, this may be a formidable task because considerable incentives may be required to get communities to collect, photocopy and send their ECs to FEMA or ISO. The data from each of these certificates could be scanned and/or manually digitized into standardized databases for entry into the registry. The cost would depend upon whether communities (A) gather and digitize their own records or (B) provide their ECs to FEMA or ISO for scanning and digitization. ? LOMA 2000. Data already residing in the LOMA 2000 database could help to "seed" an elevation registry consistent with Method 1. LAG elevations should be usable, but FEMA has raised concerns about the reliability of other data in this database. Some human intervention will be required in order to convert each LOMA 2000 single Street Address field into multiple fields for address number, street name, address suffix; apartment, unit, suite and/or building number; and post office box and route numbers. Some manual addressing will be required. Dewberry has about 130,000 LOMA 2000 records, of which 80,000 have elevation data and approximate/imprecise geocoordinates; very few have accurate coordinates from GPS surveys. Michael Baker Jr., Inc. has about 12,000 records, of which 4,200 have elevation data and approximate geocoordinates. PBS&J has about 21,000 records, of which 7,400 have elevation data and approximate coordinates. Recommendation A.2: Convert as many as possible of the hardcopy ECs to the registry's database format. FEMA may choose to centralize the scanning and conversion of hardcopy ECs held by ISO, Dewberry and URS into the registry's database format. With sufficient quantities, such conversions can be performed for $5 per EC. Communities could either provide their hardcopy ECs to FEMA for centralized conversion or they could choose to convert the records locally, consistent with FEMA guidelines and examples. Recommendation A.3: After hardcopy ECs are digitized, communities should attempt to geocode their registry records using the best of the options below and recording the horizontal accuracy code for the method used (see item 21 at APPENDIX R). ? Communities that already have geocoded building footprints or centroids linked to street addresses have the best and most accurate methodology for geocoding ECs using their existing GIS tools. ? Communities that have geocoded parcel polygons linked to street addresses and/or Assessor Parcel Numbers (APNs) can overlay their parcel polygons over digital orthophotos (available nationwide), manually identify the centroid of the main structure visible in each parcel polygon, and extract the accurate geographic coordinates. The APN can be used only on those ECs for which the APN number (normally the Assessor's tax book number, page number, and parcel number) are included in the Property Description block on the EC form. APNs are more commonly referenced in rural areas without standard street addresses. ? Alternatively, the centroid of each parcel polygon could be generated, without overlay on digital orthophotos. This would provide estimated coordinates that could be reasonably accurate and acceptable for small lots with single structures, but which would become less accurate as parcel sizes increase. ? The least accurate method is to use commercial geocoding services which can be reasonably accurate for some addresses, but which were found to have errors of hundreds of feet at the 95% confidence level because of interpolation procedures used. Also, commercial geocoding services often fail to match addresses queried, especially PO box and RR addresses. ? The latitude and longitude fields may be left blank if no method works for address matching, but such records cannot be automatically updated in the future as new flood studies are implemented. Recommendation A.4: Where other records are incomplete, get communities to help in filling in missing information. Communities are best for resolving issues from missing or confusing addresses and for providing the correct FIRM and BFE information needed for Section B on the current EC form, for example. Recommendation A.5: Do not duplicate on-line databases already developed by communities. When communities already have their ECs digitized and available online, Dewberry recommends that the elevation registry provide a link to such sites, without attempting to duplicate and maintain a database already being maintained by communities. This would justify CRS credits (under CRS Section 310) for these communities and would encourage other communities to do the same. Recommendation A.6: Provide additional CRS credits to communities that digitize their ECs, provide addresses linked to accurate geographic coordinates for the universe of floodprone structures in their communities, and assume "ownership" of their records in the registry by taking effective steps to quality control their data, complete missing information, and resolve potential discrepancies. More CRS credits should be awarded for provision of accurate coordinates and few credits for providing approximate coordinates from automated geocoding of EC records. Strategy B: Maximizing the use of existing LIDAR or photogrammetric data (when equivalent to 2 ft contours or better). Recommendation B.1: Process existing LIDAR data to extract LAG and HAG elevations and geographic coordinates of structures. This is best performed by intersecting a bare-earth LIDAR triangulated irregular network (TIN) with building footprint files, then using GIS software to determine the LAG and HAG elevations along the intersection polygon for each structure. Provide accurate latitude and longitude for the approximate centroid of each footprint. Recommendation B.2: If LIDAR data is not available, process existing photogrammetric data to extract corner spot heights, LAG and HAG elevations and geographic coordinates of structures. This is best performed by hiring a photogrammetric firm to use photogrammetric aerial triangulation data to reset the stereo models and compile LAG/HAG elevations and spot heights on the ground at all visible corners of each structure in or near the community's SFHA. Normally, only 2 or 3 corners are visible of the ground in stereo, immediately adjacent to each building. If building footprints do not already exist, compile 2-D footprints in addition to the 3-D spot heights. Provide accurate latitude and longitude for the approximate centroid of each footprint. Recommendation B.3: Hire a surveyor or utilize trained government employees to locate and travel to each of the structures in or near the SFHA in order to collect ancillary information and make on-site measurements. For Methods 8 and/or 17, the on-site surveyors or specialists should perform the following tasks, recording data onto a hand-held computer if possible to complete a database that "mirrors" the data dictionary at APPENDIX R: ? Complete all entries required for the data dictionary — except for the geographic coordinates (items 18 and 19) and elevations (items 43, 44, 45, 46, 47, 48 and 49) which are determined by other means. Ensure that the following items are completed: street address (items 4 through 14), building diagram number (item 40), and number and area of flood vents (items 50 and 51). ? Locate the LAG point for each structure; measure and record the vertical offset measurement from the LAG to the Top of Bottom Floor and the Top of Next Higher Floor (TNHF) in A-zones, or the Lowest Horizontal Structural Member (LHSM) in V-zones. Also measure and record the vertical offset measurements to the Lowest Elevation of Machinery (LEM) and garage floor elevation for an attached garage. ? Using the LAG values from photogrammetry or LIDAR for each structure, use the vertical offset measurements to compute and record the elevations of the top of bottom floor, top of next higher floor, lowest horizontal structural member, lowest elevation of machinery and/or garage floor, as applicable. Also record the LAG and HAG elevations in the database. If communities have collected LAG and HAG elevations from either photogrammetry or LIDAR, but cannot afford the additional cost of the on-site surveys, the LAG and HAG information alone may be acceptable for mass production of LOMAs, or for advising owners of some pre-FIRM homes when it would be advisable for them to acquire a conventional EC to determine if actuarial rates might be less expensive than pre-FIRM rates. LIDAR Method 14, which collects LAG/HAG elevations without additional on-site surveys, is very cost effective (15.76:1) in determining structure addresses for which the purchase of flood insurance should be mandatory, based solely on the elevation of the LAG compared with the BFE. Recommendation B.4: Perform quality control and reformat the database as necessary to fit the data dictionary. Quality control steps are recommended for the community that generates the database and again by the registry's administrator responsible for resolving any database conflicts. Recommendation B.5: We recommend that data from Pictometry imagery not be used to populate the elevation registry for the following reasons: ? All elevations are relative, rather than absolute, requiring other data sources to provide the basis for all elevation comparisons ? Although the technology could be used to view an insured structure from all sides, it has an unacceptably high error rate in detection of the presence or absence of basements. ? On-site data collection is still required for other missing information. Recommendation B.6: Provide additional CRS credits to communities that utilize their existing LIDAR and/or photogrammetric data to generate spot heights, LAG and HAG elevations for all structures in or near floodplains; perform on-site surveys to provide lowest floor elevations and other data required for EC records; rigorously assess the accuracy of their data; provide their data for the registry in the correct format; and assume "ownership" of their records in the registry by taking effective steps to quality control their data, complete missing information, and resolve potential discrepancies. Strategy C: Utilizing data from mobile photogrammetric vans Recommendation C.1: We recommend that data from mobile photogrammetric vans not be used to populate the elevation registry for the following reasons: ? This technology was rated poor in its ability to produce ECs because of the high number of target points (top of bottom floor, lowest horizontal structural member, LAG and HAG points, and vents) that could not be surveyed because of landscape shrubbery that blocked stereo views from the street ? This technology was rated poor because of its inability to see and survey key features in backyards such as walk-out basements and air conditioner pads. Strategy D: Web-entry of future ECs. Recommendation D.1: The web-based elevation registry should have a front-end portal with functionality to: (1) tutor the surveyor on correct survey procedures to be followed for completion of an EC, and (2) receive individual online submissions of new ECs needed to maintain/update the registry. Recommendation D.2: FEMA should web-enable the generation of new ECs, not allowing them to be finalized until mandatory data fields are completed, including latitude and longitude and the surveyor's use of a benchmark with Permanent Identifier (PID) number listed in the National Spatial Reference System. Recommendation D.3: When all mandatory items are complete, and the surveyor or engineer enters his/her name and state registration number, the certificate should be recorded as final, printed in hardcopy for sealing and signature as at present, but with the data automatically transferred into the elevation registry, excluding the homeowner's name because of Privacy Act considerations. FEMA may also consider the use of electronic signatures for this process. Recommendation D.4: The registry should provide immediate feedback to the surveyor, acknowledging receipt of input. If the name and/or registration number are inconsistent with records from the state licensing Board, follow-up action should be required to ensure data certification. Strategy E: Leveraging alternative data sources. Recommendation E.1: Import relevant BureauNet data into the registry. There are currently 4.5 million active policies in force which include such information as date of construction; LAG, HAG and lowest floor elevation; approximately geo- referenced coordinates, and policyholder information. FEMA's NextGen project is in the process of updating the system to an Oracle database environment and revamping the analysis tools and policy rating engine. Excluding the policyholder information, other information relevant to the registry's data dictionary should be imported for initial population of the registry Recommendation E.2: Import relevant NEMIS data into the registry. There are currently over 400,000 structures in the NEMIS database of structures damaged by Presidentially-declared disasters. NEMIS automates federal disaster programs including incident activities, preliminary damage assessment, declaration processing, human services, infrastructure support, mitigation, and associated administrative and financial processing. NEMIS includes a structure's address, type (basement, no basement), and approximate UTM coordinates. Although it does not include structural elevation information, NEMIS information could be used to populate items 70 and/or 71 of the registry's data dictionary, i.e., the depth(s) and/or date(s) of prior interior flooding. Recommendation E.3: Consider buying needed data. First American Flood Data Services (FAFDS) provided pricing to Dewberry/FEMA of $2.50 per "hit" for GIS automated geocoding of addresses and $3.02 per "hit" for automatic flood zone determinations from their database. Lower unit costs pertain to transactions involving over 10,000 addresses. Higher unit costs pertain when manual look-up procedures are used and FAFDS provides guarantees for the accuracy of their determinations. However, a FAFDS official indicated that they could not provide FEMA or anyone else a list of all addresses in or near to floodplains, to define the total universe of floodprone structures, because that would jeopardize their market advantage. Although not yet truly nationwide, the National Parcelmap Data Portal (NPDP) was considered because it has nearly 60 million geocoded parcels linked to addresses and/or Assessor Parcel Numbers (APNs) in about 200 major metropolitan areas, and they plan to compile 800-900 of the most highly populated counties by the end of the current decade. However, this is just a fraction of the 3,150 counties nationwide and would therefore not include the total "universe" of structures desired. The cost to FEMA for this service may be $1 to $2 million per year for access to all records, but NPDP's Dynamic Server option would be less expensive if, for example, only 1 million records were "hit" at 25 cents each. Because the NPDP purchases digitized parcel data from communities that update their records periodically, an argument could be made that FEMA should receive such data free from these same communities. The counter argument is that FEMA would only have to deal with NPDP to receive normalized data, rather than with thousands of communities each providing data with different data formats and technical issues to be resolved. Whereas FEMA would have difficulty justifying commitments costing FEMA millions of dollars per year, it is possible that arrangements could be made with either of the above-listed services so that FEMA is charged on a "per hit" basis, with costs paid by registry users who utilize the registry and provide credit card numbers or have active draw-down accounts for routine servicing. Users could be charged a slightly higher mark-up fee to help reimburse FEMA for its expenses in maintaining agreements with FAFDS or NPDP. Recommendation E.4: Award additional CRS credits to those communities that provide up-to-date geocoding or reverse geocoding of all structures in or near floodplains. CRS administrators should determine if this recommendation is feasible or not, and the number of credit points warranted. As with many of Dewberry's CRS credit recommendations, this could be a larger one-time credit for geocoding all existing structures, followed by a small annual credit for maintaining and updating the data. Although not mandatory, it is desirable for the registry to include the universe of addresses in or near to floodplains so FEMA/CRS would have a way to track progress towards a goal of having all such addresses completed with elevation data in the registry. Attempts to leverage data from the U.S. Census Bureau and the U.S. Postal Service were unsuccessful. Because of Title 13 limitations, the Census Bureau is not allowed to share its geocoded Master Address File (MAF), either with or without geocoding, and Postal Service addresses are not georeferenced. Ultimately, the best source of floodprone addresses is from the communities themselves. This would require that thousands of communities take actions to populate the registry with such addresses in a standard format. This could be a formidable task, and most communities would expect some incentive for performing this task, preferably in the form of additional CRS credits. Recommendation E.5: When feasible, "piggy-back" on other FEMA initiatives, to include Housing Inspection Services and the Map Modernization Program. FEMA conducts thousands of housing inspections annually in disaster areas nationwide. Personnel from FEMA's housing inspection service contractors could be trained to make vertical offset measurements and collect other data needed to leverage the photogrammetric and/or LIDAR data so as to complete high quality EC records for damaged structures. Before FEMA deploys teams for housing inspections, they would need to coordinate with the Mitigation Division or check the registry's web site to determine if offset data are needed to upgrade the mid-value data from Method 14 to high-value data from Method 17, for example. This would cost FEMA only an estimated $5 more per house, rather than $50 per house for on-site surveys described above. FEMA's Map Modernization (MAPMOD) Program is working towards the goal of having DFIRM Databases (i.e., floodplain and flood elevation data in GIS format) within 5 years for all NFIP communities, and to have DFIRM Database coverage for 98 percent of the U.S. population by the end of FY2008. When developing the implementation plan for the elevation registry, it would be prudent to review the MAPMOD schedule. Phased implementation of the elevation registry following the MAPMOD schedule would ensure that any eRating tools that depend on DFIRM Databases for BFE and flood zones would have the appropriate data available. The National Service Provider (NSP) plans to store DFIRM Databases in Geodatabase format, making them available online through the Multi-Hazard Information Platform (MIP) PART II — PROVIDING STRUCTURAL ELEVATION DATA Purpose Insurance agents and WYO companies have long affirmed that the requirement for Elevation Certificates (ECs) is a major impediment in selling flood insurance. The purpose of this study is to determine if it is appropriate, feasible, and legally possible for FEMA to obtain the elevation data on individual structures and to make this elevation information available in an elevation registry to properly rate the structures for flood risks and flood insurance premiums so that ECs costing hundreds of dollars each would not be needed in most cases for insurance rating. Part I of this study addresses the legal and technical issues in implementing an elevation registry, whereas Part II addresses feasibility issues. Summary of Part I Requirements for eRating For eRating purposes, insurance agents need to know if a structure is Pre-FIRM or Post-FIRM, and they need information traditionally included on ECs: (1) street address and FEMA building diagram number (2) elevation of the top of bottom floor in A-zones or bottom of the lowest horizontal structural member in V-zones (3) elevations of the next higher floor, lowest adjacent grade (LAG), highest adjacent grade (HAG), attached garage floor slab, and lowest elevation of machinery or equipment servicing the building (4) base flood elevation (BFE) and flood zone (5) number, area and location of flood openings (vents). The latitude and longitude of each structure is desired for long-term maintenance and update of records but these geographic coordinates are not required for rating of structures. Whereas the highest accuracy elevation data from ground surveys is preferred (±6 inches), FEMA can accept lesser accuracy for eRating by implementing a system of "judgment ratings," but with no elevation errors larger than 4 ft at the 95% confidence level. Summary of Part I Legal Findings The Dewberry/FEMA Law Associates/EOP Foundation "Final Report on Legal Issues" identified no legal issues that would preclude FEMA from establishing, maintaining and making available to insurance companies and agents, or to the general public, an elevation registry containing this required information so long as personal information is not included. As such the registry would not be a "system of records" regulated by the Privacy Act. This legal analysis assumed that the registry includes specific property addresses, but does not include personal identifiers of individuals such as names, policy numbers, or social security numbers. FEMA may maintain individual identifier information in separate data bases, and link to those separate databases for purposes authorized for those data bases. Other legal findings, relevant to implementation of an elevation registry, are summarized as follows: ? The National Flood Insurance Act clearly authorizes FEMA to obtain and distribute to the public information about flood risk and information relevant to the determination of premiums under federal flood insurance policies. Questions regarding ownership of ECs have no bearing on FEMA's right to obtain elevation data and place it in the registry. ? FEMA is not restricted by the Privacy Act or other privacy policy principles from making elevation registry information available to the insurance companies and agents, which are the intended audience of the registry. The registry would contain all addresses for which FEMA had elevation data, from a number of sources, and hence the registry would not disclose any company's customer list or disclose whether a listed property is insured by FEMA directly, insured by a competitor, or whether the property is insured at all. ? FEMA would be obligated by the Freedom of Information Act to make information in the registry available to any person on request, and to make it available in an electronic format to anyone who asks, if it is made available to companies in that format. FEMA may, but need not, design the elevation registry to be publicly available and accessible to any person on the Internet. ? FEMA and any FEMA contractors establishing and maintaining the elevation registry would not incur any significant increased liability exposure from creation of the elevation registry. FEMA can reduce any potential liability exposure by: (a) using the registry only for the purpose of rating insurance policies, and continuing to require communities to obtain Elevation Certificates to support construction permits and other floodplain management purposes; (b) amending FEMA's regulations and manuals to allow WYO Companies and agents to rely on the elevation registry for rating of policies thus supporting the argument that reliance on the registry's data satisfies the professional standard of care; and (c) including in the elevation registry a warning notice that the information in the registry has been developed solely for purposes of determination of premiums for insurance policies and that more accurate elevation determinations may be required for purchase and development decisions by property owners. ? FEMA should design and implement the quality control standards, processes, and documentation for populating the registry with data. These processes will specify the types of data sources and the documentation and certification requirements for data before it can be incorporated into the registry. Summary of Part I Technical Findings Whereas ground-surveyed ECs are the best, having elevations accurate to ±0.5 ft at the 95% confidence level, aerial surveys, including photogrammetry and LIDAR, can cost-effectively provide elevations accurate to 1.0 to 1.5 ft at the 95% confidence level when the lowest and highest adjacent grade (LAG and HAG) are visible from the air; then, vertical offset measurements could be made on-site to compute the other elevations required in a registry. Federal Geographic Data Committee (FGDC) standards require all geospatial data accuracy, including aerial survey data accuracy, to be reported in ground distances at the 95% confidence level in accordance with the National Standard for Spatial Data Accuracy (NSSDA). For example, 2 foot contours, or equivalent digital elevation datasets, have 1.2 ft vertical accuracy at the 95% confidence level; statistically, this is the same as 1.0 ft vertical accuracy at the 90% confidence level used by the National Map Accuracy Standard published in 1947, a standard now obsolete for digital elevation data since publication of the NSSDA. For quality control purposes, there are several acceptable methods for certifying the accuracy of elevation data: ? When an elevation dataset is tested against checkpoints of higher accuracy, vertical accuracy is reported as: "Tested __ feet vertical accuracy at 95% confidence level." This method is routine for LIDAR data because production procedures have not yet been standardized by the immature LIDAR industry. ? When an elevation dataset is not tested against checkpoints of higher accuracy but produced according to procedures that have been demonstrated to produce data with particular vertical accuracy, vertical accuracy is reported as: "Compiled to meet __ feet vertical accuracy at 95% confidence level." This method is routine for photogrammetric data for which industry production procedures are standardized and mature. ? Either of these methods would be suitable for a community to certify the accuracy of its elevation data if used by FEMA to establish LAG/HAG elevations and vertical offset measurements for lowest floor and other elevations in the registry. Although they have some limitations, existing hardcopy ECs could easily and cost effectively be digitized into the registry's database format. Highest quality ECs could be more easily acquired in the future if FEMA develops and encourages surveyors to use a FEMA on-line tutorial to prepare and print hardcopy ECs and automatically populate the registry with selected EC information. The owner's name would be excluded for Privacy Act considerations. This tutorial would encourage the use of National Geodetic Survey benchmarks and global positioning system (GPS) surveys that provide geographic coordinates in addition to required elevation data — helping to ensure that new ECs are accurate and complete and will support spatial queries by users. BFEs are best determined from Flood Insurance Study (FIS) profiles, but Dewberry suspects few are actually determined by this method. Most BFEs are probably estimated by interpolating between BFEs shown on FIRMs, and BFE values can be in error by up to 0.5 ft since they are rounded to the nearest whole foot on the effective FIRM. FEMA's new DFIRM databases include BFE values at all cross sections. When DFIRM databases become available nationwide (2007-2009), these cross section BFEs can be interpolated to determine BFE values at other locations; these values will be superior to BFEs interpolated from the FIRMs but may still be inferior to FIS profiles. Accurate geographic coordinates for structures are needed to track changes to flood zones and BFEs. Elevation Registry The registry could be available to all via the web. The registry should focus on structure EC data used by insurance agents, floodplain managers, realtors and potential owners to determine flood risks. Registry information, including a downloaded copy of a "virtual EC" produced from registry records, but excluding owner names, could be free to some but available to others for a fee, similar to the way that users pay a fee to FEMA's Map Service Center for downloading flood maps and DFIRM databases. Selected users would be able to query the registry for all records in a community that satisfy certain criteria. Ideally, to avoid data redundancy, the registry could be hosted by FEMA's NextGen data warehouse. However, for administrative simplicity, FEMA may want to ensure that the registry avoids potential Privacy Act issues. To do this, names and other personally identifiable information should not be included in the registry and cannot be permanently linked to a system containing these items. Therefore, despite some inefficiencies of storage, FEMA may prefer to implement the registry as a standalone database that would merely feed data into the NextGen data warehouse. Depending on the strategies chosen by FEMA, the registry could be populated by several means: (a) digitization of existing ground-surveyed ECs from communities, ISO and others; (b) web entry of future ECs by licensed surveyors using an on-line tutorial on how to correctly prepare ECs; (c) reformatting of existing FEMA databases (BureauNet, NEMIS and LOMA 2000), including deletion of personal information; and (d) batch entry of elevation records provided by communities using their existing LIDAR or photogrammetry data to determine LAG/HAG elevations and offset measurements to determine other needed elevations. Each of these data sources have different levels of accuracy and reliability that would be tracked in the registry. It is recommended that the registry be developed as a Geodatabase or other comparable spatial database. A spatial database would include the locations of structures as geographic features (in this case, geographic point features) along with the attributes as described in the data dictionary shown in APPENDIX R. Use of a spatial database will facilitate display of the elevation registry data with other GIS data such as topographic data or DFIRMs. Additionally, it will facilitate spatial queries, allowing users to more easily see all records within a community or all records near another feature. To be effective for eRating, the elevation registry would need to have a web portal with interfaces for security-controlled input of data to the registry's database, and output of information needed for eRating. In addition to insurance agents and WYO companies, this portal should also be available to others involved in the NFIP, e.g., floodplain managers who may need to review all records for his/her community, or an individual user who may need to review only a single address record at a time. The registry must identify the source of the elevation information, its accuracy and effective date, and have the ability to track multiple records per address that may differ and/or change over time. An administrator who can resolve data conflicts should monitor the registry. For maintenance of the information, the elevation registry requires the following capabilities: ? Accept community Master Address Files (MAFs) of addresses to be included in the registry for communities participating in the NFIP, with provisions for community NFIP coordinators to update their MAFs to track new construction in or near floodplains. This is needed to help communities track the percentage of total structures for which elevation data are available, with the goal of achieving 100% availability. ? Allow for batch entry of new or existing elevation databases, reformatted for consistency with the data dictionary. ? Allow for on-line preparation of ECs both for hardcopy printing and for automated entry of individual ECs into the registry by surveyors or other authorized personnel, with provisions for feedback to surveyors and validation of credentials. This capability would build upon the functionality of the latest NFIP/CRS Elevation Certificate software. It would require the surveyor to enter the latitude and longitude of the structure and indicate the source of the coordinate information from a pick list. ? The "front end" web portal would provide a How-To Guide to tutor surveyors through the correct process to perform EC surveys to higher levels of accuracy than at present and require them, for example, to enter the PID number of the NGS benchmark on which elevations are based. ? Allow for updating registry data and tracking the source of new data, while archiving historical data. ? Ability to compare files/records and perform address matching to determine if two or more records pertain to the same structure; ability to correlate addresses that differ slightly, e.g., 123 5th St, 123 5th St., 123 5th Str., 123 5th Street, 123 Fifth St., 123 Fifth Street, etc.; ability to correlate addresses that have changed zip codes for otherwise-unchanged addresses or have multiple community names for the same zip code. Web Services Dewberry's detailed recommendations for the web-based registry are documented at APPENDIX S. For all recommended strategies, it will be necessary to establish a web-based elevation registry, preferably with a Geodatabase (or comparable spatial database) that includes the attributes in the data dictionary at APPENDIX R. XML data format is recommended for all input and output protocols to minimize interoperability issues. Before ESRI's Geodatabase or alternative spatial database format is adopted, other NextGen stakeholders should be consulted to ensure a common or compatible approach to FEMA's database(s). Dewberry was informed by Mr. Jack Way of Optimal Solutions & Technologies (OST) that a Geodatabase is compatible with the NextGen database design. Publication of the elevation registry data through a Web Map Service (WMS) and/or Web Feature Service (WFS) would be required to allow users to view the elevation registry data within their GIS application. Elevation Certificate (EC) spatial data (i.e., structure points) could be used in conjunction with FEMA DFIRM data (ideally also made available through WMS and/or WFS) and/or local GIS data. ? A WMS would provide the data in the form of a map, generally in a static pictorial format such as PNG, GIF, or JPEG. The user could specify what portion of the earth to view, the coordinate system, and the map size. The map could be overlaid on data from other WMSs (e.g., a DFIRM Database or a USGS DOQ) or data stored locally on the user's system. However, a WMS provides little ability to customize the symbolization of features in the map and no ability for users to make updates to the maps. Therefore, a WMS is not recommended when changes are anticipated to the geographic coordinates of structures such as when approximate coordinates are upgraded to precise coordinates. ? The alternative WFS is recommended because it would provide the data as geospatial features that could be manipulated by FEMA and authorized users. This would include the ability for users to set symbology for features and optionally to create, update, or delete features. As with the WMS, the user could specify what portion of the earth to view and the coordinate system. In addition, a WFS would allow users to filter data by user-defined conditions (e.g., view all ECs within a particular community; all ECs for structures with lowest floor elevations below the BFE; or all ECs within a particular community for structures with flood insurance — if the source of this information came from the policies-in-force database). A WFS would provide a mechanism for spatial updates to the latitude and longitude fields of the elevation registry to be provided to FEMA by local communities or surveyors. However, a validation process would need to be developed before such updates are loaded into the main spatial database. Populating the Registry Community Input. During population of the registry, the data will be "spotty," depending on each community's ability and willingness to support the overall objectives. The following initiatives should be taken (voluntarily) by communities to help populate the registry: ? Digitize their hardcopy ECs and/or reformat their digital elevation records to fit the registry's data dictionary format. ? Digitize building footprints from digital orthophotos and link each footprint to its address, whether floodprone or not. ? For communities with existing LIDAR or photogrammetric data equivalent to 2 ft contours or better, determine LAG and HAG elevations for each structure in or near floodplains. Measure vertical offsets on-site and complete other elevations relative to the LAG/HAG. While on-site, complete other information required for a complete EC record. ? Provide tax assessor data to enhance registry records. ? Inform FEMA of typical database queries expected to be made by the community so that the registry is developed to satisfy user query requirements. FEMA Support. The following actions should be taken by FEMA to develop and populate the registry: ? Develop a user-friendly, web-based registry with functionality described in APPENDIX S. ? Digitize hardcopy ECs not digitized by communities; reformat digital EC records from ISO, Dewberry, Corps of Engineers, and LOMA 2000; and populate the registry with records from the BureauNet and NEMIS databases. ? Develop efficient procedures and tutorial for professional land surveyors, and mandate their on-line preparation and submission of future ECs. ? Award additional CRS credits for communities that take initiatives described above. ? Evaluate options for cost-sharing with communities willing to pay a major portion of costs involved. ? Encourage and promote wide support of the registry as a benefit to all. Registry Maintenance and Updates The registry can be maintained and updated by on-line preparation and submission of new ECs by surveyors. Cooperating communities could maintain and update their datasets by EC surveys or through an acceptable airborne remote sensing option combined with on-site measurements. Communities could also help to maintain the registry by volunteering to track their permit files and input new ECs into the registry if not done so automatically by the surveyors; and communities would be encouraged to add additional information such as assessed value and square footage of the structure as needed for other floodplain management purposes. All community input to the registry should be voluntary, with no FEMA mandates. Alterations to structures are difficult to identify from the air or even from the street. Insurance agents should obtain owner certifications, during the insurance application process, to verify no significant structural changes since the last EC was entered into the registry. Accuracy Verification. With high resolution LIDAR, FEMA and communities now have a tool for verifying the accuracy of old EC records in the registry. When new LIDAR data becomes available for a community that has building footprint files, FEMA could pay the additional $5,000 estimated per community to generate LAGs and HAGs for addresses community-wide. These LAGs and HAGs could then be compared with EC records in the registry to see if they are logical or not, and LAG/HAG errors are a good indicator of other elevations that might also be in error. For example, Dewberry used LIDAR data of Prince George's County, MD and found two ECs for which LAGs and other elevations were in error. When building footprint files are available for areas with LIDAR data, either FEMA or communities could verify the accuracy of LAG and HAG elevations. Also, FEMA occasionally surveys structures and compares elevations with ECs on file at communities. CAV visits could also be used to help ensure that communities act appropriately to maintain the quality of their elevation information in the registry; additional CRS credits could be earned for conscientious execution of these responsibilities. Addition of New Structures to the Database. It would be ideal if communities would initiate a new record in the registry at the time that a building permit is approved, and then update the record when an "as built" EC is received. However, FEMA should not make this a community requirement but instead rely upon CRS credits for motivating communities to do so voluntarily. Alteration to Structures. There are several questions involving structure alterations that need to be answered for insurance rating at the time an applicant applies for renewal. "Since the last EC was entered into the registry: ? "Was habitable space constructed in garages, basements or crawl spaces previously used for storage only? ? "Was habitable space constructed beneath elevated structures in V-zones? ? "Were rooms added or enlarged? Were rooms deleted? ? "Was new heating, ventilating and/or air conditioning equipment installed below the BFE? ? "Were flood vents removed or closed during remodeling (e.g., garage door replaced with a new one that has no vents; new siding that covered prior openings)? ? "Were breakaway panels in V-zones replaced with permanent walls?" Whereas CRS credits could be awarded to communities that track such alterations, illegal construction is rarely visible from the air, or even from the street, but requires interior access. For these reasons, structure alterations are best addressed by the insurance agent, requiring the owner to complete a certification form answering the above questions. Then, if the owner submits a claim for flood insurance, the agent would have the opportunity to determine if this certification is true or false. The above questions/answers have been added to the bottom of the registry's data dictionary at APPENDIX R, items 75-80. Changes to Flood Zones and BFEs. Once a DFIRM database is available for a community, it will be easy to update SFHA boundaries and BFEs and track changes to DFIRMs including the history of BFE and flood zones for a given study area. New BFEs will be interpolated from cross sections rather than from BFE lines and values. One reason for being adamant that latitude and longitude are required in the registry is because geographic coordinates are necessary to enable a GIS to automatically identify address records that need to be updated with changes in SFHA boundaries and BFEs. The other reason is that latitude and longitude are essential for spatial queries from users of the registry. Community Rating System (CRS) Credits. FEMA does not intend to require communities to populate, maintain or use the elevation registry. If a community agrees to enter information into the registry to get CRS credit, then FEMA would presumably need to ensure the community was doing this properly. APPENDIX T provides full details of CRS credits currently authorized in all relevant categories. APPENDIX T summarizes CRS credit points used to encourage communities, by the use of flood insurance premium adjustments, to initiate activities beyond those required by the NFIP to reduce flood losses, facilitate accurate insurance rating, and promote the awareness of flood insurance. CRS communities currently prepare and implement those activities which best deal with their local problems, whether or not they are creditable under the CRS. Few, if any, of the CRS activities will produce premium reductions equal to or in excess of their implementation costs. In considering whether to undertake a new floodplain management activity, a community must consider all of the benefits the activity will provide (not just insurance premium reductions) in order to determine whether it is worth implementing. Dewberry recommends that the CRS consider awarding additional CRS credit points for the following community activities: ? Identification of all addresses, assessor parcel numbers (APNs) and accurate geographic coordinates (from digital orthophotos or GPS surveys) for all structures in or near to floodplains. ? Development of building footprint (GIS) files linked to the building addresses and APNs. ? Processing of community high accuracy photogrammetric or LIDAR data to identify accurate LAGs and HAGs for all geocoded structures in or near to floodplains. ? On-site measurements, community-wide (by a person qualified to determine the correct points to be surveyed) to determine elevations of the top of bottom floor, lowest horizontal structural member, lowest machinery and garage floor relative to LAGs and HAGs, and to collect vent and other information required by the elevation registry. ? Comparison of photogrammetry or LIDAR LAGs with those on existing ECs to identify and isolate existing ECs that have major errors. Each of these initiatives would appear to warrant a larger one-time CRS credit, followed by a small annual credit for maintaining and updating the data in the elevation registry. To implement these recommendations, much work is needed to be completed by communities to address the issues cited above. It is not certain that the CRS could provide enough incentives (credit points) to make it worth the communities' efforts. For some communities the task is not unreasonable, but for large communities this may be a problem. The small incentives (current credit points) already in the CRS have not motivated a large number of CRS communities to provide the older ECs for credit (ECPO and ECPR) to date. Adding large incentives for this type of activity may cause an imbalance in the program. The bigger credit items are in the 400 and 500 series where good mitigation results are received from community actions. The 300 series has low CRS credit points because it is harder to justify the mitigation results from these types of projects. Dewberry realizes that EC data alone doesn't result in better mitigation. Therefore, to increase the credits to where it is an incentive for communities to provide the information requested (either to correct old ECs or to provide information in the future as it becomes available) may be difficult to justify. If FEMA concurs with these recommendations, they will need to be discussed with the CRS Task Force for consideration in future changes to the CRS Manual. Registry Use by Insurance Agents From our prior legal analysis at APPENDIX A, we understand that elevation information required for use in determining premiums for an actuarially sound flood insurance program need not be as accurate as information required for evaluating the true flood risk of individual structures. An actuarially sound program can average out modest positive and negative errors in elevations of individual buildings, whereas those same errors could hide true flood risk for the owner of a particular structure. To ensure that potential users of the registry are aware of its limitations, the registry should include a prominent notice stating that it may be used in lieu of ECs in rating or writing flood insurance policies but that the information may not be sufficiently accurate for other purposes, particularly in determining whether to purchase a structure in the flood plain or to permit new construction or renovation in the floodplain. Judgment Ratings Based on the cost model developed for this report, the registry could be populated by several means that are cost effective. However, not all of these strategies provide the same level of completeness or accuracy as the current ECs. FEMA could choose to build the registry based only on ECs produced by surveyors or could choose to include alternative, less accurate elevation methods. If FEMA chose to include alternative EC records that are less accurate than conventional ECs, having errors of 1.2 ft at the 95% confidence level, for example, when photogrammetric or LIDAR data are equivalent to 2 ft contours, FEMA could implement a system of "judgment ratings" that would increase premiums proportionally to the increased uncertainty in the true flood risk. Owners could always choose to pay for a normal EC to reduce uncertainty and premiums, but they will probably do so only if they believe their true elevations should be higher, which should result in lower premiums. Insurance analysts use judgment to create and modify the mathematical models used to assess risk and determine premiums. The following example will be used to demonstrate how increased uncertainty in the accuracy of structural elevation data could be offset by increased flood insurance premiums. The following example was cited previously in this report, using NFIP premiums as of May 1, 2004. For a post-FIRM building in the SFHA, annual premiums shown below are for $150,000 in building coverage and $75,000 in contents coverage for a one-story building with no basement and a $500 deductible. Assuming the top of bottom floor elevations are known with vertical accuracy of 0.5 ft at the 95% confidence level: ? When the top of bottom floor is 2 ft above the BFE: $418 ? When the top of bottom floor is 1 ft above the BFE: $595 ? When the top of bottom floor equals the BFE: $892 ? When the top of bottom floor is 1 ft below the BFE: $3,201 ? When the top of bottom floor is 2 ft below the BFE: $4,040 If we assumed the top of bottom floor elevations instead have vertical accuracy of 1.0 ft (instead of 0.5 ft) at the 95% confidence level, the above rates might be increased by 5%, if such an increase is consistent with actuarial logic. Similarly, if we assumed the top of bottom floor elevations instead have vertical accuracy of 1.5 ft at the 95% confidence level, the above rates might be increased by 10%; and if we assumed the top of bottom floor elevations instead have vertical accuracy of 2.0 ft at the 95% confidence level, the above rates might be increased by 15%. For this same example, we will next assume that the top of bottom floor elevations are derived from either photogrammetry or LIDAR data tested as equivalent to 2' contours, with on-site vertical offset measurements. This equates to vertical accuracy of 1.2 ft at the 95% confidence level. For the cited example, the flood insurance premiums could be interpolated between 1.0 ft (5% increase) and 1.5 ft (10% increase) so that structures without conventional ECs but with alternative aerial surveyed ECs accurate to ±1.2 ft at the 95% confidence level would pay a 7% increase in premiums to compensate for the elevation uncertainty. The annual premiums on the same valued structure would then be as shown in Table 30 under the column depicting ±1.2 ft at the 95% confidence level: Table 30 — Theoretical Flood Insurance Premium Increases Top of bottom floor elevation Assumed annual premiums when the top of bottom floor elevation has this accuracy at the 95% confidence level When ±0.5 ft at 95% conf level, assume no increase When ±1.0 ft at 95% conf level, assume 5% increase When ±1.2 ft at 95% conf level, assume 7% increase When ±1.5 ft at 95% conf level, assume 10% increase 2 ft above the BFE $418 $439 $447 $460 1 ft above the BFE $595 $625 $637 $655 Equals the BFE $892 $937 $954 $981 1 ft below the BFE $3,201 $3,361 $3,425 $3,521 2 ft below the BFE $4,040 $4,242 $4,323 $4,444 The conclusion from this example is that judgment ratings could be used to handle elevation data that is less accurate than the best ECs, and FEMA could apply higher premiums to compensate for increased risk. For EC data equivalent to 2 ft contours (1.2 ft at the 95% confidence level), for the example structure determined to be 2 ft above the BFE, the 7% increased cost of uncertainty in this example is only $29 per year ($447 - $418), but for the home estimated at 2 ft below the BFE, the 7% increased cost of uncertainty is $283 per year ($4,323 - $4,040) for this example, almost covering the cost of a ground-surveyed EC when mass produced community-wide. Dewberry believes that having higher premiums for poorer quality data is an incentive for getting owners to acquire higher accuracy elevation data. Education will also be needed to help communities and home owners realize the benefits of normal EC data, but actions must also be taken to ensure that future ECs are more accurate than in the past. Users should be able to see the Judgment Rating on the web portal and be able to evaluate different insurance premiums if rated by a surveyed EC or by available alternatives such as photogrammetry, LIDAR, or NEMIS data, for example. Unfortunately, this logic could encounter arguments that flood insurance studies themselves do not all have the same accuracy and rigor in computation of BFEs and delineation of SFHAs. Detailed studies do not all have the same level of accuracy; and limited detailed studies and approximate studies are less accurate as well. At some point, the variables become too complex in determining premium increases or decreases justified by alternative methods used to derive the key parameters in flood risk determinations. Homeowners themselves may decide to apply their own judgment as to whether or not it is desirable to pay for a higher accuracy ground survey. If they believe that a survey will help their premiums to decrease, they may well decide to pay for a new EC. However, if they feel that alternative aerial surveys yielded elevations that are favorable to them (higher than true elevations, indicating lower flood risk), they will probably not choose to pay for a survey that would document their premiums should be higher. Thus, elevation corrections to the registry by homeowners will be most likely to occur when owners believe their aerial surveyed elevations are too low, and less likely to occur when owners believe their aerial surveyed elevations are too high. In the latter case, errors that support lower premiums are more likely to remain in the registry indefinitely. Registry Use by Others Community Floodplain Managers. When registry records are enhanced to include accurate geographic coordinates, street addresses, assessed values, square footage, and elevation data for all floodprone structures in a community, floodplain managers can implement proactive floodplain management principles. HAZUS models can be used to accurately estimate the damages to individual buildings from past or from predicted future flood events, and sum the total damages to all flood-prone buildings in the community so that the floodplain manager can quantify the potential cost to the community from such predictable flood events. This risk assessment helps to identify and prioritize the need for subsequent steps to mitigate flood risks: (1) drainage improvement projects, (2) floodproofing projects, (3) public education and flood insurance marketing, (4) flood warning systems, and (5) post-flood rapid damage assessments. Charlotte-Mecklenburg, NC already does this with great cost-effectiveness, and their lessons learned could be used to encourage other communities to do the same. FEMA. Immediately following a riverine flood or a hurricane tidal surge, FEMA seeks to know the total losses to a community. The same information described above for proactive floodplain management is vital for accurate and rapid damage assessments by FEMA. When the elevation of flood waters are known for a flood event, information in the enhanced registry will provide (1) depth of interior flooding, (2) square footage, and (3) assessed value — the three parameters required by HAZUS for accurate damage assessments. FEMA might also use the registry for checking insurance policies and community compliance. Georeferenced ECs and policies-in-force data can be plotted on top of digital orthophotos to determine floodprone structures that are currently uninsured, but this will only be effective when all addresses are accurately geocoded. LIDAR data can be used to determine if existing ECs have accurate or inaccurate LAGs and HAGs and whether a LAG is above or below the BFE, for example, but remote sensing alone cannot determine the accuracy of lowest floor elevations. Airborne remote sensing can be used to count the total number of structures in floodplains and determine those structures for which ECs are available and non-available — the goal being to maintain ECs on all such structures if possible; but aerial survey products can not determine if structures are pre-FIRM or post-FIRM since dates of construction are unknown. LIDAR and/or photogrammetry alone will not be able to identify the majority of non- compliance issues. For example, aerial survey products can not determine the following: (1) lowest floor elevations surveyed to the wrong story, (2) illegal construction below elevated structures, (3) illegal finishing and use of crawl space or storage space, (4) elevations of basements below the BFE, (5) lowest elevation of machinery below the BFE, (6) non-breakaway walls in V-zones, and/or (7) inadequacy of flood vents. All of these require on-site inspections to verify compliance. A broader FEMA database of structures could include all structures, not just those determined to be floodprone, for the following reasons: ? The NFIP has a vested interest in understanding the numbers and elevations of structures outside the SFHA, and their proximity to floodplains, for the purpose of marketing preferred risk policies and estimating the impacts of map revisions that raise BFEs and increase the size of SFHAs within communities. SFHA buffers, used to identify candidates for preferred risk policies, would change as new flood studies are completed or future conditions are taken into account. ? An expanded database could also support other FEMA programs that require structure data on a broad basis. As part of the Department of Homeland Security (DHS), it is important for FEMA to have an inventory of all structures in the U.S., both floodprone and non-floodprone, to support individual and public assistance following all natural and technical disaster. For example, multi-hazard response and recovery activities routinely need accurate geographic coordinates linked to street addresses. Computer models that predict the spread of wildfires also utilize elevation data. ? For all forms of pre-disaster mitigation planning, DHS needs to maintain current information and GIS-based imagery on all structures that could be impacted, to include address, geographic coordinates, assessed value, and information about each structure such as used by realtors and insurance agents. A complete and up-to-date geocoded inventory of all structures is needed to determine where to send inspectors for preliminary damage assessments following natural and technical disasters. This inventory would help to identify legitimate claimants and reduce fraudulent claims. The U.S. Census Bureau, U.S. Postal Service, state emergency management centers, local communities and E-911 services all have similar requirements. Recognizing Title 13 constraints on the Census Bureau, the challenge for DHS is to develop effective incentives for working with other federal agencies, states, counties, and communities to satisfy common needs. Individuals. Individuals such as real estate agents and potential home buyers could benefit by accessing the registry for an approximate assessment of flood risk for a specific address. Increased public awareness of flood risk will ultimately result in smarter decisions by those who build, sell and buy structures in or near to floodplains. However, all users must be warned that the registry was designed for determination of insurance premiums and that more accurate elevation determinations may be required for purchase and development decisions by property owners, especially when alternative methods were used to determine lowest floor elevations. FEMA Implementation Costs Dewberry estimates that it would cost approximately $4 million to develop a web- based registry supported by a geodatabase or comparable spatial database, assuming the registry is an enhancement to a current FEMA web site such as that of the Map Service Center which is already geared to provide web-based products and services and receive cost reimbursements. In part I of this report, these start-up costs were artificially recouped by applying pro-rated charges of $2.50 each for records that are batch processed and $10 each for records that are individually entered. Strategies A and D alone would account for more than $4 million in cost-effectiveness calculations. In addition to the estimated $4 million ramp-up cost for developing the web- based registry with front-end portal for individual users and individual data providers and back-end portal for the system developer and administrator, other short-term estimated costs to FEMA include the following items that total $392,000 to populate the registry and provide linkage to other FEMA databases: ? $16,000 for scanning, digitization and quality control of nearly 3,200 hardcopy ECs from URS, at $5 each ? $44,000 for reformatting and quality control of nearly 17,600 digital ECs from Dewberry and USACE at $2.50 each ? $50,000 for reformatting and quality control of an estimated 50,000 digital records in the ISO database ? $50,000 for reformatting and quality control of an estimated 91,600 digital records in the LOMA 2000 database ? $100,000 cost to FEMA for extracting and reformatting data for the registry from an estimated 400,000 digital records in the NEMIS database and an estimated 4,500,000 records in the BureauNet database, 80,000 of which include elevation data. Additional costs to FEMA would include an estimated $350,000 per year for operation of the registry, plus additional costs incurred to reformat and quality control data provided by communities. Dewberry estimated $2.50 per record to reformat and quality control data batch processed by communities, but costs truly depend on FEMA's ability to train communities how to correctly format their data from the beginning so that FEMA's subsequent role is minimal. Cost Recovery For maximum cost effectiveness, the elevation registry could "piggy-back" onto FEMA's existing Map Service Center web site at www.msc.fema.gov. The MSC already offers products and services available for a fee to users with a credit card or draw-down account. Some current users are fee-exempt customers. Scanned images of LOMCs, for example, are available for free on-line viewing, or customers can purchase digital images for downloading on-line. Similarly, the web site provides free How-To Guides online on how to use Map Search and how to download FIRMettes for printing. FEMA already owns the hardware, has established "firewalls," has a web site that is fully certified and accredited, and has the eCommerce account in place to collect fees that go directly into the Treasury. Dewberry sees no need to establish a totally new web site when this existing site could be expanded to include new EC products and services. Scanned and digitized EC records could be treated as new products or services. Communities that scan and digitize their ECs and provide them free to the registry could, in turn, be fee-exempt when needing to download EC data from the registry. Regular customers such as flood insurance agents or WYO companies could establish draw-down accounts and pay a standard fee for EC records that they download, unless they too submit their total holdings of EC records to become fee-exempt. Individual inquirers would be the ones most likely to use their credit cards to pay for the service; there would be no fee to individuals if FEMA simply provides a link back to a database operated and maintained by a community. If approved by FEMA, a working group would need to be established to work out the technical details and develop cost estimates. Also, procedures would need to be developed to delete or blacken the owner's name from scanned EC images. If it actually costs $350,000 per year to operate, maintain and upgrade the registry, FEMA should attempt to recover at least this much annually with user fees. For example: ? $10 each for a copy of a virtual EC that looks like a regular EC except that it would be generated from the registry's database and would exclude the name of the owner and the name and seal of the Land Surveyor who produced the original EC. (FEMA would have the scanned image of the original EC on file, with the file name and path included in the database record (see APPENDIX R, items 68 and 69) but FEMA would not make the scanned EC available to the public.) ? $5 each for an alternative record from remote sensing that lists elevations needed for insurance rating, but without certification by a Land Surveyor. ? $2 each for an alternative record from remote sensing that lists LAG/HAG elevations that could be usable for a LOMA, but without certification by a Land Surveyor. Each of these costs compare favorably with the alternative of paying hundreds of dollars for a new EC survey. These alternative EC costs are admittedly higher than the $1.50 that users pay for a scanned FIRM image from the Map Service Center, but alternatives to scanned FIRM images are paper FIRMs that might cost $10 should they be sold commercially at a map store. Thus, getting a scanned EC for only $10 would be a real bargain compared with the much higher cost of an individual EC. Dewberry has no way to accurately estimate how many individual users would seek to purchase ECs from the registry because much depends on FEMA policies to be developed as well as marketing initiatives to promote the registry. Currently, FEMA receives about $1 million annually from Map Service Center products, much from the sale of scanned FIRM images at $1.50 each. Community Implementation Costs Nothing is mandatory for community participation with any of the Strategies. It will cost communities an estimated $5 for each of their hardcopy ECs that they voluntarily digitize, quality control and insert in the registry, but it will cost very little if ECs are already digitized. It will cost communities an estimated $55 to $60 per structure (primarily for on-site vertical offset measurements) to voluntarily convert their existing LIDAR or photogrammetric data into alternative EC records suitable for batch processing into the registry. Community costs to digitize building footprints linked to street addresses are normally borne by GIS-enabled communities using this information for tax assessment purposes. Incentives Community Rating System (CRS) credits are FEMA's primary incentive to encourage communities to support the registry, but it is questionable whether CRS credits alone would be sufficient to make a community willing to spend an estimated $55,000 to $60,000, for example, to convert their existing LIDAR or photogrammetric data into 1,000 alternative EC records for the registry. They will need to be convinced of a "greater good" such as demonstrated by Charlotte- Mecklenburg whose development of a local EC database allows them to implement proactive floodplain management principles. FEMA's Cooperating Technical Partner (CTP) program encourages communities to acquire LIDAR data for general mapping purposes, made available to FEMA for hydrologic and hydraulic modeling for a FIS, in exchange for FEMA giving higher priority to Map Modernization funding for such CTPs. Community tax records can provide assessed values and structure square footage needed to complete records for proactive floodplain management initiatives summarized above. Insurance firms could voluntarily provide EC files for the registry, but they too would need to be convinced of a "greater good" by sharing their data with others including competitors. Overall, since the basic purpose of the registry is to help insurance agents and WYO companies who have long affirmed that the requirement for ECs is a major impediment in selling flood insurance, they must be convinced of the benefits of supporting the registry or else there is no point in proceeding. Advantages and Disadvantages Dewberry concludes that it is appropriate, feasible, and legally possible for FEMA to obtain the elevation data on individual structures and to make this elevation information available in an elevation registry. However, in addition to cost factors, FEMA and its major constituencies must support the registry for it to be successful. ? FEMA itself might use the registry for checking policies, community compliance, post-disaster response and recovery, and to help insurance agents and WYOP companies sell more flood insurance by helping everyone recognize true flood risks and simplify the flood insurance application process. The major disadvantage to FEMA is the estimated $4+ million start- up cost, $5 each for digitizing hardcopy ECs into the registry, and $350,000 annual operating costs. ? With an elevation registry, the insurance industry should ultimately find it easier to sell flood insurance; but until the registry matures, they may still complain that the registry is incomplete or unreliable for its intended use. The insurance industry may be reluctant to provide elevation data to the registry if they believe it negates a competitive advantage. ? With an elevation registry, communities could be more-proactive floodplain managers, and increased CRS credits would result in lower insurance premiums. Yet, the major disadvantage is the time and money (potentially $60,000 per community) necessary to collect data needed to populate the registry. Thus, success may be spotty, successful in communities that provide the strongest support, and less successful elsewhere until they learn from other communities that demonstrate strong benefits. Summary Dewberry recommends that FEMA open a dialog with the insurance industry and the floodplain management community -- to promote the concept of the registry. Efforts with these constituencies must succeed before FEMA begins attempts to implement an elevation registry. Only then can steps be taken to implement a registry that is as affordable, accurate, reliable and as useful as possible. Once the decision is made by FEMA to proceed in development of a registry: ? FEMA should proceed with implementation plans to obtain needed funding and contract for the development of the web-based registry as described above. This would include plans for ways the stand-alone registry could feed data into the NextGen data warehouse. ? FEMA should design and implement quality control standards, processes, and documentation for populating the registry with data. These processes will specify the types of data sources (with different accuracy levels) and the documentation and certification required for data before it can be incorporated into the registry. ? FEMA should obtain structural elevation data from all potential sources following the various strategies described above. This includes incentives to encourage cooperation and active support from floodprone communities and the insurance industry itself. ? FEMA would need to amend regulations and manuals to allow WYO Companies and agents to rely on the registry for rating of policies, thus supporting the argument that reliance on the registry's data satisfies the professional standard of care. ? The FEMA general counsel should determine if there are any prohibitions to nominal user fees for access to data in the registry, especially if FEMA provides scanned copies of ECs originally paid for by others, but deletes the names of the owners. All must recognize that the registry will have modest gains at first, but will grow in utility and value as the registry becomes fully populated with reliable data and has effective means for updates. APPENDIX A — REPORT ON LEGAL ISSUES The Dewberry/FEMA Law Associates/EOP Foundation "Final Report on Legal Issues" identified no legal issues that would preclude FEMA from establishing, maintaining and making available to insurance companies and agents, or to the general public an elevation registry. The full report follows. FINAL REPORT ON LEGAL ISSUES Prepared for Dewberry Davis LLC In Connection with FEMA’s Evaluation of Alternatives in Obtaining Structural Elevation Data This report summarizes our legal research and analysis on legal issues relevant to a determination by the Federal Emergency Management Agency (“FEMA”) of whether it can develop a nationwide registry of structural elevation data for National Flood Insurance Program (“NFIP”) purposes. We have sought to identify and evaluate the significance of potential legal obstacles to developing this nationwide elevation registry (“registry”) in these areas: (1) the Privacy Act of 1974 and other privacy issues; (2) potential exposure to liability for inaccurate elevation information; and (3) potential ownership rights that third parties may have to elevation data. We have identified no legal issues that would preclude FEMA from establishing and maintaining an elevation registry and making it available to insurance companies and agents writing NFIP policies, or even to the general public. Creation of the proposed registry is an activity well within the authority granted by the National Flood Insurance Act. An elevation registry as described in the Statement of Work, and in subsequent meetings with FEMA, would not violate federal or state privacy law or policy or significantly expand the liability exposure of participants in the National Flood Insurance Program. This Report was prepared by Ernest B. Abbott and Maya A. Bernstein of FEMA Law Associates, PLLC. The section on electronic signatures was prepared by Terry Banks of the EOP Foundation. The Section on Strategy D, covering right of entry issues, was prepared by Ernest B. Abbott and Maya Bernstein with research assistance from Terry Banks of the EOP Foundation. TABLE OF CONTENTS SUMMARY 177 BACKGROUND 181 1. Content and Structure of an Elevation Registry 182 2. Information Gathering Authority in the NFIP 183 Program Overview 183 Risk Identification 184 Risk Reduction 185 Risk Spreading 187 Comments on Information Gathering Authority 188 3. Sources of Existing Elevation Data 188 FEMA and its Contractors 189 Communities 190 Insurance Agents and WYO Companies 190 CROSS CUTTING LEGAL ISSUES 191 1. Applicability of the Privacy Act of 1974 191 Retrieval by "Individual Identifiers" 192 "Information about an Individual" 194 Implications of Added Data Elements 198 Relationship to Existing FIMA System of Records 199 Linking Databases or Computer Matching Programs 201 2. Freedom of Information Act Issues 202 Overview 202 FOIA Analysis of Risk of Disclosure 204 Public Domain Waiver 205 Exemption 4 207 Exemption 6 208 "Similar Files" 209 "Invasion of Personal Privacy" 209 Names and Addresses 211 Other Exemptions 213 Electronic FOIA 213 3. Liability Issues 214 Liability of FEMA Generally 214 Implications of "Horizontal" vs. "Vertical" Mapping Activities 216 Liability of Other Parties 218 Liability Implications of Correct Registry Information 219 Liability Created by Inaccurate Data in Registry 220 Registry Shows Flood Risk Higher Than Actual 221 Registry Shows Flood Risk Lower Than Actual 221 FEMA Engineering and Surveying Contractors 222 Insurance Agents 222 Engineers and Surveyors 223 Local Governments and Communities 223 Lenders 225 Home Sellers & Real Estate Agents 225 Liability Summary 226 4. Legal Effect of Electronic Signatures and Verification 227 E-SIGN 227 State E-Commerce Laws 229 California 230 Florida 231 Louisiana 231 North Carolina 231 Modification of Manual Required 232 LEGAL ISSUES RELATED TO STRATEGIES FOR ACQUISITION OF DATA Strategy A: Maximize use of existing Elevation Certificates to populate the elevation registry. 1. FEMA authority to request, but not require, holders to provide data 233 Insurance Companies and Agents 233 State and Local Governments 234 2. FEMA authority to require submission of elevation data 236 FEMA Contractors 236 WYO Companies 236 Insurance Agents 237 State and Local Governments 237 3. Relevance of Ownership of Elevation Certificates 237 Strategies B & C: Maximize use of remote sensing: LIDAR, IFSAR and airborne photogrammetry; mobile photogrammetric vans. 1. Privacy Rights and Remote Surveillance 240 Strategy D: Maximize cost-effectiveness of future ECs. 1. Basic Elements of Trespass 245 California 246 Florida 246 Louisiana 247 North Carolina 248 2. No Trespass if Landowner Consents; Ability to Condition Policy Issuance and Renewal Where On Consent 249 3. Trespass Issues Where Consent Not Obtained 250 No Clear Entry Authority: Federal 250 Varied Entry Authority for Surveys: State 251 Government Right to Conduct Preliminary Surveys 254 Defenses to Trespass Actions 256 4. Common Law Right of Privacy 257 Strategy E: Leverage alternative data sources for an elevation registry. Sharing Data with the U.S. Census Bureau 258 Sharing Data with other Government Organizations 259 Sharing of Community Tax Parcel Data and/or Other Community Data Bases 260 California 260 Florida 261 Louisiana 261 North Carolina 262 CONCLUSION………………………………………………………………………..263 SUMMARY We have identified no legal issues that would preclude FEMA from establishing, maintaining and making available to insurance companies and agents, or to the general public an elevation registry. SUMMARY OF CONCLUSIONS ON CROSS-CUTTING ISSUES 1. The National Flood Insurance Act clearly authorizes FEMA to obtain and distribute to the public information about flood risk and information relevant to the determination of premiums under federal flood insurance policies. 2. An elevation registry taking the form proposed in the Statement of Work, and as further described by FEMA in subsequent meetings, would not be a “system of records” regulated by the Privacy Act, and FEMA is not precluded by privacy principles from remotely surveying elevation data on structures without the consent of the owner. This analysis assumes that the registry includes specific property addresses, but does not include personal identifiers of individuals such as names and policy numbers, or social security numbers. FEMA may maintain individual identifier information in separate data bases, and link to those separate databases for purposes authorized for those data bases. 3. FEMA is not restricted by the Privacy Act or other privacy policy principles from making elevation registry information available to the insurance companies and agents, which are the intended audience of the registry. While WYO companies might assert that disclosure of the addresses of their insured properties would compromise proprietary customer lists, we note that the registry would contain all addresses as to which FEMA had elevation data, from a number of sources, and hence the registry would not disclose any company’s customer list or disclose whether a listed property is insured by FEMA directly, insured by a competitor, or, indeed, whether the property is insured at all. Further, when the Arrangement is modified to allow companies to rely on the registry (rather than on Elevation Certificates) for rating of policies, FEMA can evaluate the strength of any “competitive information” argument and, based on that analysis, add language to the Arrangement advising that address and elevation data, without personal or company identifiers, will be available in the registry. 4. Given its proposed content, FEMA would be obligated by the Freedom of Information Act to make information in the registry available to any person on request, and to make it available in an electronic format to anyone who asks, if it is made available to companies in that format. FEMA may, but need not, design the elevation registry to be publicly available and accessible to any person on the Internet. 5. FEMA and any FEMA contractors establishing and maintaining the elevation registry would not incur any significant increased liability exposure from creation of the elevation registry. 6. Creation of the elevation registry will not have a major impact on the liability exposure of other participants in the National Flood Insurance Program. Liability for distributing information about a property is limited to those with an actual relationship to that property or who suffer actual damages as a result of the dissemination of information about the property. Potential plaintiffs meeting these tests would likely have access to direct sources of data to support claims of liability even in the absence of an elevation registry. FEMA can further reduce any potential liability exposure by: a. using the registry only for the purpose of rating insurance policies, and continuing to require communities to obtain Elevation Certificates to support construction permits and other floodplain management purposes; b. amending FEMA’s regulations and manuals to allow WYO Companies and agents to rely on the elevation registry for rating of policies thus supporting the argument that reliance on the registry’s data satisfies the professional standard of care (these amendments would in any event be required to achieve the purposes of the registry); and c. including in the elevation registry a warning notice that the information in the registry has been developed solely for purposes of determination of premiums for insurance policies and that more accurate elevation determinations may be required for purchase and development decisions by property owners. 7. The validity of the elevation registry for use in determining flood insurance premiums is not impaired by the inability of the elevation registry to reproduce the original signature and seal of the professional engineer or surveyor who measured the elevation of particular properties. Under state and federal law, certification of documents can be effected electronically. Electronic certification has the same legal validity as written certification. FEMA should design and implement the quality control standards, processes, and documentation for populating the registry with data. These processes will specify the types of data sources and the documentation and certification requirements for data before it can be incorporated into the registry. ISSUES RELATED TO STRATEGIES FOR ACQUISITION OF DATA STRATEGY A: EXISTING ELEVATION CERTIFICATES: 8. FEMA has authority to ask FEMA contractors, insurance agents, WYO companies, and communities participating in the NFIP to make available to FEMA existing elevation certificates or data used in writing flood insurance policies or in issuing construction permits or other authorizations related to floodplain management. Moreover, communities do not appear to face any prohibitions under state privacy laws from providing this information to FEMA, and, indeed, all communities participating in the Community Rating System already do so. 9. FEMA would face significant legal and practical obstacles in seeking to require FEMA contractors, insurance agents, WYO Companies, and communities to provide elevation data for inclusion in the elevation registry without their agreement and without compensation for doing so. 10. It is unclear what party is the actual “owner” of an Elevation Certificate that was paid for by a property owner and provided to an insurance agent or community. However, we do not believe that the issue of ownership affects FEMA’s ability, noted above, to obtain the elevation data contained in that certificate. STRATEGIES B & C: ELEVATION DATA FROM REMOTE SURVEILLANCE 11. Fourth Amendment principles, which have been developed in some depth in the context of remote surveillance by police of suspected sites for cultivation of marijuana, do not restrict FEMA from obtaining elevation data using airborne surveillance or surveillance from public streets and other public property. STRATEGY D: ELEVATION DATA FROM SURVEYING ON PRIVATE PROPERTY 12. The potential legal consequences of entry onto private property to collect elevation data are primarily matters of state law. Unauthorized entry onto another’s property may constitute criminal and civil trespass or nuisance. However, if FEMA has obtained consent for entry from the landowner, then actions in trespass or for invasion of privacy will not lie. FEMA can obtain consent from NFIP policy holders by adding to the SFIP, by regulation, a provision under which policy holders consent to inspections for purposes of obtaining the information required for rating of new or renewed policies. This consent will of course not provide authorization for entry on properties not insured by the NFIP. 13. FEMA does not have authority under its statute to enter upon private land without consent. FEMA does have authority to “make arrangements” with state and federal agencies to obtain data that they obtain under their own authorities. However, although there are provisions of both federal and state law that allow rights of entry for survey purposes, these provisions are, except in some states, not available for purposes not related to the specific purpose (such as the right of eminent domain) for which the authorization was adopted. 14. In certain circumstances, unauthorized entry onto another’s property can constitute criminal trespass in the four states we reviewed. However, criminal trespass laws generally require conduct — such as ignoring “no trespassing” signs or ignoring express requests to leave a property — in addition to the mere unauthorized entry upon land. Even if FEMA agents were to enter on land briefly without authorization, risk of criminal prosecution would be low if those agents heeded all no trespassing signs and promptly left the premises on request. FEMA would also be unlikely to suffer significant civil liability for unauthorized entry onto private land for the collection of elevation data, due to the probable absence of any measurable damages. 15. Nonetheless, we would not recommend that FEMA adopt a policy of directing its agents to enter on private land without seeking the landowner’s consent or the authorization of a state or local government able to provide it. In the absence of proper authorization, entry upon land would constitute technical trespass and a possibly significant public and customer relations problem. STRATEGY E: ELEVATION DATA FROM THE CENSUS BUREAU AND OTHER SOURCES 16. The Census Bureau is barred under Title XIII, its data acquisition authority, from sharing any data at the level of individual properties. It may be possible for the Census Bureau and FEMA to work together to acquire data under FEMA’s authority, using funds provided by the Census Bureau. Note: FEMA met with Census on April 10, 2003 to discuss potential collaborative efforts to obtain data. 17. There are significant other sources of data that are relevant and potentially helpful to FEMA’s mapping activities in both public (e.g. NASA, USGS and the U.S. Postal Service) and private (phone and utility companies) hands. Much of this data is available for a fee. 18. Community Tax parcel data showing property specific information is required by law in most, if not all, states to be available to the public and hence would be available to FEMA. BACKGROUND FEMA’s intent in creating a nationwide elevation registry is to expedite and simplify the rating and issuance of flood insurance policies by insurance agents, WYO companies, and the FEMA contractors issuing FEMA flood insurance policies directly. The data will be available to WYO companies and agents in a format capable of linking to their existing computer systems. Further, for purposes of rating and writing policies, FEMA intends that agents and companies be able to rely on elevation data in the registry and that policies properly written and rated consistent with elevation data in the registry will be deemed correct until the registry information is changed. The registry, at minimum, will provide to insurance agents and companies improved and simplified access to a key element of evaluating flood risk: elevation of the structure as compared to the elevation of the ‘base flood’ as determined in that area. As noted in the analysis below, registry data will likely also be available and accessible to homeowners, potential homeowners, communities, lenders, and any private companies requesting access to this data. While the registry is not designed for this purpose, homeowners or prospective homeowners might seek to use the data to evaluate flood risk of their homes, or of properties prior to purchase. Communities might use this data in studies of flood prone areas or as part of a building permit process. However, we understand that elevation information required for use in determining premiums for an actuarially sound flood insurance program need not be as accurate as information required for evaluating the true flood risk of individual structures. An actuarially sound program can average out modest positive and negative errors in elevations of individual buildings, whereas those same errors could hide true flood risk for the owner of a particular structure. Elevation information used for floodplain management purposes must be as accurate as possible for any proposed construction in the floodplain. This elevation information includes the Base Flood Elevation, any topographic information, and the proposed building elevations of all new and substantially improved structures that are provided to the community as part of the application for a development permit. It also includes “as built” elevation information the community must obtain once the structure is completed before it can issue a certificate of occupancy or compliance. Information in a registry cannot properly be used as a substitute for “as built” information because it is generally not available at the time the building is completed and may not be of the required level of accuracy. To ensure that potential users of the registry are aware of its limitations, the registry should include a prominent notice stating that it may be used in lieu of elevation certificates in rating or writing flood insurance policies but that the information may not be sufficiently accurate for other purposes, particularly in determining whether to purchase a structure in the flood plain or to permit new construction or renovation in the floodplain. 1. Content and Structure of an Elevation Registry. The legal obstacles that FEMA would encounter in creating, maintaining, and publicizing an elevation registry are extremely dependent on the data elements that FEMA chooses to include in the registry. For example, as will become clear in the discussion of the Privacy Act below, the legal analysis would change significantly if this registry were to contain individual identifiers such as social security numbers. The data included in the registry will be limited to some or all of the following information for each structure: ? One or more unique identifiers of the structure. These identifiers might include the property address, a metes and bounds description of a structure’s location, or the geographic coordinates (longitude and latitude) of a structure obtained from Global Positioning System (GPS) or other sources. ? The NFIP flood map panel in which the structure is located. ? The Flood Zone in which the structure is located and the base flood elevation (“BFE”), if one has been determined by FEMA, or as determined by the community in that zone if a base flood elevation has not been determined by FEMA. ? Elevation data for the structure: either (a) the elevation of the top of the bottom floor (including basement or enclosure) of a building and of the next higher floor (from current Elevation Certificate), or (b) the elevation of the lowest floor of the structure (from older Elevation Certificates); or (c) elevation information from remote sensing technology. ? The elevations of the highest and lowest grades adjacent to the structure. ? Selected information about the structure itself: o Type: Residential or commercial or industrial o Existence of basement and basement type (e.g. walkout or fully underground) ? The source of the elevation data for the structure. For purposes of our analysis, we are assuming that FEMA would not disclose individual identifying information in identifying the source of the elevation data used in the database, but rather would provide general information such as “Elevation Certificate March 20, 1998”, or “Remote Survey (LIDAR) by _____ Company, March 20, 2003.” Further, this analysis is based on the description provided by FEMA of its intended plans — that the registry will be maintained by FEMA, or by a contractor under a direct contract with FEMA, rather than maintained by a private company marketing elevation data in its own name. 2. Information Gathering Authority in the National Flood Insurance Program FEMA, its contractors, WYO companies, state floodplain managers, and communities participating in the NFIP have been gathering, using and making available to the public elevation information — including information about individual structures — from the outset of the program more than thirty years ago. The proposed elevation registry would expand on prior information activities primarily by centralizing in one national database information now generally held in files or databases of individual WYO companies and insurance agents, along with information generally held in land use planning, zoning, and floodplain management records of local communities. In order to evaluate the legal issues associated with this proposed data centralization, it is important first to review the authority under which the National Flood Insurance Program operates. Program Overview. The National Flood Insurance Program (NFIP) was established in 1968 pursuant to the National Flood Insurance Act (the Act). The NFIP is a federal program that provides flood insurance at, or for certain older properties below, actuarial rates as part of a program of mitigation against flood hazards. The NFIP consists of three distinct elements: risk identification, under which areas susceptible to flooding are identified and publicized; risk reduction, under which communities participating in the NFIP adopt and enforce floodplain management regulations to restrict development in areas susceptible to flood; and risk spreading — that is, insurance — under which the owners of property in communities participating in the NFIP can obtain insurance against loss due to flood. Elevation data on structures is used in all three components of the NFIP: ? Risk Identification: the NFIP is required by the Act to identify and publicize maps and related information identifying areas or zones, which are at significant, risk of flooding. The elevation of land and the elevation of the estimated high water level of a flood of a specified likelihood (such as the 1% probability per year or “base flood”) are the key components of flood risk identification. ? Risk Reduction: any community that wants flood insurance to be available to its residents must join the NFIP and adopt floodplain management regulations restricting development in areas which have been identified (mapped) as special flood hazard areas. ? Insurance: premiums for structures built after a community joins the NFIP, and the premium for a preexisting structure where the owner establishes an elevation above the base flood level, are dependent on elevation certificates establishing the elevation of the lowest floor of the structure and the elevation of the lowest adjacent grade. Accordingly, the National Flood Insurance Act provides FEMA with authority to collect, use, and publish elevation data in sections dealing with each of these components. This authority is briefly reviewed below. Risk Identification. Section 1360 of the National Flood Insurance Act, governing the “identification of flood-prone areas,” broadly authorizes FEMA to make arrangements with a wide range of data sources — federal agencies, state and local agencies, or persons or private firms — to get information about and to publicize “information with respect to all flood plain areas, including coastal areas located in the United States, which have special flood hazards.” This information is gathered and used to “establish or update flood risk zone data” and to “make estimates with respect to the rates of probable flood caused loss.” The section also directs the Director to review the flood maps at least once every five years, and to revise and update flood maps whenever new data requires it or at the request of a State or local government. An important element of the program is the public dissemination of these flood maps: the Director is required to make “flood insurance rate maps and related information” available free of charge to local communities, state floodplain management agencies, federal agency lenders, and federal entities for lending regulation; this information must be made available “at reasonable cost” to everyone else. Further, the Director is required to give public notice of any changes in the maps caused by letters of map amendment or letters of map revision. Recognizing the regulatory and financial significance of a special flood hazard area (“SFHA”) designation, Section 1363 (“Flood Elevations Determinations”) mandates that FEMA provide notice to communities of a proposed designation, and further provides specific procedures by which communities can challenge (on technical grounds alone) and ultimately appeal this designation. Section 1364 (“Notice Requirements”) requires that federally supported mortgage lenders notify the borrower/owner if the property is located in a special flood hazard area. The statute requires that this notice include a “warning” that the property is subject to flood risk and information about insurance purchase requirements, but FEMA is permitted to require that the notice also include “any other information that the Director considers necessary to carry out the purposes of the National Flood Insurance Program.” Lenders must document their determination that a property was within or without the SFHA on a form mandated by FEMA. Risk Reduction. Section 1361(c) provides that, after conducting studies and investigations, and gathering “such other information as he deems necessary,” the Director shall develop comprehensive criteria encouraging the adoption of adequate State and local measures restricting development of land exposed to flood damage and guiding construction away from locations threatened by flood hazards. These criteria carry real teeth: under Section 1315, no new federal flood insurance may be issued in any area unless “an appropriate public body” has adopted adequate land use and control measures, with effective enforcement provisions, that are consistent with the criteria adopted under Section 1361(c). Elevation data is critical to the criteria developed by FEMA under these sections. Communities participating in the NFIP must obtain the elevation of proposed developments and of proposed new or substantially improved structures whenever there is some information about the base flood elevation with which building elevations can be compared. Depending on the nature of the proposed development or construction and the elevations involved, communities must either prohibit the construction, or require that the structure be built with various flood mitigation measures (such as elevation). Although FEMA publishes an Elevation Certificate Form and recommends that it be used for floodplain management purposes, FEMA’s regulations do not require that a community obtain and maintain elevation data on this Form. However, communities are required to obtain the elevation of the lowest floor (including the basement) of all new and substantially improved structures and maintain a record of all such information. Thus, a community’s permit files must have an official record that documents how high new and substantially improved buildings were elevated. Elevation data in a particular format is, however, critical to another NFIP risk reduction program — the Community Rating System (“CRS”). Under the CRS, flood insurance premium rates are adjusted to reflect the reduced flood risk resulting from community activities that meet the three goals of the CRS: (1) reduce flood losses; (2) facilitate accurate insurance rating; and (3) promote the awareness of flood insurance. In order to participate in the CRS program, CRS communities must require and maintain in its files the FEMA Elevation Certificate for all new and substantially improved structures in the SFHA, and must make the certificates available to any requester. Although the CRS was created using other broad authorities of the NFIP, Congress amended the NFIA in 1994 to give express authority for the CRS program. Risk Spreading. Congress specified in three sections of the statute that the Director establish and administer the flood insurance program through uniformly applicable rules and regulations. Section 1306(a) provides: The Director shall from time to time... provide by regulation for general terms and conditions of insurability which shall be applicable to properties eligible for flood insurance coverage under section 4012 of this title, including (1) the types, classes and locations of any such properties which shall be eligible for flood insurance; [and] (2) the nature and limits of loss or damage (or subdivisions thereof), which may be covered by insurance. Section 1307 of the NFIA authorizes the Director to undertake and carry out studies and investigations, and receive or exchange such information as may be necessary to estimate, and shall from time to time estimate, on an area, subdivision, or other appropriate basis (1) the risk premium rates for flood insurance. Section 1313 authorizes the Director to take such action as may be necessary in order to make information and data available to the public, and to any State or local agency or official, with regard to (1) the flood insurance program, its coverage and objectives; and (2) estimated and chargeable flood insurance premium rates, including the basis for and differences between such rates. In setting these rates, the Director is expressly (and perhaps obviously) authorized to consider “the respective risks involved” — which, of course, includes information on relative elevations. And indeed, information on the elevation of structures, taken from Elevation Certificates, is used to calculate NFIP insurance premiums. Comments on Information Gathering Authority. Based simply on this brief review of the principal statutory provisions that support FEMA’s ability to obtain and publish elevation data, the following assertions can be made: 1. Congress expressly provided exceptionally broad authority to collect and maintain data in order to create and maintain “maps and related information” of flood prone areas and zones. 2. While dedicating less attention to the flood risk of individual structures than to the mapping program on which the regulatory program of the NFIP is based, Congress also gave FEMA authority to collect and disseminate data on flood risk of individual structures. 3. Congress expressly bestowed on FEMA broad authority to obtain data from a wide variety of sources: other federal agencies (on a reimbursement basis), state and local communities; and under contracts with any persons or private firms. 4. Congress expressly authorized and, for “maps and related information,” directed the agency to make available to the public information that it collects and develops on flood risk. 5. With respect to FEMA’s maps, which affect property development rights, insurance obligations, and premium rates, Congress required FEMA to employ formal procedures for amendment and revision, which are subject to administrative and judicial review. 3. Sources of Existing Elevation Data A major challenge in creating an elevation registry is to populate the registry with reliable data as quickly as possible. A principal source of data for this purpose is the substantial amount of elevation information already in existence and held by FEMA, its contractors, communities, insurance agents and WYO companies, and perhaps by others. In this section of this Report, we describe briefly the nature and sources of this existing data. FEMA and its Contractors. FEMA already maintains structure specific elevation data in conjunction with several of its programs. This data derives from a variety of sources. First, FEMA’s regulations provide a procedure for individual property owners to submit technical information to FEMA to show that the owner’s property should not have been designated in a flood zone. Under these regulations, the owner submits — among other things — a “certification by a Registered Professional Engineer or Licensed Land Surveyor that the lowest grade adjacent to the structure is above the base flood elevation.” Under these regulations, FEMA processes thousands of requests for Letters of Map Amendment (LOMA) each year; whether or not a Letter of Map Amendment is ultimately issued, a record exists of the elevation data submitted with each request. Second, FEMA’s regulations provide a procedure for revision of flood maps at the request of communities or at the request of individuals “through the community,” where a project would revise the topography of the land through addition of fill or other construction activities. In many cases, the information submitted in these Requests for Map Revision, and Conditional Requests for Map Revision, include certified elevation data for individual properties and structures. Again, thousands of Letters of Map Revision are processed under these regulations each year, creating a sizable pool of elevation data available for inclusion in the registry. Third, a small proportion of federal flood insurance policies are directly issued by FEMA rather than by a private insurance company operating under the WYO program. As to these policies, FEMA (through its contractor) obtains a property owner’s application for insurance and provides the coverage. Although elevation certificates are not required to rate all policies (such as pre-FIRM structures in a Special Flood Hazard Area (SFHA)), elevation certificates are required in order to rate policies on a number of structures, particularly post-FIRM construction in an SFHA. Fourth, after a flood, FEMA sometimes commissions licensed engineers and surveyors to create detailed maps of the flooding event. These maps are used to assist in recovery from the disaster and in the review of mitigation proposals under the Robert T. Stafford Disaster Relief and Emergency Assistance Act, as amended (“Stafford Act”). We understand that at least some of these projects may generate elevation data applicable to individual structures. Elevation data obtained for Letters of Map Amendment and Revision, or from flood studies funded under the Stafford Act, might be held by FEMA, or, in some cases, might be held in the files of the FEMA contractors who originally generated the data or processed data provided by third parties. Communities. As noted above, a community participating in the NFIP must adopt and enforce floodplain management ordinances. These ordinances must generally prohibit construction in the floodway and require that a structure in the floodplain be built with various flood mitigation measures (such as elevation a minimum number of feet above the base flood elevation). Floodplain management ordinances generally enforce these requirements through the building permit process: an applicant for a building permit for a new or substantially modified structure in the SFHA must provide an elevation certificate from a registered engineer or licensed surveyor demonstrating compliance with the elevation requirements. Communities maintain these elevation certificates. In communities, which participate in the Community Rating System (CRS) program, Elevation Certificates must be submitted by the engineer or surveyor on an Elevation Certificate form prescribed by FEMA, and these forms must be available for public inspection. CRS communities obtain additional credit for placing Elevation Certificate information in a computerized database that is provided to FEMA each year. Insurance Agents and WYO Companies. Over 90% of the flood insurance policies issued under the NFIP are not issued by FEMA but by an insurance company operating under the WYO Arrangement. For these policies, property owners submit applications to insurance agents; the agents review this information and require the owner to provide an Elevation Certificate where a Certificate is required in order to rate the policy. The agent then transmits the application to the WYO Company in the format and with the information that that Company requires in order to write the policy. The WYO Company, in turn, provides to FEMA a computer tape each month containing information about new policies or activities under existing policies as required by FEMA. Although Elevation Certificates may be required to rate a policy, and Elevation Certificates are used by agents in providing companies the information required to write a policy, FEMA does not at present require that WYO companies submit the Elevation Certificates themselves, nor are WYO Companies required to transmit to FEMA all of the information contained in an Elevation Certificate. We understand that WYO companies vary in the amount of information they require agents to capture from an Elevation Certificate and transmit to the WYO Company. Record keeping practices of agents across the country may also vary. CROSS-CUTTING LEGAL ISSUES 1. Applicability of the Privacy Act of 1974 Overview. The Privacy Act of 1974 (the Privacy Act) regulates the collection, maintenance, use, and disclosure by federal executive branch agencies of certain information about individuals, which is personally identifiable. It can generally be characterized as an information resources management statute which incorporates “fair information practices” to guide federal agencies in dealing with personally identifiable information. It guarantees access by individuals to information about themselves but prohibits disclosure to others without the written authorization of the subject individual (with some exceptions in both directions, of course). It also imposes requirements on agencies in managing Privacy Act data from collection to disposition. The lynchpin of the Privacy Act is the “system of records,” a term with a particular meaning defined in the Privacy Act. If information is contained, or is proposed to be contained, in a “system of records,” it is subject to the provisions of the Privacy Act, and an agency must collect, maintain, use and disclose that information only in accordance with the Privacy Act. Since the Privacy Act regulates initial collection of information, an agency cannot wait until it has acquired data to consider whether the Privacy Act applies. The agency must consider the entire life cycle of the data it intends to collect before beginning collection so that at the time of collection, the agency does not inadvertently violate the Privacy Act (for example, by failing to provide the proper notice in advance). A “system of records” is defined by the Privacy Act as A group of any records under the control of any agency from which information is retrieved by the name of the individual or by some identifying number, symbol or other identifying particular assigned to the individual. The term “record” is also defined by the Privacy Act: Any item, collection, or grouping of information about an individual that is maintained by an agency, including, but not limited to, his education, financial transactions, medical history, and criminal or employment history and that contains his name, or the identifying number, symbol, or other identifying particular assigned to the individual, such as a finger or voice print or a photograph. In order for the Privacy Act to apply to the elevation registry, the registry must first include records, each of which is “information about an individual” connected to an individual identifier of some sort. Those records, in order to qualify as a “system of records,” must be retrieved by that individual identifier. We turn now to an analysis of whether the proposed elevation registry includes “records.” A “record” includes two elements: 1) information “about” an individual which is connected to 2) an identifier “assigned to the individual.” The nub of the legal question is encapsulated by two questions, relying on the assumptions stated above about the contents of the database. First, do the proposed data elements constitute “information about an individual”? Second, if the data is, as proposed, to be retrieved by street address, is the street address an individual identifier as contemplated by the Privacy Act? Retrieval by “Individual Identifiers.” As a practical matter, almost all of the Privacy Act cases dealing with the definition of “record,” are concerned with whether some particular piece of information in a clearly established system of records is “about” the subject individual and do not address the question of identifiers. There are barely a handful of cases, which directly address the question of whether the information includes an individual identifier. It may therefore seem in reviewing the cases that the courts confound these two concepts. This is due to the fact that in the most common factual situation presented to the courts, a grouping of information includes an individual’s name or social security number, or some other obvious identifier, and usually the agency has recognized that the Privacy Act applies by complying with the requirement to publish a notice describing the system of records in the Federal Register. In understanding the difference, it is useful to remember that the Privacy Act was passed when the model for a system of records was a filing cabinet, rather than a database. The Congress was interested in covering the type of filing cabinet in which one could use the indexing system to immediately pluck out a file about an individual, such as a collection of folders alphabetized by last name, or in numerical order by social security number. The identifier was the indexing element, the sort of information one might find on the label of a file folder, and the “information about” the individual was the contents of the file folder. By contrast, they excluded from the definition of “system of records” a filing cabinet organized chronologically, where one might have to search the entire filing cabinet to be sure to find the file on a particular individual—such a filing system would not be a “system of records” under the Privacy Act. To qualify as a Privacy Act “record,” the information must identify an individual. Our question is whether a street address does so. The definition of individual is limited to living, natural persons who are U.S. citizens and permanent resident aliens. An identifier need not be unique, but it must identify an individual. The Act itself lists both unique and non-unique identifiers, using the following examples: name, number, fingerprint, voice print, and photograph. While an assigned number or fingerprint would be unique, it is common in American society to find more than one individual with the same name, such as John Smith, Sr., and John Smith, Jr. The list of individual identifiers in the Privacy Act includes examples, and is not an exhaustive list. The Supreme Court has addressed home addresses in the context of the Privacy Act in DOD v. FLRA. In that case, federal employee unions sought access to the home addresses of employees in a bargaining unit to support their representation responsibilities under the Federal Service Labor-Management Relations statute. The Supreme Court rejected the unions’ argument that the Labor Statute afforded the unions special access to the records, and treated the unions as if they were any other third-party requester under the Freedom of Information Act. As a precedent, the Supreme Court stated without discussion or citation that “the employee addresses sought by the unions are ‘records’ covered by the broad terms of the Privacy Act.” Using a FOIA analysis, the Court concluded that the disclosure of employees’ home addresses would constitute a “clearly unwarranted invasion of personal privacy.” However, the case is inapposite, because the Court did not have to consider whether a home address was an individual identifier. The personnel systems in question were indexed not by address, but by name and social security number. The home addresses, in this case, were the “information about” the employees, not the indexing individual identifiers. If it were already clear that FEMA’s elevation registry were a “system of records,” the home addresses in it would be protected from disclosure. But this does not answer the threshold question of whether there is a system of records in the first place. It is possible that a court might consider a street address to be an identifier of an individual, but only where only one person owns or resides at the property. If more than one person lives at a particular address, the address alone cannot be associated with just one person in the household, but could identify, in addition, a spouse or domestic partner, child, other family member living in the household, a roommate, a live-in employee, or other arrangement. In that case, the address would be associated with two or more individuals, and could not be an individual identifier. Although an individual identifier does not have to be unique, it must identify a unique individual. The closest case to FEMA’s street address problem may be the recent case of Fleming v. United States Railroad Retirement Board in which summary information about an RRB investigation of the plaintiff was disclosed in a report to Congress. The District Court for the Northern District of Illinois decided that since the report did not identify the plaintiff but only described the case, it did not constitute a “record.” The court said that RRB’s report “would have identified plaintiff only to an individual who had other information that would have caused that individual to infer from the report that plaintiff was the subject of the investigation.” Even in the case of sole ownership or residence, a street address, as in Fleming, would only identify the owner or resident to a person who had other information that would allow the person to infer that the property was associated with a particular individual. More important, the definition of record in the statute uses the phrase “identifying particular assigned to the individual.” In the case of a street address, even where only one person owns or resides at an address, when ownership or residence changes, the address comes to identify a completely different individual, or, in some cases, even an organization. There is a strong argument that a street address is assigned to the real property, and not to the individual, and therefore would not qualify as the identifier element necessary to defining a “record.” “Information About An Individual” To meet the Privacy Act definition of “information” in a record, data does not have to be particularly personal or broad in its descriptive qualities. The Office of Management and Budget’s Guidelines state that the term “record” means “any item of information about an individual that includes an individual identifier” and “can include as little as one descriptive item about an individual.” The federal courts differ as to how broad or narrow the definition of “record” is. Consistent with the OMB Guidelines, the Courts of Appeals for the Second and Third Circuits have broadly interpreted the term “record.” The Third Circuit held that the term “encompass[es] any information about an individual that is linked to that individual through an identifying particular” and is not “limited to information which taken alone directly reflects a characteristic or quality.” The Second Circuit, after analyzing the tests established by the other courts of appeals, adopted a test “much like the Third Circuit's test.” The Second Circuit found the Third Circuit's test to be closest to the statutory language;” it found the Third Circuit's test to be the only one consistent with the Supreme Court's decision in DOD v. FLRA, and, finally, it found the Third Circuit's test to be supported by the legislative history and OMB’s guidelines. Emphasizing that “the legislative history makes plain that Congress intended 'personal information' . . . to have a broad meaning,” the Second Circuit held that the term “record” “has 'a broad meaning encompassing,' at the very least, any personal information 'about an individual that is linked to that individual through an identifying particular.'“ Other courts have also applied a broad interpretation of the term “record.” The Courts of Appeals for the Ninth and Eleventh Circuits have limited Privacy Act coverage by adopting a narrow construction of the term “record” — requiring that in order to qualify, the information “must reflect some quality or characteristic of the individual involved.” The Court of Appeals for the District of Columbia Circuit, the Circuit of universal jurisdiction for the Privacy Act, also has adopted a narrow construction of the term by holding that in order to qualify as a “record” an item must contain “information that actually describes the individual in some way.” Examining the Third Circuit's statement in Quinn that information could qualify as a record “'if that piece of information were linked with an identifying particular (or was itself an identifying particular),'“ the D.C. Circuit rejected the Third Circuit's interpretation “[t]o the extent that . . . [it] fails to require that information both be 'about' an individual and be linked to that individual by an identifying particular.” In order to qualify as a “record,” the D.C. Circuit ruled that the information “must both be 'about' an individual and include his name or other identifying particular.” On the other hand, the D.C. Circuit rejected as “too narrow” the Ninth and Eleventh Circuits' definitions in Unt and Boyd, and stated that: “So long as the information is 'about' an individual, nothing in the Act requires that it additionally be about a 'quality or characteristic' of the individual.” Ultimately, the D.C. Circuit, “[w]ithout attempting to define 'record' more specifically than [necessary] to resolve the case at bar,” held that an NLRB computer system for tracking and monitoring cases did not constitute a “system of records,” because its files contained no information “about” individuals, despite the fact that the case information contained the initials or identifying number of the field examiner assigned to the case. Although the D.C. Circuit recognized that the case information could be, and apparently was, used in connection with other information to draw inferences about a field examiner's job performance, it stated that that “does not transform the [computer system] files into records about field examiners.” Several other courts have also limited Privacy Act coverage by applying narrower constructions of the term “record.” On the rare occasions when the federal courts have heard Privacy Act cases involving information about property belonging to an individual, they have found that it does not constitute information about an individual covered by the Act. For example, in Shewchun v. United States Customs Service, the District Court for D.C. reviewed a request for a letter concerning the Customs Service’s disposition of the plaintiff’s seized merchandise and held that the letter lacked “sufficient informational nexus with [the plaintiff] to bring it within the definition of ‘record’.” In Arizona, the District Court reviewed a request for a Postal Service claim form and information concerning estimated value of an item sent through the mail, and ruled that it was “not a ‘record’ within the meaning of the [Privacy Act]” because it “disclosed no information about the plaintiff” and did not reflect any “ ‘quality or characteristic’ concerning the plaintiff.” The type of information contemplated by FEMA for the flood database is closer to information about the property at a particular address than personal information about the owner of that property. The elevation of a particular structure’s floors, its lowest or highest adjacent grade, or whether or not it has a basement, are characteristics of the structure and not a “quality or characteristic” of the individual who owns the structure. Should ownership change, the previous individual owner might acquire a new structure with completely different characteristics, and the new owner would come to be associated with the characteristics of the property at the address alienated by the previous owner. The characteristics of a structure at a particular address would not change just because ownership had transferred. Therefore, we believe that the information contemplated for the elevation registry is about the property, not “information about an individual” as defined by the Privacy Act, and would not constitute a record under the Act. Of course, a court will react to the particular facts before it in a particular case, and there is always a risk that, if challenged under the Privacy Act, a court would find the proposed elevation registry to be a “system of records”. A court might adopt a very broad definition of “record” and conclude that characteristics of real estate reflect on one personally. A court might further decide that a street address identifies any sole owner of a property, or even that just as a single name (John Smith) may identify more than one individual, a street address may identify all individuals whose names appear on the deed or the mortgage. However, even under the Second and Third Circuits broad test for determining whether information is a “record” under the Privacy Act, the information must still be “about an individual” and linked to the individual by an identifier, although it need not, taken alone, directly reflect a “characteristic or quality” of the individual. Since FEMA anticipates retrieving the data in the database by street address, and since the information associated with the addresses is likely to be found to describe information about the property at that address, and not the individual owner or resident, we firmly believe that the proposed data does not constitute “records” under the Act, that the registry would not fall within the definition of a “system of records” under the Privacy Act, and that, therefore, the Privacy Act would not apply. Implications of Added Data Elements. As noted above, our conclusion depends upon the contents for the database as described by FEMA. If FEMA were to add to this registry data elements that are individual identifiers, such as names or policy numbers, the Privacy Act is much more likely to apply. If the Privacy Act applies, FEMA would be required to publish a notice identifying the registry as a “system of records,” and describing the registry in the Federal Register, give direct notice to individuals at the time information was collected for the registry, and most important, FEMA would not be able to disclose information in the registry to the general public without the written consent of the individuals who are the subjects of the record. Disclosures for program purposes would occur under a disclosure exception, most likely a routine use. Disclosure pursuant to a routine use would be limited to particular program purposes, such as to insurance companies, or local governments that have particular needs for the data in carrying out the flood program. The addition of a routine use to a system of records requires 30-day advance notice in the Federal Register providing the opportunity for public comment, and approval by the Office of Management and Budget. In addition, an agency is required to maintain for at least five years an accounting of all disclosures made via the routine use provision, to make the accounting available upon request to the individual who is the subject of the record, and use the accounting to inform anyone to whom the record has been disclosed of any subsequent correction or notation of dispute about the record. It would be difficult, although not impossible, for FEMA to justify affirmative disclosure of data designated as a “system of records” to the public at large. Because we understand this is not the planned use of the registry, we have not analyzed this possibility in detail. Relationship to Existing FIMA Systems of Records. In addition, while we understand that FEMA does not contemplate storing personally identifiable data in the elevation registry, we are aware that FEMA nevertheless already maintains databases of information related to the National Flood Insurance Program that do identify individuals and have been designated by FEMA as Privacy Act systems of records. The Federal Insurance and Mitigation Administration (FIMA) last published notices for their systems of records on January 23, 2002. Below is a summary of these six systems of records taken from the information in that notice. We have not made any evaluation about the currency of the notice or the quality of these notices with respect to their compliance with the Privacy Act of 1974. FIMA-2, “National Flood Insurance Direct Servicing Agent Application and Related Documents Files,” covers applicants for flood insurance and individuals insured directly by FEMA for flood insurance. In addition to identifying information such as name, address, and taxpayer identification number, it contains records about the subject’s policy, mortgage lender, loans, and claims including Group Flood Insurance Program certificates. The records are used to carry out the NFIP and verify no duplication of benefits. FIMA-3, “National Flood Insurance Bureau and Statistical Agent Data (BSA) and Related Files,” covers information about insured individuals that is required to be reported by private insurance agencies in the Transaction Record Reporting and Processing Plan. FIMA-4, “National Flood Insurance Program Marketing Records and Related Files,” includes marketing information about consumers, flood insurance policyholders, insurance agents, Write-Your-Own company employees and lenders, such as identifying information and data about awareness, attitudes, and satisfaction related to the flood program. The records are used in the campaign to increase awareness of flood risks and the availability of flood insurance. FIMA-5, “National Flood Insurance Program Telephone Response Center (TRC) Consumer and Policyholder Records and Related Documents Files,” covers consumers and policyholder identifying information, consumer research, and records of inquiries for NFIP marketing material. FIMA-6, “National Flood Insurance Special Direct Facility (SDF) Repetitive Loss Target Group Records and Related Files,” includes underwriting and claims data about individuals who have been designated as RLTG policyholders. The data includes identifying information, including taxpayer identification number, and may include application forms, claims and loss information, and information about the lender, loans and dates of mortgages, Most relevant to our current inquiry, this system of records includes Elevation Certificates. FIMA-7, “National Flood Insurance Community Rating System and Related Documents Files,” includes information on individuals in communities applying to the Community Rating System, Repetitive Loss property owners, and other applicants for insurance or policyholders. FEMA may collect information already in FEMA’s possession for the registry from these systems of records. Under an exception to the “no disclosure without consent rule,” FEMA is permitted to disclose information from a system of records to “officers and employees of the agency …who have a need for the record in the performance of their duties.” As long as the record stays within the agency for a mission purpose, disclosure is permitted. However, the fact that a record initially was part of a system of records does not mean it is always protected by the Privacy Act. The exact same record in a grouping of records, which is not retrieved by an individual identifier, is NOT protected by the Privacy Act because it does not meet the definition of a “system of records.” Therefore, even if elevation data or elevation certificates were transferred from FIMA-6, “National Flood Insurance Special Direct Facility (SDF) Repetitive Loss Target Group Records and Related Files,” to the registry, as long as the registry records were not retrieved by an individual identifier, none of the information in the registry would be protected, or regulated, by the Privacy Act. However, if FIMA-6 records are to be used on a routine basis as a source of data for the registry, FEMA would be wise to review the sources from which information for FIMA-6 records are collected. If any of that information is collected directly from the subject individual (for example, on an application form), FEMA could update the notice given to the subject individual, and the associated system of records notice published in the Federal Register, to properly reflect the expanded ultimate use of the information. Linking Databases or Computer Matching Programs. If FEMA intends that the new registry not be covered by the Privacy Act, FEMA must maintain such systems of records separately from the registry. FEMA may match the data from the registry together with these systems of records, as long as the databases are not combined, and there is no permanent link between a designated Privacy Act system of records and the registry. We understand that since the registry is contemplated for disclosure, which would be more administratively burdensome if it were covered by the Privacy Act, FEMA has a strong incentive to keep the databases separate. Furthermore, an agency is permitted to use its own databases for program purposes, including for computer matching. The Computer Matching and Privacy Protection Act (CMPPA) which regulates the comparison of systems of records, does not prohibit comparisons, it merely requires certain procedural safeguards for interagency matches which affect individuals’ federal benefits or loans, or matches involving federal personnel records. Intra-agency matches for program purposes are not regulated. So, if FEMA compared its existing Privacy Act systems of records with the new elevation registry, such a match would not be covered by the CMPPA. Finally, we note that there is some possibility that documents could be attached to the registry that contain personally identifying information. For example, one way to alert users of the source of data placed in the registry would be to attach to the registry an electronic copy (say, a .pdf file) of the source document, such as an elevation certificate from community files or a LOMA application. This attached document might include the name of the property owner. However, we do not believe that the presence of this information on an attached file would convert the registry into a Privacy Act system of records, at least if it were not technically possible for a person using the registry to conduct a computer search using the ‘individual identifier’ of the individual’s name. In order to be a “system of records” triggering coverage under the Privacy Act, information in a filing system must actually be retrievable by individual identifiers. 2. Freedom of Information Act Issues Overview. With the passage of the Freedom of Information Act (FOIA) in 1966, Congress firmly established a right of access to federal records, and the right to enforce that access in federal court. FOIA incorporates a presumption of openness, based on the principle that in a democratic society, citizens must be informed in order to check corruption and ensure the government is accountable for the performance of its statutory duties. Since enactment of FOIA, other open records laws have been passed to strengthen these goals, such as the Sunshine Act, which governs federal open meetings, and the Federal Advisory Committee Act, governing meetings of councils of outside advisors to the executive branch, and numerous other statutes governing access to specific types of information. FOIA is a disclosure statute. FOIA requires disclosure, on request, of any information in government files unless the information falls within one of FOIA’s exemptions. FOIA permits, but does not require, agencies to withhold exempt information. Thus, although FOIA does exempt some types of records from the requirement to disclose, FOIA itself never prohibits any type of disclosure. The Justice Department’s Freedom of Information Act Guide states, “[i]nasmuch as the FOIA's exemptions are discretionary, not mandatory, agencies may make discretionary disclosures of exempt information, as a matter of their administrative discretion, where they are not otherwise prohibited from doing so.” In other words, while the FOIA accommodates certain prohibitions embodied in other statutory authorities, it does not, itself, require an exemption to be exercised. Barring another statute that would prohibit disclosure, agencies are legally permitted to disclose information to the public that is exempt under FOIA. As demonstrated above, the dissemination of information in FEMA’s possession about flood risk is one of the key elements of the National Flood Insurance Program. These elements of the NFIP are wholly consistent with FOIA and longstanding federal policy favoring affirmative disclosure of information in support of an agency’s mission. In the case of the Elevation Database, FEMA’s specific intent is to disclose elevation data to the WYO companies and their agents, and to change FEMA’s regulations to allow those companies and agents to rely upon the Elevation Database in writing policies. Under FOIA, data in the Database will be “records” under the control of an “agency.” Accordingly, FEMA very well may receive a FOIA request for the data in the elevation registry from others — homeowners or prospective homebuyers, communities, mortgage lenders, or flood zone determination companies. When FEMA receives a request, it should make no inquiry into the requester’s motives for seeking documents, as a requester’s basic rights to access “are neither increased nor decreased” by virtue of having a greater interest in the records than that of an average member of the general public. We have not been advised of any desire by FEMA to withhold information so requested, assuming FEMA is not legally barred or subjected to undue legal risk in doing so. Nonetheless, we note that the policy of the Justice Department, as most recently enunciated by Attorney General Ashcroft, is that agencies considering discretionary disclosure of exempt materials do so “only after full and deliberate consideration of the institutional, commercial, and personal privacy interests that could be implicated by disclosure of the information.” Accordingly, we turn to a review of the possible legal risks that FEMA would incur in disclosing this elevation data in response to a FOIA request — bearing in mind that the most significant legal risk an agency may incur under FOIA would arise were the agency to fail to disclose non-exempt information. ‘FOIA’ Analysis of Risk of Disclosure. Although FOIA is a disclosure statute, third parties whose information is held by the federal government have frequently filed suit to prevent disclosure of “their” information by the federal government. These “reverse-FOIA” suits must argue both that the information is exempt from disclosure, and that disclosure would substantially invade a protectible interest of the plaintiff. The exemption at issue in most reverse- FOIA cases is Exemption 4, covering “trade secrets or other commercial and confidential information, and Exemption 6 covering “personnel and medical files or similar files the disclosure of which would constitute a clearly unwarranted invasion of personal privacy. In a reverse FOIA suit “the party seeking to prevent a disclosure the government itself is otherwise willing to make” assumes the “burden of justifying nondisclosure.” A reverse-FOIA challenge to an agency's disclosure decision is reviewed in light of the “basic policy” of the FOIA to “open agency action to the light of public scrutiny” and in accordance with the “narrow construction” afforded to the FOIA's exemptions. The landmark case in the reverse-FOIA area is Chrysler Corp. v. Brown, in which the Supreme Court held that jurisdiction for a reverse-FOIA action cannot be based on the FOIA itself “because Congress did not design the FOIA exemptions to be mandatory bars to disclosure” and, as a result, the FOIA “does not afford” a submitter “any right to enjoin agency disclosure.” In Chrysler Corp. the Court found that review of an agency's decision to disclose requested records can be brought under the Administrative Procedure Act (APA). Accordingly, reverse- FOIA plaintiffs ordinarily argue that an agency's contemplated release would violate the Trade Secrets Act (or, sometimes, the Privacy Act or another statute) causing the plaintiff harm, and thus would “not be in accordance with law” or would be “arbitrary and capricious” within the meaning of the APA. However, any reverse-FOIA action challenging disclosure of elevation registry data would face severe hurdles. Public Domain Waiver. First, the Court of Appeals for the District of Columbia Circuit has held that even if government information would otherwise fall within one of FOIA’s exemptions, the government must disclose that information on request if the information is in the “public domain.” In the court’s view, once information becomes public, withholding data pursuant to an exemption would serve no purpose, and the government is deemed to have waived its right to invoke the exemption. This “public domain” doctrine may be very critical should FEMA seek to withhold data in the registry, as much of the data in the elevation registry will be derived from sources in the public domain. In the context of individual privacy, the “public domain” doctrine does not wholly eliminate the ability of an agency to withhold information that some time ago appeared publicly. In United States Dep’t of Justice v. Reporters Committee for Freedom of the Press, the U.S. Supreme Court made clear that it is possible to have a strong interest in the privacy of information “even where the information may have been at one time public.” In that case, the plaintiffs requested from the FBI its “rap sheets” on individuals; these rap sheets collected into one file the individual’s arrest and conviction records obtained by the FBI over time from multiple local and state authorities. The Court reasoned that if the information in question was at some time or place available to the public, but is now “hard-to- obtain information,” the individual to whom it pertains may have a privacy interest in maintaining its “practical obscurity.” The elevation registry, however, does not collect into a single file scattered information from disparate sources, as with a rap sheet, nor is it likely to become less available to the public from its original public source, unlike information once published in a newspaper. Rather, the database, as proposed, will be indexed by an address, not a person. It will not gather together or link information on an individual (such as multiple properties owned by an individual). Many of the records in the registry would derive solely from information in publicly available records of state or local government. The entire database would not be easily reproduced, but all of the information about a particular address could very easily be retrieved from the one place in which the information originally resided. Thus, the concept of “practical obscurity” as envisioned by the Reports Committee court does not apply to the elevation registry. Accordingly, we believe that the D.C. Circuit’s “public record doctrine” cases cited above would control the FOIA status of any records in the elevation registry that have been in the public domain. Exemption 4. Exemption 4 covers “trade secrets and commercial or financial information obtained from a person and privileged or confidential.“ The Trade Secrets Act prohibits the unauthorized disclosure of information falling within the exemption for confidential commercial information constraining an agency’s ability to make a discretionary disclosure absent an agency regulation, authorized by statute, that expressly authorizes disclosure. A number of the stakeholders in the NFIP could not readily assert reverse-FOIA action based on disclosure of data in the registry. Elevation data collected from existing sources at FEMA or from other governmental entities is not “obtained by a person” under Exemption 4. Information obtained via contractors hired by FEMA to collect elevation data is governed by contracts that, presumably, would require the information to be made available to the public . We do not understand FEMA to be contemplating obtaining elevation data directly from engineers or surveyors other than those contracted by FEMA, because it would be too inefficient and administratively burdensome to establish relationships with the myriad engineers and surveyors in each community. The strongest potential source of Exemption 4 reverse-FOIA actions are WYO companies and perhaps their agents. WYO companies participate in the flood insurance program by agreeing to an arrangement promulgated, after notice and comment rulemaking, by FEMA under the National Flood Insurance Act. That agreement requires the WYO companies to submit policy and transaction data to FEMA. We understand that WYO companies compete with each other for business, and jealously guard from one another information that might allow one company to target marketing activity to another company’s existing policy holders. To the extent the elevation registry would allow such targeted marketing, it is at least arguable that information provided by the WYO companies for the registry could be claimed proprietary and that WYO companies could object to disclosure. We have substantial doubt, however, that Exemption 4 “trade secret or commercial information” status could be bestowed on an elevation registry which included all addresses for which FEMA had elevation data. The data in the registry would come not just from insurance companies, but also from FEMA contractors and from local governments participating in the CRS or submitting data derived from their own elevation surveys. Thus, the registry would not disclose any company’s customer list or disclose whether a listed property is insured by FEMA directly, insured by a competitor, or, indeed, whether the property is insured at all. The most that can be said is that, taken together, the addresses might reveal the location of the flood plain, or flood prone areas, information that is available directly from FEMA or from public libraries, and cannot be said to be proprietary to any company. The purpose of the registry is to reduce the costs of writing flood insurance by providing access to elevation data that is more quickly and easily accessible, nearly as accurate as current sources of elevation data, and free of charge to agents, WYO companies, and their customers. In order to implement the registry, FEMA must by rule modify the arrangement (and related Transaction Record Reporting and Processing Plan) to advise companies that Elevation Certificate information should be provided to it and that it can rate policies based on information in the registry rather than by review of an Elevation Certificate. As part of this rulemaking, FEMA should make clear that it will place in the registry the address and elevation certificate data (but not any personal identifiers) it obtains from a number of sources, including the WYO companies themselves. In this rulemaking, FEMA would have an opportunity to balance any assertions of proprietary disclosure — should any be asserted — against the benefit — in terms of reducing the cost to insureds and all WYO companies — to the entire NFIP. Thus, even if some low level of proprietary interest would derive, for example, from the possibility of using the address data as a mailing list for marketing service to new customers, FEMA would have the opportunity to determine whether this imposition on the proprietary interests of the WYO companies is greatly outweighed by the benefits of establishing the elevation registry. As there is no requirement that WYO companies participate in the flood program, any who do not wish to report information about their customers as required by FEMA may choose to discontinue participation. Exemption 6. FOIA Exemption 6 permits withholding of all information about individuals in "personnel and medical files and similar files the disclosure of which would constitute a clearly unwarranted invasion of personal privacy.” In evaluating whether this exemption applies, we must first consider whether the data in the registry meet the threshold requirement of being “personnel and medical files and similar files.” They clearly are not personnel or medical files — but are they “similar files” — the sort intended to be covered by Exemption 6? “Similar Files.” In United States Department of State v. Washington Post Co., the Supreme Court held, based upon a review of the legislative history of the FOIA, that Congress intended the term “similar files” to be interpreted broadly; “similar files” under this exemption covers all information that “applies to a particular individual.” More recently, in Na Iwi O Na Kupuna v. Dalton, the District Court in Hawaii was explicit that “to trigger Exemption Six protection, the actual production of the documents must constitute a clearly unwarranted invasion of ‘personal’ privacy.” That court went on to observe, “[o]bviously, that can only occur when the documents disclose information directly attributable to an individual.” The court also cited the legislative history, which states, “[E]xemption [6] is. . . intended to cover detailed Government records on an individual which can be identified as applying to that individual.” As we have previously discussed in the context of the Privacy Act, property addresses do not correspond to particular individuals. Only where a property is a single family residence rather than a multi-unit dwelling or other commercial establishment, where the owner is a single individual, and where the owner and the resident are one and the same would an address even correspond to a unique individual. Moreover, the elevation registry will not include any kind of individual identifier with which to connect a property address to the individual, so even though such a connection is theoretically possible, the connection will not exist in the registry. “Invasion of Personal Privacy.” If FEMA finds, arguendo, that property addresses could meet the threshold test of being “similar files” qualified for exemption, FEMA must then analyze whether disclosure “would constitute a clearly unwarranted invasion of personal privacy.” In this analysis, an agency employs the test elucidated in Reporters Committee, which requires a balancing of the public interest in disclosure of the information with the harm to personal privacy that would result from the disclosure. First the agency must ascertain whether a protected privacy interest exists that would be threatened by disclosure. If no privacy interest is found, further analysis is unnecessary and the records must be disclosed. This step eliminates from Exemption 6 all records placed in the registry regarding properties of corporations and business associations: corporations and business associations do not possess protectible privacy interests. This rule probably applies to decedents’ estates, as well. Next we review potential privacy interests for other (non-commercial) addresses in the elevation registry. For Exemption 6 to apply, the threat to privacy must be real rather than speculative. In the context of the Privacy Act, this report has discussed at length the very scant privacy interest in the elevation data and concluded that it most likely would be considered information about the property and not about the individual. In National Association of Retired Federal Employees v. Horner, the Court of Appeals for the District of Columbia Circuit explained that Exemption 6 applies where a “substantial likelihood that any concrete facts about a particular individual could be inferred.” As noted infra in our discussion of Privacy Rights and Remote Surveillance, even in communities that are not participating in the CRS, this same information is generally required by state law to be publicly available, reducing the potential privacy interest that might attach to the data. In the DOD v. FLRA case discussed supra, the Supreme Court found a privacy interest in federal employees’ names and home addresses even though they “often are publicly available through sources such as telephone directories and voter registration lists.” Many courts have protected from disclosure compilations of addresses linked to names, but these cases all differ from FEMA’s disclosure of elevation data, because the contemplated registry does not link names with addresses, but only identifies properties, not indicating which are residences, which are multi-unit dwellings, or which are commercial, and not identifying individual persons. It is difficult to imagine how records indicating the location and elevation of properties could be viewed as generating any viable privacy interest when land sale and elevation records are public in most states, and as noted in our discussion of the Privacy Act, no record in the registry will be linked to an identifier of a specific individual. Indeed, we are not aware of any case deciding that government records of addresses, without names, are exempt from disclosure under Exemption 6. Names and Addresses. Even though the registry will not include names, perhaps a court might stretch and require FEMA to analyze the contemplated disclosure of an address as a disclosure of “a name and address.” But even this possibility does not, in our view, generate a real likelihood that a successful reverse-FOIA action based on Exemption 6 could be maintained. In NARFE, the D.C. Circuit explained that for Exemption 6 to apply, there must be a “substantial probability that disclosure will cause an interference with personal privacy.” Lists of names and addresses alone, without more, do not do so. As explained by the NARFE court: Every list of names and addresses sought under FOIA is delimited by one or more defining characteristics, as reflected in the FOIA request itself; no one would request simply all “names and addresses” in an agency's files, because without more, those data would not be informative. The extent of any invasion of privacy that release of the list might occasion thus depends upon the nature of the defining characteristics, i.e., whether it is significant that an individual possesses them. A non-embarrassing characteristic may or may not be otherwise significant, in a manner relevant to the individual's privacy interests, depending upon whether many parties in addition to the party making the initial FOIA request would be interested in obtaining a list of and contacting those who have that characteristic. In DOD v. FLRA, names and addresses were all linked to a particular fact about the names and addresses in question: union membership, or membership in a bargaining unit — a fact that would differentiate a household on the list from those not requested. Similarly, in the cases in which exemption of the addresses of FOIA requesters have been upheld, disclosure would have included not just the names and addresses of individuals, but the fact that each household included a person who had made a FOIA request in the past. In the case of Professional Programs Group, the names and addresses were connected to registration for the patent bar examination. In Bibles v. Or. Natural Desert Ass'n, addresses were connected to those receiving, and presumably interested in, the Bureau of Land Management’s publications. By contrast, in the case of the elevation registry, the names and addresses are connected only to elevation data, a characteristic of the property, not of an individual resident or owner of the property. The existence of elevation measurements, by itself, does not differentiate the group of addresses in the database from addresses not in the database, since every address has elevation. The particular elevation measurement serves to differentiate one address from another, but not one individual from another. The individuals associated with a particular address may change as ownership or residence changes, but the elevation of the address remains associated with the address. Even if FEMA were unpersuaded by these arguments and concluded that there is a privacy interest in the subset of addresses in the database which are associated with personal residences of individuals, it must then go on to balance the privacy interests of the resident-owner of the property with the public interest in disclosure of the addresses and elevation data. In considering the privacy interest in addresses, the NARFE court considered whether disclosure of names and addresses would “interfere with the subjects' reasonable expectations of undisturbed enjoyment in the solitude and seclusion of their own homes.” That court observed, the disclosure of names and addresses is not inherently and always a significant threat to the privacy of those listed; whether it is a significant or a de minimis threat depends upon the characteristic(s) revealed by virtue of being on the particular list, and the consequences likely to ensue. The list at issue in that case revealed not only the names and addresses of a large group of individuals, but indicated that “each is retired or disabled (or the survivor of such a person) and receives a monthly annuity check from the federal Government.” The court pointed out that, “any business or fund-raising organization for which such individuals might be an attractive market could get from the Government, at nominal cost, a list of prime sales prospects to solicit. Armed with this information, interested businesses, charities, and individuals could, and undoubtedly would, subject the listed annuitants to an unwanted barrage of mailings and personal solicitations.” It is possible that WYO companies might want to solicit the business of persons who own properties listed in the registry by sending “Dear Occupant,” letters, and that some individuals might be annoyed by receiving such solicitations. Nevertheless, the disclosure of the information in the registry would not reveal personal, medical, financial, or other embarrassing information about any individual, and the list is unlikely to be of interest to entities other than WYO companies, so a “barrage of mailings,” is not likely. Given the nature of the registry, the public nature of much of the data in the registry, the purpose of the registry in communicating flood risk to those responsible for insuring against flood risk, and even (since it is not a stated purpose of the registry) the importance of communicating about flood risk to persons that may have an interest in whether a property could flood, we believe that FEMA could easily conclude that the balance of interests rests in favor of disclosure. Finally, we note a practical consideration which would make the invocation of Exemption 6 extremely difficult. if resident-owners were found to have a protectible privacy interest, and FEMA chose to withhold records based on that conclusion under Exemption 6, upon every FOIA request for records in the database, FEMA would be required to investigate the current ownership of each property before disclosure. Since withholding of records about commercial entities or decedents’ estates based on Exemption 6 would be a violation of FOIA’s requirement to disclose non-exempt records, FEMA would risk violation of the statute and resulting litigation if it chose to invoke Exemption 6 without a thorough check of the ownership of each address in the database. The transfer of property ownership from an individual to a commercial entity is rather common, as is the transfer of property from a decedent to his or her estate upon death, and, since ownership and residence are not included in the database, FEMA would have no way of knowing whether the records were currently eligible for exemption without investigating the current ownership in each case. We imagine this would be prohibitively burdensome to implement. Other Exemptions. We are not aware of any other specific authority that would prohibit the records from disclosure. Addresses of buildings are not classified national security information even if there may be elements of the nation’s critical infrastructure whose exact location may have national security implications. As for other FOIA exemptions, addresses of buildings, even if ‘georeferenced,’ are not “geological or geophysical information and data, including maps, regarding wells.” Electronic FOIA. Under the “Electronic Freedom of Information Act,” information must be provided in electronic format if the request seeks data in that format and the information is readily reproducible by the agency in that format. Thus, if the federal government creates an elevation registry in a computerized format and makes that database accessible — in words taken from our Task Order — “to select NFIP stakeholders such as mortgage companies and insurance companies with a legitimate need to know,” then this information would appear to be “readily reproducible” in that computerized format. Accordingly, this information must be available (upon request) in that same computerized format to any requester — whether or not the requester is perceived as having a “legitimate” need to know. 3. Liability Issues. Disputes over flood risk maps, including FEMA’s Flood Insurance Rate Map (“FIRM”), and over flood risk data for individual structures and land parcels, sometimes end up in court. The courts have reviewed various theories of liability involving a wide range of possible defendants: the seller of land, the seller’s or buyer’s real estate agent, the buyer’s insurance agent, the buyer’s lender, the community which issued a construction permit, the surveyor who provided a survey or elevation certificate at closing, the engineer who performed a flood study for FEMA, and FEMA itself. Of course, all case law with respect to liability for erroneous flood map or elevation information was developed prior to establishing an elevation registry. The question reviewed in this report is whether, or how, the creation of an elevation registry would affect the liabilities of the many participants in the elevation process. We note at the outset that FEMA is designing this registry for a particular purpose — the rating of flood insurance policies — and will develop the quality control procedures and acceptable error limits of elevation data in the registry specifically for that purpose. The analysis below assumes that persons or entities use the data for that purpose, also use the data for other purposes, and suffer harm as a result. To reduce the frequency of these situations FEMA should include a specific disclaimer as to the suitability of the registry for any purpose other than the rating and writing of flood insurance policies. Liability of FEMA Generally. We believe that there is little likelihood that FEMA can be held liable for information in the registry. It is well established that the United States and its agencies enjoy sovereign immunity, except to the extent that immunity is waived. The National Flood Insurance Act waives immunity only in two very limited ways. First, Section 1341 waives sovereign immunity for challenges to the agency’s disallowance of flood insurance claims. Second, Section 1364(g) provides for administrative review of elevation determinations, and appeal to the U.S. District Courts. Publication of information about elevation of specific properties is not the disallowance of a flood insurance claim, so Section 1341 does not apply. Judicial review of a FEMA decision not to change elevation information in the elevation registry would, at most, require the agency to change elevation information in a manner specified by the plaintiff. It would not lead to monetary damages. Three cases involving FEMA’s mapping authority demonstrate the strength of FEMA’s sovereign immunity defenses. In Normandy Pointe Assocs. v. FEMA, Normandy Pointe, a developer, sued FEMA and FEMA’s contractor, Dewberry and Davis, LLC, as well as its own engineering firm and the local government, seeking to have the court decide where the flood plain really was. (Normandy Pointe had constructed and sold several homes in an area mapped by FEMA as being in the flood plain, based on its own engineer’s independent flood study showing that the flood plain did not extend beyond the river bank. After the homes flooded, the township commissioned a third flood study that showed the homes to be in the flood plain). The court had no difficulty dismissing FEMA and FEMA’s consultant from the litigation on sovereign immunity grounds. In doing so, the court found neither a waiver of sovereign immunity in the NFIA, nor a waiver of immunity in general statutes providing courts with jurisdiction to resolve “federal questions,” nor in the Declaratory Judgment Act. The court used a different analysis to throw out a challenge to NFIP maps in Britt v. U.S. In Britt, homeowners alleged that they had relied on erroneous and negligently prepared NFIP flood maps in building their homes; these homes were then severely damaged by flooding. The court threw out a lawsuit against the United States seeking recovery of floodwater damages, holding that the immunity provisions of 33 U.S.C. § 702c precluded the action. Section 702c — enacted as part of the Flood Control Act of 1928 (after the disastrous Mississippi floods of 1927) — provides in pertinent part: “No liability of any kind shall attach to or rest upon the United States for any damage from or by flood waters at any place.” This section had previously been construed to prevent actions against the United States for flood damage unless the damage arose out of negligence of the United States unconnected with any “Congressionally mandated flood control initiative.” The court held that preparation and dissemination of maps under the NFIA were “flood control initiatives,” and therefore that Section 702c precluded the action. In Segall v. Rapkin, defendant Goodkind & O’Dea had performed a “Flood Insurance Study” as contractor for FEMA, and in that study specified the base flood elevation (BFE). Plaintiffs alleged that due to Goodkind’s survey errors, the BFE adopted by FEMA was inaccurate; relying on this inaccurate BFE, plaintiffs’ homes were constructed three and one half feet below the actual base flood elevation. Plaintiffs sued FEMA’s engineering contractor, Goodkind. The court granted Goodkind’s motion to dismiss, holding that the NFIA provides no private right of action for erroneous map information. The court noted: It is not necessary for private parties to have a right of action under the Act to achieve or further its purposes. Indeed, to allow plaintiffs to hold Goodkind liable would discourage future surveyors from reporting their views concerning flood levels to FEMA. In addition, courts have consistently held that no private cause of action exists for breach of duty by a government contractor for violation of the underlying agencies’ duties. While it would be dangerous to assert that no attack on FEMA’s sovereign immunity would ever be successful, we believe that this defense is quite strong and does not appear to be weakened by creation of a registry containing — and centralizing — information already required for administration of FEMA’s current flood insurance and mitigation programs. Liability of FEMA: Implications of “Horizontal” vs. “Vertical” Mapping Activity. Our Task Order asked us to review whether the immunity for “vertical” mapping — meaning the mapping of elevations of land and structures — differs from the traditional two-dimensional “horizontal” maps shown on FIRMs. It does not. Sovereign immunity applies to FEMA’s activities under the National Flood Insurance Act in acquiring and disseminating information about flood risk. It applies to the “horizontal issues involving Special Flood Hazard Areas.” It applies to FEMA’s actions in publicizing elevation data obtained by FEMA contractors, and it applies to FEMA’s actions in publicizing elevation data obtained from the files of participating NFIP communities and from WYO companies. We recognize that there are more statutory provisions granting FEMA authority to create and disseminate map information on flood hazard zones or areas, than there are granting authority to collect and disseminate information on individual structures. Further, FEMA’s maps designating special flood hazard areas trigger mandatory flood insurance purchase requirements and can have a substantial impact on the ability of owners to develop land; these ‘horizontal’ maps are the cornerstone of the NFIP’s floodplain management and insurance purchase regulations. Congress was very concerned about the technical accuracy of FEMA’s flood maps and provided special procedures by which communities and individuals could appeal mapping errors. Indeed, we believe that FEMA would have an obligation to correct erroneous information in the registry that came to its attention, and that a person who could show that it had been aggrieved by erroneous information in the registry may well have a right to judicial review and potentially an order requiring FEMA to change the map should FEMA refuse to do so. Nonetheless, our conclusion is the same for all of FEMA’s efforts to obtain and disseminate flood risk information: the United States and its component agencies, including FEMA, are immune from suit unless Congress has explicitly waived sovereign immunity. The National Flood Insurance Act does not waive sovereign immunity for obtaining and disseminating flood risk information, and certainly contains no distinction between horizontal or vertical maps or risk information. There is always a risk that a court will find liability in a particular case. Courts may occasionally stretch to reach what they believe to be a just result and may attempt to find a waiver of sovereign immunity in a general statute (such as the Federal Tort Claims Act ) even where a specific statute is silent. Nonetheless, this risk appears to be small, is not affected in a significant manner by the nature of the “flood risk information” that FEMA is authorized to collect and distribute, and would be reduced still further by including a disclaimer in the registry that the information is appropriate only for the rating and writing of flood insurance policies. Liability of Other Parties. Elevation determinations, as well as determinations of the location of a property inside or outside a floodplain, have been a frequent subject of litigation. Most of the reported cases have arisen as an outgrowth of real estate transactions. The range of defendants exposed to liability in cases of this type is extensive: ? The seller of property, who knew the property was in a flood zone but failed to so advise the buyer; ? The real estate agent who represented both buyer and seller in the transaction, who both provided to and interpreted for the buyer a prior flood elevation survey, and in doing so erroneously advised that the survey showed the property was not in a flood zone; ? The insurance agent who advised that property was not in a flood zone and could not be insured; ? The engineer or surveyor who erroneously determined that the property was above the base flood elevation; ? The mortgage lender who did not advise the buyer, as required by the NFIP, that the property was located in a Special Flood Hazard Area; ? The local government that issued a building permit to the buyer without advising the buyer that the property was in a flood hazard area or that development was restricted in that area; ? The commercial general liability insurer of a land developer, who constructed homes in a flood area upon advice from his engineer (who did not look at FEMA’s maps) that the homes were not in a flood area. The developer faced liability from the owners of the homes flooded in the development. A detailed legal analysis of all of the possible ways in which a particular engineer, surveyor, insurance agent, real estate agent, real estate seller, mortgage lender, flood zone determination company, or local government might incur liability is neither possible nor within the scope of this project. Liability of parties due to the new elevation registry will depend substantially on the particular facts of particular transactions, the particular language in the agreements between the parties involved, and the existence and wording of a disclaimer advising users that the data is not appropriate for uses other than the determination of the proper premium in writing flood insurance policies. Our task in this report is not to analyze the circumstances in which potential defendants run the risk of liability for their direct or indirect involvement with flood elevation or flood zone data. Rather, our task is to analyze the likelihood that by establishing an elevation registry FEMA will increase the risk of liability faced by these parties. Liability Implications of Correct Registry Information. In discussing the potential liability implications of establishing an elevation registry, we make several initial observations applicable to liability exposure arising from accurate and reliable information in the elevation registry; that is, where the registry correctly specifies the elevation of a structure, its adjacent grades, and the base flood elevation. Accurate communication about flood risk to people and organizations making development and insurance decisions is a critical objective of the NFIP, and the elevation registry would play a central role in advancing that objective. It would make elevation information more available to insurance agents and insurance companies and, to a lesser extent, to homeowners, homebuyers, mortgage lenders, and real estate agents. Much of the litigation documented in the notes to this report arose because flood elevation or risk information was not readily available, causing one party to a transaction to rely upon another party in making a real estate purchase, development, or insurance decision. To the extent that the elevation registry provides accurate elevation data upon which insurance agents and WYO companies rely (and upon which even buyers, developers, or insureds might rely, albeit contrary to a disclaimer about proper use of the registry), many transactions that might have led to litigation will simply not take place. Second, third parties participating in some way in the creation of elevation information or in transactions using elevation information — surveyors and engineers, insurance and real estate agents, developers — should not suffer increased liability because reliable information is given greater distribution through placement in an elevation registry. A property may well lose value because it is located below base flood elevation and cannot be developed under floodplain management ordinances. Its owner may attempt litigation against the community, or other available defendants, asking the court to permit development or to shift to defendants the loss in value of land caused by its high flood risk. But the greater availability of accurate elevation data is unlikely to help in this owner’s efforts to impose liability on others. Third, although the error rates for data in the registry may be higher than is appropriate for floodplain management purposes, the availability of the registry to property owners may encourage closer review of elevation information and possibly lead more property owners to obtain elevation certificates. Fourth, disputes will arise because an insurance agent or company could have, but did not, review the information in the elevation registry before taking action. For example, an insurance agent might advise that a property is eligible for low premiums, or need not be insured, because the agent thinks that the property is elevated above base flood elevation, when, in fact, the elevation registry accurately shows that it is not above base flood elevation. While failure to check the elevation registry would not necessarily establish liability of insurance agents, the existence of (and publication of information about) this registry would weaken the agent’s legal defenses and hence increase his or her potential liability. Similarly, the ready availability of flood elevation information in the registry may well weaken the defenses, and increase the liability, of other parties who are found to have had a duty to check, and who did not check, the registry. It is not clear, however, that FEMA should be concerned with increased liability created when FEMA makes it easier for those with a duty to check flood risk information to do so. Liability Created by Inaccurate Data in the Registry. Concern about increased liability from an elevation registry is properly directed toward the probability that a database involving millions of structures will not be error free. Inaccuracies in the registry — and potential liability — may result from a number of sources, such as the original surveyor, an intermediary who transferred the data to FEMA, a FEMA engineering contractor, a FEMA data entry contractor, or FEMA itself. There are two potential types of errors in the registry, leading to different liability results: the registry shows an elevation lower than the true elevation of a structure and a higher risk of flood than the true risk, or the registry shows an elevation higher than the true elevation and a lower risk of flood. Registry Data Shows Flood Risk Higher Than Actual Flood Risk. Where the registry reports elevation too low, the structure will actually be higher, and hence less susceptible to flooding, than is implied by the registry. The registry’s low reported elevation for the structure could trigger insurance premiums for property owners and development restrictions under local ordinances that are higher and more stringent than warranted by the structure’s true elevation. In order to reduce the likelihood of litigation arising from this source of error, FEMA must ensure that there are procedures in place to allow correction of inaccurate data in the registry. Thus, an owner adversely affected by erroneous information about his or her property must be able to provide information — such as an elevation certificate performed by a licensed engineer —that would cause FEMA to correct the information in the database. If such a procedure were available, the economic consequence to owners affected by the erroneous information in the registry — and hence the damages that could be sought in potential litigation — would be limited: with the corrected data, premiums could be reduced and construction of additions to existing structures could go forward without being subject to any, or at least as extensive flood control measures. We note that one of the “losses” that might be claimed is that excess premiums were paid over an extended period of time until the error is corrected. We anticipate that FEMA could be asked to review its policy limiting refund of excess premiums earned by the NFIP where erroneous information in the registry caused the excess premiums. Registry Data Shows Flood Risk Lower Than Actual Flood Risk. In the more troublesome case — and the one that is more likely to give rise to litigation — the registry reports elevation too high, indicating a property is less susceptible to flooding than it really is. Litigation in these situations normally arises either because an owner experiences uninsured flood losses, or because an owner must either satisfy an unanticipated requirement for flood insurance or pay much higher premiums than the owner had anticipated when the property was acquired. We provide the following observations on the potential for increased exposure to liability for six different types of parties caused by the creation of an elevation registry. FEMA Engineering or Surveying Contractors: To the extent the database is populated with data provided by engineering or survey companies under contract with FEMA, there should be no change in the potential liability of these companies as a result of the publication of the data in an elevation registry. The standard of care of these contractors, and liability of these contractors, should be determined by their contracts with FEMA. Absent contrary provisions in the contracts, these contractors are not liable to third parties who may rely on the information published by FEMA. “Courts have consistently held that no private cause of action exists for breach of duty by a government contractor for violation of the underlying agency’s statutory duties.” We note — since FEMA does not always use its NFIP authority in contracting for elevation data — that this result should apply even where the contractors are providing flood elevation data to FEMA under contracts funded by the Disaster Relief Fund and authorized under the Robert T. Stafford Disaster Relief and Emergency Assistance Act, 42 U.S.C §§ 5121 et seq. Insurance Agents. Simplifying the process of writing flood insurance for insurance agents is the primary purpose behind creation of the registry. Insurance agents have been held liable in some cases for providing their clients with misinformation about the risk of flooding and the availability of flood insurance, and establishment of the registry may reduce the overall number of situations in which misinformation is provided by simplifying acquisition of flood risk data. Where the registry is inaccurate, agents will be disseminating the inaccurate information taken from the registry (we presume unknowingly), rating insurance policies based on the inaccurate information, and perhaps even discouraging applicants from insuring property. These actions might lead to liability to which the agent would not have been exposed had the registry not existed. In the absence of a registry, perhaps the agent would have required the owner to provide a new elevation certificate from a licensed engineer. The basis of an action against an agent in this context would be that the agent owed a duty of professional care to the insured, and that the agent did not act with the required level of care by relying on the registry. While this possibility cannot be completely discarded — particularly if there were evidence that the agent knew that the registry information for the property at issue was incorrect — we believe that the possible increased risk to the agent is relatively small compared to the significant benefits of the program. The primary purpose of FEMA in establishing the registry is to provide agents with available information about the elevation of structures, which will reduce the complexity and expense of the NFIP application process and the proper determination of premium. We therefore anticipate that FEMA would encourage and even require agents to use this database in order to write policies. Since FEMA is the federal agency charged with creating and administering the entire flood insurance program, its judgments and policies asserting the level of care that it demands for agents writing NFIP policies would likely be given deference. Engineer/Surveyors: Engineers or surveyors not working as FEMA contractors supply elevation data under contract to various clients, including home owners and developers, and also to local governments. Engineers or surveyors clearly have potential liability to their clients — unaffected by the existence of a registry — for errors in the elevation surveys they provide, with the extent of liability dependent on the scope of their work and whether it was performed in accordance with professional standards of care. The law is mixed on the degree to which engineers or surveyors might also be liable to persons who were not parties to the professional services contract but who nonetheless rely upon the erroneous data to their detriment. For example, two years ago one Georgia court dismissed a homebuyer’s lawsuit against a surveyor because the survey had been requested by the closing attorney at the request of a lender; the court held that the homebuyer (who had paid for the survey as part of “closing costs”) was not a party to the contract. Another Georgia court held six years earlier that a homeowner could sue an engineer in very similar circumstances. In any event, for a third party to incur liability, that party must rely upon the erroneous data about flooding and suffer damages as a result. This limits the class of potential plaintiffs to those who are parties to a contract to acquire or sell property that the engineer surveyed. Thus, even if an elevation registry made available to everyone in the country, via the Internet, an engineer’s faulty elevation work, the liability of the engineer would be limited to those for whom there was potential exposure prior to the creation of the registry: those who might base decisions to buy, sell, or develop particular property on the engineer’s work. In consequence, establishing the registry should not significantly increase engineers’ and surveyors’ liability for erroneous work. Local Governments/Communities. At present, local governments participating in the NFIP obtain and use elevation data in a number of ways. Elevation certificates may be obtained from property owners seeking development or construction permits. Communities may contract for elevation data, either to provide data in support of FEMA flood map changes, to aid in recovery from a flood disaster, or as part of community land use planning efforts. Although for some purposes no particular format for elevation information is required, for the roughly 1000 communities in the CRS (in which a majority of NFIP flood policies are located) communities must maintain the elevation certificates in a particular FEMA approved form, and they are required to make these forms available to the public. In administering their responsibilities under the NFIP to adopt and enforce flood plain management regulations, local governments have been subject to litigation arising out of errors in flood zone and elevation determinations on individual structures. In the reported cases reviewed for this study, the local government escaped liability, either on sovereign immunity grounds (based on an exemption from the state tort claims act for unintentional misrepresentation ) or based on a finding of no negligence. Nonetheless, in some states local governments do not have sovereign immunity, and it is certainly possible that a court could find a community liable for a community’s negligence in providing data to a property owner who suffers losses as a result. The question we address here is whether the likelihood of a finding of liability against a community would be increased if an elevation registry were established. Potential liability attributable to the registry would depend on whether the community is the source of inaccurate information in the registry, or whether the community had or should have had information suggesting that the registry information was inaccurate, but relied on the registry anyway. In the former case, increased liability from providing inaccurate information to the registry is likely to be small: the community’s exposure is created primarily from the unreliability of information in its own files, upon which buyers or owners would presumably have relied on in any event, rather than its transfer of the information to FEMA where others might have access to the inaccurate information, but are less likely to be parties in interest. This is particularly true in CRS communities, which are already under an obligation to make elevation data publicly available. As to the latter case, we note that FEMA does not intend to revise its flood plain management regulations requiring communities to obtain actual elevation certificates in issuing construction permits. FEMA recognizes that some of the data that will be placed in the registry will be obtained from sources, which have greater margins of error than those present in elevation certificates. A community might nonetheless issue construction permits based on data in the registry (even though issuance is contrary to its ordinances adopted to comply with FEMA’s floodplain management regulations). Should it do so, in violation of federal floodplain management guidelines and its own ordinances, and should the property owner subsequently encounter significant losses due to flooding, a court could conceivably find the community liable due to its failure to follow its ordinances and to review an accurate Elevation Certificate before allowing construction. Lenders. Federally sponsored lenders have an important role in the Flood Insurance program: they are required to determine whether a property is or is not in an area mapped by FEMA as a special flood hazard area. If the area is in an SFHA, the lender is required to ensure that the property has flood insurance. Lenders are required to document their flood hazard determination on a Flood Hazard Determination Form, which includes information on the flood zone a property is in, but does not include information about the elevation of particular structures. Where a property is found in a SFHA, the lender must require that there be flood insurance, and elevation information may well be available to the lender as part of the process of ensuring that the owner obtains a flood insurance policy. However, there is no decision for which the lender is responsible that requires elevation information and lenders would not normally have information in their files that would be provided to the registry. Accordingly, the registry would not obtain information from lenders and hence would not create possible liability for lenders by publicizing incorrect information in their files. Lenders could conceivably suffer liability by using information from (rather than supplying information to) the registry for their required flood determinations. However, this liability would appear to be self-inflicted. First, FEMA intends to advise users of the registry that it is designed for and should be used for determination of flood insurance premiums and not for other purposes. Second, to use the registry in this manner, it appears that lenders would have to ignore an existing statutory requirement that a lender may provide for the acquisition or determination of such information [regarding location of a property in an SFHA] by a person other than the lender (or other person) only to the extent that such person guarantees the accuracy of the information. The registry with its notice that it should be used only for purposes of determining premium would not carry with it the guarantee required by the statute. Home Sellers and Real Estate Agents. Home sellers and real estate agents have been held liable to buyers when they provide false or misleading information about the flood risk of a property. Occasionally, liability attaches where the seller or agent knew that the property was in a flood zone or had recently flooded, and failed to so advise the buyer. Liability in these cases is almost exclusively determined by state law. Some states maintain a ‘buyer beware’ policy backed up by provisions in standard real estate purchase contracts; other states require sellers to disclose flood risk information to the buyer. If the registry shows flood risk to be low, and buyers rely on the data in the registry in purchasing property, it might be possible, in some states, for a buyer to hold a seller liable if the buyer could show that the seller knew, and failed to advise the buyer, of significant flood risk. Other scenarios of liability, involving a seller breaching a duty to provide flood risk information to the buyer are possible. The importance of any increase in liability generated by scenarios of this type should of course be weighed against the potential reduction of litigation that would ensue if real estate agents (working with insurance agents) and buyers were aware of and reviewed information in the registry before making purchase decisions. Liability Summary: As these brief observations on liability show, we do not believe that the creation of an elevation registry would generate a major increase in liability for any of the groups who create elevation data or use elevation data for insurance, floodplain management, property acquisition, or development purposes. Our belief that the registry would not cause significant new liability exposure recognizes that litigation is unpredictable: there may be a factual pattern in which reliance on erroneous information in the registry gives rise to real economic loss and to major monetary damages due to flood, causing a court in egregious situations to hold liable an entity that it finds to be responsible for the losses. Our analysis of liability issues highlights the importance — if an elevation registry is created — of populating the registry with accurate data. At present, the principal data published by the NFIP are in the form of maps. While these maps are not always accurate, they are adopted by FEMA after review of all available data and after providing opportunities for comment and appeal. From a regulatory point of view, what is critical is that these maps are the maps adopted by the agency charged by statute with administering the flood program. The existence of an insurance requirement for a property does not, under the law, depend on what its actual risk of flooding is, but on whether the property is located in an area that is mapped by FEMA as a Special Flood Hazard Area. An elevation registry provides a source of information about the flood risk of particular properties in addition to that provided by FEMA’s maps. It is quite possible, with two different sources of elevation and map data (the FIRM and the registry) and the lapse of time between generation of the two types of data, that information about a property in the registry will not be consistent with information on the official FEMA flood map for the area in which the property is located. Inconsistency in “official” elevation information will give rise to claims that an agent, lender, owner, or community relied on the wrong information, or, at a minimum, that a party should have investigated the discrepancy. Inconsistency will breed disputes and litigation. This concern is best addressed by (1) including a disclaimer as to the use to which registry information is to be put; (2) carefully designing the procedures to be used in collecting registry information and the standard for determining reliability of data that will populate the registry; and (3) establishing a workable procedure to allowing for corrections to registry data at the request of property owners, insurance agents, and communities. 4. Legal Effect of Electronic Signatures and Verification Under current FEMA insurance manuals, when insurance agents submit NFIP insurance policy applications for certain properties, they are required by FEMA to obtain an Elevation Certificate and to attach it to the application. The Elevation Certificate, in turn, must bear the normal signature and seal of a licensed Professional Engineer or other qualified certifier. The registry will capture only data fields (such as address, map panels, and elevation data itself). It almost certainly will not capture the signature and seal of the many engineers and surveyors who determined the elevations of the millions of structures that will be included in the registry. Accordingly, the Task Order requested that the EOP Foundation review how elevation data in an elevation registry should, “be certified for accuracy when [FEMA does not] have the normal signature and seal of a Professional Surveyor or other qualified certifier”, and further requested EOP Foundation to “evaluate the need for and legality of ‘electronic’ signatures.” We first review the federal legislation applicable to electronic signatures. E-SIGN. In an effort to encourage uniform standards affecting electronic transactions, the U.S. Congress enacted the Electronic Signatures In Global and National Commerce Act (E-SIGN). E-SIGN governs transactions that involve international or interstate commerce. E-SIGN preempts state law, but permits itself to be partially overridden by comparable state legislation. By enacting E- SIGN, Congress intended to promote the acceptance and use of electronic signatures. The central provision of E-SIGN validates the legitimacy of electronic signatures in interstate commerce. Notwithstanding any statute, regulation, or other rule of law . . . with respect to any transaction in or affecting interstate or foreign commerce— (1) a signature, contract, or other record relating to such transaction may not be denied legal effect, validity, or enforceability solely because it is in electronic form; and (2) a contract relating to such transaction may not be denied legal effect, validity, or enforceability solely because an electronic signature or electronic record was used in its formation. An “electronic record” under the statute, is a contract or other record created, sent, communicated , received, or stored by electronic means. An “electronic signature” is “an electronic sound, symbol, or process, attached to or logically associated with a contract or other record and executed or adopted by a person with the intent to sign the record.” In other words, an electronic signature can be as simple as an e-mail message or fax from one person to another agreeing to a contract, or as complex as a technologically sophisticated digital signature. E-SIGN also specifically authorizes the electronic notarization, acknowledgment, or verification of documents. Congress specifically identified insurance as a business to which E-SIGN was intended to apply. Thus, electronic signatures and verification apply to the creation of contracts of insurance between private parties under the National Flood Insurance Program. The federal government is not required to accept electronic signatures for contracts to which it is a party. State E-Commerce Laws. E-SIGN applies to transactions involving interstate and international commerce. However, states may modify, limit, or supersede the provisions of E-SIGN by enacting the Uniform Electronic Transactions Act (UETA), which was issued by the National Conference of Commissioners of Uniform State Laws (NCCUSL) in 1999. Either a state version of UETA that deviates from the model law, or passage of an electronic signature law not based on UETA, such that the state law is inconsistent with E-SIGN, could be preempted by E-SIGN. California, Florida, Louisiana, and North Carolina have all adopted versions of UETA, but with varying levels of uniformity. The model UETA is comparable to E-SIGN in that it specifically legitimizes electronic records, contracts, and signatures. UETA also provides for electronic verification: If a law requires a signature or record to be notarized, acknowledged, verified, or made under oath, the requirement is satisfied if the electronic signature of the person authorized to perform those acts, together with all other information required to be included by other applicable law, is attached to or logically associated with the signature or record. UETA also permits state agencies to decide whether or not to accept and use electronic signatures. California. California passed a non-uniform version of UETA that included significant consumer protection provisions making it likely that the California UETA will be susceptible to federal preemption. In addition to their UETA law, California has also grappled with digital signatures. The California digital signature law provides that the use of a digital signature shall have the same force and effect as the use of a manual signature if and only if it embodies all of the following attributes: (1) It is unique to the person using it. (2) It is capable of verification. (3) It is under the sole control of the person using it. (4) It is linked to data in such a manner that if the data are changed, the digital signature is invalidated. (5) It conforms to regulations adopted by the Secretary of State. The California statute does not mandate the use of or digital signatures by state agencies: The use or acceptance of a digital signature shall be at the option of the parties. Nothing in this section shall require a public entity to use or permit the use of a digital signature. California, of course, cannot mandate the acceptance of electronic signatures or digital signatures by the federal government. Florida. Florida enacted UETA on May 26, 2000. The Florida law contains all of the provisions in the uniform UETA, including provisions relating to notaries, the time and place of sending and receiving electronic records, and the acceptance and distribution of electronic records by governmental agencies. Louisiana. Louisiana’s version of UETA was enacted on June 1, 2001. The Louisiana statute includes the uniform provisions discussed above. North Carolina. North Carolina enacted its version of UETA on August 2, 2000, and amended its law on April 5, 2001. The North Carolina statute includes the uniform provisions discussed above except for the provision allowing state agencies to decide whether or not to use and recognize electronic signatures. The state’s Electronic Commerce Act, enacted in 1998, allows for the use of electronic signatures by public agencies. The Act provides for the legal validity and enforceability of electronic signatures, as well as their admissibility into evidence. In sum, under E-SIGN and state UETA laws, records and signatures may not be denied legal effect solely because they are in electronic format. If a law requires a record to be in writing, an electronic record satisfies the law, and an electronic signature satisfies a legal requirement for a signature. Electronic signatures may be used when a law requires a signature or record to be notarized, acknowledged, verified, or made under oath. FEMA may choose to require that there be a written or electronic certification of the data for each property in the elevation registry. The method of certification could vary depending upon the method of determining the elevation: a written or paper seal for a traditional elevation certificate prepared on-site by a surveyor or inspector, or an electronic signature if obtained by airborne remote sensing, by mobile photogrammetric vans, or other methods. Whatever methods of collection FEMA chooses to authorize, it could require certification of the accuracy of the data by the individual data collector by either written or electronic means. Modification of Manual Required. Agencies are thus encouraged by law to allow use of electronic signatures in those situations where appropriate controls against counterfeit signatures are in place. However, even if this were not the case, FEMA would not be precluded from establishing the registry and requesting that insurance agents use the registry in submitting NFIP insurance applications. As noted above, FEMA requires that agents “attach” Elevation Certificates to those applications where elevation affects premium. FEMA mandates use of a FEMA approved form Elevation Certificate, which must be signed by a registered and state-licensed engineer or surveyor. In order for the registry to be established, FEMA must modify its Flood Insurance Manual to allow agents to rely on data from the registry in lieu of a signed Elevation Certificate. Once this change is made, it is of no particular legal consequence whether the data provided to FEMA and placed in the registry was (1) provided in an original Elevation Certificate with original signature and seal; (2) provided in a photocopy of an original Elevation Certificate; (3) transmitted to FEMA in electronic form with an electronic signature; or (4) developed using remote sensing techniques. We expect FEMA would establish quality control procedures to assure the validity of the data placed in the registry, as well as documentation and audit procedures to assure that agents in fact obtain and properly interpret elevation data from the registry when rating policies. LEGAL ISSUES RELATED TO STRATEGIES FOR ACQUISITION OF DATA As this project advances, Dewberry & Davis will be evaluating five different strategies for obtaining data to populate the registry. Each of these strategies raises somewhat different legal issues. The remainder of this report reviews the principal legal issues raised by the five strategies; some of the analysis is clearly applicable to more than one strategy. Strategy A: Maximize use of existing Elevation Certificates to populate the elevation registry. 1. FEMA authority to request, but not require, holders to provide elevation data Congress has authorized FEMA to request elevation data from private entities and from state and local governments. Insurance Companies and Agents. With respect to insurance companies and agents, FEMA has express authority to [e]nter into any contracts, agreements, or other appropriate arrangements which may, from time to time, be necessary for the purpose of utilizing, on such terms and conditions as may be agreed upon, the facilities and services of any insurance companies or other insurers, insurance agents and brokers, or insurance adjustment organizations Under the WYO arrangement, the insurance companies write the policies and collect a percentage of the premiums, while FEMA underwrites the risk. With respect to insurance companies operating under the WYO arrangement, FEMA already requires that companies comply with the WYO Transaction Record Reporting and Processing Plan, under which the WYO provides to FEMA monthly data tapes of transactions (such as new policies written, existing policies renewed, and claims activity). The WYO Arrangement — which is promulgated as a federal regulation and agreed to by companies participating in the WYO Program — itself provides that [t]he Company shall furnish to FEMA such information and analyses of information including claim file information, and property address, location, and/or site information in its records as may be necessary to carry out the purposes of the National Flood Insurance Act of 1968, as amended, in such form as the FIA [Federal Insurance Administration], in cooperation with the Company, shall prescribe. Elevation information held by insurance agents and companies was originally obtained, and is maintained, because it is necessary to determine proper premiums. Elevation information is clearly “necessary to carry out the purposes of the National Flood Insurance Act.” Accordingly, FEMA is authorized to revise its Transaction Record Reporting and Processing Plan and the WYO arrangement (in consultation with the WYO Companies) in order to request this data from the WYO companies and agents. However, actual collection of the data may be expensive, since the data might not have previously been requested or required by FEMA, and, therefore, might not be held in centralized locations, or in compatible formats, or captured electronically in the first place. The increased cost to WYO companies could trigger requests for adjustment of the expenses allowed to be retained by the WYO Companies under the WYO Arrangement. State and Local Governments. FEMA also has broad authority to request specific elevation data from state and local governments. The NFIA expressly provides that FEMA Director can ? consult with, receive information from, and enter any agreements with …the head of any State or local agency …in order that he may … identify and publish information with respect to all floodplain areas …which have special flood hazards …and …establish or update flood-risk zone data in all such areas, and make estimates with respect to the rates of probable flood caused loss” ? undertake and carry out studies and investigations, and receive or exchange such information as may be necessary to estimate, and shall from time to time estimate, on an area, subdivision, or other appropriate basis (1) the risk premium rates for flood insurance. If FEMA can request data from state and local governments, are there significant legal (non-budgetary) restrictions on these governments’ ability to provide this information to FEMA? We believe that there are not. To illustrate, we focus this review primarily on a brief review of applicable laws in Florida, North Carolina, Louisiana, and California. First, records held by local governments in each of these states — and we believe in virtually all of the other states — are subject to laws governing the inspection of public records. Each of the four states we have been asked to review has a public records act, which requires that governmental agencies make available for inspection or copying records about the conduct of public business. Information about structural elevation is not among those items exempted from disclosure requirements. We have not found any state statute that otherwise prevents the disclosure of elevation data. Therefore, although we have not scoured every State code, our belief is that there is no impediment in principle to obtaining elevation information. We do not expect, however, that FEMA would rely on state freedom of information laws to request elevation data. In practice, FEMA would most likely conclude agreements with the States about the type of information FEMA wishes to collect, the formats compatible with state data systems, and the appropriate schedule on which the information would be collected. FEMA should be aware that any collection of information, whether mandatory or voluntary, that involves ten or more persons (including individuals, companies, or State or local governments), is subject to the administrative requirements of the Paperwork Reduction Act, which has a six month minimum lead time for new or expanded collections of information. The operation of FEMA’s Community Rating System confirms our view that there are not significant legal obstacles to communities making available elevation data they have obtained in carrying out their floodplain management responsibilities. Every local community participating in the Community Rating System must maintain in its files flood elevation certificates for new structures or construction in the SFHA built from the time the community first submits its application for the CRS program. Further, the elevation certificates must use the prescribed FEMA form, and the community must make the certificates available to any requester. About 1000 communities participate in the CRS, and these communities encompass a significant percentage of all of the properties insured under the NFIP. Since the certificates must be available to every requester, there would be no legal impediment to FEMA requesting and reviewing the data in the communities’ files. (However, FEMA may find review of data in each community’s files impracticable.) In addition, under the CRS, “extra credit” is available if the community’s “elevation and flood-proofing certificate data are kept in computer format and provided to FEMA each year.” So, FEMA may already be collecting a significant portion of available elevation data originating in CRS communities. The decision of many communities participating in the NFIP to join CRS indicates, consistent with our review of state public records laws, that those communities have not found it legally impermissible to make elevation data publicly available. We recognize that it is theoretically possible that some communities have hesitated to join the CRS because they face, or believe that they face, a legal obstacle under local law to disclosing elevation data that participating CRS communities in their state do not face. However, since public records laws and exemptions to public records laws are generally adopted by states, and generally apply to all governmental jurisdictions within the state, we believe that this theoretical possibility is unlikely to reflect a real legal concern. 2. FEMA Authority to Require Submission of Elevation Data Our analysis to this point focused on legal restrictions on FEMA’s ability to request, but not require, elevation data held principally by FEMA contractors, insurance agents, WYO companies, and communities. We have also reviewed whether there may be legal obstacles preventing these entities from providing elevation data to FEMA voluntarily at FEMA’s request. We now examine FEMA’s potential ability to mandate that these entities provide existing data. The short answer is “FEMA cannot,” at least unless FEMA arranges to compensate sources for the cost of providing the data. We review briefly the analysis for the different entities that may hold elevation certificate data. FEMA Contractors. Except in rare instances not applicable here, FEMA does not have authority to mandate private entities to enter into contracts. The contractor’s obligations to provide data developed under the contract will be determined by the contract itself. We have not reviewed the scope of work of the FEMA contractors who might hold elevation certificates, but it is common practice for government contracts to include a requirement that the contractor provide the government, on request, with whatever information and documents were generated or obtained in performance under the contract. Accordingly, it is quite probable that FEMA can “require” that its contractors holding elevation certificates or elevation data provide the certificates or data to FEMA. However, this request would likely be considered a new task order or change in scope of work, and FEMA would likely be obligated, pursuant to the contract, to pay the cost incurred by contractors in complying with the requirement. WYO Companies. FEMA also cannot require insurance companies to act as WYO Companies; insurance companies become WYO Companies voluntarily, by agreeing to the terms and conditions of the WYO Arrangement. FEMA is authorized to enter into this arrangement only with “terms and conditions as may be agreed upon.” FEMA is authorized to amend the Arrangement, prospectively, by rule, to “require” that WYO Companies provide FEMA with elevation certificates or elevation certificate data. However, if these costs are significant, and WYO Companies do not believe that they would be compensated for incurring them, WYO Companies may simply drop out of the WYO program. Insurance Agents. FEMA has even less authority over agents than over WYO Companies to mandate submission of data, since most agents have a relationship to the NFIP only through the WYO Companies. To the extent WYO Companies are required under the Arrangement to provide elevation certificates or data, and this information is initially collected by agents, then WYO Companies can “require” agents to submit the information with any policy application or renewal. State and Local Governments. The NFIP is a voluntary program, so while there are strong incentives for communities to participate and make federal flood insurance available to their residents, no community is required to do so. In order to join the NFIP, a community must adopt “land use and control measures” consistent with “comprehensive criteria” developed by FEMA to: (1) Constrict the development of land which is exposed to flood damage where appropriate; (2) Guide the development of proposed construction away from locations which are threatened by flood hazards, (3) Assist in reducing damage caused by floods, and (4) Otherwise improve the long range land management and use of flood-prone areas. This section might be broad enough to allow FEMA, through informal rulemaking, to amend its current “comprehensive criteria” to include a requirement that communities submit to FEMA their elevation certificates or data. Absent indication that FEMA would attempt to do so, we have not analyzed this question in any depth. As with any rulemaking, the proposed and final rules would be subject to the requirements of the Administrative Procedure Act, including compliance with the Paperwork Reduction Act, review by the Office of Management and Budget under Executive Order 12866, and other regulatory analyses and certifications necessary to that process. Such a regulatory change, if imposed on State and local governments without concomitant funding, might be criticized as an “unfunded mandate.” 3. Relevance of Ownership of Elevation Certificates. FEMA has requested that we analyze the issue of “who owns the elevation data” — the owner who paid for an elevation certificate, the insurance agent or WYO company that required a certificate to rate a policy, the community that required a certificate before issuing a construction permit for a structure, or other entities. In addition, we were tasked to evaluate the degree to which persons that have elevation data derived from elevation certificates, but are not the “owners” of the certificates, may provide this data to FEMA. Newly created data will be governed by the contract under which it is collected, and FEMA can include in each data collection contract appropriate provisions regarding ownership and use of data. Further, even if “ownership” concerns exist with respect to the transfer of existing elevation data into a FEMA database, FEMA may be able to address those concerns prospectively by making appropriate changes to the language of the agreements under which it obtains elevation data from third parties. However, our review of the restrictions applicable to insurance agents, WYO companies, and state and local governments has cast substantial doubt in our minds that ascertaining the “owner” of the certificate is of any real relevance to FEMA’s ability to obtain elevation data and to place that data into the registry. With respect to retrospective elevation data, we can assume that “original” Elevation Certificates, with original signatures and bearing the seals of licensed surveyors or engineers, exist in a number of places. They are likely to have been provided originally by the engineer to the requester: a property owner, a potential buyer and developer of the property, a community that funded detailed elevation surveys in flood prone areas, or perhaps even an escrow agent or mortgage banker who arranged insurance as part of the closing of a real estate loan. This original certificate, or copy of this certificate with or without a formal certification of the copy, may then have been provided to an insurance agent (for purposes of obtaining insurance), to a community (to obtain an as built elevation certification), to FEMA or its contractors (for purposes of obtaining a Letter of Map Amendment or Revision), or to some other person. Having received a certificate from the original “holder,” the recipient insurance agent, WYO company, or government clearly has at least a right to physical possession of that certificate whether or not it “owns” that certificate. Moreover, even if the first “holder” of the certificate had a right, as “owner”, to request return of the “original” certificate from the recipient, we are confident that the recipient would have the right, if not the obligation, as a matter of audit and federal and state recordkeeping requirements, to make and keep a copy of that certificate. Further, we have already reviewed whether FEMA can request data from certificates in the files of agents, WYO companies, and participating NFIP communities, and concluded that ownership does not appear to be relevant to FEMA’s ability to acquire this data. Public record keeping laws, for example, require governments to make available for public inspection information lawfully in their files regardless of where “ownership” may reside. The issue of ownership may well be important if FEMA seeks not just the elevation data contained on elevation certificates, but physical possession of the elevation certificates themselves. We question — but have not analyzed — whether FEMA would be able to obtain physical possession of “original” elevation certificates, for example, those filed with a community’s building permit records. In any event, FEMA has advised that it has no intent to do so as part of the elevation registry project, and so we have not pursued this issue further. We have considered what other types of ownership issues might be raised where FEMA seeks data from airborne sensing, photogrammetric vans, or conventional surveys. We are aware that the owners of the technology used for remote sensing may, in an effort to preserve competitive advantage with respect to their technology, have retained some rights with respect to disclosure of their work product. However, FEMA would have the opportunity to review, evaluate, and negotiate removal of any such restrictions when entering into the contracts under which it would acquire the data. We have also considered other legal issues that might be relevant, but are more tangential to FEMA’s current objectives. For example, does a property owner have a right to control information about his or her land in the same way celebrities have a “right of publicity” in their voices, images, and “likenesses.” Does state trade secret law in some situations restrict a person from using overflight photographs, noted as a possibility in the Dow Chemical case discussed infra, . In the last few years, a particularly controversial topic of legislation involves determining the appropriate intellectual property status of complex collections of public information, which companies invest significant resources to collect and organize, and which may have significant value in the market. A database of public information is not eligible for copyright protection, but without some sort of protection for their investments, companies will be unwilling to make this type of product available. This is similar to the problem of obtaining elevation certificates, since elevation is essentially a characteristic of the earth — public information — but which is obtained only by an expert surveyor, or expensive remote sensing equipment. There is even a recent case in which the plaintiff attempted creatively to sue in “trover,” an ancient common law tort, to challenge another who appropriated, for profit, a laboriously constructed World Wide Web page. Indeed, in constructing a registry, FEMA may face variations on this private concern from flood zone determination companies who have expended considerable effort in their business of advising and certifying to mortgage lenders whether a property is or is not in an SFHA. However, the range of potential issues raised by these questions is quite broad, detailed legal research of these issues would be expensive, and the issues appear tangential to the registry. Accordingly, we have not pursued them. Strategies B & C: Maximize use of remote sensing: LIDAR, IFSAR and airborne photogrammetry; mobile photogrammetric vans. 1. Privacy Rights and Collection of Data by Remote Surveillance The remote sensing technologies that FEMA might utilize to collect elevation data include the use of aerial or "drive by" photogrammetry that uses stereo photography to measure the elevation of land or structures; Light Detection and Ranging (LIDAR), a remote sensing technology that employs eye-safe airborne laser technology to measure the elevation of land or structures; or Interferometric Synthetic Aperture Radar (IFSAR), a remote sensing technology that employs airborne radar technology to measure the elevation of land or structures. In our analysis, we have primarily reviewed the case law examining whether remote sensing by criminal or regulatory investigators violates the Fourth Amendment’s protection against warrantless searches by the government. Initially, the test for whether a search violated the Fourth Amendment asked whether physical intrusion was involved, but as technology advanced, the Supreme Court found this inquiry insufficient to deal with new kinds of privacy invasions and modified its thinking. At present, the test used by the Supreme Court is that elucidated in Katz v. United States, which considers 1) whether the person had an expectation of privacy and (2) whether society recognizes that expectation as “reasonable.” The data collection FEMA intends to conduct requires observation directly outside of and near private homes. In determining a person’s expectation of privacy in the area outside a person’s home, the Supreme Court distinguishes between the home and that area immediately adjacent to it, known as the “curtilage,” from “open fields,” which the Court has described as “any unoccupied or undeveloped area outside of the curtilage.” Within the curtilage an owner has the greatest expectation of privacy; but in an “open field” an owner has no expectation of privacy. That distinction was made clear in Oliver v. United States, in which the Court reviewed a search conducted by police officers who walked onto defendant’s property, passing “no trespassing” signs along the way, and found marijuana growing in two fenced patches in the woods behind the house. The Court explained that “[a]t common law, the curtilage is the area to which extends the intimate activity associated with the sanctity of a man's home and the privacies of life, and therefore has been considered part of the home itself for Fourth Amendment purposes.” But the Court found that the secluded woods, away from the curtilage, was an “open field,” and did not garner the same level of privacy as the curtilage. Consequently, the court found that the search by the officers was not unconstitutional. The Supreme Court has considered a number of times the application of the Katz test to remote surveillance. In the case of California v. Ciraolo, the Court held that observation by police inside the curtilage of a home was not a violation of the homeowner’s Fourth Amendment rights where the property in question was surrounded by a fence and observed with the naked eye from a fixed-wing airplane flying within FAA sanctioned airspace at 1000 feet. In the companion case of Dow Chemical Co. v. United States, decided on the same day as Ciraolo, the Court addressed a warrantless search of Dow’s Midland, Michigan, plant by the Environmental Protection Agency which “employed a commercial aerial photographer, using a standard floor-mounted, precision aerial mapping camera, to take photographs of the facility from altitudes of 12,000, 3,000, and 1,200 feet.” The camera was mounted in an airplane flying in lawful navigable airspace. The data and images collected apparently permitted, using simple magnification, identification of objects such as wires as small as 1/2-inch in diameter. The Dow Court distinguished the private activities of home from activities conducted on property used for commercial or industrial purposes, which are afforded a lesser expectation of privacy. In holding the search constitutional, the Court pointed out that warrantless government observations of workplaces are less likely to violate the Fourth Amendment. Significantly, the Dow Court relied on the fact that even though equipment was used to enhance human vision, the images “remain limited to an outline of the facility's buildings and equipment.” The Court also took into account the fact that the EPA was conducting a legitimate compliance investigation under the Clean Air Act. Just three years after those cases, the Court ruled in Florida v. Riley, another aerial observation case, that activities conducted in plain view, even within the curtilage of one’s home, will generally not be protected. In the course of deciding remote surveillance cases, the courts have distinguished among places, equipment, behaviors, and circumstances for which there is a reasonable expectation of privacy and for which an expectation of privacy is less reasonable. Eight different elements of the courts’ reasoning are apparent. An expectation of privacy is less likely to be present in an “open field” than within the “curtilage” of a property;” in a commercial establishment than in a private home; when observation is conducted remotely as opposed to where there is a physical intrusion; when carried out from a location where the government agents have a legal right to be (e.g. navigable air space), rather than when they are trespassing; where the observation collects information exclusively about activities outside the buildings rather than where it also collects information about the activities inside; where the government is carrying out a regulatory activity authorized by statute rather than conducting a “fishing expedition” to develop leads for possible investigation; where human observation or readily available equipment is employed rather than advanced sensory enhancing technologies not generally available to the public; where the observed have not taken measures to avoid the loss of privacy, rather than where they have taken such measures. Of these eight different elements, which assist a court in deciding whether surveillance is lawful under the Fourth Amendment, FEMA’s proposed data collection for the elevation registry would lean toward a greater expectation of privacy in only two of the elements. FEMA’s collection of structural elevation data would certainly be an observation inside the “curtilage” of some private homes. While some of the structures mapped may be commercial, the mapping will surely include homes, which are afforded the highest expectation of privacy under the Constitution. The area that is required to be surveyed is that area immediately adjacent to a structure — the lowest adjacent grade and the highest adjacent grade. For this part of the analysis, we assume that there would be no physical intrusions onto the land of any homeowner. Instead, FEMA will collect data exclusively outside of the structures and with readily available commercial photogrammetric equipment. The collection of new elevation data would be carried out by FEMA under its authority to run the National Flood Insurance Program, and as such, FEMA would be conducting a statutorily authorized function from legal, navigable airspace or from a public street. As for the last element — whether property owners will try to prevent FEMA from mapping the correct elevation of their land — it is certainly possible, but very unlikely. It is possible that property owners will have erected fences or coverings over their yards, or that there are other impediments to observation. However, as we discussed in the previous section on the Privacy At of 1974, the type of data being collected is not the sort of information the Court seems concerned with protecting—the private activities and behaviors of people. Elevation data is for the most part about the characteristics of property, and probably not the sort of thing that could or should be protected from observation even if a homeowner wanted to protect that information for some reason. So far, the Supreme Court has not yet decided a case with facts that exactly match the activities FEMA proposes to engage in to collect data for the elevation registry. In Dow, the Court approved surveillance of a commercial property using sophisticated, although commercially available, vision-enhancing equipment. In Ciraolo and Riley, the Court approved surveillance with the naked eye inside the curtilage of private homes from within navigable airspace, even as close as 400 feet. The states, too, have subtle differences in how they interpret their own constitutions with respect to government searches. It is true that in Dow, the Court suggested that were the government to use equipment generally not available to the public in surveillance of a private home, they might decide differently. But the Court did not consider the camera at issue in the case to fall into that category, inasmuch as the Court referred to it as “a conventional, albeit precise, commercial camera commonly used in mapmaking.” Therefore, we think it unlikely that, even should the Court grant certiorari in a case involving photogrammetric surveillance of private homes by the police, it would find that activity unconstitutional. We believe that collecting the proposed registry data using photogrammetry from the air or street would be permissible. Even though FEMA would not be making observations solely with the use of the naked eye, the Supreme Court sanctioned the use of a $22,000 aerial camera in the Dow case as being, a “common” and “standard” tool for mapmaking. Like EPA’s reliance in Dow on its statutory authority to conduct Clean Air Act investigations, FEMA would be conducting the collection of structural elevation data under its authority to make maps to support the National Flood Insurance Program. Strategy D: Utilize conventional/GPS surveys only when necessary, because these cost the most and would be unaffordable for FEMA to pay for nation-wide coverage with Elevation Certificates. We have been advised that the remote sensing techniques of Strategies B and C are likely to be adequate for much of the information required for insurance purposes, and may even be quite accurate for some properties. However, some information — such as whether a property has a walkout basement — is difficult to see remotely. Strategy D will evaluate a ‘compromise’ technique under which remote sensing would be used rapidly to gather most of the required information, but with a very brief “walk on” to the property by the engineer or surveyor to confirm structural data not visible from the street. These physical intrusions onto private property by agents of the federal government raise substantial legal issues: physically entering onto private property for this purpose without consent may be considered a possible criminal or civil trespass or tortious invasion of privacy. As more fully set forth in the discussion below, there are a number of limitations and defenses to the criminal and civil actions in trespass or for invasion of privacy. As a result there may be relatively low risk that agents of the federal government would be prosecuted or would suffer liability for damages from entering onto private property very briefly to render more accurate elevation surveys. Nonetheless, a federal agency may and should have some hesitation in directing its agents to enter on the private property of hundreds of thousands of homeowners without the consent of those owners and without a clear statutory authorization to do so. Even if legal defenses were available to actions in trespass or for invasions of privacy, as a matter of policy the agency would want to obtain an owner’s consent where possible, to provide landowners with advance notice of when inspections will occur, and to obtain concurrence and perhaps even participation in the inspection program from participating NFIP communities. 1. Basic Elements of Trespass. We first review applicability of trespass law in California, Florida, Louisiana, and North Carolina. In each of the four states, the unauthorized entry upon a landowner’s property can be a tort giving rise to a potential action for damages or a misdemeanor under the state’s criminal law, or perhaps both. California: Criminal Law. California’ criminal trespass law contains 21 separate offenses; the ones most relevant to this inquiry are (a) entering on real property marked by no trespassing signs; (b) entering on real property and refusing to leave at the request of the owner, and (c) entering and occupying real property or structures of any kind without the consent of the owner, the owner's agent, or the person in lawful possession. These offenses are misdemeanors. California: Civil Liability. The Supreme Court of California has held that, unless a defendant causes actual damage to the land, he cannot be held liable for trespass. However, California law proscribes as a “nuisance” the obstruction or free use of property, so as to interfere with the comfortable enjoyment of life or property. The California courts have interpreted the law of nuisance much more broadly, allowing recovery for discomfort or annoyance. In Judson v. Los Angeles Suburban Gas Co., the landowner complained that the fumes, smoke, noxious odors, and noise emanating from a gasworks owned by the gas company interfered with the use and enjoyment of his property. The court held that causing a landowner mere annoyance was sufficient to justify liability for nuisance. The fact that respondent proved no damage to the dwelling-house or herbage on his land or to the rental, or vendible value of the property, does not prevent the court from awarding damages. In the very nature of things the amount of detriment sustained is not susceptible of exact pecuniary computation. It is for the court to say what sum of money the plaintiff should receive in view of the discomfort or annoyance to which he has been subjected. Florida: Criminal Law. Under Florida law, “[a] person who, without being authorized, licensed, or invited, wilfully enters upon or remains in any property other than a structure or conveyance: As to which notice against entering or remaining is given . . . commits the offense of trespass . . . ”, which offense is a misdemeanor. Florida: Civil Liability. The plaintiff in Coddington v. Staab alleged that the defendant entered plaintiff’s apartment without plaintiff’s consent and destroyed property in the apartment. The Court held that “[t]respass to real property has been defined as ‘an unauthorized entry onto another's property,’” and that the measure of damages is the loss of use and enjoyment of the land. The Coddington case involved a tenant. The Court held the measure of damages for the landowner in Stockman v. Duke to be the difference in value of the land before and after the trespass. Louisiana: Criminal Law. Under Louisiana law, “[n]o person shall intentionally enter immovable property owned by another: (1) when he knows his entry is unauthorized, or (2) under circumstances where he reasonably should know his entry is unauthorized.” Violation of the trespassing provision is a misdemeanor. However, registered land surveyors are exempt from this law. Louisiana: Civil Liability. The Louisiana Civil Code provides that, “every act whatever of man that causes damage to another obliges him by whose fault it happened to repair it.” The tort of trespass is defined as the unlawful physical invasion of the property of another. A trespasser is one who goes on another's property without the other's consent. Louisiana courts permit the recovery for the tort of trespass as a means to correct the damage caused when an owner is unjustly deprived of the use and enjoyment of his or her land. However, damages which cause mere discomfort, disturbance, inconvenience, and even sometimes financial loss as an ordinary and general consequence of public improvements are not compensable, and are considered damnum absque injuria, loss without a legal remedy. North Carolina: Criminal Law. Under North Carolina law, “[a] person commits the offense of second degree trespass if, without authorization, he enters or remains on premises of another: (1) After he has been notified not to enter or remain there by the owner, by a person in charge of the premises, by a lawful occupant, or by another authorized person.” Violation of the trespassing provision is a misdemeanor. North Carolina: Civil Liability. The elements of a trespass claim in North Carolina are that a defendant made an unauthorized entry on land of which plaintiff was in possession at the time of the alleged trespass, and that plaintiff was damaged by the alleged invasion of his rights of possession. To succeed in a nuisance claim, plaintiffs must show an unreasonable interference with the use and enjoyment of their property. An intentional invasion or interference occurs when a person acts with the purpose to invade another's interest in the use and enjoyment of their land, or knows that it will result, or will substantially result. An intentional invasion or interference, however, is not always unreasonable. The factors bearing on whether an invasion is unreasonable include: (1) the surroundings and conditions under which defendant's conduct is maintained, (2) the character of the neighborhood, (3) the nature, utility and social value of defendant's operation, (4)the nature, utility and social value of plaintiffs' use and enjoyment which have been invaded, (5) the suitability of the locality for defendant's operation, (6) the suitability of the locality for the use plaintiffs make of their property, (7) the extent, nature and frequency of the harm to plaintiffs' interest, (8) the priority of occupation as between the parties, (9) and other considerations arising upon the evidence. No single factor is decisive; all the circumstances in the particular case must be considered. To be actionable, "the interference must be substantial and unreasonable. Substantial simply means a significant harm to the plaintiff and unreasonable means that it would not be reasonable to permit the defendant to cause such an amount of harm intentionally without compensating for it." Once plaintiff establishes that the invasion or intrusion is unreasonable, the plaintiff must prove the invasion caused substantial injury to his or her property interest. 2. No Trespass if Landowner Consents to Entry; Ability to Condition Policy Issuance and Renewal on Consent to Entry. Our brief summary of trespass law demonstrates that a fundamental element of criminal and civil trespass is the absence of authorization, either by the owner of land, or by law. If the owner gives consent, entry is no longer “unauthorized” and no trespass action can lie. Accordingly, FEMA’s ability to obtain consent from the owners of significant numbers of properties in the flood plain, in an administratively feasible manner, eliminates any potential trespass claims from those owners. FEMA has clear authority to determine the conditions under which it will extend flood insurance coverage. At present, FEMA already requires, as a condition of extending coverage, that Elevation Certificates be obtained by any applicant for insurance in post FIRM properties in the SFHA; to obtain the Certificate, the owner must allow a registered surveyor to enter onto the property to be insured. (One of the principal purposes of the elevation registry proposal is to reduce the burden of this “condition” by allowing policies to be issued by merely checking elevation data in the data base rather than by requiring the owner, at an expense of several hundred dollars, to contract with a registered surveyor to create a new certificate.) FEMA could, when implementing the registry, add, by regulation, a new condition to the Standard Flood Insurance Policy specifying that the policy holder consents to inspection of conditions, such as structure elevation, relevant to rating of the policy. FEMA already has exercised its “conditioning” authority under the NFIP to “require” a policy holder to provide other information needed for issuance and administration of flood insurance policies — including information derived from “inspections”. The policy specifies that after a loss, the owner must “cooperate with the adjuster or representative in the investigation of the claim,” and, if requested, “[s]how [the insurer] or our representative the damaged property.” The policy also specifies that “[i]n connection with the renewal of this policy, we may ask you during the policy term to certify, on a Recertification Questionnaire we will provide to you, the rating information used to rate your most recent application for or renewal for insurance.” FEMA’s pilot inspection procedure applicable in Monroe County and in the city of Islamorada, Florida, further demonstrates FEMA’s ability to require new or renewing insurance applicants to consent to inspections as a condition of flood insurance. The Standard Flood Insurance Policy issued in Monroe County and the City of Islamorada now includes language advising policy holders that during the several years that this inspection program will be in place, you may be required to obtain and submit an inspection report from your community certifying whether or not your insured property is in compliance with the community’s floodplain management ordinance before you can renew your policy. This requirement was formally proposed in 1999 and promulgated one year later. We have already noted that FEMA must revise its rules in order to implement the elevation registry; FEMA currently requires that agents obtain Elevation Certificates to rate certain policies, and the registry cannot fulfill its purpose unless insurance agents are allowed to rely on the registry instead. In the rulemaking proceeding making this change, FEMA could and we believe should include language in which the insured consents to inspections of structural information relevant to flood risk during the term of the policy. 3. Trespass Issues where Consent Not Obtained Placing language authorizing elevation inspections of insured property in the SFIP will not generate any consent from uninsured property. We next review potential sources of authorization — other than the owner’s consent — to enter land., No Clear Entry Authority: Federal. First, we note that the flood insurance program’s extensive information gathering authorities nowhere state that FEMA can command, or mandate, a person to provide information or to allow entry onto property against his or her will. Rather, these provisions only give FEMA the ability to conduct “studies and investigations” and “receive or exchange data” relevant to flood insurance premiums, and to “make arrangements” with federal agencies, state and local agencies, or persons or private firms in order to obtain information about flood risk. Varied Entry Authority for Surveys: State. If state or local government entities have broad authority to enter upon private property for purposes of surveying flood risk, FEMA could “make arrangements” with these state or local governments under which they could obtain elevation data exercising their own authority and provide it to FEMA. However, a cursory review of inspection or survey authorities in four states suggests that most jurisdictions do not have inspection or survey authorities broad enough affirmatively to authorize entry onto private property for elevation determinations. We have selected from some of the state inspection and survey authorities to illustrate their scope and limitations. Florida. In the state of Florida, local governments have a number of inspection authorities applying principally to enforcement of varying types of public health and safety codes — but it appears that this inspection right is limited. For example, government bodies have clear authority to inspect assisted living facilities, but cannot inspect the residential unit of occupants of the nursing home without the resident’s (or their representative’s) consent. Indeed, FEMA’s Pilot Inspection procedure in Monroe County was promulgated in response to the position taken by Monroe County and the City of Islamorada that under Florida law they do not have authority to inspect owner-occupied primary residences. Florida’s statutes provide: An inspection warrant shall be issued only upon cause, supported by affidavit, particularly describing the place, dwelling, structure, or premises to be inspected and the purpose for which the inspection is to be made. In addition, the affidavit shall contain a statement that consent to inspected has been sought and refused or a statement setting forth facts or circumstances reasonably justifying the failure to seek such consent. Owner-occupied family residences are exempt from the provisions of this act. Given the exemption for owner-occupied residences from this general inspection statute and the position taken by local governments in Florida in the Monroe County rulemaking proceeding, it appears unlikely that FEMA will be able to use local government rights of entry to authorize the “walk on” inspections contemplated by Strategy D. California. California appears to have a somewhat broader inspection warrant procedure. California’s Civil Procedure Code provides generally for “inspection warrants” relating to a wide variety of local laws and regulations: An inspection pursuant to this warrant may not be made between 6:00 p.m. of any day and 8:00 a.m. of the succeeding day, nor in the absence of an owner or occupant of the particular place, dwelling, structure, premises, or vehicle unless specifically authorized by the judge upon a showing that such authority is reasonably necessary to effectuate the purpose of the regulation being enforced. An inspection pursuant to a warrant shall not be made by means of forcible entry, except that the judge may expressly authorize a forcible entry where facts are shown sufficient to create a reasonable suspicion of a violation of a state or local law or regulation relating to building, fire, safety, plumbing, electrical, health, labor, or zoning, which, if such violation existed, would be an immediate threat to health or safety, or where facts are shown establishing that reasonable attempts to serve a previous warrant have been unsuccessful. Where prior consent has been sought and refused, notice that a warrant has been issued must be given at least 24 hours before the warrant is executed, unless the judge finds that immediate execution is reasonably necessary in the circumstances shown. Again, this procedure — which relates inspections to determine compliance with a “regulation being enforced” — would not necessarily be available for purposes of assisting insurers to rate insurance policies. The process of obtaining inspection warrants through a judge may prove administratively burdensome. Louisiana. The existence of and potential limitations on authority to enter onto land for an elevation inspection is illustrated by Louisiana’s Code. Louisiana bestows on levee boards a number of significant powers, including the right to construct and maintain levees, the right to take property by eminent domain for this purpose, and the right to enter upon any lands, waters, and premises in the state for the purpose of making such surveys, soundings, drillings, and examinations as they may deem necessary or convenient for carrying out the purposes of this [levee district] Chapter, which entry shall not be deemed a civil or criminal trespass nor a temporary construction servitude, nor shall it be deemed an entry under any eminent domain proceedings which may be then pending, provided that prior written notice of five days to resident owners and fifteen days to nonresident owners be given to the last record property owner as reflected in the parish assessment rolls. Written notice shall consist in mailing the notice by certified mail to the last known address of the owner as shown in the current assessment records. The levee boards and/or levee and drainage board shall indemnify the property owner for any loss or injury resultant from entry upon the property and shall make reimbursement for any actual damages resulting to lands, waters, and premises as a result of these activities. Thus, a levee board has power to enter onto land for purposes of making preliminary surveys, as long as 5 or 15 days notice is provided to resident and non-resident landowners, respectively. Entry under this section is for surveys as necessary “to carry out the purposes of this chapter.” Determination of the elevation of a structure for inclusion in an elevation registry (to allow the National Flood Insurance Program to correctly determine flood insurance premiums but not to allow local governments to use this information in processing permits for building modifications) is arguably, but not clearly, within the purposes of this statute: the statute provides that levee districts may undertake any activities “related directly to … Flood Protection [or] “Cooperative activities with other public bodies for public purposes.” North Carolina. North Carolina statues have provisions allowing government employees to enter onto property in a number of contexts: for purposes of health inspections, hospital inspections, nursing home inspections, and general administrative warrants. These inspection rights are generally restricted to specific purposes and times, and include specific provisions for advance notice (except in emergencies). Maryland. The Real Property law in Maryland, while it is not one of the states that we have been asked to review for this report, provides a broader authority to surveyors than appears in the other states, and serves as a useful contrast. The Maryland law authorizes surveyors to enter onto private property “to obtain information …for any governmental report [or] undertaking:” (a) Civil engineers, land surveyors, real estate appraisers, and their assistants acting on behalf of the State or of any of its instrumentalities or any body politic or corporate having the power of eminent domain after every real and bona fide effort to notify the owner or occupant in writing with respect to the proposed entry may: (1) Enter on any private land to make surveys, run lines or levels, or obtain information relating to the acquisition or future public use of the property or for any governmental report, undertaking, or improvement; (2) Set stakes, markers, monuments, or other suitable landmarks or reference points where necessary; and (3) Enter on any private land and perform any function necessary to appraise the property Summary: Inspection Authorities. State and local governments have varying arrays of inspection or survey authorities, with separate authorities enacted to facilitate enforcement of individual administrative codes and standards (such as regulation of nursing homes), or to facilitate exercise of the right of eminent domain. In some cases, these authorities may be broad enough to encompass inspection for purposes of floodplain management or surveys related thereto. Except in situations of imminent threat to the health or safety of citizens, these inspection and survey authorities are generally qualified by requirements that landowners be given prior notice of the time of the inspection, and that the time of the inspection be at times likely to be convenient to the property owner. Finally, in most cases statutes provide inspection authority in connection with exercise of specific regulatory responsibilities exercised by the local government. An inspection for the sole purpose of allowing the accurate determination of insurance premium may not be a purpose for which local governments are authorized to inspect private land. (As discussed above, FEMA does not plan to relax its requirement that local governments obtain Elevation Certificates from registered surveyors for floodplain management purposes.) The Government’s Right to Conduct Preliminary Surveys. Federal law and the law in many states recognizes the right of entities that enjoy the right of eminent domain to enter onto private property to take preliminary surveys. It is assumed that before an entity can determine whether or not to exercise its right of eminent domain, it must conduct preliminary physical inspections of the affected properties. So long as the land is not damaged by these analyses, the inspection does not subject the entity conducting the analysis to liability for trespass or nuisance. As noted by the California Supreme Court in Fox v. The Western Pacific Railroad Company, The right to take for public use implies the right to enter for the purpose of ascertaining whether the public need will be subserved by the taking. If a railroad is to be constructed, a survey must be made before the corporation can determine the precise land which will be required; and the corporation may lawfully enter for that purpose and may lawfully do what would otherwise be a trespass. Under no circumstances, then, can the entry be regarded as the taking. Nor, indeed, can it be said in any legal sense that the land has been taken until the act has transpired which divests the title or subjects the land to the servitude. So long as the title remains in the individual, or the land remains uncharged by the servitude, there can have been no taking under conditions, which, as already stated, preclude the commission of a trespass. The pre-condemnation right of entry is frequently provided by statute specifying terms and conditions for the exercise of this right. For example, the North Carolina statute provides: Any condemnor without having filed a petition or complaint, depositing any sum or taking any other action provided for in this Chapter, is authorized to enter upon any lands, but not structures, to make surveys, borings, examinations, and appraisals as may be necessary or expedient in carrying out and performing its rights or duties under this Chapter. The condemnor shall give 30 days' notice in writing to the owner at his last known address and the party in possession of the land of the intended entry authorized by this section. Entry under this section shall not be deemed a trespass or taking within the meaning of this Chapter, however, the condemnor shall make reimbursement for any damage resulting from such activities. And as noted above, Maryland has a statute allowing surveyors generally to enter upon land — not just for purposes of eminent domain, but for “any governmental report or undertaking” — and Louisiana levee boards have a statutory right to survey property for purposes related flood protection. Indeed, the federal government has a statutory right of right of eminent domain for the purpose of flood control, and after a particular property is identified for potential acquisition and after written notice is provided to the landowner, the Corps of Engineers will, with or without the accompaniment of the landowner, enter on the property to obtain the survey and appraisal information required for the condemnation proceeding. However, we strongly doubt that FEMA can make use of this theory to support its walk-on inspections, except possibly in those states, such as Maryland, whose law allows surveyors working for governments to enter onto land to survey for the purpose of “any governmental report [or] undertaking.” The basic problem is that even though FEMA’s mapping activities have been held to be “flood control initiatives” which trigger sovereign immunity under a federal flood control statute, FEMA’s mapping activities do not include any authority to condemn land. Moreover, even though the Corps of Engineers has a (narrow) pre- condemnation right of entry, (and FEMA has authority to make arrangements with the Corps to obtain information), the Corps’ authority is exercisable only in connection with a specific project and the proposed condemnation of specific parcels of land. In sum, we have not found a federal or (except in a few states) state authorization for entry on land that would eliminate legal risk of trespass actions. This does not, of course, mean that the brief “trespasses” contemplated by Strategy D would generate criminal prosecutions or the risk of significant civil liability. Defenses to Trespass Actions. In the four states whose laws have been reviewed in this report, criminal trespass appears to be of relatively minor concern; this offense requires elements in addition to that of entry onto property. In Louisiana, surveyors are exempt from the criminal trespass statute. In Florida, criminal trespass is committed only where a person defies warnings or requests not to enter, or (if in the curtilage of a home) commits an additional offense other than trespass. In California, criminal trespass is found only in the presence of aggravating factors (such as willfully damaging property or refusing to leave premises when requested). And in North Carolina, criminal trespass requires disregard of warning signs or requests to leave the property. Given the limited duration and activity involved in Strategy D’s “walk on” surveys, we believe that it is unlikely that Strategy D would involve criminal trespass as long as surveyors are instructed to and do obey all “no trespassing signs” and leave any property when requested to do so. Defenses to civil trespass actions also exist that would minimize any potential liability. The most important is the absence of measurable damages. In each of the four states reviewed, a landowner can only recover from a trespasser the actual damages or reduction in value of the property as a result of the trespass. It is unlikely that the brief “walk on” inspections contemplated by Strategy D would impair the value of property in a way that might generate civil liability. (Of course, if FEMA’s agent caused damage to the property (e.g. breaking a window) this would generate liability under the Federal Tort Claims Act.) FEMA may not and should not wish to depend on the absence of damage as a policy justification for planning to trespass on private property in a massive and systematic way. A brief review of the common law right of privacy — while again finding that risk of legal liability from walk on inspections may be limited — nonetheless does not eliminate concern that a federal agency should be wary of directing its agents to enter onto private property without clear statutory authorization and without consent. 4. Common Law Right of Privacy One who invades the privacy of another can be subject to tort liability for the resulting harm to the interests of the other. If the landowner has advance knowledge that the data will be collected, he or she may seek to have the government enjoined from entering onto his or her property. The traditional standard for granting a preliminary injunction requires a plaintiff to show that, in the absence of its issuance, he will suffer irreparable injury and also that he is likely to prevail on the merits. It is unclear whether Plaintiffs could show that allowing a government agent to determine whether a dwelling has a walkout basement, for purposes of qualifying the property for flood insurance, will cause the property owner “irreparable harm.” Nonetheless, it is not FEMA’s intention to generate bad will or bad press by challenging efforts by private landowners with no connection to the flood insurance program to prevent entry onto their land, and we would not recommend that it do so. In general, to constitute an invasion of a landowner’s right of privacy, an act must be of such a nature that a reasonable person would conclude that the act would likely cause mental distress and injury to anyone possessed of ordinary feelings and intelligence. The acts constituting the invasion of privacy must be highly offensive to a reasonable person for three out of the four forms of invasion of privacy: (1) Intrusion upon the plaintiff’s seclusion; (2) Publicity given to the plaintiff’s private life; and (3) Placing plaintiff in a false light. In an action for invasion of privacy based on public disclosure of private facts regarding the plaintiff, the information disclosed must actually be of a private nature. The law does not recognize a right of privacy in connection with that which is already public. The right of privacy is not infringed by the publication of matters of public record. In Wehling v. Columbia Broadcasting System, a claim of violation of the right of privacy was denied when a broadcaster televised the plaintiff’s residence. The Court held that "the broadcast provided the public with nothing more than could have been seen from a public street. Consequently, no invasion of privacy occurred." While the Wehling case is not directly applicable to elevation information that cannot be determined from a public street, it highlights the requirement that the revealed information must be private in nature. The elevation or structural data that a surveyor seeks to confirm by a “walk on” survey is unlikely to trigger successful actions for violation of right to privacy; a reasonable person is not likely to find knowledge of such a basement to be “highly offensive.” However, it is not difficult to construct scenarios in which privacy issues are raised by a survey. The owner may have constructed a high fence from the back of his house around a swimming pool and patio; many home owners might find it “highly offensive” if a government surveyor were to climb over this fence, without consent, to confirm the existence of a basement — and by happenstance also to observe activities in this “curtilage” of the home. In sum, while there may be legal defenses to actions in trespass or for rights of privacy that would limit the agency’s damage exposure, we cannot find that “walk on” inspections would not constitute at least technical violations of law — let alone a potential public relations problem. Accordingly, where FEMA is unable to obtain consent to walk on inspections, or cooperation from state and local governments who in fact do have authority for such entry, we would not recommend that FEMA direct its agents to enter onto land in absence of consent. Strategy E: Leverage alternative data sources for an elevation registry. A final strategy is to obtain elevation data from other possible sources with no previous relationship to the National Flood Insurance Program. We were specifically directed to review restrictions on obtaining data from the Census Bureau, and also to provide observations on the likely legal issues affecting cooperation with other data sources such as the U.S. Postal Service, community E-911 databases, etc. 1. Sharing data with the U.S. Census Bureau. The Census Bureau collects and updates a national database of address information for use in the decennial and other censuses conducted by the Bureau. The address data is collected under the authority of Title 13 of the U.S. Code, which prohibits the use of the data for anything but the statistical purpose for which it was originally collected. This very strict confidentiality law also precludes the sharing of any data outside of the Census Bureau that was collected under the authority of Title 13. Census does make available to the public its Topologically Integrated Geographic Encoding and Referencing (TIGER®) files, a digital database of geographic features, such as roads, railroads, rivers, lakes, legal boundaries, census statistical boundaries, etc. covering the entire United States. The data base contains information about these features such as their location in latitude and longitude, the name, the type of feature, address ranges for most streets, the geographic relationship to other features, and other related information. These data are publicly available for a nominal fee, but do not include individual addresses. For the most part, we understand that there is no good source of government address data which is referenced by longitude and latitude, or by some other method that would be able to link street addresses with FEMA’s elevation data. However, whether or not FEMA may obtain data from the Census Bureau, we understand that FEMA is exploring the possibility that the Census Bureau would find value in the elevation data which FEMA plans to collect. Our brief research indicates that there are some groups within the Census Bureau using georeferenced data, and, we understand the use of georeferenced data is increasing significantly as the Census Bureau prepares for the 2010 decennial census. In addition to existing systems to keep track of locations in rural areas of the United States, which enable Census to say with certainty whether there is a structure at particular longitudinal and latitudinal coordinates, Census is investing significant resources to map much larger sections of the country in this manner for use in 2010. It appears possible, given the way its authorities are structured, that the Census Bureau could provide the funding for some layers of mapping activity that would then be performed under FEMA’s authority (not Census’ use-restricted Title XIII authority) and shared with both agencies. 2. Sharing data with other government agencies, or other organizations Other organizations have address data or georeferenced data that may be useful to FEMA in establishing the elevation registry. For example, we understand that the United States Postal Service has very complete street address data, but that the data is not linked to any tax parcel or georeferencing, so that it could not be used by FEMA to match up latitude/longitude data with street addresses. Similarly, FEMA might find more complete georeferenced address data from private sector organizations that use such data for their own purposes, such as the regional telephone companies, power and gas utilities, Federal Express, or the National Emergency Number (9-1-1) Association, although obtaining address data from a commercial entity would likely be very expensive. Since researching the potential for sharing data with Postal Service or private sector organizations is outside the scope of our work, we have not pursued these avenues further. 3. Sharing of Community Tax Parcel Data and/or other Community Data Bases. As discussed above, we believe that there are no significant legal restrictions that would prevent communities from sharing elevation data with FEMA. Although at first blush tax parcel data might appear more confidential in nature and more likely to trigger disclosure protections, upon review we have determined that the same conclusion also applies to tax parcel or tax assessment data. To summarize, records held by the State and local governments of each of these states are subject to freedom of information laws that require government agencies to make their records — including tax parcel and tax assessment data — available for inspection and copying on request. California. In California, the property tax assessor of any county with a population of 50,000 or more must maintain a list of transfers of interest in property going back two years. The list must be made available to the public, and must contain the names of the parties if available, the assessor's parcel number; the street address of the sales property; the date of transfer, the date of recording and recording reference number; and, where it is known by the assessor, the sales price. A separate section of the California Code requires an assessor in a county with population exceeding 4,000,000 to open any of its office’s records to public inspection. In that case, the stated purpose of the statute is to permit identification of claimants who have been granted the homeowner’s exemption. California law also specifically requires that information about the physical characteristics of property maintained by the assessor is a public record open to public inspection. Property characteristics include, but are not limited to the year of construction of improvements to the property, their square footage, the number of bedrooms and bathrooms of all dwellings, the property's acreage, and other attributes of or amenities to the property, such as swimming pools, views, zoning classifications or restrictions, use code designations, and the number of dwelling units of multiple family properties. Fees are permitted for obtaining access to the records. Finally, in California, where the assessor possesses a complete, accurate map of any land, the assessor may adopt numbers or letters for the parcels and revise the maps accordingly. If approved in the statutorily prescribed manner, this scheme may be used in lieu of other description of the land in all assessment proceedings and documents, and copies of these maps are required to be publicly displayed in the office of the assessor. Florida. The Clerks of the Circuit Courts of Florida are charged with recording all instruments required to be recorded in the county in which they reside. These include deeds, leases, bills of sale, agreements, mortgages, notices or claims of lien, notices of levy, tax warrants, tax executions, and other instruments relating to the ownership, transfer, or encumbrance of or claims against real or personal property or any interest in it; extensions, assignments, releases, cancellations, or satisfactions of mortgages and liens; and powers of attorney relating to any of the instruments. All of these data are likely to have street addresses associated with the recordings about the properties, in addition to information about the parcel for tax purposes. The clerk of the circuit court may maintain books where maps, plats, and drawings are recorded. All of these records are required to be open to the public for inspection. Louisiana. In every municipality of Louisiana, the clerk is required to keep a book with a record of all deeds to individuals, and the list of lands sold to the municipality by the tax collector, showing (a) description of the land, (b) as whose property sold, (c) date of sale, (d) amount of taxes, costs, and damages due, and to whom the costs are owing, (e) when redeemed, (f) by whom redeemed, (g) date of redemption, and (h) amount paid therefore. A series of opinions by the Attorney General of Louisiana makes clear that records of assessors, including computer records, are public records available for inspection, with minor exceptions. North Carolina. Although North Carolina does not seem to have a specific law making records of the tax assessor, tax parcels, or registrar of deeds publicly available, these records are not exempt from the public records statute. In addition, public records are required to be disclosed in any media in which the agency is capable of providing them. Address data, which is part of the automatic number location identification information contained in a county 911 database, is not a public record. However, the fact that such data is not available to the public at large via the public records statute does not necessarily mean that FEMA could not obtain the data. Further coordination with the State of North Carolina would be necessary to determine whether acquisition of automatic number locator information were possible (assuming it would be of use to FEMA). In North Carolina, a specific statute governs geographical information systems. Databases and data files of GIS information developed and operated by counties and cities are designated as public records. The county or city is required to provide public access to its GIS information by public access terminals or “other output devices” and to furnish copies in “documentary” or electronic form to anyone requesting at reasonable cost. However, as a condition of furnishing an electronic copy, the county or city may require a certification in writing that the copy will not be resold or otherwise used for trade or commercial purposes. Presumably, use by the federal government would not be a commercial purpose, although if FEMA intended to disclose GIS information obtained from North Carolina to the public at large, FEMA would not be able to attach a ‘no resale or commercial use’ restriction to the data. As a result, coordination with North Carolina authorities would be appropriate to determine how best to obtain and use the GIS data. It is important to note that the existence of a public records law permitting the disclosure of records relating to tax parcels, tax assessments, or recording of deeds is only an indication that the data is publicly available, and, therefore, there should be no legal impediment preventing the State from disclosing the data to FEMA. We do not expect, however, that FEMA would actually obtain property or address data by employing a State’s public records law, which is generally an inefficient way of obtaining records or information. On the contrary, we expect that FEMA would conclude agreements with the State as to how best to obtain data that will fulfill the needs of the National Flood Insurance Program. CONCLUSION Based on the analysis set forth in some detail above, we do not see that FEMA is precluded by law from creating, populating, maintaining, or disseminating elevation data in an elevation registry APPENDIX B — GEOSPATIAL ACCURACY STANDARDS Accuracy vs. Precision. The following definitions are from Appendix 1-A, Glossary of Terms, of FGDC-STD-007.1-1998, Geospatial Positioning Accuracy Standards. "Accuracy - closeness of an estimated (e.g., measured or computed) value to a standard or accepted [true] value of a particular quantity. NOTE: Because the true value is not known, but only estimated, the accuracy of the measured quantity is also unknown. Therefore, accuracy of coordinate information can only be estimated … Precision - in statistics, a measure of the tendency of a set of random numbers to cluster about a number determined by the set. NOTE: If appropriate steps are taken to eliminate or correct for biases in positional data, precision measures may also be a useful means of representing accuracy." Precision essentially defines the consistency of multiple measurements as those measured values cluster about a number determined by those same measurements. But when these multiple measurements include systematic errors or biases, highly precise measurements may nonetheless yield highly inaccurate results. For example, suppose benchmark A (see *) has a published value of 100.00 ft in elevation, and the difference in elevation between benchmark A and new benchmark B is measured 10 times by differential leveling. If all 10 measurements indicate that benchmark B is between 0.99 ft and 1.01 ft higher than benchmark A, it is logical to conclude that benchmark B has an elevation of 101.00 ft (and some people would say 101.00 ft ±0.01 ft). Although "±0.01 ft" may be a measure of precision in this example, it may be very misleading if erroneously understood to represent accuracy, especially if it is later determined that the elevation of benchmark A is really 98.26 ft, for example, instead of 100.00 ft. When correcting for the systematic error or bias of 1.74 ft, then the true elevation of benchmark B would be 99.26 ft instead of 101.00 ft, assuming there were no other systematic errors in the measurements. * Note, a benchmark is a relatively permanent, natural or artificial, material object bearing a marked point whose elevation above or below an adopted vertical datum is known; a benchmark surveyed with differential leveling normally does not have surveyed geographic coordinates, latitude and longitude, as has a survey monument surveyed with GPS. A survey monument may either be a 2-D monument (latitude and longitude only) or a 3-D monument (latitude, longitude, and elevation). Relative and Absolute Accuracy. As defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) in "Digital Elevation Model Technologies and Applications: The DEM Users Manual," page 471, Relative accuracy is a "measure that accounts for random errors in a data set. Relative accuracy may also be referred to as point-to-point accuracy. The general measure of relative accuracy is an evaluation of the random errors … in determining the positional orientation (e.g., distance, azimuth) of one point or feature with respect to another." In construction surveying for example, it is important for construction stakes to have good accuracy relative to the boundary markers used to define the corners of the lot on which the construction is to occur, but it may be immaterial whether or not the corner boundary markers are accurately surveyed relative to the geodetic datum so long as the boundary markers are authoritative and define legal ownership rights. Similarly, ASPRS defines absolute accuracy as "a measure that accounts for all systematic and random errors in a data set. Absolute accuracy is stated with respect to a defined datum or reference system." When the "reference system" is the National Spatial Reference System (NSRS), the "defined datum" is the North American Vertical Datum of 1988 (NAVD 88) for elevation surveys and the North American Datum of 1983 (NAD 83) for horizontal surveys. Both NAVD 88 and NAD 83 are geodetic datums used to control the surveying and mapping of North America. NAD 83 is based on the Geodetic Reference System of 1980 (GRS 80) ellipsoid which is nearly identical to the World Geodetic System of 1984 (WGS 84) ellipsoid used internationally in conjunction with the Global Positioning System (GPS) which is based on the WGS 84 ellipsoid and datum. All ECs are surveyed with relative accuracy — relative to a survey monument or benchmark from which elevations are derived. Local and Network Accuracy. Closely related to relative accuracy, the local accuracy of a control point, as defined in FGDC-STD-007.1-1998, is "a value that represents the uncertainty in the coordinates of the control point relative to the coordinates of other directly connected, adjacent control points at the 95-percent confidence level. The reported local accuracy is an approximate average of the individual local accuracy values between this control point and other observed control points used to establish the coordinates of the control point." Closely related to absolute accuracy, network accuracy, as defined by FGDC-STD-007.1- 1998, "is a value that represents the uncertainty in the coordinates of the control point with respect to the geodetic datum at the 95-percent confidence level. For NSRS network accuracy classification, the datum is considered to be best expressed by the geodetic values at the Continuously Operating Reference Stations (CORS) supported by NGS. By this definition, the local and network accuracy values at CORS sites are considered to be infinitesimal, i.e., to approach zero." Before the satellite era, geodesists were unable to establish a geocentric (center of earth) ellipsoid and datum, but current geodetic datums are geocentric because the center of the earth can be defined as that point about which satellites orbit, and GPS surveys can be referenced to the center of the earth as well as to multiple CORS stations on the earth's surface, surveyed so accurately that they are assumed to have absolute positioning errors of zero. When using differential GPS survey procedures with survey-grade receivers, GPS surveys are normally tied directly or indirectly to CORS stations, providing both local accuracy and network accuracy of a few centimeters at the 95% confidence level. However, when using single, inexpensive mapping-grade GPS receivers, GPS surveys are not relative to any local survey monument or benchmark, and the network accuracy is on the order of 10-meters horizontally and 20-meters vertically at the 95% confidence level. Land Surveying Standards. Traditional land surveying procedures yield relative accuracy, expressed in terms or orders and classes of surveys. Table B.1 shows how the various orders and classes of conventional vertical surveys have relative accuracies expressed in terms of the distance between two points, i.e., the reference benchmark and the newly surveyed point. Table B.1 — Relative Accuracy Standards for Conventional Vertical Surveys Vertical Survey Order Vertical Survey Class Relative Accuracy between directly connected points 1st I Standard Error = 0.5 mm vK 1st II Standard Error = 0.7 mm vK 2nd I Standard Error = 1.0 mm vK 2nd II Standard Error = 1.3 mm vK 3rd N/A Standard Error = 2.0 mm vK where vK is the square root of the distance K in kilometers between the two points Table B.2 shows a similar distance-based relative accuracy standard for a survey method called trilateration which measures the distances of lines for horizontal positioning. Table B.2 — Relative Accuracy Standards for Conventional Horizontal Surveys Horizontal Survey Order Horizontal Survey Class Relative Accuracy between directly connected points 1st N/A Standard Error = 1 part in 1,000,000 2nd I Standard Error = 1 part in 750,000 2nd II Standard Error = 1 part in 450,000 3rd I Standard Error = 1 part in 250,000 3rd II Standard Error = 1 part in 150,000 Table B.3 shows a different distance-based relative accuracy standard for differential GPS surveys that measure distances to multiple satellites to compute baselines between a GPS base station and "rover," for both horizontal and vertical positioning. Table B.3 — Relative Accuracy for GPS Horizontal Surveys GPS Class Local Accuracy at 95% Confidence Level AA 0.3 cm + 1 part per 100,000,000 A 0.5 cm + 1 part per 10,000,000 B 0.8 cm + 1 part per 1,000,000 First 1.0 cm + 1 part per 100,000 When GPS surveys correctly use CORS or selected NSRS monuments as the GPS base stations for the differential surveys, then the network accuracies of the newly surveyed points can be estimated and centimeter-level accuracies can be achieved. But when only a single GPS receiver is used, errors are typically on the order of 10 meters horizontally and 20 meters vertically at the 95% confidence level. National Map Accuracy Standards (NMAS). Whereas surveying standards have traditionally referenced relative accuracy, mapping standards have traditionally referenced absolute accuracy. The NMAS, in use since 1947, defines absolute accuracy at the 90% confidence level. Horizontally, for large-scale maps, 90% of clearly defined checkpoints must be accurate within 1/30th of an inch at the publication scale of the map; there is no real limit on the 10% of errors that may be larger than 1/30th of an inch. Vertically, 90% of checkpoints used to validate the accuracy of contour lines should be accurate within ½ the contour interval with no errors larger than the full contour interval; and 90% of checkpoints used to validate the accuracy of spot heights should be accurate within ¼ the contour interval with no spot height errors larger than ½ the contour interval. Apparent vertical errors can be offset by permissible horizontal errors; this makes it difficult to perform vertical accuracy checks. National Standard for Spatial Data Accuracy (NSSDA). Implemented in 1998 to replace the NMAS for all digital mapping products, the NSSDA defines absolute accuracy at the 95% confidence level, compared with the NMAS' 90% standard. This equates to the FGDC's definition of network accuracy. The FGDC has specified: "Federal agencies collecting or producing geospatial data, either directly or indirectly (e.g., through grants, partnerships, or contracts with other entities), shall ensure, prior to obligating funds for such activities, that data will be collected in a manner that meets all relevant standards adopted through the FGDC process" and specifically mandates that the "accuracy of new or revised spatial data will be reported according to the NSSDA." According to FGDC-STD-007.1-1998, "The reporting standard in the horizontal component is the radius of a circle of uncertainty, such that the true or theoretical location of the point falls within that circle 95-percent of the time. The reporting standard in the vertical component is a linear uncertainty value, such that the true or theoretical location of the point falls within ± of that linear uncertainty value 95- percent of the time." The NSSDA provides root-mean-square error (RMSE) criteria for computing accuracy values at the 95% confidence level, but only when errors are known to follow a normal error distribution. However, the National Digital Elevation Program (NDEP) has determined that many forms of elevation errors do not follow a normal error distribution (bell curve) and specifies that an alternative 95th percentile method be used to establish accuracy at the 95% confidence level. The ASPRS "DEM Users Manual" defines percentile as follows: "As used in this manual, a percentile is any of the values in a dataset of errors dividing the distribution of the individual errors in the dataset into one hundred groups of equal frequency. Any of these groups can specify a specific percentile, e.g., the 95th percentile. The 95th percentile indicates that 95% of the errors will be of equal or lesser value and 5 percent of the errors will be of larger value." In testing the accuracy of a dataset at the 95% confidence level, the NSSDA states: "A minimum of 20 check points shall be tested, distributed to reflect the geographic area of interest and the distribution of error in the dataset. When 20 points are tested, the 95% confidence level allows one point to fail the threshold given in product specifications." Table B.4 compares NMAS and NSSDA vertical accuracy standards for checking contours or digital terrain models or individual points based on their equivalent contour interval. The NMAS and NSSDA standards for spot heights are half the values shown in this Table. For example, for 2 ft equivalent contours, 95% of spot heights should be accurate within 0.6 ft. Table B.4 — Comparison of NMAS/NSSDA Standards NMAS Equivalent Contour Interval NMAS Vertical Accuracy Standard 90% Confidence Level NSSDA RMSEz NSSDA Vertical Accuracy at 95% Confidence Level 1 ft 0.5 ft 0.3 ft 0.6 ft 2 ft 1.0 ft 0.6 ft 1.2 ft 5 ft 2.5 ft 1.5 ft 3.0 ft 10 ft 5 ft 3.0 ft 6.0 ft 20 ft 10 ft 6.1 ft 11.9 ft NOAA Technical Memorandum NOS NGS-58. Normally referred to as "NGS- 58," the correct title of this document is NOAA Technical Memorandum NOS NGS-58, "Guidelines for Establishing GPS-Derived Ellipsoid Heights (Standards: 2 cm and 5 cm)," November, 1997. This is the "bible" for GPS elevation surveys, and NOAA contracted with Dewberry in 2002 to perform research necessary to validate the various operational procedures necessary to ensure that 2 cm local accuracy , 5 cm local accuracy, or 5 cm network accuracy are achieved with specified baseline lengths, observation times, satellite configurations that control vertical accuracy, and other variables. Survey procedures consistent with NGS- 58 were followed for all check surveys to establish "ground truth" for this study and the evaluation of various remote sensing technologies. APPENDIX C — ELEVATION SURVEYS Vertical Datums. In 2001, the American Society for Photogrammetry and Remote Sensing (ASPRS) published a manual entitled: "Digital Elevation Model Technologies and "Applications: The DEM Users Manual." Chapter 2 of this manual provides an excellent reference on vertical datums. It explains reasons why the North American Vertical Datum of 1988 (NAVD 88) has replaced the obsolete National Geodetic Vertical Datum of 1929 (NGVD 29). NGVD 29 was based on an erroneous assumption that mean sea level at 26 tidal gauge sites all represented the same (zero) elevation. Furthermore, NGVD 29 benchmarks throughout the U.S. suffered from an accumulation of relative errors, exceeding 1.5 meters in some locations. NAVD 88 is the only official vertical datum of the U.S., and it is best suited for GPS surveys that yield network accuracies. However, the NSRS still includes many NGVD 29 benchmarks that were surveyed with differential leveling and have never been rigorously surveyed with GPS to establish accurate ellipsoid heights. For any point on the Earth being surveyed with GPS, the ellipsoid height is the height above or below the WGS84 reference ellipsoid, i.e., the distance between a point on the Earth's surface and the WGS84 ellipsoidal surface, as measured along the normal (perpendicular) to the ellipsoid at the point and taken positive upward from the ellipsoid. Defined as "h" in the equation: h = H + N (see Figure C.1). The geoid is that equipotential (level) surface of the Earth's gravity field which, on average, coincides with mean sea level in the open undisturbed ocean. In practical terms, the geoid is the imaginary surface where the oceans would seek mean sea level if allowed to continue into all land areas so as to encircle the Earth. The geoid undulates up and down with local variations in the mass and density of the Earth. The local direction of gravity is always perpendicular to the geoid. What we call the elevation on a FEMA EC is technically its orthometric height. The orthometric height is the height of a point above the geoid as measured along the plumbline between the geoid and a point on the Earth's surface, taken positive upward from the geoid. It is defined as "H" in the equation: H = h - N (transposed from the above equation). Figure C.1 — Relationships between Ellipsoid, Geoid*, and Orthometric Heights * In the U.S., "N" is a negative number because the geoid is below the ellipsoid, making this formula (h = H + N) correct, although it appears in the above example to have its algebraic sign reversed. Throughout much of the Earth elsewhere, the geoid is above the ellipsoid. Differential Leveling. Differential leveling establishes a horizontal line-of-sight that is perpendicular to the direction of gravity. Differential leveling follows the rules of gravity, and all elevations are above the geoid. Differential leveling has traditionally been the most common way to determine differences in elevation between points A and B, between points B and C, between points C and D, and on and on using multiple surveyors with diverse instruments over many decades of time until the last surveyor arrives at a theoretical final benchmark somewhere in the U.S. and calculates the elevation as __ ft above mean sea level, assuming point A was at mean sea level with zero elevation. The accuracy of each benchmark is relative to the accuracy of each of the preceding benchmarks surveyed enroute to the theoretical final benchmark. Furthermore, the elevation at the theoretical final benchmark is dependent upon the route surveyed, because of variations in the slope of the geoid along different routes; thus different elevations can be surveyed for the same point when using differential leveling simply by following different routes to the final destination (the final benchmark). Although these differences are insignificant for short distances surveyed, they accumulate and become significant over large distances. Furthermore, differential leveling does not provide geographic coordinates (latitude and longitude). When approximate latitude and longitude are required, a map is usually scaled to estimate these coordinates; when accurate latitude and longitude are required, GPS procedures are the preferred option. GPS Surveying. Whereas traditional surveying follows the rules of gravity, GPS surveys follow the rules of geometry. The GPS surveyor does not establish elevations (orthometric heights) directly, but indirectly, because GPS yields ellipsoid heights which are converted into orthometric heights (elevations) by applying the latest geoid model from NGS which models the undulation of the geoid (the Geoid Height "N" in the above formula, i.e., the distance of the geoid below the ellipsoid in the U.S.) at any given latitude and longitude. NGS' latest geoid model is Geoid 03, released in January, 2004. GPS surveys best support network accuracy because they are most easily tied to CORS stations that are used to define the geodetic datum defined by WGS84. Furthermore, GPS surveys always yield accurate latitude and longitude values. National Spatial Reference System (NSRS). NOAA's National Geodetic Survey (NGS) defines and manages the National Spatial Reference System (NSRS) -- a consistent coordinate system that defines latitude, longitude, height, scale, gravity, and orientation throughout the U.S. NSRS comprises a consistent, accurate and up-to-date network of continuously operating reference stations (CORS) which support 3-dimensional positioning activities, a network of permanently marked survey points (monuments and benchmarks), and a set of accurate models describing dynamic, geophysical processes that affect spatial measurements. The accuracy and accessibility of NSRS is dependent on contributions of GPS or leveling observations by state, local, and private surveyors. Survey data must meet rigorous "bluebook" standards and achieve minimum accuracies of first- order horizontal or second-order vertical, with accuracies verified using NGS- approved software. NGS Data Sheets are available nationwide from the NSRS at www.ngs/noaa.gov. These Data Sheets include 6-digit Permanent Identifiers (PID numbers), name stamped on the monuments, latitude and longitude based on the NAD 83 horizontal datum, elevations (orthometric heights) based on the NAVD 88 vertical datum, geoid height, ellipsoid height, horizontal order and class, vertical order and class, stability, station descriptions (to-reach directions) and station recovery information. Because these NSRS monuments and benchmarks are considerably more accurate and stable than FEMA's elevation reference marks (ERMs), NSRS monuments are mandatory as GPS base stations from GPS land surveys as well as airborne GPS surveys for photogrammetry, LIDAR and IFSAR. National Height Modernization Study. In 1998, NGS contracted with Dewberry to prepare the "National Height Modernization Study, Report to Congress." This report documented the advantages of GPS over traditional differential leveling and provided recommendations for modernizing the National Height System in the U.S. based on GPS. The recommendations of this study are now being implemented, and Height Modernization surveys are now in progress nationwide that will further serve to improve the ability of GPS surveyors to routinely establish network accuracies of 5-cm and local accuracies of 2-cm and 5-cm when specified survey procedures are followed. On behalf of NGS, Dewberry is currently researching and revising these specifications in NGS-58. APPENDIX D — ISO ELEVATION CERTIFICATE DATA Data Dictionary of Elevation Certificates Provided by ISO for CRS Communities Column headings for the spreadsheet below are explained as follows: ? cEC: credit for Elevation Certificates -- this is credit for communities that join the CRS and agree to maintain FEMA Elevation Certificates (post CRS). The maximum credit is 56 points. A community is assigned a lesser value if there are problems with the ECs or if it is not maintaining 100% of the ECs. Credit has been assigned for 958 communities. ? cECPO: credit for Elevation Certificates (post FIRM). This is credit for maintaining ECs since joining the NFIP. The maximum credit is 56 points. A community is assigned a lesser value if it does not have ECs for 100% of the post FIRM properties in the Special Flood Hazard Area (SFHA). Credit has been assigned for 268 communities. ? cECPR: credit for Elevation Certificates (pre FIRM). This is credit for maintaining ECs before joining the NFIP. The maximum credit is 15 points. A community is assigned a lesser value if it does not have ECs for 100% of pre FIRM ECs in the SFHA. Credit has been assigned for only 26 communities. ? cECCF: credit for Elevation Certificates in Computer Format -- this credit is for maintaining the ECs that they get credit for, above, in a Computer Format. The maximum credit is 15 points. A community is assigned a lesser value if it does not have 100% of the ECs in computer format. Credit has been assigned for 162 communities. ? Data Entered: this refers to a special project that ISO completed where hard copy ECs were scanned and populated into an Access database. ISO has 33,865 such ECs for 315 CRS communities. ? Additional Data Available: ISO has over 300 diskettes, which represent 17,751 ECs for 90 CRS communities. ? Total Data Available: ISO has 51,616 ECs for 351 CRS communities. Note, for 54 communities, ISO has data for both scanned hard copy and diskettes. Some duplication of addresses may occur. Summary of Elevation Certificates for CRS Communities NFIP# Community Name State cEC cECPO cECPR cECCF Data Entered Other Data Available Total Data Available 010002 PRATTVILLE, CITY OF AL 56 0 0 0 0 0 010070 WETUMPKA, CITY OF AL 56 0 0 0 0 0 010071 ATMORE, CITY OF AL 56 0 0 15 0 0 010116 BIRMINGHAM, CITY OF AL 56 0 0 0 0 0 010123 HOOVER, CITY OF AL 56 0 0 0 0 0 010153 HUNTSVILLE, CITY OF AL 56 0 0 0 0 0 010176 DECATUR, CITY OF AL 56 0 0 3.75 34 34 010189 PELL CITY, CITY OF AL 56 0 0 0 10 10 010418 DAUPHIN ISLAND, TOWN OF AL 56 0 0 15 0 0 015000 BALDWIN COUNTY* AL 56 0 0 0 32 32 015005 GULF SHORES, TOWN OF AL 56 0 0 0 59 59 015006 HOMEWOOD, CITY OF AL 56 56 0 0 0 0 015011 ORANGE BEACH, CITY OF AL 56 0 0 0 7 7 020005 ANCHORAGE, MUNICIPALITY OF AK 56 14 0 15 0 164 164 020012 KENAI PENINSULA BOROUGH AK 56 14 0 0 0 0 020094 VALDEZ, CITY OF AK 56 0 0 0 0 0 040012 COCHISE COUNTY * AZ 44.8 0 0 0 20 20 040019 COCONINO COUNTY * AZ 48.7 3.92 0 0 0 0 040020 FLAGSTAFF, CITY OF AZ 56 0 0 15 18 42 60 040037 MARICOPA COUNTY * AZ 56 56 0 15 182 932 1114 040040 CHANDLER, CITY OF AZ 56 56 0 15 20 20 040044 GILBERT, TOWN OF AZ 56 56 0 0 0 0 040045 GLENDALE, CITY OF AZ 56 0 0 0 0 0 040051 PHOENIX, CITY OF AZ 56 56 12 15 216 216 040054 TEMPE, CITY OF AZ 56 0 0 0 0 0 040056 WICKENBURG, TOWN OF AZ 46.5 56 0 0 3 3 040058 MOHAVE COUNTY * AZ 56 43.68 0 0 0 0 040066 NAVAJO COUNTY * AZ 56 0 0 0 0 0 040067 HOLBROOK, CITY OF AZ 56 0 0 0 0 0 040069 SHOW LOW, CITY OF AZ 56 0 0 0 20 20 040073 PIMA COUNTY * AZ 56 56 0 0 2 2 040076 TUCSON, CITY OF AZ 56 29.12 0 0 56 56 040080 CASA GRANDE, CITY OF AZ 56 45.36 0 0 0 0 040093 YAVAPAI COUNTY * AZ 56 46.48 0 15 126 539 665 040094 CHINO VALLEY, TOWN OF AZ 56 49.28 0 15 2 3 5 040095 CLARKDALE, TOWN OF AZ 56 56 0 15 0 17 17 040098 PRESCOTT, CITY OF AZ 56 0 0 0 31 31 040130 SEDONA, CITY OF AZ 56 0 0 0 0 0 040131 CAMP VERDE, TOWN OF AZ 56 49.84 0 15 62 125 187 045012 SCOTTSDALE, CITY OF AZ 56 0 0 15 42 840 882 050012 BENTONVILLE, CITY OF AR 50.4 0 0 0 0 0 050029 ARKADELPHIA, CITY OF AR 56 56 0 0 9 9 050046 BONO, CITY OF AR 56 0 0 0 1 1 050048 JONESBORO, CITY OF AR 56 0 0 0 0 0 050055 WEST MEMPHIS, CITY OF AR 56 0 0 0 0 0 050084 HOT SPRINGS, CITY OF AR 56 0 0 0 0 0 050140 BLYTHEVILLE, CITY OF AR 56 0 0 0 0 0 050180 JACKSONVILLE, CITY OF AR 56 0 0 0 0 0 050181 LITTLE ROCK, CITY OF AR 56 0 0 0 0 0 050192 BENTON, CITY OF AR 56 0 0 0 0 0 050308 BRYANT, CITY OF AR 56 0 0 0 0 0 050433 GARLAND COUNTY * AR 56 0 0 0 0 0 060001 ALAMEDA COUNTY * CA 56 0 0 15 154 154 060012 PLEASANTON, CITY OF CA 56 56 0 0 77 1 78 060025 CONTRA COSTA COUNTY * CA 56 0 0 15 0 27 27 060035 RICHMOND, CITY OF CA 56 56 0 0 0 0 060048 FRESNO, CITY OF CA 56 0 0 0 161 392 553 060075 KERN COUNTY * CA 48.7 56 0 0 0 0 060090 LAKE COUNTY * CA 56 0 0 15 0 60 60 060136 LONG BEACH, CITY OF CA 56 56 0 0 42 42 060137 LOS ANGELES, CITY OF CA 56 0 0 0 0 0 060178 NOVATO, CITY OF CA 56 14 0 0 0 0 060195 MONTEREY COUNTY * CA 56 56 0 15 45 374 419 060202 SALINAS, CITY OF CA 56 0 0 0 20 20 060207 NAPA, CITY OF CA 56 0 3.75 0 0 0 060212 ORANGE COUNTY * CA 56 56 0 0 0 0 060213 ANAHEIM, CITY OF CA 56 43.68 0 15 0 41 41 060218 FOUNTAIN VALLEY, CITY OF CA 56 56 0 0 0 0 060222 IRVINE, CITY OF CA 56 0 0 0 0 0 060227 NEWPORT BEACH, CITY OF CA 56 0 0 0 34 34 060228 ORANGE, CITY OF CA 56 0 0 0 0 0 060231 SAN JUAN CAPISTRANO, CITY OF CA 56 0 0 0 3 3 060238 YORBA LINDA, CITY OF CA 56 0 0 0 0 0 060239 PLACER COUNTY * CA 56 26.88 0 0 20 20 060243 ROSEVILLE, CITY OF CA 56 56 0 0 35 35 060257 PALM SPRINGS, CITY OF CA 56 0 0 0 0 0 060262 SACRAMENTO COUNTY * CA 56 56 3.75 3.75 55 55 060266 SACRAMENTO,CITY OF CA 56 14 0 0 158 312 470 060294 OCEANSIDE, CITY OF CA 56 56 3.75 0 0 0 060299 SAN JOAQUIN COUNTY * CA 56 0 0 15 0 106 106 060302 STOCKTON, CITY OF CA 56 56 0 0 0 0 060310 SAN LUIS OBISPO, CITY OF CA 56 56 15 0 36 68 104 060331 SANTA BARBARA COUNTY * CA 56 49.28 0.15 0 0 0 060341 LOS ALTOS, CITY OF CA 56 0 0 0 4 4 060344 MILPITAS, CITY OF CA 56 56 0 0 256 143 399 060347 MOUNTAIN VIEW, CITY OF CA 56 12.18 0 0 0 0 060348 PALO ALTO, CITY OF CA 56 0 0 0 0 0 060349 SAN JOSE, CITY OF CA 44.8 0 0 0 0 0 060350 SANTA CLARA, CITY OF CA 56 0 0 0 0 0 060352 SUNNYVALE, CITY OF CA 56 0 0 0 0 0 060355 SANTA CRUZ, CITY OF CA 56 56 0 0 0 0 060357 WATSONVILLE, CITY OF CA 56 0 0 0 0 0 060360 REDDING, CITY OF CA 56 14 0 0 0 0 060370 FAIRFIELD, CITY OF CA 56 0 0 0 0 0 060373 VACAVILLE, CITY OF CA 56 14 0 0 0 0 060379 PETALUMA, CITY OF CA 56 0 0 0 0 0 060421 SIMI VALLEY, CITY OF CA 56 56 0 15 0 13 13 060631 SOLANO COUNTY* CA 56 0 0 0 0 0 060710 SAN RAMON, CITY OF CA 56 56 0 15 0 0 060729 SANTA CLARITA, CITY OF CA 56 0 0 0 0 0 060751 MURRIETA, CITY OF CA 56 0 0 0 0 0 065028 FREMONT, CITY OF CA 56 56 0 0 0 0 065029 FRESNO COUNTY* CA 56 14 0 0 133 133 065034 HUNTINGTON BEACH, CITY OF CA 56 0 0 0 0 0 065043 LOS ANGELES COUNTY * CA 56 37.52 0 15 0 0 065070 WALNUT CREEK, CITY OF CA 56 56 0 0 45 45 065074 MORENO VALLEY, CITY OF CA 56 0 0 0 0 0 080001 ADAMS COUNTY * CO 56 0 0 0 0 0 080002 AURORA, CITY OF CO 56 0 0 0 0 0 080007 THORNTON, CITY OF CO 56 0 0 0 0 0 080008 WESTMINISTER, CITY OF CO 56 56 0 15 0 0 080009 ALAMOSA COUNTY * CO 56 0 0 0 0 0 080010 ALAMOSA, CITY OF CO 56 0 0 0 0 0 080011 ARAPAHOE COUNTY * CO 56 0 0 0 0 0 080013 CHERRY HILLS VILLAGE, CITY OF CO 56 0 0 0 0 0 080017 LITTLETON, CITY OF CO 56 0 0 0 0 0 080018 SHERIDAN, CITY OF CO 56 0 0 0 0 0 080023 BOULDER COUNTY * CO 56 0 0 0 2 2 080024 BOULDER, CITY OF CO 56 0 0 0 0 0 080027 LONGMONT, CITY OF CO 56 0 0 0 5 5 080043 DELTA, CITY OF CO 56 0 0 0 0 0 080046 DENVER, CITY OF CO 56 56 0 0 0 0 080049 DOUGLAS COUNTY * CO 56 0 0 0 0 0 080054 VAIL, TOWN OF CO 56 56 0 0 0 0 080059 EL PASO COUNTY * CO 44.8 0 0 0 0 0 080060 COLORADO SPRINGS, CITY OF CO 56 0 0 0 0 0 080061 FOUNTAIN, CITY OF CO 56 0 0 0 0 0 080063 MANITOU SPRINGS, CITY OF CO 56 0 0 0 0 0 080067 FREMONT COUNTY * CO 44.8 0 0 0 22 22 080068 CANON CITY, CITY OF CO 56 0 0 0 0 49 49 080078 GUNNISON COUNTY * CO 56 56 0 0 3 3 080080 GUNNISON, CITY OF CO 56 0 0 0 0 0 080090 GOLDEN, CITY OF CO 56 0 0 0 0 0 080092 MORRISON, TOWN OF CO 56 56 0 0 0 0 080099 DURANGO, CITY OF CO 56 14 0 0 0 0 080102 FORT COLLINS, CITY OF CO 56 42 0 15 82 1 83 080130 BRUSH, CITY OF CO 56 0 0 0 0 0 080159 STEAMBOAT SPRINGS, TOWN OF CO 56 0 0 0 38 38 080168 TELLURIDE, TOWN OF CO 56 0 0 0 0 0 080201 SILVERTHORNE, TOWN OF CO 56 0 0 0 0 0 080245 FRISCO, TOWN OF CO 56 0 0 0 0 0 080287 PITKIN COUNTY * CO 56 0 0 0 2 2 080310 PARKER, TOWN OF CO 56 56 0 15 0 0 085072 ARVADA, CITY OF CO 56 0 0 0 0 0 085074 ENGLEWOOD, CITY OF CO 56 56 0 0 0 0 085075 LAKEWOOD, CITY OF CO 56 0 0 15 0 0 085076 LOUISVILLE, CITY OF CO 56 0 0 0 1 1 085079 WHEAT RIDGE, CITY OF CO 56 0 0 15 0 0 090011 NEWTOWN, TOWN OF CT 56 56 0 0 0 0 090015 STAMFORD, CITY OF CT 56 0 0 0 0 0 090019 WESTPORT, TOWN OF CT 56 0 0 0 0 0 090074 CHESHIRE, TOWN OF CT 56 0 0 0 0 0 090078 HAMDEN, TOWN OF CT 56 0 0 0 0 0 090096 EAST LYME, TOWN OF CT 56 0 0 0 0 0 095082 WEST HARTFORD, TOWN OF CT 56 56 0 0 0 0 100025 NEWARK, CITY OF DE 56 56 0 0 0 0 100026 NEW CASTLE, CITY OF DE 44.8 0 0 0 0 0 100041 LEWES, CITY OF DE 56 23.52 0 0 16 16 100048 SEAFORD, CITY OF DE 56 0 0 0 0 0 100056 DEWEY BEACH, TOWN OF DE 56 0 0 0 18 18 105084 FENWICK ISLAND, TOWN OF DE 56 0 0 0 1 1 105086 REHOBOTH BEACH, CITY OF DE 56 0 0 0 0 0 120001 ALACHUA COUNTY * FL 56 0 0 0 0 0 120004 BAY COUNTY * FL 56 0 0 0 19 19 120005 CALLAWAY, CITY OF FL 56 0 0 0 17 17 120009 LYNN HAVEN, CITY OF FL 56 0 0 0 5 5 120011 PARKER, CITY OF FL 56 0 0 0 0 0 120012 PANAMA CITY, CITY OF FL 56 0 0 0 36 36 120020 COCOA, CITY OF FL 56 7.84 0 0 21 21 120025 MELBOURNE, CITY OF FL 56 0 0 0 94 94 120027 ROCKLEDGE, CITY OF FL 56 0 0 0 33 33 120028 SATELLITE BEACH, CITY OF FL 56 0 0 0 0 0 120031 COCONUT CREEK, CITY OF FL 56 0 0 0 0 0 120032 COOPER CITY, CITY OF FL 56 0 0 0 301 301 120033 CORAL SPRINGS, CITY OF FL 56 0 0 0 1,779 1779 120034 DANIA, CITY OF FL 44.8 0 0 0 0 0 120035 DAVIE, CITY OF FL 56 0 0 0 1 1 120040 HILLSBORO BEACH, TOWN OF FL 56 0 0 10 0 0 120044 LAUDERHILL, CITY OF FL 56 0 0 0 0 0 120047 MARGATE, CITY OF FL 56 0 0 0 817 648 1465 120048 MIRAMAR, CITY OF FL 56 0 0 0 0 0 120049 NORTH LAUDERDALE, CITY OF FL 56 0 0 0 0 0 120050 OAKLAND PARK CITY OF FL 56 0 0 0 32 32 120053 PEMBROKE PINES, CITY OF FL 56 0 0 0 0 0 120054 PLANTATION CITY OF FL 56 56 0 0 4,385 4385 120055 POMPANO BEACH, CITY OF FL 56 0 0 0 87 87 120058 TAMARAC, CITY OF FL 56 0 0 0 433 433 120061 CHARLOTTE COUNTY * FL 56 0 0 0 1,289 1289 120062 PUNTA GORDA, CITY OF FL 56 0 0 0 0 0 120063 CITRUS COUNTY * FL 56 14 0 0 0 0 120064 CLAY COUNTY * FL 56 0 0 0 0 0 120067 COLLIER COUNTY * FL 56 0 0 0 0 261 261 120070 COLUMBIA COUNTY* FL 56 0 0 0 0 0 120077 JACKSONVILLE, CITY OF FL 56 0 0 0 0 0 120078 JACKSONVILLE BEACH, CITY OF FL 56 0 0 0 340 340 120079 NEPTUNE BEACH, CITY OF FL 56 0 0 0 6 6 120080 ESCAMBIA COUNTY * FL 56 0 0 0 142 142 120082 PENSACOLA, CITY OF FL 56 0 0 0 0 0 120087 FLAGLER BEACH, CITY OF FL 56 0 0 0 0 0 120088 FRANKLIN COUNTY * FL 56 0 0 0 17 17 120090 CARRABELLE, CITY OF FL 56 0 0 0 40 40 120098 GULF COUNTY * FL 44.8 0 0 0 16 16 120099 PORT ST. JOE, CITY OF FL 56 0 0 0 0 0 120103 HARDEE COUNTY * FL 56 0 0 0 0 0 120107 HENDRY COUNTY * FL 56 0 0 0 0 0 120110 HERNANDO COUNTY * FL 56 50 0 0 0 0 120111 HIGHLANDS COUNTY * FL 56 0 0 0 19 19 120112 HILLSBOROUGH COUNTY * FL 56 33.6 0 0 0 0 120114 TAMPA, CITY OF FL 56 0 0 15 0 0 120115 TEMPLE TERRACE, CITY OF FL 56 0 0 0 0 0 120119 INDIAN RIVER COUNTY * FL 56 0 0 0 1 1 120120 FELLSMERE CITY OF FL 56 56 0 15 0 7 7 120121 INDIAN RIVER SHORES, TOWN OF FL 56 0 0 0 141 141 120123 SEBASTIAN, CITY OF FL 56 0 0 0 0 0 120124 VERO BEACH, CITY OF FL 56 0 0 0 0 0 120125 JACKSON COUNTY FL 56 0 0 0 0 0 120144 TALLAHASSEE, CITY OF FL 44.8 0 0 0 0 0 120145 LEVY COUNTY * FL 56 0 0 0 171 171 120146 FANNING SPRINGS, TOWN OF FL 56 0 0 0 0 0 120147 YANKEETOWN, TOWN OF FL 56 0 0 0 30 30 120149 MADISON COUNTY * FL 56 0 0 0 0 0 120153 MANATEE COUNTY * FL 56 0 0 15 8 8 120155 BRADENTON, CITY OF FL 56 0 0 15 0 0 120159 PALMETTO, CITY OF FL 56 10.64 0 0 253 253 120160 MARION COUNTY* FL 56 0 0 15 119 119 120161 MARTIN COUNTY * FL 56 0 0 0 224 224 120162 JUPITER ISLAND, TOWN OF FL 56 0 0 0 0 0 120164 SEWALL'S POINT, TOWN OF FL 56 0 0 0 0 0 120169 LAYTON, CITY OF FL 56 0 0 0 0 0 120172 FERNANDINA BEACH, CITY OF FL 56 0 0 0 80 80 120173 OKALOOSA COUNTY * FL 56 14 0 0 12 12 120174 FORT WALTON BEACH, CITY OF FL 56 0 0 0 0 0 120177 OKEECHOBEE COUNTY FL 56 0 0 0 0 0 120179 ORANGE COUNTY * FL 56 0 0 0 722 722 120180 APOPKA, CITY OF FL 56 0 0 0 0 0 120186 ORLANDO, CITY OF FL 56 0 0 10 0 0 120189 OSCEOLA COUNTY * FL 56 0 0 0 19 19 120190 KISSIMMEE, CITY OF FL 56 0 0 15 0 0 120191 ST CLOUD, CITY OF FL 56 0 0 0 0 0 120192 PALM BEACH COUNTY * FL 56 0 0 0 494 938 1432 120193 ATLANTIS, CITY OF FL 56 0 0 0 0 0 120195 BOCA RATON, CITY OF FL 56 0 0 0 0 0 120196 BOYNTON BEACH, CITY OF FL 56 0 0 0 0 0 120198 CLOUD LAKE, TOWN OF FL 56 0 0 0 0 0 120200 GLEN RIDGE, TOWN OF FL 56 0 0 0 0 0 120207 HYPOLUXO, TOWN OF FL 56 0 0 0 0 0 120208 JUNO BEACH, TOWN OF FL 56 56 0 15 0 0 120211 LAKE CLARKE SHORES, TOWN OF FL 56 0 0 0 0 0 120212 LAKE PARK, TOWN OF FL 56 51.52 0 0 0 0 120213 LAKE WORTH, CITY OF FL 56 0 0 0 0 0 120214 LANTANA, TOWN OF FL 56 0 0 10 9 17 26 120215 MANALAPAN, TOWN OF FL 56 0 0 0 0 0 120216 MANGONIA PARK, TOWN OF FL 56 0 0 0 0 0 120217 NORTH PALM BEACH, VILLAGE OF FL 44.8 0 0 0 0 0 120220 PALM BEACH, TOWN OF FL 56 0 0 0 220 220 120223 PALM SPRINGS, VILLAGE OF FL 56 0 0 0 0 0 120227 SOUTH PALM BEACH, TOWN OF FL 56 0 0 0 0 0 120228 TEQUESTA, VILLAGE OF FL 56 0 0 0 0 0 120229 WEST PALM BEACH, CITY OF FL 56 0 0 0 0 0 120230 PASCO COUNTY * FL 56 0 0 15 952 952 120232 NEW PORT RICHEY, CITY OF FL 56 0 0 0 32 59 91 120234 PORT RICHEY, CITY OF FL 56 56 0 0 0 0 120245 KENNETH CITY, TOWN OF FL 56 0 0 0 3 3 120250 OLDSMAR, CITY OF FL 56 0 0 0 0 0 120251 PINELLAS PARK, CITY OF FL 56 0 0 0 38 1 39 120259 TARPON SPRINGS, CITY OF FL 56 0 0 15 0 538 538 120261 POLK COUNTY * FL 56 0 0 15 0 0 120267 LAKELAND, CITY OF FL 56 0 0 0 0 0 120274 SANTA ROSA COUNTY * FL 44.8 0 0 0 0 0 120275 GULF BREEZE, CITY OF FL 56 0 0 0 0 0 120279 NORTH PORT, CITY OF FL 56 0 0 0 0 0 120285 ST. LUCIE COUNTY * FL 56 56 0 0 205 347 552 120286 FORT PIERCE, CITY OF FL 56 0 0 0 0 0 120287 PORT ST. LUCIE, CITY OF FL 56 0 0 15 27 457 484 120289 SEMINOLE *UNINC AREAS FL 56 0 0 0 0 0 120290 ALTAMONTE SPRINGS, CITY OF FL 56 0 0 0 0 0 120292 LONGWOOD, CITY OF FL 56 0 0 0 0 1 1 120295 WINTER SPRINGS, CITY OF FL 56 0 0 0 0 0 120296 SUMTER COUNTY * FL 56 0 0 0 0 0 120300 SUWANNEE COUNTY * FL 56 0 0 0 0 0 120302 TAYLOR COUNTY * FL 44.8 0 0 0 0 0 120308 EDGEWATER, CITY OF FL 56 0 0 0 23 23 120313 PORT ORANGE, CITY OF FL 56 0 0 0 0 0 120314 SOUTH DAYTONA, CITY OF FL 56 0 0 0 0 0 120315 WAKULLA COUNTY * FL 56 0 0 0 135 135 120316 ST. MARKS, TOWN OF FL 56 0 0 0 6 6 120328 SUNRISE, CITY OF FL 56 0 0 0 0 0 120330 OCALA, CITY OF FL 56 0 0 0 0 0 120331 JEFFERSON COUNTY * FL 44.8 0 0 0 0 0 120338 NICEVILLE, CITY OF FL 56 0 0 0 0 0 120402 SANIBEL, CITY OF FL 56 0 0 0 2 2 120404 PALM BAY, CITY OF FL 56 0 0 10 323 132 455 120419 BAKER COUNTY * FL 56 0 0 0 0 0 120421 LAKE COUNTY * FL 56 0 0 0 124 124 120426 MARCO ISLAND, CITY OF FL 56 56 3.75 15 0 0 120579 SHALIMAR, TOWN OF FL 56 0 0 15 0 0 120635 MIAMI-DADE COUNTY FL 56 0 0 0 11 11 120636 BAL HARBOUR FL 44.8 0 0 0 0 0 120637 BAY HARBOR ISLANDS FL 56 19.04 0 0 69 69 120639 CORAL GABLES, CITY OF FL 56 0 0 0 44 44 120643 HIALEAH FL 56 0 0 0 0 0 120648 KEY BISCAYNE, CITY OF FL 56 0 0 0 0 0 120650 MIAMI, CITY OF FL 44.8 0 0 0 91 91 120651 MIAMI BEACH, CITY OF FL 56 14 0 15 0 20 20 120652 MIAMI SHORES VILLAGE FL 56 0 0 15 0 0 120655 NORTH MIAMI, CITY OF FL 56 0 0 15 33 33 120656 NORTH MIAMI BEACH, CITY OF FL 56 0 0 0 7 7 120658 SOUTH MIAMI, CITY OF FL 44.8 0 0 0 27 46 73 120659 SURFSIDE, TOWN OF FL 56 0 0 0 0 0 120673 FORT MYERS BEACH, TOWN OF FL 56 0 0 0 0 0 120676 AVENTURA, CITY OF FL 56 0 0 0 0 0 125087 ANNA MARIA, CITY OF FL 56 0 0 15 0 0 125089 BELLEAIR BEACH, CITY OF FL 56 0 0 0 101 101 125091 BRADENTON BEACH, CITY OF FL 56 0 0 0 37 37 125092 BREVARD COUNTY * FL 56 0 0 0 107 107 125093 BROWARD COUNTY * FL 44.8 0 0 0 139 139 125094 CAPE CANAVERAL, CITY OF FL 56 0 0 0 0 0 125095 CAPE CORAL, CITY OF FL 56 0 0 0 0 0 125096 CLEARWATER, CITY OF FL 56 0 0 15 153 153 125097 COCOA BEACH, CITY OF FL 56 0 0 0 0 0 125099 DAYTONA BEACH, CITY OF FL 56 0 0 0 9 39 48 125100 DAYTONA BEACH SHORES, CITY OF FL 56 0 0 0 0 0 125101 DEERFIELD BEACH, CITY OF FL 56 0 0 0 7 7 125102 DELRAY BEACH, CITY OF FL 56 0 0 0 15 15 125103 DUNEDIN, CITY OF FL 56 0 0 0 36 36 125105 FORT LAUDERDALE, CITY OF FL 56 0 0 0 0 0 125106 FORT MYERS, CITY OF FL 56 0 0 0 0 0 125107 GAINESVILLE, CITY OF FL 56 0 0 0 0 0 125108 GULFPORT, CITY OF FL 56 0 0 10 0 0 125109 GULF STREAM, TOWN OF FL 56 0 0 0 0 0 125110 HALLANDALE BEACH, CITY OF FL 56 0 0 0 8 8 125111 HIGHLAND BEACH, TOWN OF FL 56 0 0 0 34 34 125112 HOLLY HILL, CITY OF FL 56 0 0 0 0 0 125113 HOLLYWOOD, CITY OF FL 56 0 0 0 0 0 125114 HOLMES BEACH, CITY OF FL 56 0 0 0 0 0 125117 INDIAN ROCKS BEACH, CITY OF FL 56 0 0 15 0 0 125118 INDIAN SHORES, TOWN OF FL 56 0 0 10 0 0 125119 JUPITER, TOWN OF FL 56 0 0 0 0 0 125121 KEY COLONY BEACH, CITY OF FL 56 0 0 0 0 0 125122 LARGO, CITY OF FL 56 0 0 15 27 27 125123 LAUDERDALE BY THE SEA, CITY OF FL 56 0 0 0 0 0 125124 LEE COUNTY * FL 44.8 44.8 0 0 0 0 125125 LIGHTHOUSE POINT, CITY OF FL 56 0 0 0 16 16 125126 LONGBOAT KEY, TOWN OF FL 56 0 0 15 182 57 239 125127 MADEIRA BEACH, CITY OF FL 56 0 0 0 0 0 125130 NAPLES, CITY OF FL 56 36.4 0 0 0 0 125132 NEW SMYRNA BEACH, CITY OF FL 56 39.2 0 0 0 0 125133 NORTH REDINGTON BEACH, TOWN OF FL 56 0 0 0 9 9 125134 OCEAN RIDGE, TOWN OF FL 56 0 0 0 17 17 125136 ORMOND BEACH, CITY OF FL 56 0 0 0 0 0 125137 PALM BEACH SHORES, TOWN OF FL 56 0 0 0 0 0 125138 PENSACOLA BEACH, CITY OF FL 56 0 0 0 1 1 125139 PINELLAS COUNTY * FL 56 0 0 0 23 23 125140 REDINGTON BEACH, TOWN OF FL 56 0 0 0 17 17 125141 REDINGTON SHORES, TOWN OF FL 56 0 0 15 0 0 125143 SAFETY HARBOR, CITY OF FL 56 0 0 0 30 30 125144 SARASOTA COUNTY * FL 44.8 0 0 0 5 5 125145 ST. AUGUSTINE, CITY OF FL 48.7 0 0 0 0 0 125146 ST. AUGUSTINE BEACH, TOWN OF FL 56 0 0 0 0 0 125147 ST. JOHNS COUNTY * FL 56 0 0 0 0 0 125148 ST. PETERSBURG, CITY OF FL 56 0 0 15 621 621 125149 ST. PETE BEACH, CITY OF FL 56 0 0 0 0 0 125150 SARASOTA, CITY OF FL 44.8 14 0 0 29 29 125151 SOUTH PASADENA, CITY OF FL 44.8 0 0 0 0 0 125152 TITUSVILLE, CITY OF FL 56 0 0 0 0 0 125153 TREASURE ISLAND, CITY OF FL 56 0 0 15 36 57 93 125154 VENICE, CITY OF FL 44.8 0 0 0 0 0 125155 VOLUSIA COUNTY * FL 56 0 0 0 26 26 125158 DESTIN, CITY OF FL 56 0 0 0 2 2 130030 CHATHAM COUNTY * GA 56 0 0 0 6 6 130052 COBB COUNTY * GA 56 0 0 0 0 0 130059 COLUMBIA COUNTY * GA 56 0 0 0 0 0 130065 DEKALB COUNTY * GA 56 0 0 0 0 1240 1240 130074 DOUGHERTY COUNTY * GA 52.1 0 0 0 16 16 130075 ALBANY, CITY OF GA 56 0 0 0 32 32 130078 PEACHTREE CITY, CITY OF GA 56 56 0 0 0 0 130086 COLLEGE PARK, CITY OF GA 56 56 0 0 0 0 130088 ROSWELL, CITY OF GA 56 56 0 0 0 0 130092 GLYNN COUNTY * GA 56 0 0 0 0 0 130093 BRUNSWICK, CITY OF GA 56 0 0 0 0 0 130098 DULUTH, CITY OF GA 56 0 0 0 0 0 130144 COVINGTON, CITY OF GA 56 0 0 0 0 0 130201 JEKYLL ISLAND, CITY OF GA 56 0 0 0 0 0 130261 POOLER, TOWN OF GA 56 0 0 15 7 7 130306 DOUGLAS COUNTY * GA 56 56 0 0 0 0 130322 GWINNETT COUNTY * GA 56 0 0 0 189 189 135158 COLUMBUS, CITY OF GA 56 0 0 0 0 0 135159 DECATUR, CITY OF GA 56 0 0 0 0 0 135160 FULTON COUNTY * GA 56 0 0 0 0 0 135163 SAVANNAH, CITY OF GA 56 0 0 15 0 0 135164 TYBEE ISLAND, CITY OF GA 56 0 0 0 0 0 150003 MAUI COUNTY * HI 56 0 0 0 0 0 160001 ADA COUNTY * ID 56 14 0 0 13 19 32 160002 BOISE, CITY OF ID 56 56 0 0 0 0 160003 EAGLE, CITY OF ID 56 0 0 0 0 0 160004 GARDEN CITY, CITY OF ID 56 14 3.75 0 0 0 160009 BANNOCK COUNTY * ID 44.8 14 0 0 48 48 160012 POCATELLO, CITY OF ID 56 56 0 0 0 0 160022 HAILEY, CITY OF ID 56 0 0 0 60 60 160023 KETCHUM, CITY OF ID 56 56 0 0 45 45 160024 SUN VALLEY, CITY OF ID 56 29.68 0 0 0 0 160058 MOUNTAIN HOME, CITY OF ID 56 56 0 0 11 11 160076 KOOTENAI COUNTY * ID 56 0 0 0 114 203 317 160090 MOSCOW, CITY OF ID 56 56 0 0 0 0 160101 NEZ PERCE COUNTY* ID 56 0 0 0 0 0 160114 SHOSHONE COUNTY * ID 44.8 14 0 0 0 0 160120 TWIN FALLS, CITY OF ID 56 11.2 0 0 53 53 160131 KELLOGG, CITY OF ID 56 0 0 0 0 0 160212 ELMORE COUNTY * ID 56 0 0 0 0 0 160220 VALLEY, UNINCORPORATED AREAS ID 56 56 0 0 2 2 165167 BLAINE COUNTY * ID 56 14 0 0 55 55 170001 ADAMS COUNTY * IL 56 56 0 0 0 0 170059 BARTLETT, VILLAGE OF IL 56 0 0 0 0 0 170072 CALUMET CITY, CITY OF IL 56 0 0 15 0 0 170078 COUNTRY CLUB HILLS, CITY OF IL 56 56 0 0 8 8 170081 DES PLAINES, CITY OF IL 56 0 0 0 0 0 170091 FLOSSMOOR, VILLAGE OF IL 56 56 0 15 0 0 170107 HOFFMAN ESTATES, VILLAGE OF IL 56 0 0 0 4 4 170116 LANSING, VILLAGE OF IL 56 8.4 0 15 0 0 170129 MOUNT PROSPECT, VILLAGE OF IL 56 14 12.45 0 26 26 170132 NORTHBROOK, VILLAGE OF IL 56 3.92 0.69 13.35 24 92 116 170163 SOUTH HOLLAND, VILLAGE OF IL 56 26.32 0 15 0 21 21 170172 ORLAND HILLS, VILLAGE OF IL 56 56 0 15 0 0 170173 WHEELING, VILLAGE OF IL 48.7 56 0 15 37 149 186 170198 ADDISON, VILLAGE OF IL 56 0 0 0 0 0 170204 DOWNERS GROVE, VILLAGE OF IL 56 0 0 0 0 0 170206 GLENDALE HEIGHTS, VILLAGE OF IL 56 0 0 0 0 0 170211 LISLE, VILLAGE OF IL 48.2 56 0 0 0 0 170214 OAK BROOK, VILLAGE OF IL 56 0 0 0 0 0 170222 WILLOWBROOK, VILLAGE OF IL 56 0 0 0 0 0 170224 WOOD DALE, CITY OF IL 56 0 0 0 0 0 170298 CARBONDALE, CITY OF IL 56 56 0 0 0 0 170330 ST CHARLES, CITY OF IL 44.8 0 0 0 0 0 170361 DEERFIELD, VILLAGE OF IL 56 0 0 0 0 0 170378 LINCOLNSHIRE, VILLAGE OF IL 56 56 0 15 119 119 170533 PEORIA COUNTY IL 44.8 56 0 0 21 21 170912 SANGAMON COUNTY* IL 56 0 0 10 0 0 170919 PROSPECTS HEIGHTS, CITY OF IL 56 0 0 0 0 0 175170 PALATINE, VILLAGE OF IL 56 0 0 0 0 0 180001 DECATUR, CITY OF IN 56 0 0 0 0 0 180003 FORT WAYNE, CITY OF IN 56 0 0 15 75 75 180006 BARTHOLOMEW COUNTY * IN 56 0 0 0 26 26 180007 COLUMBUS, CITY OF IN 56 0 0 0 13 13 180080 HAMILTON COUNTY UNINCORPORATED IN 56 56 0 0 1 1 180082 NOBLESVILLE, CITY OF IN 56 0 0 0 0 0 180093 KOKOMO, CITY OF IN 56 56 0 0 0 0 180121 KOSCIUSKO COUNTY * IN 56 0 0 0 0 0 180122 SYRACUSE, TOWN OF IN 56 56 0 0 0 0 180256 VANDERBURGH COUNTY * IN 56 0 0 0 0 0 180257 EVANSVILLE, CITY OF IN 56 0 0 0 0 0 180263 VIGO COUNTY * IN 56 14 0 0 0 0 180302 ALLEN COUNTY IN 56 0 0 0 2 2 180382 MILFORD JUNCTION, VILLAGE OF IN 56 56 0 0 0 0 180465 NORTH WEBSTER TOWN OF IN 56 56 0 0 0 0 190227 DES MOINES, CITY OF IA 56 56 0 15 10 82 92 190242 DAVENPORT, CITY OF IA 56 0 0 0 0 0 200096 HAYS, CITY OF KS 56 56 0 0 12 12 200173 OLATHE, CITY OF KS 56 0 0 0 18 18 200177 SHAWNEE, CITY OF KS 56 0 0 0 0 0 200215 LINDSBORG, CITY OF KS 56 0 0 0 0 0 210051 GRAYSON, CITY OF KY 56 0 0 0 0 0 210067 LEXINGTON, CITY OF KY 56 56 0 15 0 46 46 210072 PRESTONSBURG, CITY OF KY 56 0 0 0 19 19 210075 FRANKFORT, CITY OF KY 56 0 0 0 0 0 210120 JEFFERSON COUNTY KY 56 0 0 0 0 0 210122 LOUISVILLE, CITY OF KY 56 0 0 0 0 0 210126 NICHOLASVILLE, CITY OF KY 56 56 0 3.75 99 72 171 210127 PAINTSVILLE, CITY OF KY 56 0 0 0 10 10 210193 PIKEVILLE, CITY OF KY 56 0 0 0 42 42 210203 ROWAN COUNTY* KY 52.1 0 0 0 0 0 210219 BOWLING GREEN, CITY OF KY 56 56 0 0 0 0 210298 PIKE COUNTY * KY 56 0 0 0 0 0 210312 WARREN COUNTY * KY 56 0 0 0 0 0 210366 RADCLIFF CITY OF KY 56 0 0 0 0 0 220008 RAYNE, CITY OF LA 56 56 0 0 50 50 220013 ASCENSION PARISH * LA 51.5 0 0 0 0 0 220015 GONZALES, TOWN OF LA 44.8 0 0 0 0 0 220016 SORRENTO, TOWN OF LA 52.1 0 0 0 5 5 220027 DERIDDER, CITY OF LA 56 0 0 0 0 0 220033 BOSSIER CITY, CITY OF LA 56 0 0 0 0 0 220036 SHREVEPORT, CITY OF LA 56 39.2 0 10 906 906 220037 CALCASIEU PARISH * LA 44.8 14 0 0 0 0 220058 EAST BATON ROUGE PARISH LA 56 0 0 0 0 0 220061 ZACHARY, TOWN OF LA 56 0 0 0 0 0 220113 LIVINGSTON PARISH * LA 56 0 0 0 0 0 220116 DENHAM SPRINGS, CITY OF LA 56 56 0 0 0 0 220117 FRENCH SETTLEMENT,VILLAGE OF LA 56 0 0 0 0 0 220121 WALKER, TOWN OF LA 56 0 0 0 0 0 220135 OUACHITA PARISH* LA 56 0 0 0 0 0 220160 ST CHARLES PARISH * LA 56 39.2 0 0 702 702 220164 ST. JOHN THE BAPTIST PARISH * LA 48.7 0 0 0 0 0 220196 MORGAN CITY, CITY OF LA 56 56 0 0 315 315 220202 MANDEVILLE, TOWN OF LA 49.3 0 0 0 0 0 220204 SLIDELL, CITY OF LA 48.7 0 0 0 57 57 220206 TANGIPAHOA PARISH * LA 56 0 0 0 0 0 220220 HOUMA, CITY OF LA 56 43.85 0 0 0 0 220239 WEST BATON ROUGE PARISH * LA 48.7 0 0 0 0 0 220248 LUTCHER, TOWN OF LA 56 0 0 0 0 0 220261 ST JAMES PARISH* LA 56 0 0 15 2 2 220347 RUSTON, CITY OF LA 56 0 0 0 0 0 220361 CADDO PARISH * LA 56 14 0 0 0 21 21 225193 BAKER, CITY OF LA 56 56 0 0 32 32 225198 GRETNA, CITY OF LA 50.4 0 0 15 0 0 225199 JEFFERSON PARISH * LA 56 0 0 0 1,114 1114 225201 KENNER, CITY OF LA 56 0 0 0 93 93 225202 LAFOURCHE PARISH * LA 56 0 0 0 233 233 225203 ORLEANS PARISH LA 56 0 0 0 0 0 225205 ST TAMMANY PARISH * LA 56 0 0 0 102 102 225206 TERREBONNE PARISH LA 56 0 0 0 0 0 230001 AUBURN, CITY OF ME 56 0 0 0 0 0 230004 LEWISTON, CITY OF ME 56 0 0 15 21 21 230018 FORT KENT, TOWN OF ME 56 0 0 0 0 0 230043 CAPE ELIZABETH, TOWN OF ME 56 56 0 0 4 4 230051 PORTLAND, CITY OF ME 56 0 0 0 0 0 230057 FARMINGTON, TOWN OF ME 56 0 0 0 6 6 230069 HALLOWELL, CITY OF ME 56 0 0 0 0 0 230120 PHIPPSBURG, TOWN OF ME 56 56 0 0 0 0 230153 OLD ORCHARD BEACH, TOWN OF ME 56 0 0 0 6 6 230155 SACO, CITY OF ME 56 0 0 0 60 60 230157 SOUTH BERWICK, TOWN OF ME 56 0 0 0 0 0 230158 WELLS, TOWN OF ME 44.8 0 0 0 0 0 230159 YORK, TOWN ME 56 56 0 0 11 11 230178 NORRIDGEWOCK, TOWN OF ME 56 56 0 0 0 0 230191 ALFRED, TOWN OF ME 56 56 0 0 0 0 230208 ARROWSIC, TOWN OF ME 56 56 0 0 0 0 230209 GEORGETOWN, TOWN OF ME 56 0 0 0 0 0 230293 SOUTHWEST HARBOR, TOWN OF ME 56 56 0 0 0 0 230632 OGUNQUIT, VILLAGE OF ME 56 56 0 0 0 0 240012 NORTH BEACH, CITY OF MD 44.8 0 0 0 0 0 240040 HARFORD COUNTY * MD 56 0 0 0 14 14 240042 BEL AIR, TOWN OF MD 56 56 0 0 0 0 240130 CAROLINE COUNTY * MD 56 0 0 0 0 0 245207 OCEAN CITY, TOWN OF MD 56 0 0 0 243 921 1164 245208 PRINCE GEORGE'S COUNTY * MD 56 56 0.9 0 0 0 250004 CHATHAM, TOWN OF MA 56 56 0 0 0 0 250008 HARWICH, TOWN OF MA 56 0 0 0 0 0 250010 ORLEANS, TOWN OF MA 56 0 0 0 0 0 250060 NORTON, TOWN OF MA 56 0 0 0 0 0 250085 HAVERHILL, CITY OF MA 56 0 0 0 16 16 250218 TEWKSBURY, TOWN OF MA 56 0 0 0 0 0 250233 BRAINTREE, TOWN OF MA 56 0 0 0 7 7 250273 MARSHFIELD, TOWN OF MA 56 0 0 0 0 0 250278 PLYMOUTH, TOWN OF MA 56 0 0 0 0 0 250282 SCITUATE, TOWN OF MA 56 0 0 0 104 104 250349 WORCESTER, CITY OF MA 56 0 0 15 0 0 255219 QUINCY, CITY OF MA 56 0 0 0 0 0 260118 HAMBURG, TOWNSHIP OF MI 56 0 0 15 0 0 260128 STERLING HEIGHTS, CITY OF MI 56 56 0 0 0 10 10 260140 MIDLAND, CITY OF MI 56 31.92 0 15 2 2 260142 BEDFORD, TOWNSHIP OF MI 56 0 0 15 0 0 260150 LUNA PIER, CITY OF MI 56 0 0 15 0 0 260175 NOVI, CITY OF MI 56 0 0 15 0 0 260221 DEARBORN HEIGHTS, CITY OF MI 44.8 56 0 3.75 66 66 260226 GIBRALTAR, CITY OF MI 56 0 0 15 0 0 260243 SUMPTER, TOWNSHIP OF MI 56 0 0 15 0 0 260577 PORTAGE, CITY OF MI 56 56 0 15 0 0 270307 MOWER COUNTY * MN 56 0 0 0 0 0 275228 AUSTIN, CITY OF MN 56 56 0 0 0 0 275240 LAKE ST. CROIX BEACH, CITY OF MN 56 0 0 0 0 0 280016 CLEVELAND, CITY OF MS 56 0 0 15 8 8 280053 HATTIESBURG, CITY OF MS 44.8 0 0 15 0 0 280070 HINDS COUNTY* MS 56 0 0 0 0 0 280072 JACKSON, CITY OF MS 56 0 0 0 0 0 280096 MERIDIAN, CITY OF MS 56 0 0 0 14 14 280110 RIDGELAND, CITY OF MS 56 0 0 0 0 0 280176 VICKSBURG, CITY OF MS 56 0 0 0 0 0 280179 GREENVILLE, CITY OF MS 56 0 0 0 0 0 280229 MADISON, TOWN OF MS 56 0 0 0 0 0 280332 GAUTIER, CITY OF MS 56 0 0 0 39 39 285251 BAY ST. LOUIS, CITY OF MS 56 0 0 15 0 0 285252 BILOXI, CITY OF MS 56 0 0 0 0 6 6 285253 GULFPORT, CITY OF MS 56 0 0 0 0 0 285257 LONG BEACH, CITY OF MS 56 0 0 0 0 0 285259 OCEAN SPRINGS, CITY OF MS 56 0 0 0 0 0 285261 PASS CHRISTIAN, CITY OF MS 56 0 0 14.85 561 561 285262 WAVELAND, CITY OF MS 56 0 0 15 0 0 290172 INDEPENDENCE, CITY OF MO 56 0 0 0 0 0 290188 ARNOLD, CITY OF MO 56 56 0 0 0 0 290315 ST. CHARLES COUNTY * MO 56 0 0 0 0 0 300008 CASCADE COUNTY * MT 56 14 0 0 0 0 300009 BELT, CITY OF MT 56 0 0 0 0 0 300010 GREAT FALLS, CITY OF MT 56 0 0 0 0 0 300014 MILES CITY, CITY OF MT 56 56 0 0 0 0 300023 FLATHEAD COUNTY * MT 56 21.28 0 0 0 0 300028 BOZEMAN, CITY OF MT 56 0 0 0 5 5 300029 THREE FORKS, TOWN OF MT 56 44.8 0 15 1 250 251 300038 LEWIS AND CLARK COUNTY * MT 44.8 34.72 0 0 0 0 300048 MISSOULA COUNTY * MT 56 0 0 0 0 0 300049 MISSOULA, CITY OF MT 56 56 0 0 0 0 300108 CIRCLE, TOWN OF MT 56 56 0 0 0 0 310069 FREMONT, CITY OF NE 56 56 0 2.4 134 82 216 315273 LINCOLN, CITY OF NE 56 0 0 0 166 166 320001 CARSON CITY, CITY OF NV 56 56 0 0 138 138 320003 CLARK COUNTY* NV 56 56 0 0 148 148 320005 HENDERSON, CITY OF NV 56 0 0 0 134 134 320007 NORTH LAS VEGAS, CITY OF NV 56 0 0 3.75 1 1 320008 DOUGLAS COUNTY * NV 56 42.56 0 0 0 0 320033 STOREY COUNTY NV 56 56 0 0 479 479 320035 MESQUITE, CITY OF NV 56 14 0 0 0 0 325276 LAS VEGAS, CITY OF NV 56 0 0 0 144 144 330023 KEENE, CITY OF NH 56 0 0 0 0 0 330024 MARLBOROUGH, TOWN OF NH 56 56 0 0 0 0 330028 WINCHESTER, TOWN OF NH 56 0 0 0 0 0 340067 RIDGEWOOD, VILLAGE OF NJ 56 56 0 15 0 7 7 340246 HAMILTON, TWP OF (MERCER CO) NJ 56 0 0 0 15 15 340289 BRADLEY BEACH, BOROUGH OF NJ 56 56 0 0 0 0 340329 SPRING LAKE, BOROUGH OF NJ 56 0 0 0 0 0 340355 PARSIPPANY-TROY HILLS, TWP OF NJ 56 0 0 0 0 0 340359 RIVERDALE, BOROUGH OF NJ 56 0 0 0 0 0 340383 MANTOLOKING, BOROUGH OF NJ 44.8 0 0 0 0 0 340393 STAFFORD, TOWNSHIP OF NJ 56 0 0 15 48 48 340427 BEDMINISTER, TOWNSHIP OF NJ 56 0 0 0 0 0 340467 LINDEN, CITY OF NJ 56 0 0 0 0 0 340472 ROSELLE, BOROUGH OF NJ 56 0 0 0 0 0 340517 MULLICA, TOWNSHIP OF NJ 56 0 0 0 24 24 340518 OCEAN, TOWNSHIP OF (OCEAN CO.) NJ 56 0 0 0 0 0 340570 HACKENSACK MEADOWLANDS COMMSN NJ 56 0 0 15 8 8 345278 ATLANTIC CITY, CITY OF NJ 56 0 0 0 114 114 345279 AVALON, BOROUGH OF NJ 56 0 0 15 0 0 345280 BARNEGAT LIGHT, BOROUGH OF NJ 51.5 0 0 0 46 46 345281 BAY HEAD, BOROUGH OF NJ 50.4 0 0 0 0 0 345282 BEACH HAVEN, BOROUGH OF NJ 56 0 0 0 0 0 345286 BRIGANTINE, CITY OF NJ 56 0 0 0 0 0 345287 BURLINGTON, CITY OF NJ 56 0 0 0 0 0 345288 CAPE MAY CITY, CITY OF NJ 52.1 0 0 0 0 0 345289 CAPE MAY POINT, BOROUGH OF NJ 56 0 0 0 0 0 345293 DOVER, TOWNSHIP OF NJ 56 0 0 0 0 0 345296 HARVEY CEDARS, BOROUGH OF NJ 52.1 0 0 0 0 0 345300 LINCOLN PARK, BOROUGH OF NJ 56 0 0 0 0 0 345301 LONG BEACH, TOWNSHIP OF NJ 56 0 0 15 0 1858 1858 345302 LONGPORT, BOROUGH OF NJ 56 0 0 0 0 0 345303 MANASQUAN, BOROUGH OF NJ 56 0 0 0 43 43 345304 MARGATE CITY, CITY OF NJ 44.8 0 0 0 36 36 345307 NORTH PLAINFIELD, BOROUGH OF NJ 56 0 0 0 0 0 345308 NORTH WILDWOOD, CITY OF NJ 56 0 0 0 0 0 345310 OCEAN CITY, CITY OF NJ 56 0 0 0 317 317 345313 POINT PLEASANT, BOROUGH OF NJ 56 0 0 15 71 71 142 345314 RAHWAY, CITY OF NJ 56 0 0 15 0 0 345320 SHIP BOTTOM, BOROUGH OF NJ 56 0 0 15 32 32 345323 STONE HARBOR, BOROUGH OF NJ 56 0 0 0 0 0 345324 SURF CITY, BOROUGH OF NJ 56 0 0 15 35 108 143 345326 VENTNOR, CITY OF NJ 56 0 0 13.05 0 30 30 345327 WAYNE, TOWNSHIP OF NJ 52.1 0 0 0 0 0 345328 WEST WILDWOOD, BOROUGH OF NJ 56 0 0 0 13 13 345330 WILDWOOD CREST, BOROUGH OF NJ 56 0 0 0 2 2 350001 BERNALILLO COUNTY * NM 56 0 0 0 0 0 350002 ALBUQUERQUE, CITY OF NM 56 0 0 0 0 0 350006 ROSWELL, CITY OF NM 56 14 0 0 10 10 350010 CLOVIS, CITY OF NM 56 35.28 0 14.7 0 0 350029 HOBBS, CITY OF NM 56 56 1.35 15 0 378 378 350045 ALAMOGORDO, CITY OF NM 56 0 0 0 0 0 350054 PORTALES, CITY OF NM 56 0 0 0 0 0 350067 FARMINGTON, CITY OF NM 56 56 3.9 15 0 0 355332 LAS CRUCES, CITY OF NM 56 29.12 0 0 126 33 159 360047 JOHNSON CITY, VILLAGE OF NY 56 0 0 0 0 0 360056 UNION, TOWN OF NY 56 37.8 0 0 0 0 360147 ASHLAND, TOWN OF (CHEMUNG CO.) NY 56 0 0 0 0 0 360148 BIG FLATS, TOWN OF NY 44.8 56 0 15 0 0 360149 CHEMUNG, TOWN OF NY 56 0 0 0 0 0 360150 ELMIRA, CITY OF NY 56 0 0 0 0 0 360151 ELMIRA, TOWN OF NY 56 0 0 0 0 0 360153 HORSEHEADS, TOWN OF NY 56 0 0 0 0 0 360154 HORSEHEADS, VILLAGE OF NY 56 0 0 0 0 0 360156 SOUTHPORT, TOWN OF NY 56 56 0 0 0 0 360157 WELLSBURG, VILLAGE OF NY 56 56 0 0 0 0 360226 AMHERST, TOWN OF NY 56 56 0 15 0 0 360308 ILION, VILLAGE OF NY 56 56 0 0 0 0 360417 GREECE, TOWN OF NY 56 56 0 0 42 42 360463 EAST ROCKAWAY, VILLAGE OF NY 56 0 0 0 0 0 360464 FREEPORT, VILLAGE OF NY 56 0 0 15 0 100 100 360476 LAWRENCE, VILLAGE OF NY 56 0 0 0 0 0 360506 NIAGARA FALLS, CITY OF NY 56 0 0 0 0 0 360595 SYRACUSE, CITY OF NY 56 0 0 0 0 0 360667 ONEONTA, CITY OF NY 56 0 0 0 0 0 360772 CORNING, CITY OF NY 56 0 0 0 0 0 360774 ERWIN, TOWN OF NY 56 0 0 0 0 0 360801 NORTHPORT, VILLAGE OF NY 56 0 0 0 0 0 360932 SCARSDALE, VILLAGE OF NY 56 0 0 0 0 0 365342 SOUTHAMPTON, TOWN OF NY 44.8 0 0 0 0 0 370015 BELHAVEN, TOWN OF NC 56 0 0 0 1 1 370017 WASHINGTON, CITY OF NC 56 0 0 0 14 14 370028 SOUTHPORT, CITY OF NC 56 0 0 0 0 121 121 370036 CABARRUS COUNTY * NC 46.5 56 0 0 14 14 370037 CONCORD, CITY OF NC 56 43.12 0 0 0 0 370039 CALDWELL COUNTY * NC 56 0 0 0 0 0 370043 CARTERET COUNTY * NC 56 33.6 0 0 14 14 370044 ATLANTIC BEACH, TOWN OF NC 56 0 0 0 35 35 370046 CAPE CARTERET, TOWN OF NC 56 0 0 0 0 0 370047 EMERALD ISLE, TOWN OF NC 56 0 0 0 341 341 370048 MOREHEAD CITY, TOWN OF NC 56 0 0 0 135 70 205 370049 NEWPORT, TOWN OF NC 56 0 0 0 6 6 370062 EDENTON, TOWN OF NC 44.8 0 0 0 0 0 370071 WHITEVILLE, CITY OF NC 56 0 0 0 0 0 370072 CRAVEN COUNTY * NC 56 56 0 0 0 0 370074 NEW BERN, CITY OF NC 56 0 0 0 363 363 370076 CUMBERLAND COUNTY * NC 56 56 0 15 0 361 361 370078 CURRITUCK COUNTY * NC 56 56 0 0 253 253 370081 LEXINGTON, CITY OF NC 56 0 0 0 0 0 370085 DURHAM COUNTY * NC 56 0 0 0 0 0 370092 ROCKY MOUNT, CITY OF NC 56 0 0 0 0 0 370111 GUILFORD COUNTY * NC 56 0 0 0 0 0 370133 HYDE COUNTY * NC 56 0 0 0 0 0 370144 LENOIR COUNTY * NC 56 0 0 0 7 7 370145 KINSTON, CITY OF NC 56 0 0 0 21 21 370158 MECKLENBURG COUNTY * NC 56 0 0 15 0 811 811 370159 CHARLOTTE, CITY OF NC 56 0 0 15 0 6 6 370160 PINEVILLE, TOWN OF NC 56 0 0 15 0 0 370167 NASHVILLE, TOWN OF NC 56 0 0 10 2 2 370168 NEW HANOVER COUNTY * NC 56 14.11 0 15 350 350 370178 JACKSONVILLE, CITY OF NC 56 50.4 0 15 108 108 370181 PAMLICO COUNTY * NC 44.8 0 0 0 0 0 370183 BAYBORO, TOWN OF NC 44.8 0 0 0 0 0 370187 TOPSAIL BEACH, TOWN OF NC 56 0 0 0 0 0 370191 GREENVILLE, CITY OF NC 56 0 0 15 0 0 370231 BREVARD, TOWN OF NC 56 0 0 0 0 0 370243 RALEIGH, CITY OF NC 56 0 0 0 106 71 177 370247 WASHINGTON CO(CRESWELL & ROPER NC 52.1 0 0 0 24 24 370249 PLYMOUTH, TOWN OF NC 52.1 0 0 0 6 6 370251 WATAUGA COUNTY * NC 56 0 0 0 25 25 370253 BOONE, TOWN OF NC 56 56 15 14.85 5 5 370254 WAYNE COUNTY * NC 44.8 0 0 0 2 2 370255 GOLDSBORO, CITY OF NC 56 0 0 0 16 16 370263 CLINTON, CITY OF NC 56 0 0 0 0 0 370265 HAVELOCK, CITY OF NC 56 0 0 0 0 0 370267 PINE KNOLL SHORES, TOWN OF NC 56 0 0 0 15 105 120 370268 WASHINGTON PARK, TOWN OF NC 56 0 0 0 0 0 370270 WILSON, CITY OF NC 56 8.57 0 0 51 51 370279 ORIENTAL, TOWN OF NC 44.8 0 0 0 0 0 370372 PITT COUNTY NC 56 0 0 0 0 0 370391 CASWELL BEACH, TOWN OF NC 44.8 23.24 0 0 0 0 370404 ALLIANCE, TOWN OF (PAMLICO CO) NC 44.8 0 0 0 0 0 370418 MINNESOTT BEACH, TOWN OF NC 44.8 0 0 0 0 0 370421 ROPER, TOWN OF NC 52.1 0 0 0 0 0 370430 SOUTHERN SHORES, TOWN OF NC 56 0 0 15 43 753 796 370437 STONEWALL, TOWN OF NC 44.8 0 0 0 0 0 370438 VANDEMERE, TOWN OF NC 44.8 0 0 0 0 0 370439 KITTY HAWK, TOWN OF NC 56 0 0 0 0 0 370443 CRESWELL, TOWN OF NC 52.1 0 0 0 4 4 370465 CEDAR POINT TOWN OF NC 56 0 0 0 0 0 370466 NORTH TOPSAIL BEACH,TOWN OF NC 56 0 0 0 0 0 370523 OAK ISLAND, TOWN OF NC 56 0 0 0 0 0 375346 BEAUFORT, TOWN OF NC 56 0 0 0 8 8 375347 CAROLINA BEACH, TOWN OF NC 56 0 0 0 13 13 375348 DARE COUNTY- SOUTHERN SHORES NC 56 55.44 0 0 40 40 375349 FORSYTH COUNTY * NC 52.1 0 0 0 0 0 375352 HOLDEN BEACH, TOWN OF NC 56 0 0 0 205 205 375353 KILL DEVIL HILLS, TOWN OF NC 56 0 0 0 0 0 375355 MANTEO, TOWN OF NC 56 33 0 0 32 32 375356 NAGS HEAD, TOWN OF NC 56 0 0 15 157 171 328 375357 OCEAN ISLE BEACH, TOWN OF NC 56 0 0 0 143 143 375359 SUNSET BEACH, TOWN OF NC 56 0 0 15 120 188 308 375360 WINSTON SALEM CITY OF NC 52.1 0 0 0 0 0 375361 WRIGHTSVILLE BEACH, TOWN OF NC 56 11.2 0 0 0 0 385365 GRAND FORKS ND 56 56 8.1 15 0 0 390038 FAIRFIELD, CITY OF OH 56 56 0 0 0 0 390071 NEW RICHMOND, VILLAGE OF OH 44.8 56 0 12.9 13 13 390131 SOUTH EUCLID, CITY OF OH 56 56 0 0 0 0 390176 OBETZ, VILLAGE OF OH 56 0 0 0 0 0 390183 DELTA, VILLAGE OF OH 56 0 0 0 0 0 390328 LICKING COUNTY * OH 56 56 0 15 0 0 390412 KETTERING, CITY OF OH 56 0 0 0 0 0 390419 WEST CARROLLTON, CITY OF OH 56 0 0 0 0 0 390432 OTTAWA COUNTY * OH 44.8 0 0 0 0 0 390460 PREBLE COUNTY * OH 56 0 0 0 0 0 390472 OTTAWA, VILLAGE OF OH 56 0 0 0 0 0 390479 SHELBY, CITY OF OH 44.8 56 0 0 12 12 390737 ORANGE, VILLAGE OF OH 56 56 0 0 0 0 400049 LAWTON, CITY OF OK 56 39.76 0 0 0 0 400062 ENID, CITY OF OK 56 0 0 0 0 0 400078 BLACKWELL, CITY OF OK 56 56 0 15 22 27 49 400211 SAND SPRINGS, CITY OF OK 56 56 15 0 890 890 400220 BARTLESVILLE, CITY OF OK 56 0 0 0 0 0 400221 DEWEY, CITY OF OK 56 0 0 0 0 0 400234 CHICKASHA, CITY OF OK 56 0 0 0 59 59 400236 BROKEN ARROW, CITY OF OK 56 56 0 15 66 66 400252 EDMOND, CITY OF OK 56 0 0 0 0 0 405380 STILLWATER, CITY OF OK 56 0 0 0 0 0 405381 TULSA, CITY OF OK 56 56 0 1.5 0 199 199 410008 BENTON COUNTY OR 56 0 0 15 8 8 410009 CORVALLIS, CITY OF OR 56 56 0 0 0 0 410029 CANNON BEACH, CITY OF OR 56 56 0 0 0 0 410039 SCAPPOOSE, CITY OF OR 56 56 0 0 2 2 410059 DOUGLAS COUNTY * OR 56 0 0 0 0 0 410067 ROSEBURG, CITY OF OR 56 56 0 0 0 0 410090 ASHLAND, CITY OF OR 56 56 0 0 0 0 410092 CENTRAL POINT, CITY OF OR 56 56 0 15 0 0 410096 MEDFORD, CITY OF OR 56 56 0 0 0 0 410098 ROGUE RIVER, CITY OF OR 56 56 0 0 0 0 410100 TALENT, CITY OF OR 56 0 0 0 0 0 410108 GRANTS PASS, CITY OF OR 56 56 0 0 0 0 410122 EUGENE, CITY OF OR 56 56 0 15 77 162 239 410137 ALBANY, CITY OF OR 48.7 0 0 0 0 0 410154 MARION COUNTY * OR 56 0 0 0 0 0 410183 PORTLAND, CITY OF OR 56 14 0 0 0 0 410186 POLK COUNTY OR 56 0 0 0 0 0 410196 TILLAMOOK COUNTY * OR 56 0 0 15 0 0 410213 STANFIELD, CITY OF OR 56 0 0 0 0 0 410257 SHERIDAN, CITY OF OR 56 14 0 0 0 0 415589 JACKSON COUNTY * OR 56 56 0 0.9 35 30 65 420339 BLOOMSBURG, TOWN OF PA 44.8 0 0 0 0 0 420380 HARRISBURG, CITY OF PA 56 56 0 15 14 14 420612 KINGSTON, BOROUGH OF PA 56 0 0 0 0 0 420631 WILKES-BARRE, CITY OF PA 56 0 0 0 0 0 420642 JERSEY SHORE, BOROUGH OF PA 56 0 0 0 0 0 420687 LEWISTOWN, BOROUGH OF PA 56 0 0 0 0 0 420754 NEWPORT, BOROUGH OF PA 56 0 0 0 0 0 420831 LEWISBURG, BOROUGH OF PA 56 0 0 0 1 1 421062 ETNA, BOROUGH OF PA 56 0 0 0 0 0 421101 SHALER, TOWNSHIP OF PA 56 0 0 0 0 0 421119 UPPER ST. CLAIR, TOWNSHIP OF PA 56 0 0 0 0 0 421134 GRANVILLE, TWP (MIFFLIN CO) PA 56 0 0 0 0 0 425384 MILTON, BOROUGH OF PA 56 0 0 0 0 0 440022 PAWTUCKET, CITY OF RI 56 56 0 0 0 0 445401 MIDDLETOWN, TOWN OF RI 56 0 0 0 6 6 445402 NARRAGANSETT, TOWN OF RI 56 0 0 0 6 6 445404 NORTH KINGSTOWN, TOWN RI 56 0 0 0 6 6 450002 AIKEN COUNTY * SC 56 0 0 0 5 5 450025 BEAUFORT COUNTY * SC 56 42.56 0 5.7 0 323 323 450026 BEAUFORT, CITY OF SC 56 0 0 15 0 0 450039 MCCLELLANVILLE, TOWN OF SC 56 0 0 0 0 0 450040 MEGGETT, TOWN OF SC 56 0 0 15 0 0 450043 RAVENEL SC 56 0 0 15 0 0 450078 FLORENCE, CITY OF SC 56 0 0 0 2 6 8 450087 GEORGETOWN, CITY OF SC 56 0 0 0 4 4 450089 GREENVILLE COUNTY * SC 56 44.8 0 3.75 51 51 450091 GREENVILLE, CITY OF SC 56 0 0 0 0 0 450109 MYRTLE BEACH, CITY OF SC 56 0 0 0 38 38 450110 NORTH MYRTLE BEACH, TOWN OF SC 56 0 0 15 0 0 450129 LEXINGTON COUNTY * SC 56 0 0 15 0 0 450166 PICKENS COUNTY* SC 56 0 0 0 0 0 450170 RICHLAND COUNTY * SC 56 0 0 0 0 26 26 450182 SUMTER COUNTY SC 56 0 0 0 22 22 450184 SUMTER, CITY OF SC 56 0 0 0 8 8 450249 ROCKVILLE, TOWN OF SC 56 0 0 15 0 0 450250 HILTON HEAD ISLAND, TOWN OF SC 56 56 0 0 0 0 450256 SEABROOK ISLAND, TOWN OF SC 56 0 0 15 0 0 450257 KIAWAH ISLAND,TOWN OF SC 56 0 0 15 0 0 450262 AWENDAW, TOWN OF SC 56 0 0 15 0 0 455412 CHARLESTON, CITY OF SC 56 0 0 0 0 0 455413 CHARLESTON COUNTY * SC 56 0 0 15 0 0 455414 EDISTO BEACH, TOWN OF SC 56 44.8 0 0 104 104 455415 FOLLY BEACH, TWP OF SC 56 0 0 0 0 0 455416 ISLE OF PALMS, CITY OF SC 56 15.68 0 0 0 0 455417 MOUNT PLEASANT, TOWN OF SC 56 0 0 15 448 448 465420 RAPID CITY, CITY OF SD 50.4 0 0 0 0 38 38 470040 NASHVILLE & DAVIDSON COUNTY TN 56 0 0 0 0 0 470176 CARTHAGE, CITY OF TN 56 28 0 0 13 13 470211 ATHENS, CITY OF TN 56 0 0 0 2 2 475425 ELIZABETHTON, CITY OF TN 50.4 0 0 15 107 107 475426 GATLINBURG, CITY OF TN 56 0 0 0 5 5 475433 KNOX COUNTY TN 56 0 0 2.5 2 2 475434 KNOXVILLE, CITY OF TN 56 0 0 0 21 21 480082 BRYAN, CITY OF TX 56 0 0 0 0 0 480140 PLANO, CITY OF TX 56 56 0 0 0 0 480167 CARROLLTON, CITY OF TX 56 0 0 0 40 40 480170 COPPELL, CITY OF TX 56 56 0 0 0 0 480171 DALLAS, CITY OF TX 56 56 0 0 0 0 480173 DUNCANVILLE, CITY OF TX 56 56 0 0 11 11 480184 RICHARDSON, CITY OF TX 56 14 0 0 0 0 480194 DENTON, CITY OF TX 56 30.24 0 0 80 80 480195 LEWISVILLE, CITY OF TX 56 0 0 0 0 0 480206 ODESSA, CITY OF TX 56 56 0 0 0 0 480214 EL PASO, CITY OF TX 56 14 0 0 326 326 480289 BELLAIRE, CITY OF TX 56 18.48 0 0 0 0 480291 DEER PARK, CITY OF TX 56 0 0 0 0 0 480296 HOUSTON, CITY OF TX 56 0 0 0 0 0 480452 LUBBOCK, CITY OF TX 56 0 0 0 66 66 480477 MIDLAND, CITY OF TX 56 56 0 0 302 302 480484 CONROE, CITY OF TX 56 14 0 15 0 35 35 480502 SWEETWATER, CITY OF TX 56 56 0 0 2 2 480586 BENBROOK, CITY OF TX 56 56 0 15 50 54 104 480601 HURST, CITY OF TX 56 56 0 0 64 64 480607 NORTH RICHLAND HILLS, CITY OF TX 56 0 0 0 27 27 480624 AUSTIN, CITY OF TX 56 56 3.75 0 0 0 480662 WICHITA FALLS, CITY OF TX 56 0 0 0 667 667 481585 TIKI ISLAND, VILLAGE OF TX 56 14 0 0 0 0 485454 ARLINGTON, CITY OF TX 56 53.2 0 0 0 0 485456 BAYTOWN, CITY OF TX 56 0 0 0 0 0 485459 BURLESON, CITY OF TX 56 0 0 0 45 45 485462 CLEBURNE, CITY OF TX 56 0 0 0 0 0 485464 CORPUS CHRISTI, CITY OF TX 56 0 0 0 0 0 485468 FRIENDSWOOD, CITY OF TX 56 0 0 0 0 0 485471 GARLAND, CITY OF TX 56 56 15 15 59 59 485472 GRAND PRAIRIE, CITY OF TX 56 56 0 0 9 9 485481 KEMAH, CITY OF TX 56 35.28 6.15 0 0 30 30 485487 LA PORTE, CITY OF TX 56 0 0 0 0 0 485488 LEAGUE CITY, CITY OF TX 0 0 0 0 0 0 485491 NASSAU BAY, CITY OF TX 56 34.72 0 0 22 22 485499 PORT ARTHUR, CITY OF TX 44.8 14 0 3.75 112 157 269 485505 SAN MARCOS, CITY OF TX 56 14 0 0 0 0 485507 SEABROOK, CITY OF TX 56 0 0 0 0 0 485513 TAYLOR LAKE VILLAGE, CITY OF TX 56 0 0 0 0 0 490019 LOGAN, CITY OF UT 56 0 0 0 0 0 490039 BOUNTIFUL, CITY OF UT 56 56 0 0 0 0 490040 CENTERVILLE, CITY OF UT 56 0 0 15 0 0 490052 WEST BOUNTIFUL, CITY OF UT 56 0 0 0 0 0 490072 MOAB, CITY OF UT 56 0 0 0 0 0 490159 PROVO, CITY OF UT 56 56 0 0 4 4 490177 ST. GEORGE, CITY OF UT 56 28.56 4.35 0 0 0 490178 SANTA CLARA, TOWN OF UT 56 0 0 0 0 0 490214 NORTH OGDEN, CITY OF UT 56 56 0 0 0 0 490216 OREM, CITY OF UT 56 56 15 10 0 0 500013 BENNINGTON, TOWN OF VT 56 0 0 0 0 0 500126 BRATTLEBORO, TOWN OF VT 56 0 0 0 0 0 505518 MONTPELIER, CITY OF VT 56 0 0 0 0 0 510001 ACCOMACK COUNTY * VA 56 56 0 0 0 0 510002 CHINCOTEAGUE, TOWN OF VA 56 0 0 0 0 0 510005 WACHAPREAGUE, TOWN OF VA 56 0 0 0 0 0 510053 VIENNA, TOWN OF VA 56 0 0 0 0 0 510071 GLOUCESTER COUNTY * VA 48.7 0 0 0 0 75 75 510090 LOUDOUN COUNTY * VA 56 0 0 0 0 0 510104 NORFOLK, CITY OF VA 56 0 0 0 19 19 510119 PRINCE WILLIAM COUNTY * VA 56 45.36 11.25 6.45 0 0 510130 ROANOKE, CITY OF VA 56 0 0 0 0 0 510134 BRIDGEWATER, TOWN OF VA 56 0 0 0 0 0 510183 POQUOSON, CITY OF VA 56 56 0 0 0 0 510190 ROANOKE COUNTY * VA 44.8 0 0 0 6 6 510201 JAMES CITY COUNTY * VA 56 0 0 0 12 12 515519 ALEXANDRIA, CITY OF VA 56 0 0 15 0 0 515520 ARLINGTON COUNTY VA 56 0 0 15 6 6 515525 FAIRFAX COUNTY * VA 56 0 0 0 0 0 515529 PORTSMOUTH, CITY OF VA 56 0 0 0 18 18 530051 EPHRATA, CITY OF WA 56 0 0 0 0 0 530071 KING COUNTY * WA 56 14 0 0 0 0 530073 AUBURN, CITY OF WA 56 0 0 0 2 3 5 530074 BELLEVUE, CITY OF WA 56 0 0 0 0 0 530079 ISSAQUAH, CITY OF WA 56 56 0 0 0 0 530085 NORTH BEND, CITY OF WA 56 14 0 0 0 0 530088 RENTON, CITY OF WA 56 0 0 15 1 2 3 530090 SNOQUALMIE, CITY OF WA 56 56 3.75 0 0 0 530102 LEWIS COUNTY * WA 56 11.2 0 0 49 49 530103 CENTRALIA, CITY OF WA 56 56 15 0 0 0 530104 CHEHALIS, CITY OF WA 56 56 0 0 0 0 530138 PIERCE COUNTY * WA 56 56 0 15 0 0 530151 SKAGIT COUNTY * WA 56 0 0 0 0 0 530153 BURLINGTON, CITY OF WA 56 56 3.75 15 0 131 131 530156 LA CONNER, TOWN OF WA 50.4 14 0 0 0 0 530158 MOUNT VERNON, CITY OF WA 56 14 0 0 0 0 530166 INDEX, TOWN OF WA 56 0 0 0 0 0 530169 MONROE, CITY OF WA 56 56 15 0 7 103 110 530188 THURSTON COUNTY* WA 56 56 0 0 0 0 530198 WHATCOM COUNTY * WA 44.8 0 0 0 2 11 13 530200 EVERSON, CITY OF WA 56 56 0 0 0 0 530204 SUMAS, CITY OF WA 56 14 0 0 86 9 95 530316 LOWER ELWHA INDIAN RESERVATION WA 56 22.4 0 0 0 0 550001 ADAMS COUNTY * WI 56 0 0 0 0 0 550022 GREEN BAY, CITY OF WI 50.4 0 0 0 0 0 550085 MAZOMANIE, VILLAGE OF WI 56 0 0 0 0 0 550107 WATERTOWN, CITY OF WI 56 0 0 0 2 2 550108 WAUPUN, CITY OF WI 56 0 0 0 0 0 550128 EAU CLAIRE, CITY OF WI 56 0 0 0 0 0 550310 OZAUKEE COUNTY * WI 56 0 0 0 0 0 550537 WINNEBAGO COUNTY WI 56 0 0 0 0 0 550578 ELM GROVE, VILLAGE OF WI 56 0 0 0 0 0 550612 ALLOUEZ, TOWN OF WI 52.1 0 0 0 0 0 555562 LA CROSSE, CITY OF WI 56 0 0 0 8 8 560013 DOUGLAS, TOWN OF WY 56 0 0 0 0 0 560037 CASPER, CITY OF WY 56 0 0 0 0 0 560044 SHERIDAN, CITY OF WY 56 56 0 0 0 0 Totals 33,865 17,751 51,616 Number of Communities 958 268 26 162 315 90 351 Elevation Certificates for Non-CRS Communities Community Name ECs 100029 Sussex County, DE 7 120010 Town of Mexico Beach, FL (Bay County) 2 120060 Hillsboro Beach, FL 5 120092 City of Chattahoochee, FL (Gadsden County) 7 120136 City of Leesburg, FL (Lake County) 1 120586 Town of Inglis, FL (Levy County) 16 130147 Paulding County, GA 16 160208 Canyon County, ID 12 200298 Riley County, KS 24 210055 City of Hopkinsville, KY (Christian County) 1 210339 Johnson County, KY 1 220001 Acadia Parish, LA 4 240011 Calvert County, MD 33 270626 Olmsted County, MN 4 275246 City of Rochester, MN (Olmsted County) 56 280075 Town of Goodman, MS (Holmes County) 2 280198 Warren County, MS 1 285255 Harrison County, MS 34 340512 Township of Pennsville, NJ (Salem County) 24 345311 Township of Pequannock, NJ (Morris County) 3 370013 Beaufort County, NC 1 370171 City of Wilmington, NC (New Hanover County) 12 370238 Town of Cary, NC (Wake County) 2 370295 NC 1 370340 Onslow County, NC 1 370370 Wilson County, NC 2 390001 New Richmond, OH 28 400462 Tulsa County, OK 10 415591 Lane County, OR 324 420372 Township of Upper Allen, PA (Cumberland County) 2 421040 Township of Loyalsock, PA (Lycoming County) 3 450076 Florence County, SC 6 470105 City of Fayetteville, TN (Lincoln County) 12 470123 Maury County, TN 7 475423 City of Columbia, TN (Maury County) 74 480385 Jefferson County, TX 2 480510 Orange County, TX 2 481189 Wichita County, TX 9 481239 Midland County, TX 19 481496 City of Lakeside City, TX (Archer County) 1 040017 City of Sierra Vista, AZ (Cochise County) 113 040048 City of Mesa, AZ (Maricopa County) 31 050109 City of Pine Bluff, AR (Jefferson County) 335 050168 City of Helena, AR (Phillips County) 39 060421 City of Simi Valley, CA (Ventura County) 6 080101 Larimer County, CO 58 080104 Town of Wellington, CO (Larimer County) 9 090012 City of Norwalk, CT (Fairfield County) 5 Total 1,367 Data Dictionary for Sample Elevation Certificate Records Provided by ISO ISO provided Dewberry with a spreadsheet of Elevation Certificate data for CRS communities within four counties: Pinellas County, FL; Beaufort County, SC; Jefferson County, CO; and Harris County, TX. This database had the following column headings: ? NFIP Community Number ? Property's Community ? Property's Zip Code ? Property's State ? NFIP Policy Number ? Property Owner's Last Name ? Property Owner's First Name ? Property Owner's Middle Initial ? Property's Zip+4 ? Date of the Elevation Certificate (EC) or Floodproofing Certificate (FC) ? Certificate Type (EC or FC) ? Source of Data ? Data Provided by a WYO Company? ? FIRM Panel Number ? Panel Suffix ? FIRM Panel Date ? FIRM Zone ? Base Flood Elevation ? Elevation Datum used for Elevation Certificate ? Lowest floor elevation required by Community ? Date of Elevation Certificate ? Building Diagram Number ? Elevation of Lowest Floor if building is in an A Zone ? Lowest Adjacent Grade (LAG) ? Elevation Reference Mark (ERM) on the FIRM (Y/N) ? If Elevation Certificate is based on as-built or drawings (A or D) ? Start of Construction Date ? Certifier's Last Name ? Certifier's First Name ? Certifier's Title ? Certifier's License Number ? Comments on Elevation Certificate ? Optional Description of Property on Elevation Certificate ? Property's House Number ? Property's House Number Suffix ? Property's Street Prefix ? Property's Street Name ? Property's Street Suffix ? Property's Apartment Number APPENDIX E — ELEVATION CERTIFICATE HOLDINGS OF DEWBERRY AND URS The first part of APPENDIX E documents the Elevation Certificate holdings of Dewberry. These holdings are in digital format and therefore could be extracted into an elevation registry with minimal effort. The second part of APPENDIX E documents the Elevation Certificate holdings of URS. These holdings are hardcopy certificates that would need to be digitized for import into an elevation registry. APPENDIX F — COMPARISON OF COMMERCIAL GEOCODING SERVICES APPENDIX F compares three leading commercial geocoding services and their ability to provide latitude and longitude information for 53 street addresses in Houston, Texas. The names of the three services were deliberately withheld from this report because Dewberry believes that erroneous conclusions could be drawn from ranking the quality of services, i.e., each could perform differently in other communities, but they all appear to utilize interpolation procedures that cause positioning errors of several hundred feet. They are referred to herein as "Service A", "Service B" and "Service C." The "ground truth" coordinates were provided by Michael Walters, GIS Section Leader of the Harris County Flood Control District who cooperated with Dewberry in several aspects of this report and whose support is greatly appreciated. For 53 street addresses provided by Dewberry (from actual ECs), Mr. Walters utilized county GIS files that linked street addresses to tax parcels. He then overlaid the tax parcel polygons over digital orthophotos to manually identify the rooftops of the principal structure within each parcel polygon. Lastly, he placed a centroid point near the center of each rooftop and provided Dewberry with the latitude and longitude of each centroid (coordinates embedded in the digital orthophotos) linked to its street address. The commercial geocoding services were then utilized to georeference (geocode) the street addresses and zip codes so as to provide latitude and longitude for each address. The results are at the spreadsheet that follows. Service A was able to address-match 50 of 53 addresses, Service B matched 52 of 53 addresses, and Service C matched all 53 of 53 addresses. The addresses not matched are highlighted in yellow at the bottom of the spreadsheet. The most important statistics are summarized in the colored boxes at the bottom of the spreadsheet. ? For Service A, the average error in Northing was 129.80 ft, the average error in Easting was 362.97 ft; and the dataset tested 575.99 ft horizontal accuracy at the 95% confidence level. ? For Service B, the average error in Northing was 152.97 ft, the average error in Easting was 206.37 ft; and the dataset tested 787.86 ft horizontal accuracy at the 95% confidence level. ? For Service C, the average error in Northing was 178.79 ft, the average error in Easting was 212.68 ft; and the dataset tested 684.24 ft horizontal accuracy at the 95% confidence level. ? In each case, the largest outliers are highlighted in their appropriate columns. Addresses that could not be matched were excluded from the accuracy statistics. The most important conclusion from this data is that the commercial geocoding services are not accurate enough to distinguish one house from neighboring houses or those across the street. Therefore, such services should not be used to geocode existing ECs (where addresses or known but latitude and longitude are unknown) or reverse-geocode remote sensing data (where latitude and longitude are known but addresses are unknown) if the intent is to accurately position a structure inside or outside a SFHA or compute its BFE. When commercial geocoding services are used, for example in populating the LOMA 2000 database, the elevation registry will require some form of qualifier to indicate that such geographic coordinates are estimated rather than surveyed coordinates. The same would pertain to the digitization of existing hardcopy ECs which normally do not include geographic coordinates, should FEMA decide to geocode those addresses in order that the registry may be spatially enabled. APPENDIX G — PHOTOGRAMMETRY ACCURACY ANALYSES APPENDIX G is a spreadsheet that computes the vertical accuracy of 40 houses surveyed photogrammetrically in 2000 by the Philadelphia District of the U.S. Army Corps of Engineers (USACE). The Corps contracted with an aerial survey firm to survey photogrammetric spot heights at the adjacent grade of all house corners visible in stereo (normally 2 or 3 spot heights per building), and the Corps contracted with a land survey firm to measure vertical offsets on-site, either up or down from selected spot heights as necessary to compute the elevation of the top of bottom floor, including basements and enclosures. Dewberry's goal for its FEMA study was to estimate the vertical accuracy of this process. Ground truth surveys were performed in 2003 by Greenhorne & O'Mara, as subcontractor to Dewberry, which randomly selected 40 houses from a much larger dataset and surveyed their top of bottom floor elevations using a combination of GPS and conventional survey techniques. All surveys were performed using the NAVD 88 vertical datum. The major statistics are highlighted in green at the bottom of the spreadsheet. The average elevation error is 0.34 ft, and the dataset tested 1.19 ft vertical accuracy at the 95% confidence level. This almost exactly equaled the accuracy expected from random elevation points interpolated between 2' contours, i.e., 1.2 ft at the 95% confidence level. At the 95% confidence level, two of 40 points will have errors larger than 1.19 ft; in this case, the two addresses with top of bottom floor errors larger than 1.19 ft are highlighted in yellow on the spreadsheet. This test verifies that conventional photogrammetric standards can be applied to photogrammetric spot heights supplemented with vertical offset measurements to indirectly determine top of bottom floor elevations to traditional photogrammetric standards. For other aspects of this FEMA study, the validity of this survey method was also extended by Dewberry to the use of on-site vertical offset measurements to indirectly determine top of bottom floor elevations from other forms of aerial remote sensing, i.e., LIDAR and IFSAR, which also provide adjacent grade elevations at varying levels of accuracy. APPENDIX H — PICTOMETRY EXPLANATION Pictometry, a small technology firm headquartered in Rochester, NY, creates libraries of a revolutionary new form of digital, full color aerial imagery and geo- spatial information. Pictometry captures every square foot of an area from as many as twelve directions, typically with pixel resolutions ranging from two feet to 6 inches. While Pictometry libraries consist of nadir (straight down) images like ordinary aerial imaging, over 80% of the images are oblique (taken from angles) so that features can be easily seen in their entirety. These images reveal the front, back, and sides of objects of interest rather than just their tops. Within seconds, a user can literally view and analyze any house, building, intersection, fire hydrant, tree or any feature in the photographed area from their laptop or workstation. Pictometry delivers its image libraries with a set of software tools that, for the first time in the commercial market, permit users to easily measure and annotate the oblique images. This new technology is dynamically changing the use of visual information systems in the following ways: ? Geo-referenced oblique images – Pictometry has broken new ground on providing photorealistic oblique images that can still be accurately geo- referenced down to the pixel level. ? Instant recognition of any location – Because of their oblique nature, Pictometry images do not require photo-interpretation skills in order to recognize features in the image. The data is presented from a view we are all used to seeing. ? Client image library – Pictometry has created a centralized storage and delivery system that allows all of the images captured to be stored in a central repository and quickly queried at the click of a mouse to find all images that point to a region of interest. ? Easy and intuitive – Pictometry’s Electronic Field Study™ software (EFS) has been designed to be both powerful and easy to use, features that are important to first responders. With very little training, users can become immediately productive with EFS. GIS expertise is not required. ? Intelligent Images™ – Pictometry’s all-digital, fully geo-referenced images include all the data necessary to use the images without any required knowledge of coordinate datums or projection systems. A user need only double click on an image and EFS does the rest. ? High resolution – Pictometry’s high-resolution images allow viewers to see detailed information of building attributes such as doors, windows, number of floors, and building composition. Users can also inspect and easily identify roads, water sources, manholes, and many other neighborhood area features. ? Renewable image libraries – Pictometry’s image libraries are periodically refreshed, allowing customers to analyze changes that have taken place over time. Applications for Pictometry are wide-ranging. From engineering firms and community planning departments, to first response agencies and homeland security measures, Pictometry provides visual intelligence that lets users see almost everywhere, measure anything, and plan everything. Typical uses include: public safety, E911, transportation, tax assessment, environmental, homeland security, community planning, utilities, real property, engineering, and public health. Among its many features, Pictometry provides users with the capability to: ? Measure the length, width, and height of any feature in an image ? Click on any feature in an image and get its geo-coordinates and/or elevation ? Determine the bearing of a road and angles of intersecting roads or physical features ? Automatically calculate perimeter, acreage or square footage of any area or building ? Annotate images with text, lines, circles, and other symbols ? Overlay shape files and other geo-referenced data directly over all images, such as street centerlines, subway lines, plume clouds, parcel boundaries, and jurisdictional boundaries ? Create/distribute sub-libraries of images for jurisdictional or geographical specialty use ? Attach an unlimited amount of text, raster, or vector data to features within the images (such as attaching floor plans, IPEX virtual tours, and other important information to buildings viewed on the image) ? Take inventories of features, such as light poles or building entrances, and export that data to a file, a database, or another shape file ? Use Pictometry’s change analysis software to easily identify and analyze change over time ? Incorporate other georeferenced files into the library, including already existing images such as USGS DOQQs. These features are in addition to the typical image manipulation capabilities, including zoom, pan, and split screen display. Pictometry’s patented capture system guides the pilot onto flight lines, fires the camera at pre-determined locations (as often as every 3 seconds), records, and merges the image and navigation data and continually monitors the system to ensure and improve quality. Pictometry uses optimized direct georegistration equations to provide accurate images at minimal cost. Pictometry has created a number of innovations to improve this process, including a tessellated ground plane to improve oblique image accuracy and a new digital camera calibration process. Community images are captured with a 2-foot ground sample distance and neighborhood images are captured with a 6-inch ground sample distance. Pixel placement accuracy is based on the combination of a number of factors, including ground sample distance, terrain, and elevation model accuracy. Typically, Pictometry achieves 2- to 5-meter pixel placement accuracy with USGS DEMs in hilly terrain, but has achieved sub-meter accuracy with LIDAR data in less varied terrain. Pictometry is conducting a large scale collection of control points to produce a statistically significant sampling of the accuracies we have achieved to date, correlated by ground sample distance, terrain, and elevation model. This will allow customers to predict the accuracies Pictometry will be able to obtain with their capture conditions. In all cases, Pictometry has achieved very accurate distance measurement accuracies. For shorter distances, Pictometry measurements are within 1% of their true measurements and this improves with longer measurements. Pictometry images are stored using a mix of industry standard and proprietary formats. The image raster data is stored in an industry standard image file format such as JPEG, TIFF or MrSID. The image geographic and capture data is stored in Pictometry’s proprietary image trailer format. However, orthogonal images can have their geographic data stored in ESRI’s world file format as well. Pictometry’s image trailers allow the images to be organized into Client Image Libraries and accessed through Pictometry’s Image Warehouse utilities. The warehouses can be on locally accessible disk volumes. Pictometry solutions can be used as stand alone applications or as part of a comprehensive solution. The EFS software is easily integrated with other applications. Pictometry can be integrated with emergency command center software, E911 dispatch system, incident management applications, automated vehicle locater systems, and remote sensing systems. It integrates with GIS applications, including ESRI’s ArcIMS and SDE. Pictometry’s Electronic Field Study software will run on most Windows platforms. Pictometry specifically tests compatibility with Windows 98, NT, XP and 2000. Installation alternatives include both server-based and browser-based applications. Pictometry’s initial approach to the market has been to sell to counties. Current inventory includes over 100 counties. Pictometry has begun to attract interest at the State level; its first State-wide contract is with Massachusetts. On the federal side, Pictometry has contracts with the Department of Energy, The U.S. Census Bureau, and the Capitol Police. Pictometry’s expertise with digital imagery had resulted in a contract with the U.S. Geological Survey for a digital camera calibration system for aerial sensors, which was successfully installed at the USGS Eros Data Center. In addition, Pictometry has a CRADA with the USGS to explore applications within the Federal government for our new technology. APPENDIX I — PICTOMETRY ACCURACY ANALYSES Three different Pictometry datasets were evaluated by Dewberry. 1. 2-view Pictometry of 29 houses in Prince George's County, MD based on a USGS digital elevation model (DEM) produced from 10 ft contours. When Pictometry initially used the USGS DEM, they were unaware that a more-accurate LIDAR dataset was available. 2. 2-view Pictometry of the same 29 houses in Prince George's County, MD based on a LIDAR dataset approximately equivalent to 2 ft contours 3. 4-view Pictometry of 27 houses in Arlington County, VA based on surveyed spot heights at corner adjacent grades The first spreadsheet, attached, is for the Pictometry top of bottom floor elevations based on a standard USGS DEM of Prince George's County. Dewberry's accuracy analysis indicated the average vertical error in top of bottom floor elevations to be 2.61 ft and the overall top of bottom floor vertical accuracy to be 6.34 ft at the 95% confidence level. Dewberry considered this to be unacceptable for the intended purpose and obtained permission to utilize the county's LIDAR dataset that had previously been tested as comparable to 2 ft contours. The second spreadsheet, attached, indicates the results when using the LIDAR dataset of Prince George's County. Dewberry's accuracy analysis indicated the average vertical error in top of bottom floor elevations to be 2.53 ft and the overall top of bottom floor vertical accuracy to be 4.66 ft at the 95% confidence level. Dewberry again considered this to be unacceptable for the intended purpose. At this point, Dewberry was unable to determine which of the following potential error sources caused the elevations to be larger than expected: (a) errors in the LIDAR, (b) errors caused by only having 2-view Pictometry imagery instead of 4-view images to see buildings from all sides, or (c) errors caused by limitations in being able to correctly interpret and measure features with Pictometry images. For the third test, Dewberry provided Pictometry with accurately surveyed spot heights around 27 houses in Arlington County, VA where Pictometry had 4-view imagery available. This would enable the source of the error to be isolated. The third spreadsheet, attached, indicates the results of this accuracy analysis in Arlington County. The average vertical error in top of bottom floor elevations in Arlington County, VA was 1.59 ft and the overall vertical accuracy was 5.01 ft at the 95% confidence level. Dewberry again considered this to be unacceptable for the intended purpose, isolating the problem as the inability to accurately determine the presence or absence of basements and to interpret the imagery to determine the correct reference level for top of bottom floor elevations. In Pictometry's defense, Dewberry recognizes that these errors were on the high side partly because these houses were complex split levels on hillsides, and the houses were surrounded by tall trees that blocked many of the aerial oblique views. Of the 27 houses, eight had misidentified the basement or split level to be surveyed for the top of bottom floor, the principal cause for the high error statistic. Pictometry may provide substantially improved results in locations where basements are not an issue. Pictometry does not measure elevations directly, but indirectly relative to digital elevation data from other sources. Dewberry concluded that Pictometry is most relevant to FEMA in providing an aerial oblique view to (1) see what houses look like from all sides, and (2) see if there has been new or illegal construction compared with earlier images. The Pictometry software includes a change analysis module that compares changes in land use over time, by leveraging existing and new Pictometry images. The change analysis tool permits users to visually inspect new developments, structural changes, and other types of improvements. The tool takes new images and matches them to current and historical Pictometry imagery. Pictometry’s Equalized Imaging Software process allows users to horizontally divide the display, in order to visually display in one Window the old image and in the second Window the updated image of the same area. As the user pans or zooms in one image, these actions are automatically replicated in the second image, providing a quick method for making “before” and “after” comparisons. The user has the full measurement capabilities to calculate the square footage of additions or other improvements to the structure. In addition to this change analysis capability, a future version of Pictometry software will use automated change detection to make the initial determination of the areas of suspected change. APPENDIX J — LIDAR AUTOMATED DATA EXTRACTION REPORT Computational Consulting Services (CCS) was contracted by Dewberry because CCS is generally recognized as the firm that is best able to automatically extract information from LIDAR raw "point cloud" data, without that data first being processed into bare-earth datasets. CCS was tasked to satisfy the following objectives: ? To automatically identify, classify and geocode building centroids within selected areas of four LIDAR datasets. ? To calculate the LAG and HAG elevations for all buildings automatically extracted from the LIDAR data. ? To investigate direct and indirect methods for calculating the top of bottom floor elevations and to actually calculate top of bottom floor elevations for geocoded buildings. ? To determine the extent of building characterization (roof slope, presence of decks and porches, 2-D footprints, etc.) that can be achieved when using LIDAR, and to determine the system parameters required to achieve those results to assist with calculating top of bottom floor elevations, and ? To provide Dewberry with the necessary data to perform an independent statistical analysis of the results when compared to actual survey data. For Mecklenburg County, NC, Prince George's County, MD, Harris County, TX, and Beaufort County, SC, Dewberry provided CCS with some ECs from various sources that CCS refers to as control data or control homes. CCS did not use these certificates to control anything, but merely for in-house comparisons. With few exceptions in Mecklenburg, Prince George's and Beaufort Counties, these certificates helped CCS to evaluate its own data; in several cases, CCS data helped to identify blunders in ECs intended assist CCS in evaluating its data. When the processed CCS data was provided to Dewberry, other ECs were compared with the LIDAR data, and the CCS data was close to expectations in these three counties. For Harris County, there were problems with the LIDAR data and with the ECs that could not be reconciled. Dewberry decided to abandon the evaluation of the Harris County dataset. Because of errors in automated building extraction and LAG/HAG/top of bottom floor estimations, Dewberry concludes that there is marginal value in using totally automated techniques when it is relatively simple to generate accurate building footprints from digital orthophotos so that more-reliable and accurate automated processes can be used to estimate LAG and HAG elevations that adjoin building footprints. None of the techniques automatically estimate top of bottom floor elevations acceptable for FEMA's criteria of 4 ft or less at the 95% confidence level, except in situations where houses are essentially slab on grade and do not involve basements, split levels or split foyers APPENDIX K — LIDAR ACCURACY ANALYSES This appendix includes three spreadsheets used for accuracy analyses of three LIDAR datasets with distinctly different characteristics, summarized in Table M.1. ? Mecklenburg County, NC where the LIDAR post spacing is approximately 16 feet. This is a low resolution dataset. ? Prince George's County, MD where the LIDAR post spacing is approximately 8 feet. This is a mid resolution dataset. ? Beaufort County, SC where the LIDAR post spacing is approximately 4 feet. This is a relatively high resolution dataset. (Truly high resolution LIDAR datasets are now being produced with post spacing measured in inches.) Table K.1 — Summary of LIDAR Accuracy Statistics LIDAR LAG/TBF Accuracy Summary Statistics Mecklenburg Prince George's Beaufort Low Resolution Mid Resolution High Resolution LAG LAG LAG TBF NoFP w/FP NoFP w/FP NoFP w/FP NoFP Number of houses 215 215 579 579 27 38 27 Avg (abs) error (ft) 1.22 0.87 0.53 0.80 0.42 0.28 2.93 Minimum error (ft) -9.39 -4.64 -5.15 -4.28 -1.37 -0.65 -0.19 Maximum error (ft) 9.47 3.63 1.91 2.51 0.27 0.55 3.63 95th Percentile (ft) 3.79 2.82 1.68 2.02 1.09 0.59 3.58 The bare-earth LIDAR datasets for both Mecklenburg and Prince George's Counties had previously been tested as satisfying accuracy standards for 2 ft contours, whereas the LIDAR dataset for Beaufort County had previously been tested as satisfying accuracy standards for 1 ft contours. ECs were previously available for hundreds of houses for comparison in Mecklenburg and Prince George's Counties, whereas only a few dozen ECs were surveyed in Beaufort County specifically for this study. For Mecklenburg and Prince George's Counties, Dewberry selected only those houses for which ECs were available and for which CCS had extracted buildings at those locations. CCS' NoFP method failed to identify and extract a significant number of structures, largely because these two LIDAR datasets lacked the high resolution needed for automated building extraction, as reported in CCS' report at APPENDIX J. For Beaufort County, Dewberry utilized all 38 ECs surveyed for this project, and CCS' automated building extraction program failed to identify 11 of 38 buildings. The top of bottom floor statistics in Beaufort County are unimpressive and would probably have been worse if some of these houses had basements. The Beaufort County dataset demonstrated that, by using the w/FP method (with footprints) and high resolution LIDAR datasets, LAG elevations can be automatically extracted with vertical accuracy of 0.59 ft at the 95% confidence level, i.e., equivalent to 1 ft contours, instead of 2 ft contours assumed herein APPENDIX L — IFSAR ACCURACY ANALYSES This appendix includes evaluations of LAG and HAG elevations extracted from IFSAR imagery of Jefferson County, Colorado in the Denver suburbs. Because building footprints were not available, and because the surveyed latitudes and longitudes pertained to the front door of each house, Dewberry devised alternative procedures to draw circles around the geographic coordinates for each house, utilizing circles of two sizes — 20 ft radius and 30 ft radius. The size of the circles made little difference. LAG and HAG errors were between 15 and 17 feet at the 95% confidence level. Intermap's Product Handbook, at www.intermaptechnologies.com, identifies limitations of IFSAR DTMs, especially DTMs in built-up areas, as follows: ? Layover and foreshortening which tend to make objects (including buildings) look shorter than they really are. ? Shadowing which causes no returns on the back sides of buildings. ? Signal saturation where too much light is returned and image detail is lost — most often a problem over urban areas because of the strong return from buildings. ? Multipath, where the radar signals bounce off of buildings and other objects before hitting the ground, making the ground appear lower than it really is. (Note: this affects LIDAR also). ? Edge effects, sometimes called "blooming," near buildings and forests where interpolation between true ground and elevated points creates intermediate elevations in transition zones up to 25 meters away from the elevated edge. ? Slope effects that degrade accuracy. The impact depends on the magnitude of the slope, where the slope is positive or negative, aspect angle, and where it lies in the radar swath (look angle). After this analysis was already complete, FEMA indicated that technologies that yielded elevations with errors larger than 4 ft at the 95% confidence level, would have no value for populating an elevation registry used for eRating of flood insurance policies. Nevertheless, IFSAR still remains potentially viable for other FEMA applications to include hydrologic modeling of floodplains and general elevation modeling for wildfire modeling and other natural and manmade disasters. APPENDIX M — VISAT ACCURACY ANALYSES This appendix provides the spreadsheet used for the accuracy assessment of VISAT photogrammetric van data in Pinellas County, FL. Because only 20% of the homes had "target points" that could be seen on VISAT stereo images, Sanborn first provided EC data for 27 houses in one community that could be surveyed in stereo. Then, Dewberry hired a survey firm in Pinellas County to use GPS and conventional survey procedures to generate traditional ECs for those same 27 houses. The VISAT-derived elevations were tested to have vertical accuracy of approximately 1.5 ft at the 95% confidence level. The checkpoint surveys indicated top of bottom floor elevations were accurate to 1.54 ft at the 95% confidence level; LAG elevations were accurate to 1.34 ft at the 95% confidence level; and HAG elevations were accurate to 1.59 ft at the 95% confidence level. However, each of these houses had a concrete pad in the back yard for the air conditioner that was not seen from the street and/or could not have been mapped in stereo. For visible features, their surveyed accuracies were acceptable; however, the inability to see (in stereo) the majority of the target points to be surveyed presents a major challenge for this technology. Communities that already have photogrammetric van imagery will need to assess on a community-by-community basis whether their existing imagery will add value to their elevation records. For example, if all houses in the community are slab on grade, the community need not worry about not being able to see the existence of walk out basements visible only from the back yard. Also, foliage could be less dense, or their photogrammetric van stereo imagery could have been taken with a better stereo camera configuration so that a high percentage of target points to be surveyed can be seen in stereo. If such conditions are satisfactory, then photogrammetric van technology does yield elevation accuracies suitable for populating the elevation registry. APPENDIX N — SideSwipe™ — VEHICLE MOUNTED SIDE SCAN LIDAR Introduction Mosaic Mapping Corporation is a publicly traded company with over 18 years of experience delivering high–quality airborne and ground–based mapping products to a diverse international customer base. Headquartered in Ottawa, Canada, and with offices in Calgary, Canada and Houston, USA, Mosaic owns and operates a total of 7 LIDAR systems — three low–altitude helicopter mount ALMIS-350 systems, and four high–altitude fixed–wing ALTMS systems — thus making the company one of the world’s largest providers of high–quality LIDAR mapping services. With the advent of its newest LIDAR innovation, SideSwipe, the firm is preparing to field a number of these revolutionary ground–based LIDAR systems. SideSwipe Background In 2002 Mosaic’s Research and Development team initiated feasibility studies that centered around the re–engineering of its ALMIS–350 LIDAR system for use on ground–based vehicles. Consultations with Ontario’s Ministry of Transportation (MTO) suggested that such a system would be useful in numerous MTO projects, and Mosaic’s own feasibility studies indicated that the technological challenges could be addressed. A prototype system was developed and successfully operated on a rolling test bed. In June 2003 Mosaic learned about a project to provide engineering grade mapping of approximately 566 kilometers of desert road in Afghanistan. Afghanistan’s main highway, known as Highway 1, starts in Kabul, passes through Kandahar, ends in Herat, and traverses some 1062km en route. Much of the road surface, and several bridges, have been destroyed as a result of more than 20 years of neglect, not to mention years of civil war. Consequently, the journey from Kabul to Herat is an arduous one that can take as long as one week to complete. Surveying crews had already employed conventional ground–based techniques to map the highway from Kabul to Kandahar, but progress was slow, and alternative methodologies were being considered. The immediate reaction was to employ one of the low–altitude helicopter–based ALMIS–350 LIDAR systems on the project. However, consultation with military experts proved that such an approach could be dangerous given Afghanistan’s state of unrest. Thus, with conventional ground–based techniques deemed to be inadequate, and now, with conventional airborne LIDAR techniques also out of the question, the decision was made to develop the ground–based LIDAR test bed into a production tool. Some Details ALMIS–350 is a high–precision low–altitude LIDAR system that utilizes a Laser Mirror Scanner, a digital SLR camera, and a tightly coupled GPS/IMU. The GPS/IMU determines the position and attitude of the moving platform and of the sensors. The software that ties each of the component parts together represents several years of intense research, development, and refinement — it is essentially the “glue” that differentiates this system from others — and lends the system an inherent flexibility that allows alternative configurations to be considered. Initial tests involved turning the laser through approximately 90 degrees so that the scanner was able to record data to the side of the vehicle, with the GPS antenna located approximately above the IMU in order to minimize errors due to the GPS to IMU lever–arm uncertainty. Clearly, the principal difference between this installation and that of a helicopter centers around the orientation of the laser. Depending on the mission and the objects that are to be scanned (e.g., tall buildings, highways etc.) a tilt angle of between 70° and 110° is applied to the laser so that it points at the required objects. As the vehicle moves along the street or any other corridor of interest, the laser constantly scans the entire right– hand–side of its trajectory with a swath width of 60°. Theoretical Accuracy SideSwipe’s theoretical accuracy is a function of several component parts, as outlined below: Scanning Distance (m) 10 50 100 GPS Accuracy (m) 1 ± (0.02 + 5ppm) ± (0.02 + 5ppm) ± (0.02 + 5ppm) Beam Divergence (m) 0.030 0.150 0.300 IMU Accuracy (0.05 deg) 0.009 0.044 0.089 Laser Distance Accuracy (m) 2 0.020 0.020 0.020 Notes: 1 Dual–frequency double differenced data, equipment operated in kinematic mode. 2 Assumes reflection off hard surfaces Table N1 – SideSwipe Error Sources In conventional airborne LIDAR work using the ALMIS–350 typical accuracies are of the order of a few cm vertically and 20 to 25cm horizontally. Table N1 indicates that the system should achieve horizontal accuracies of the order of 4 or 5cm regardless of scanning distance, and vertical accuracies of the order of 5 or 6cm at a scanning range of 10m (note that vertical accuracy degrades with increased scanning distance). Therefore, because SideSwipe essentially turns LIDAR on its side, the situation with respect to accuracy changes. It can be seen that similar accuracies are achieved in both the horizontal and vertical components. Test Results Ground truth data are available for a 10km section of Rural Route 6 (RR#6) in North Gower, Ontario. Consequently, the area represents a controlled test range where Mosaic has conducted numerous high accuracy tests using the ALMIS– 350 system. Figure N3 depicts digitally enhanced SideSwipe data collected where RR#6 and Hwy 416 intersect. Both the SideSwipe test bed and, more recently, the SideSwipe production system have been subjected to numerous rolling trials at the controlled test range where, in most cases, the vehicle maintained a more or less constant velocity of 80km/h. Table N2 summarizes SideSwipe’s accuracy when compared against the ground truth data: Surface Type Sample Size r.m.s. error (cm) Center Line Road 47 4.3 Edge of Pavement 68 3.7 Edge of Shoulder 45 2.8 Guard Rail 20 4.0 Property Line 30 4.9 Toe Slope 44 4.8 Ditch 40 4.6 Table N2 – SideSwipe Test Results Overall, the results are extremely positive, with the entire survey corridor showing an r.m.s. error of about 4cm (or 7cm at the 2? confidence level). Hard surfaces such as the road’s center line do even better, showing 2? accuracies of about 4cm (a figure that is consistent with the theoretical accuracy previously noted). Limitations Although somewhat difficult to visualize without the aid of a computer, the graphic shown in Figure N3 is generated entirely from SideSwipe data collected at ground–level. Further analysis of the graphic reveals that data are missing either side of the 416 overpass. In other words, the black areas represent areas of shadowing. This is entirely consistent with what one would expect to see, but it does nevertheless underline the system’s principal limitation. Current Field Technique The impact of shadowing can be minimized by adopting appropriate field techniques. Clearly, and once again with reference to Figure N3, if a 3- dimensional model of the entire overpass had been required the system would have been operated on both RR#6 and Hwy 416. Figure N3 – Digitally Enhanced SideSwipe Data Forward Pointing Horizontal Scan Although not previously mentioned in this discussion, the road was first driven with a forward-pointing, horizontally scanning system tilted downwards by about 10° and sweeping a 60° swath. Legal clearance issues dictate that the equipment cannot be mounted more than 4.1m above the surface of the road; all of which equates to a forward reach of approximately 23m coupled to a horizontal swath of about 27m. In some situations this might provide sufficient coverage, but it is often the case that more extensive coverage is required (e.g., 30m either side of centerline in Afghanistan) while minimizing the effects of shadowing. Side Pointing Vertical Scan Fortunately, the effective swath of the survey can be greatly extended while simultaneously reducing the effects of shadowing. In order to achieve such an outcome the laser is rotated through 90 degrees on its z–axis so that it is side pointing. The laser is then turned through 90 degrees on its x–axis so that it becomes a vertically scanning, side pointing laser. If the laser is once again tilted downwards by about 10°, the resultant vertical swath now extends from approximately 40° below the horizon to approximately 20° above the horizon. Realistically, such a swath intersects the ground some 5m to the side of the vehicle, and extends usefully to about 100m, or until it strikes a reflective surface such as a building. In Practical Terms A combination of the two scanning methods outlined above allows an entire road to be mapped in three passes as described and illustrated in Figure N4. 1st Pass — drive along the road performing a forward pointing horizontal scan; 2nd Pass — turn the vehicle around, rotate the laser through 90° about its z–axis, rotate the laser through 90° about its x–axis, and resurvey the road in the opposite direction using a side pointing vertical scan; and finally 3rd Pass — turn the vehicle around, resurvey the road in the original direction, once again performing a side pointing vertical scan. The three passes now provide sufficient data in order to map approximately 100m either side of the road’s centerline. Although obstructions such as street furniture, buildings, and trees cause shadowing, they are mapped by virtue of the fact that they are “in the way”. Potential SideSwipe Enhancements The Multi Laser Approach The current field technique illustrates that there is room for improvement — rather than adopting a three pass approach to the survey, it would be preferable to drive a single pass only. Three lasers (one forward pointing, and two scanning to either side of the vehicle) would facilitate such an approach, but would of course add to the system’s overall complexity and cost. “Spray Painting” As previously noted, if a 3-dimensional model of the overpass in the test area had been required the system would have been operated on both RR#6 and Hwy 416. Although this would have minimized the degree of shadowing, the interior surfaces of the overpass would not have been captured in their entirety — this would require that the laser be directed upwards. From an engineering point of view this is perfectly feasible as long as the laser platform is mounted on a gimbaled mechanism that can be pointed in any direction. Because the laser is rigidly fixed in space with respect to the system’s IMU it would still be able to position the platform correctly, and thus obtain accurate information. To advance everything several steps further, the gimbaled mechanism could be remotely controlled and if the data processing were performed in real–time the operator would be able to “spray paint” the interior surfaces of the overpass. It is worth noting that this “spray painting” technique could be used in order to collect data in other difficult areas (by driving around a building for example). GPS Outages The tightly coupled nature of the system’s GPS/IMU means that GPS outages of the order of 15 to 20 seconds can be accommodated without seriously impacting the system’s accuracy. If the example of the RR#6 / Hwy 416 overpass is once again considered, and if it is assumed that its underside cannot be thoroughly “spray painted” in 15 to 20 seconds, it is thought that the vehicle could be stopped just short of the overpass (i.e., while maintaining sufficient GPS satellites for positioning) while data are collected using an obliquely pointed laser. If this were repeated from the other side of the overpass (with data being collected while traveling through the overpass, at speed) a complete picture of the overpass’ interior surfaces could be acquired. Some urban canyons and longer tunnels could pose serious challenges, but it is believed that the development of additional field techniques using emerging technologies such as pseudolights may help in this respect. Conclusions In response to real–world requirements to provide an accurate surveying system that would capture highway detail in a speedy and cost–effective manner Mosaic Mapping Systems developed its ground–based LIDAR mapping system — called SideSwipe — in record breaking time. The system is already in action in Afghanistan, where it will map almost 600km of road in very demanding conditions. System tests demonstrate that an accuracy of 4cm r.m.s. can routinely be achieved using the system, although areas of shadowing do inevitably occur from time–to–time. In order to minimize the effects of shadowing and in order to streamline the data collection process a system comprised of three lasers has been discussed as a potential enhancement for the future. It is also thought that real–time “spray painting” of detail via a remotely operated gimbaled mechanism could be implemented. Relevance to FEMA On behalf of FEMA, Dewberry is investigating alternative remote sensing technologies to capture 1st floor building elevations and lowest adjacent grades in order to predict flood risks. Because a building essentially presents SideSwipe with a source of shadowing it is, as noted earlier, mapped by virtue of the fact that it is “in the way”. Mosaic believes that Dewberry’s specific requirements can be met using a very specific mode of “spray painting”. SideSwipe’s operating costs are trivial compared with those of the helicopter system from which it is derived; also noteworthy is the fact that the helicopter system can be modified for ground use in half a day. APPENDIX O — LEGAL COMMENTS ON WEB-BASED ELEVATION CERTIFICATES This appendix documents the FEMA Law Associates (FLA) response to a query from Dewberry regarding potential legal issues raised by implementation of a web-based Elevation Certificate process. The following questions were asked: 1. What if either the surveyor, or the homeowner who hired the surveyor, doesn't want the EC entered into the elevation registry, is there anything illegal or questionable if FEMA sets up a web-enabled EC process that helps the surveyor to easily generate an EC but then also captures the information for insertion into the registry? 2. What if the homeowner who paid for the EC doesn't want the world to know that his/her house is floodprone? 3. Would we need to have a choice (yes or no) as to whether or not the data is entered into the registry? Perhaps we could simply state in the foreword comments that FEMA's web-enabled process has two purposes: (1) to help the surveyor prepare the EC correctly, and (2) to automatically enter the data into the registry. If the surveyor doesn't want to accept these conditions, don't use FEMA's web-enabled software to prepare the EC, but produce it the old-fashioned way by typing in the information on a blank form. FLA's answers to these questions are attached. This document, printed from a .pdf file sent over the Internet, includes an example of an electronic signature from Ernest B. Abbott of FLA. A similar process could be used for applying the seal and signature of the surveyor or engineer authenticating an EC into the registry. APPENDIX P — ELEVATION REGISTRY SYSTEM DESCRIPTION Overview The elevation registry system will allow licensed engineers and architects and registered surveyors to enter new ECs and to edit existing ones. In addition, insurance agents will be able to review insurance rating issues related to specific structures. The system will be web-based and spatially-enabled. Access Architects, Engineers, and Surveyors Each user will be provided a unique userID and password. This access combination will be validated against a master table of valid users. To be listed in this master table, an architect, engineer, or surveyor must have successfully completed the requirements for a professional certificate and be listed with a state or federal agency as a registered professional. These users will have read, write, and edit capabilities, but only for their own records, not those of others. Insurance Agents Each user will be provided a unique userID and password. This access combination will be validated against a master table of valid users. To be listed in this master table, an insurance agent must have successfully completed the requirements for a professional certificate and be listed with a licensed insurance agency. At a minimum, these users will have read-only capabilities. Limited write and edit capabilities may be permitted to allow these users to add and/or edit their own records in the system, not those of others. Other Users Map Determination Companies, Mortgage Companies, Real Estate Agents, and other related users may be granted read-only access to the elevation registry for informational purposes. The government may be able to recuperate some of the costs associated with developing and maintaining this system by charging a fee for this service, similar to the fee currently paid by users of the Letter of Map Change Publication service. In this model, the end-user would purchase access to the elevation registry for a set period of time (e.g., one year). After paying for the service, they would receive a unique userID and password for accessing the site. Upon expiration of their term of service, their userID and password would be disabled and they would have the option to renew their membership in the service. To eliminate the need for/associated cost of an e-commerce solution, fee collection for this service would not be a function of the web-based system. Input New Submissions Architects, Engineers, and Surveyors will be provided interactive screens to allow input of new submissions for ECs. These screens will mimic, to the degree possible, the existing EC form. The forms will be self validating. If a user enters a value not expected by the system, the system will present the user with pertinent feedback, such as an example of an expected value. All essential information must be completed before the form can be successfully submitted. All new submissions will require an electronic signature. This signature will consist of three elements that must be entered correctly for the signature to be valid. To electronically sign the submission, the user will re-enter his/her userID password combination, provide his/her e-mail address, and provide his/her professional registration number. These combinations will be matched against the master table, and if all three entries are correct, the submission will be accepted as valid. Legacy Data All existing elevation information will be entered into the system. Data available in electronic format will be reconfigured and ported into the system. Data available only in hardcopy format will be scanned, edited, and ported into the system. Output Electronic data and hard copy output will be available to users with read, write, and edit access. Data Users will have the ability to download the data associated with a single EC in a variety of formats. The specific formats will be confirmed later, but these may include .pdf, .doc, .xls, .mdb, shp or others. Some users will have the ability to download data in editable format whereas others will have the ability to download data only in non-editable format, e.g., .pdf. Hardcopy Surveyors, architects, and engineers will have the ability to print out a newly submitted EC or a legacy certificate in the same format as the existing hard copy certificates. The end user can print newly submitted certificates and affix his/her signature and seal for delivery to the customer. Hard copy versions of previously submitted certificates will be printed with a notification in the footer that the document is not original. The area set aside for the seal will be overwritten with hatch marks. Database The database will be a spatially-enabled SQL level database. This geo-database will allow all users to view the location of a structure as provided in a certificate in relation to other available data layers such as floodplain maps, DOQs, et al. These data layers may be stored within the structure of the elevation registry system, or, if the government so desires, linked interactively through the Geospatial One Stop. Screens The system will be interactive. The user will be prompted for all required fields and for the formats for the required fields. All interaction with the system will be via the Internet. The system will support Internet Explorer 5.5 and higher and Netscape 6.2 and higher. No other user software will be required. Support The user will also have access to an online tutorial to guide the user through the completion of the form. Section 508 Compliance The elevation registry will be compliant with Section 508 of the Americans with Disabilities Act. APPENDIX Q — CE SPREADSHEETS FOR BASE SCENARIO The Cost-Effectiveness (CE) spreadsheet in APPENDIX Q is the Base Scenario spreadsheet, used for Table 17 and subsequently modified for the various CE sensitivity analyses summarized in Tables 18 through 26. The percentage point values assigned to different components of an EC, as well as the Elevation Accuracy Multipliers for different technologies, at the bottom of the spreadsheet, were all provided by FEMA during coordination meetings. APPENDIX R — PROPOSED DATA DICTIONARY One of FEMA's goals is to minimize Privacy Act issues; therefore, Dewberry recommends that the building owner's name not be included in the registry. Another of FEMA's goals is to facilitate automated address matching; this can be done most efficiently if each street address is separated into discrete components including: house number, house number suffix, street prefix, street name, street suffix, and apartment, unit, suite or building number. Another of FEMA's goals is for the registry database to accommodate changes to flood zones and base flood elevations; this can be done most efficiently if the registry includes the latitude and longitude of each structure, in addition to the mandatory items listed on the EC (FEMA form 81-31). Another of FEMA's goals is to improve accuracy by getting surveyors to use accurate and stable monuments (benchmarks) published by the National Geodetic Survey (NGS) rather then FEMA's Elevation Reference Marks which lack stability and are less accurate. Finally, Dewberry believes that the registry would have greater value to insurance agents if some attempt was made to quantify the accuracy of the lowest floor elevation (top of bottom floor), for which recommendations are included in Part I of the Final Report. Therefore, the data dictionary for the registry database should have the following columns, with one row for each record of elevation data, including repeat surveys of the same address performed on different dates. All of these items already exist in the LOMA 2000 database except for items annotated with an asterisk below. Note that if implemented, this data dictionary would most likely be modified for consistency with the NextGen database. 1. Unique address ID, text (20) 2. State FIPS code, text (2) 3. Community code, text (4) 4. Building's house number, text (8). 5. Building's house number suffix, text (4) 6. Building's street prefix, text (50) 7. Building's street name, text (100) 8. Building street suffix, text (50) 9. Apartment, unit, suite and/or building number, text (20) 10. P.O. box number, text (10) 11. P.O. route number, text (10) 12. City, text (50) 13. Zip code, text (5) 14. Zip code suffix (4) 15. Property description (lot & block numbers, tax parcel number, legal description, etc.), text (40) 16. Assessor's Parcel Number (APN)(book-page-parcel), text (20) * 17. Building use (residential, non-residential, comments), text (20) * 18. Latitude, decimal degrees, number (single precision) 19. Longitude, decimal degrees, number (single precision) 20. Horizontal datum, text (10) 21. Horizontal accuracy code, integer (1) 22. Survey source (GPS, conventional ground survey, USGS quad map, aerial photogrammetry, LIDAR, IFSAR, other), text (40) * 23. Estimated accuracy of lowest floor elevation (top of bottom floor) at the 95% confidence level (ft), number (single precision) * 24. B1 - NFIP community name, text (50) 25. B1 - NFIP community number, text (6) 26. B2 - County name, text (30) 27. County FIPS code 28. B3 – State name, text (50) 29. B4 - FIRM map and panel number, text (10) 30. B5 - FIRM suffix, text (2) 31. B6 - FIRM index date, date/time 32. B7 - FIRM panel effective/revised date, date/time 33. B8 - Flood zone, text (12) 34. B9 - BFE (ft), number (single precision) 35. B10 - Source of BFE data, text (20) 36. B11 - Elevation datum used for the BFE, text (20) 37. B12 - CBRS or OPA, text (10) * 38. B12 – CBRS or OPA designation date, date/time * 39. C1 - Basis for building elevations, text (30) * 40. C2 - Building diagram number, integer (1) * 41. C3 - Elevation datum used for all elevations surveyed, text (20) 42. C3 - NGS PID No. (Permanent Identifier) used as elevation reference, text (6) * 43. C3a - Elevation, top of bottom floor (ft), number (single precision) 44. C3b - Elevation, top of next higher floor (ft), number (single precision) * 45. C3c - Elevation, bottom of lowest horizontal structural member (V zones only) (ft), number (single precision) * 46. C3d - Elevation, attached garage, top of slab (ft), number (single precision) * 47. C3e - Lowest elevation of machinery and/or equipment servicing the building (ft), number (single precision) * 48. C3f - Elevation, lowest adjacent grade (LAG), number (single precision) 49. C3g - Elevation, highest adjacent grade (HAG), number (single precision) * 50. C3h - Number of permanent openings (flood vents) within 1 ft. above adjacent grade, integer (2) * 51. C3i - Total area of all permanent openings (flood vents) in C3h (sq in), number (single precision) * 52. Certifier's name, text (30) 53. License number, text (12) * 54. Certifier’s title, text (50) * 55. Certifier’s company name, text (50) * 56. Certifier’s address, text (100) * 57. Certifier’s city, text (50) * 58. Certifier’s state, text (50) * 59. Certifier’s zip code, text (5) * 60. Certifier’s telephone, text (12) * 61. Certifier’s email address, text (50) * 62. Date of survey information, date/time * 63. Surveyor comments, text (254) * 64. E2 – Elevation of top of bottom floor from HAG (ft), number (single precision) * 65. E2 – Elevation of top of bottom floor is above/below HAG, text (5) * 66. E3 – Elevation of next higher floor above HAG (ft), number (single precision) * 67. E4 – Is top of bottom floor elevated in compliance with community’s ordinance? Yes/no, Boolean * 68. Scanned EC image file name (50) * (for FEMA use only; EC image not available to the public) 69. Scanned EC image path name (50) * 70. Depth(s) of prior interior flooding, text (20) * 71. Date(s) of prior flooding, text (20) * 72. Digital images path name (50) * 73. Digital image, front view, file name (50) * 74. Digital image, rear view, file name (50) * 75. Was habitable space constructed in garages, basements or crawl spaces previously intended for storage only? Yes/No, Boolean. Explain (50) 76. Was habitable space constructed beneath elevated structures in V- zones? Yes/No, Boolean. Explain (50) 77. Were rooms added, enlarged, or deleted? Yes/No, Boolean. Explain (50) 78. Have flood vents been removed/closed during remodeling? Yes/No, Boolean. Explain (50) 79. Is lowest elevation of machinery below the BFE? Yes/No, Boolean. Explain (50) 80. Have breakaway panels in V-zones been replaced with permanent panels or walls? Yes/No, Boolean, Explain (50) APPENDIX S — WEB-BASED REGISTRY Web Portals To be effective for eRating, the elevation registry will need to have a web portal — a starting place on the Map Service Center's web site — with interfaces for input of data to the registry's database, and output of information needed for eRating. In addition to insurance agents and WYO companies, this portal should also be available to others involved in the NFIP, e.g., real estate agents (see CRS Section 340 that encourages real estate agents to disclose flood risk and the need for flood insurance to potential buyers), as well as the general public who should not need to submit a Freedom of Information Act request to obtain flood risk information for individual structures (see CRS Section 310 that encourages the availability of flood risk information on a public website). The interface should be designed with the users in mind while the underlying processes should be designed with the data sources in mind. To analyze potential options, the strategies will be categorized by the number of structures each strategy will have per transaction, and the type of user involved. At a fundamental level, any system consists of three major components: input, process, and output. This section will focus on the input and output controls and access portals within the elevation registry system (See Figure S.1). Figure S.1 — Elevation Registry System Components. In a significant number of systems these two highlighted components tend to be the most neglected because sufficient information about user requirements is never collected. To fully develop these components within the eRating system, full user requirements analysis will have to be performed before design and development of the system. Although the foundation of the system will be the data sources, the elevation registry system intended for eRating will be designed around user needs. Given the differing skill levels and needs of each user group, to ensure continued use of the system, all user types must be identified upfront and provisions made to accommodate their specific needs. Elevation Registry — Phased Implementation The system should be implemented in two phases: the ramp-up phase, and the maintenance phase. During the initial ramp-up phase, data will be loaded through back-end portals available to the developers only, with no public access. Back-end portals are managed by the system administrator who controls the database and ensures it is not corrupted. Front-end portals are the "face" the user sees at the web portal, e.g., drop-down menus and other features that need to be user friendly so that users can interact efficiently with the web portal. During the second phase, information will be available for use by insurance agents and others, and data will primarily be loaded through front-end portals, with some back-end uploads for mass data inputs such as subsequent batch processing of remote sensing data. The data collected for each strategy can be loaded into the system during one or both phases of implementation, depending on the availability of the data. For example, existing EC data (Strategy A) will be primarily loaded during the initial ramp-up phase, whereas remote sensing data (Strategy B) will be uploaded during both phases, depending on when the data is ready and validated. Follow-on ECs generated individually by surveyors (Strategy D) will be loaded during the maintenance phase. Figure S.2 — Implementation Phases and Data Access. The major distinctions between front-end and back-end portals are the level of controls, or restrictions, enforced and access to data on users. Back-end portals will generally have fewer restrictions and the developers will be able to access specific data directly, including historical data. Figure S.2 illustrates how the strategies and the input/output functionalities will be integrated with each phase. Phase I – Ramp-Up As noted above, during the initial ramp-up phase, data will be loaded into the elevation registry through back-end portals only. This will primarily take place by bulk loading of data from existing data sources that have been validated and processed for entry into the registry. This would include data acquired through Strategies A and E as well as remote sensing data acquired through Strategy B. Existing Elevation Certificates will be compiled and processed for loading into the elevation registry. These will include those ECs already in a database format as well as ones that need to be scanned and digitized and those that need to be digitized from existing scans. As outlined in Part I of this report under Strategy A, the potential sources of existing ECs include the following: ? LOMA 2000: 91,600 database records ? ISO data: 50,000 database records ? Dewberry & URS: 16,381 database records + 3,119 hardcopy ECs ? USACE: 1,200 database records ? Communities: up to 1,000,000 hardcopy ECs Most of these ECs and records either have no latitude/longitude values, or their values are estimated by some form of automated geocoding. Furthermore, most of the ISO and community ECs and LOMA 2000 records have quality issues discussed above and would potentially be of lesser value to the registry. Nevertheless, Strategy A recognizes that some of the existing EC data will be of lesser value than EC data from other sources. Data from other FEMA databases will also be processed and loaded into the registry during Phase I. As noted in Strategy E, NEMIS and BureauNet are the two existing FEMA databases that contain data most relevant to the registry. ? NEMIS Database. In FY 1999, FEMA deployed the National Emergency Management Information System (NEMIS) which serves as the information technology standard for the agency's Presidential disaster operations. During the transition to NEMIS, the data in ADAMS, the predecessor system, was transferred to the newer system. NEMIS automates federal disaster programs including incident activities, preliminary damage assessment, declaration processing, human services, infrastructure support, mitigation, and associated administrative and financial processing. During FY 2002, NEMIS supported more than 197 disasters, 42 of which were Presidential declarations. Currently, NEMIS contains information on over 400,000 structures, including a structure’s address, type (basement, no basement), and UTM coordinates (latitude and longitude). Given the complexity of NEMIS, it is more cost effective to load NEMIS data into the elevation registry in order to display NEMIS data with other data needed for eRating, and neither export data to NEMIS nor require damage inspectors to enter data into the elevation registry directly. ? BureauNet. BureauNet, or Policy-In-Force, is the reporting and user interface for the policy subsystem of an IBM mainframe database maintained by FEMA (other subsystems include claims and structures that can not be insured). The mainframe database includes historical policy data starting in 1974 and is updated by various sources including WYO companies. There are currently 4.5 million active policies (note: one active policy per structure) which include such information as date of construction, LAG, HAG, lowest floor elevation, geo-referenced coordinates, and policyholder information. FEMA’s NextGen project is in the process of updating the system to an Oracle database environment and revamping the analysis tools and policy rating engine. This database should be key for initial population of the registry. Given the diverse addressing protocols and requirements within the various databases, it is essential that the elevation registry adopt an addressing convention that is applicable universally. As discussed in Strategy B, remote sensing data that would support the registry include the following: ? Photogrammetry equivalent to 2 ft contours, combined with on-site measurements; ? LIDAR equivalent to 2 ft contours, combined with on-site measurements; and ? LIDAR LAG/HAG elevations only for an entire community or county which could be processed to extract EC data such as the LAG and HAG for all structures in the community. These data would be batch loaded into the registry through a back-end portal. Remote sensing data collected before or after the implementation of the elevation registry will be processed similarly. Before any data can be posted to the elevation registry, it will have to be quality reviewed for accuracy (in accordance with one of the Options listed below) and certified according to FEMA standards. The process of certification will depend on several factors including collection technique, local needs, and FEMA partnerships with other agencies. After certification, data will be posted to the registry by FEMA or a FEMA cooperating technical partner (CTP) or contractor using. Phase II – Maintenance During the second phase, information will be available for use by insurance agents and others, and data will primarily be loaded through front-end portals, with some back-end uploads for mass data inputs such as subsequent batch processing of remote sensing data or scheduled batch updates from existing databases. After the ramp-up phase, surveyors will be encouraged to use the front-end portal of the registry to prepare new ECs. During a typical year, thousands of structures are surveyed using GPS or conventional methods. The information is primarily recorded on a paper EC, certified by a surveyor, and given to the owner. The information may also be compiled by local government agencies. However, no national repository currently exists to compile the elevation information for surveyed structures, and this is the deficiency that would be solved through on-line entry of new ECs into the elevation registry. As part of FEMA's outreach program, surveyors and government agencies will be encouraged to use the elevation registry's web portal as part of their process to capture and store structural information in the registry. This effort will involve developing tools and products, including an Elevation Certificate How-To Guide for EC generation, to attract and maintain usage of the system by such users. We would prefer that surveyors not have a way to by-pass posting the structural information to the registry. Remote sensing data collected after the implementation of the elevation registry will be quality reviewed for accuracy (in accordance with one of the Options listed below) and certified according to FEMA standards. After certification, data will be posted to the registry by FEMA or a FEMA cooperating technical partner (CTP) or contractor using a back-end portal. During the design of the elevation registry, it is anticipated that schedules will be determined for adding and/or updating records through the back-end portal from existing data sources, including NEMIS and BureauNet. As noted above, the system administrator who controls the database will resolve any conflicts between existing and new records and ensure that the registry is not corrupted. CRS communities annually submit ECs to ISO as part of their CRS requirements. Until such time as all surveyors who prepare ECs are using the front-end portal, these ECs will be compiled and processed for loading into the elevation registry through the back-end portal. As in Phase I, ECs submitted by communities may include those ECs already in a database format as well as ones that need to be scanned and digitized and those that need to be digitized from existing scans. Elevation Registry System Requirements This section will address three options for flow of data between users and the registry: (1) centralized processing, (2) distributed processing, and (3) web portals. A key distinction among users is the quantity of structures the user will provide or request per transaction. Surveyor and insurer eRating transactions will primarily consist of single structure information, while remote sensing data providers (i.e., contractors and communities) will primarily engage in multi-unit transactions. Transactions involving LOMAs can involve both single and multi- unit transactions and will have to be considered separately. To develop the system interface, data providers are categorized into three major categories: ? Single-structure data users (requestors or providers). An example of a single- structure data requestor would be an insurance agent authorized to read and print individual records and be charged a transaction fee from a draw-down account, or perhaps a real estate agent or potential home owner seeking information on a structure of interest, for which charge cards could be used for billing purposes. An example of a single-structure data provider would be a surveyor authorized to enter individual EC records based on their surveyor's license and seal. Single-structure data users would interact directly with the front-end or back-end web portals (Option 3 in Figure 14 below), but the site would need to authenticate the surveyor's credentials before accepting new EC data into the registry. To minimize costs, Dewberry recommends that Option 3 be operated as an add-on to FEMA's current Map Service Center (MSC) web site for reasons explained previously. FEMA also operates other centralized web portals for NEMIS and LOMA 2000. ? Multiple-structure data users (requestors or providers). An example of a multiple-structure data requestor would be a community NFIP or CRS coordinator who needs to review all records for his/her community. An example of a multiple-structure data provider would be a community that is submitting batch files of digitized ECs or batch files of remote sensing data from Methods 8, 14 or 17. Depending on whether or not the user is a CTP, either Option 1 or Option 2 of Figure S.3 could pertain. For example, a CTP would agree to maintain certain levels of expertise so that the partner's specialist (normally NFIP or CRS coordinator) could perform specified transactions for input and output of data covered by the CTP agreement; then, Option 2 could be exercised with a Distributed Processing Center, and there would be no fees charged for output of data or reports. If the user is not a CTP, then data requestors and providers would both interact with a Centralized Processing Center, Option 1, operated either by FEMA's National Service Provider (NSP) or a different FEMA contractor, and fees could be charged for services provided. Figure S.3 — User Logic Process for Three Options. ? Special-case data users (requestors or providers). An example of a special- case data requestor might be a FEMA Analyst searching for repetitive loss properties in a community. An example of a special-case data provider might be a LOMA Analyst entering data into the LOMA 2000 database, linked to the registry, for input of individual LOMA records or perhaps the LOMA record for an entire subdivision (one LOMA covering multiple addresses). Such users would have access to any of the three Options. With Option 1 (Centralized Processing Center), the import or export of large datasets will be managed and quality controlled in a centralized location. Its primary function will be to process and quality control incoming data, reformat data into a standardized format, and batch enter large datasets from Strategy A or B. The advantages are: (1) easier enforcement of standards, (2) easier training of the staff involved in the process, and (3) easier and quicker procedures for upgrades and changes. The disadvantage is higher costs to FEMA compared to Option 2 where CTPs bear major costs. With Option 2 (Distributed Processing Centers), the import or export of community data will be managed and quality controlled by states, counties or communities who choose to manage their own data and sign a CTP agreement to do so. The advantage is lower cost to FEMA compared with Option 1, but the disadvantages are: (1) harder enforcement of standards, (2) harder training of the staff involved, and (3) more difficult to upgrade and change procedures. Some communities already operate their own web site for provision of ECs to the public, and FEMA should encourage such practice by others. Option 2 is a half- step in helping communities meet this goal. With Option 3 (Centralized Web Portal), the import or export of data for individual structures is direct with the web portal. Individual users interact directly with the front-end portal designed to be user friendly. The Web portal will register and authenticate users automatically as well as allow data queries. Beyond the cost of developing the web site, FEMA would not bear any major additional costs associated with data entry and requests through this system, except when protocols need to be revised and updated. This option is the major way that FEMA receives cost reimbursement from data requestors who use the registry for real estate queries or eRating, for example. General Assumptions ? No data providers will be allowed to submit data directly to the elevation registry because this may unintentionally corrupt the data base. All data will pass security checks before being posted. ? Land surveyors and remote sensing contractors are registered with and/or licensed by a state Board of Registration for Professional Engineers and Land Surveyors which protects the public from harm that could be caused by unlicensed surveyors. Remote sensing firms are similarly licensed, based on ownership or management controls by licensed professionals. Because licenses can be revoked or suspended because of non-professional performance or negligence, professional registrations helps to ensure the accuracy of data input from surveyors and remote sensing firms. ? Alternative options all have essentially the same short-term costs for setting up the web portal. ? Cost of data collected through the LOMA process and NEMIS will not be incurred by the elevation registry. General Criteria ? Minimize maintenance costs. ? Use other FEMA programs to maintain the data and absorb costs (e.g., Map Service Center, CTP and CRS). ? Adopt flexible strategies that are adaptive to technological changes and minimize interoperability issues. ? Provide added value to users by saving time through automation or elimination of specific tasks and report generation. Authentication of Data Providers. For legal and programmatic reasons, methods to authenticate data providers will be required before data can be posted to the registry. Authentication will be required when the data provider first registers with the system and a profile is generated. Depending on the available of resources, authentication may be required periodically to validate user information. Given that one solution will not be suitable for all data providers and circumstances, three alternatives will be considered: ? On-line authentication ? The data provider submits documentation directly to the elevation registry processing center, either with the National Service Provider or other FEMA representative or contractor. ? The state licensing Board submits an annual validation form to the elevation registry processing center listing all Land Surveyors and license numbers. Data Certification. Since data posted to the system will be generated from multiple sources using different techniques, not all data in the registry will have the same level of accuracy. Furthermore, some users, such as insurance providers, will require less accurate elevation information than others. As a result, to address legal and scientific concerns, the registry will capture additional information about elevation data, such as: ? Level of accuracy (dependent on quality control and collection methodology) ? Certified by a licensed Land Surveyor or uncertified ? Methodology (e.g., aerial survey; ground survey, GPS or conventional; NGS benchmark used as the basis for elevation surveys) E-signatures. When legal and feasible, users can submit data and generate legal documents (e.g., Elevation Certificates) using e-signatures. Otherwise, data providers will have to submit data and documentation using conventional methods through a processing center. Input and Output Protocols. eXtensible Markup Language (XML) and Geographic Markup Language (GML) formats are recommended to eliminate interoperability issues. XML and GMS (a subset of XML) are types of meta- languages that serve to define other languages and their data. With XML, one can define the structure of data used, have data be platform-independent, automatically process data defined by XML, and define unique markup tags that hold FEMA's data elements. The simple and consistent nature of XML makes it very useful for exchanging data between many types of applications. GML was developed to describe spatial data and spatial data properties and is used by WMS and WFS. WMS and WFS are Open GIS specifications that facilitate the interoperability of web-based applications and data, principally for portal technologies. User Output Reports: ? Capture additional information to benefit users such as surveyors ? Generate hardcopy ECs automatically ? Generate summary reports for users, e.g., all ECs generated by a specified surveyor ? Generate a report, containing a map, for the user clients. A FIRMette is already an option available from the MSC. ? Provide EC data for use in GIS applications through WMS and WFS. FEMA Reports: ? Generate exception reports ? Flag structures or users that do not satisfy specific criteria, e.g., lowest floor elevations, or lowest elevation of machinery (LEM), if below the BFE; in the case of LEMs, air conditioner pads could be elevated, or equipment could otherwise be protected from water damage. ? Flag new elevation data that is significantly different from prior data on the same structure. ? Identify data providers that consistently provide questionable elevation data for structures. ? Mitigation analyses. Identify repetitive loss structures or structures that might otherwise be candidates for retrofitting or buy-out. ? Verify flood policies or whatever else BureauNet is used for. Overall Strategy The primary data collection and delivery mechanism will be the central Web portal already operated by the MSC. The centralized user interfaces constitute the most cost effective strategies for the following reasons: ? Minimal setup and distribution costs ? Low maintenance and upgrade costs ? Universal access ? Existing security functionalities ? Existing E-commerce procedures for user authentications and payments APPENDIX T — COMMUNITY RATING SYSTEM Community Rating System (CRS). The CRS offers the greatest potential for encouraging communities to partner in development and maintenance of an elevation registry. Under the CRS, there is an incentive for communities to do more than just regulate construction of new buildings to minimum national standards. As part of the CRS program, flood insurance premiums are adjusted to reflect community activities that: (1) reduce flood damage to existing buildings, (2) manage development in areas not mapped by the NFIP, (3) protect new buildings beyond the minimum NFIP protection level, (4) help insurance agents obtain flood data, and (5) help people obtain flood insurance. Items (4) and (5) are clearly relevant to the primary purpose of an elevation registry. Table T.1 lists credit points earned, classification awarded, and premium reductions given for communities in the NFIP CRS. This table is extracted from www.fema.gov/pdf/nfip/manual10_04/19crs.pdf. Table T.1 — Premium Reductions for CRS Credit Points Earned Community CRS Credit Points Community Class Premium Reduction - SFHA Premium Reduction - Non-SFHA * 4,500+ 1 45% 10% 4,000 - 4,499 2 40% 10% 3,500 - 3,999 3 35% 10% 3,000 - 3,499 4 30% 10% 2,500 - 2,999 5 25% 10% 2,000 - 2,499 6 20% 10% 1,500 - 1,999 7 15% 5% 1,000 - 1,499 8 10% 5% 500 - 999 9 5% 5% 0 - 499 10 0% 0% * Preferred Risk Policies are available only in B, C, and X Zones for properties that are shown to have a minimal risk of flood damage. The Preferred Risk Policy does not receive premium rate credits under the CRS because it already has a lower premium than other policies. Although they are in SFHAs, Zones AR and A99 are limited to a 5% discount. Premium reductions are subject to change. The objective of the CRS is to reward communities that are doing more than meeting the minimum NFIP requirements to help their citizens prevent or reduce flood losses. The CRS also provides an incentive for communities to initiate new flood protection activities. The goal of the CRS is to encourage, by the use of flood insurance premium adjustments, community and state activities beyond those required by the NFIP to: 1. Reduce flood losses, i.e., ? Protect public health and safety ? Reduce damage to buildings and contents ? Prevent increases in flood damage from new construction ? Reduce the risk of erosion damage, and ? Protect natural and beneficial floodplain functions 2. Facilitate accurate insurance rating, and 3. Promote the awareness of flood insurance The "CRS Coordinators Manual" at www.fema.gov/pdg/nfip/crsentire.pdf indicates the activities for which CRS credits can be earned by communities. The CRS Schedule identifies 18 creditable activities, organized under four categories: Public Information (Series 300), Mapping and Regulations (Series 400), Flood Damage Reduction (Series 500), and Flood Preparedness (Series 600). The Schedule assigns credit points based upon the extent to which an activity advances the three goals of the CRS. Once it has submitted its CRS Application, a community must continue to implement its credited activities to keep its classification. Community responsibilities include cooperating with the ISO/CRS Specialist and verification procedures, and maintaining Elevation Certificates, other permit records, and old FIRMs forever. The Public Information Series 300 credits programs that advise people about the flood hazard, flood insurance, and ways to reduce flood damage. These activities also provide data needed by insurance agents for accurate flood insurance rating. They generally serve all members of the community and work toward all three goals of the CRS. ? Section 300 includes the counting of buildings within the SFHA by maintaining complete records of pre-FIRM and post-FIRM buildings in floodplains, by using recent aerial photography or digital orthophotos to count the number of such buildings, or by using community staff to travel through the floodplains and count the number of buildings. Dewberry recommends that additional CRS credit points be awarded to those communities that maintain an accurate list of geocoded addresses in or near to floodplains; this is obviously worth much more than a simple count of structures. ? Section 310 provides a maximum of 162 CRS credit points for EC data, i.e., up to 56 points for maintaining FEMA ECs on all buildings built in the SFHA after date of application to the CRS; up to 56 points for maintaining ECs on buildings built before the date of application to the CRS but after initial date of the FIRM; up to 15 points if the EC data are kept and made available in computer format; and up to 20 points for putting EC data on a publicly accessible website. Impact adjustments are applied to reflect the proportion of buildings that have ECs. This section is directly relevant to the elevation registry, and providing EC data for the registry should earn additional CRS credits. ? Section 320 provides a maximum of 140 CRS credit points for providing FIRM information to inquirers, providing information on flood insurance purchase requirements, providing information on Coastal Barrier Resources System (CBRS) requirements, keeping old FIRMs and updating the maps used for the service, and advising inquirers whether the property is subject to a special flood-related hazard. This section is also relevant to the elevation registry, and providing EC data for the registry would earn additional CRS credits. ? Section 330 provides a maximum of 315 CRS credit points for community outreach projects pertaining to the local flood hazard (for example, mapping drainage areas smaller than 1 square mile); flood safety; flood insurance requirements; property protection measures; natural and beneficial functions of the local floodplain; a map of the local flood hazard; the flood warning system; floodplain development permit requirements; substantial improvement/damage requirements; and drainage system maintenance. The elevation registry will help define flood insurance requirements, earning CRS credits for this section. Mecklenburg County, NC, for example, provides ECs on demand and has multiple programs to encourage the purchase of flood insurance; these programs are especially effective when home owners can see elevation data for their home that compares their lowest floor elevation with the BFE. ? Section 340 provides a maximum of 81 CRS credit points for real estate agent disclosure of flood hazards and flood insurance purchase requirements to those interested in purchasing properties located in the SFHA, other disclosure requirements, real estate agents' brochure, and disclosure of other hazards such as erosion, subsidence, or wetlands. A community's support of FEMA in establishing a web site that facilitates the search (by real estate agents and/or individuals) of flood hazard information, and other initiatives to get real estate agents to routinely use this service, should earn additional CRS credit points for this support. ? Section 350 provides a maximum of 66 CRS credit points for a flood protection library, locally pertinent documents, and flood protection website. Community flood protection websites should provide links to FEMA's elevation registry. ? Section 360 provides a maximum of 71 CRS credit points for flood protection technical assistance. The elevation registry could include site-specific flood data, information on historical flooding, repetitive losses, floor elevations and other information that would enable the local CRS or NFIP coordinator to provide one-on-one advice to property owners. The Mapping and Regulations Series 400 credits programs that provide increased protection to new development. These activities include mapping areas not shown on the FIRM, preserving open space, enforcing higher regulatory standards, and managing stormwater. The credit is increased for growing communities. These activities work toward the first and second goals of the CRS, damage reduction and accurate insurance rating. ? Section 410 provides a maximum of 1,373 CRS credit points for additional flood data, including regulatory flood elevations, additional data standards for a new study, more restrictive floodway standard, additional flood data for special hazards, and CTP agreements. If community input to the elevation registry included higher standards, either community wide or address-specific, it is possible that additional CRS credit points could be earned. ? Section 420 provides a maximum of 900 CRS credit points for open space preservation. This section appears to have no relevance to the elevation registry. ? Section 430 provides a maximum of 700 CRS credit points for managing the development of land in ways that minimize construction of buildings in the floodplain. Community-provided photogrammetric or LIDAR data could be used in conjunction with regulations that discourage construction of buildings in floodprone areas or encourage low density zoning. ? Section 440 provides a maximum of 231 CRS credit points for flood data maintenance, including additional map data that improves access, quality, and/or ease of updating flood data within the community. Ease of updating flood data is exactly why this report emphasizes the importance of community address lists that are accurately geocoded for all structures in or near floodplains. Communities would need to retain the old BFE, but they could update the registry with new BFEs, useful for new additions or new elevation requirements if substantially damaged. ? Section 450 provides a maximum of 670 CRS credit points for stormwater management regulations, stormwater management master plan, freeboard for new buildings in B, C, D, and X zones, erosion and sedimentation control regulations, and water quality regulations. Many communities initially acquire their high accuracy LIDAR data for stormwater management purposes, while also serving floodplain management and other applications. Thus, a community decision to acquire high accuracy LIDAR data could be used for multiple purposes and also earn additional CRS credit points in this section. The Flood Damage Reduction Series 500 credits programs for areas in which existing development is at risk. Credit is provided for a comprehensive floodplain management plan, relocating or retrofitting floodprone structures, and maintaining drainage systems. These activities work toward the first goal of the CRS, damage reduction. ? Section 510 provides a maximum of 309 CRS credit points, most points being earned for adopting and implementing a floodplain management plan that was developed using a specified standard planning process. A community's implementation of actions recommended in this report for populating an elevation registry could form a major component of this floodplain management plan, earning additional CRS credits in multiple categories. ? Section 520 provides a maximum of 3,200 credit points for buildings acquired or relocated, especially repetitive loss structures. Since the elevation registry is intended to include information on structures previously flooded, this should serve to earn additional CRS credit points. Furthermore, communities that use their elevation registry information correctly could identify those structures with the highest probability of being flooded in the future, i.e., candidates for acquisition/relocation initiatives. ? Section 530 provides a maximum of 2,800 credit points for structural flood control projects and sewer backup protection projects. Dewberry is aware of one community that used an EC-type database to compute the benefit-cost ratio used to justify the construction of a new concrete box culvert to replace an undersized culvert that caused back-up which flooded properties upstream. Provision of objective justification and cost modeling data is an added benefit of an elevation registry. ? Section 540 provides a maximum of 330 points for drainage system maintenance. This section appears to be non applicable to the elevation registry. The Flood Preparedness Series 600 credits flood warning, levee safety, and dam safety programs. These activities work toward the first and third goals of the CRS, damage reduction and hazard awareness. ? Section 610 provides a maximum of 225 CRS credit points for a flood threat recognition system, emergency warning dissemination, other response efforts, critical facilities planning, and StormReady community designation. The elevation registry, as presented in this report, includes essential information necessary for a flood threat warning system for individual structures. ? Section 620 provides a maximum of 900 CRS credit points for levee safety. While not improving the safety of levees themselves, elevation registry information can be used to identify structures that would be inundated by a levee breach, and, combined with HAZUS, can be used to estimate the cost to a community as a result of a breached levee. This information would be especially relevant should communities need to consider whether or not to deliberately breach a levee to flood farmlands, for example, in order to spare a city that would otherwise be flooded. CRS communities protected by levees should have a greater incentive for cooperating with FEMA in development and maintenance of an elevation registry. ? Section 630 provides a maximum of 175 CRS credit points for dam safety. Some communities already use EC databases to determine which structures would be inundated by floods should there be a catastrophic dam breach, flooding structures downstream and possibly destroying them completely. CRS communities immediately downstream from dams should therefore have a greater incentive for cooperating with FEMA in development and maintenance of an elevation registry. It must be noted that Dewberry and ISO share concerns about populating a new database with old information that has known quality problems. As noted previously, many of the ECs are incomplete and need additional information. Furthermore, since there were so many different versions of the EC and in various states of quality for scanning, there will be many that are missing much of the pertinent data such as top of bottom floor and HAG since this type of information was not required on many of the earlier forms or named differently (e.g., "top of bottom floor" versus "reference floor" on earlier versions). Before digitizing any community ECs, it would be advisable to check the BureauNet database to determine if these elevation records are already part of the policies-in-force database maintained by CSC. When a community's ECs are already digitized in another database, it would be wasteful to digitize them again. For this reason, the BureauNet database needs to be accessible to anyone entering EC data. If an address, when entered, duplicates an address already in the database, dates would need to be checked to see if the EC is a duplicate or perhaps supersedes an existing record. Data conversion specialty firms, when paid a negotiated unit fee to digitize records, do not want to be encumbered by decisions as to whether or not each record needs to be digitized. Furthermore, community personnel are better able to determine if addresses are truly duplicates. For these reasons, it would be best if the communities themselves could decide which records need to be digitized, avoiding decisions required by digitization firms. For these reasons, Dewberry will assume that communities themselves will determine which of their ECs to digitize. It is extremely important that the person taking these measurements be fully trained to understand the correct elevation points to be measured. Often, licensed surveyors do not understand the correct elevation points, partly because they may rarely survey ECs. For these reasons, it is imperative that anyone taking measurements for hundreds of alternative EC records be fully trained in the correct process to be followed. Although it is technically feasible, the concept of also including electronic seals for engineers and surveyors is not realistic at this time because state licensure boards would need to develop, operate and maintain such systems. There are no such initiatives known to be in progress at this time. When LIDAR data is used for floodplain mapping purposes, FEMA standards require the data to be tested and reported separately for each of the major land cover categories representative of the floodplain, e.g., open terrain, weeds and crops, scrub, forests, and built-up areas. Nevertheless, a consolidated vertical accuracy statistic is normally quoted for all land cover categories combined. Existing tax assessor records typically have value for the elevation registry, especially when tax parcels (or building footprints or centroids) are digitized in a GIS with each parcel linked to a street address, PO box or rural route address, and/or APN number. Even though these records do not include elevation data, they include georeferenced parcel polygons that can be overlaid on DFIRMs to determine the universe of structure addresses within each county that are located in or near to floodplains. This is needed to help determine how complete the registry records are for each county or community, or alternatively, how many EC records are missing. Furthermore, tax record square footage and assessed values of structures enhance an elevation registry so that proactive floodplain management initiatives can be applied. These increases of 5%, 10% and 15% are totally theoretical on Dewberry's part, used for demonstrating the concept of higher premiums based on uncertainty, and do not represent actuarially-sound increases based on the true risks. This report is focused on the privacy, ownership, and liability issues associated with the creation of a National Elevation Registry, not on any constraints imposed by the National Flood Insurance Act, as amended. As noted in this background summary, infra, the NFIA as enacted into substantive law provides ample authority for an Elevation Registry. We are not aware of any Appropriations riders or Appropriations Committee reports that might limit use of appropriated funds for establishing an Elevation Registry. While we have not reviewed all Appropriations activity from the program’s outset, we have reviewed all appropriations for the last 5 years and also for the years 1990 and 1991, when we understand Congress had expressed concern with a previous FEMA proposal to publish certain flood insurance risk information. See 42 U.S.C. § 4001, et seq. See 42 U.S.C § 4101, which provides: (a) Publication of information; establishment of flood-risk zones; estimates of flood-caused loss The Director is authorized to consult with, receive information from, and enter into any agreements or other arrangements with the Secretaries of the Army, the Interior, Agriculture, and Commerce, the Tennessee Valley Authority, and the heads of other Federal departments or agencies, on a reimbursement basis, or with the head of any State or local agency, or enter into contracts with any persons or private firms, in order that he may-- (1) identify and publish information with respect to all flood plain areas, including coastal areas located in the United States, which have special flood hazards, within five years following August 1, 1968, and (2) establish or update flood-risk zone data in all such areas, and make estimates with respect to the rates of probable flood caused loss for the various flood risk zones for each of these areas until the date specified in section 4026 of this title. An “area of special flood hazard is the land in the flood plain within a community subject to a one percent or greater chance of flooding in any given year.” 44 C.F.R. § 59.1. See 42 U.S.C. § 4101(e). See 42 U.S.C. § 4101(f) which provides: (f) Updating flood maps The Director shall revise and update any floodplain areas and flood-risk zones-- (1) upon the determination of the Director, according to the assessment under subsection (e) of this section, that revision and updating are necessary for the areas and zones; or (2) upon the request from any State or local government stating that specific floodplain areas or flood-risk zones in the State or locality need revision or updating, if sufficient technical data justifying the request is submitted and the unit of government making the request agrees to provide funds in an amount determined by the Director, but which may not exceed 50 percent of the cost of carrying out the requested revision or update. 42 U.S.C. § 4101(g). 42 U.S.C. § 4101(h); see also 42 U.S.C. § 4101(i)(requiring that a compendium of map changes be published semi-annually). We use this term to include both Federal agency lenders and regulated lending institutions as defined in 42 U.S.C. § 4003(7) and (10). Id. Section 1365, 42 U.S.C. § 4104b. (establishing standard flood determination forms) See § 1361(c) (codified at 42 U.S.C. § 4102(c)). On the basis of such studies and investigations, and such other information as he deems necessary, the Director shall from time to time develop comprehensive criteria designed to encourage, where necessary, the adoption of adequate State and local measures which, to the maximum extent feasible, will — (1) constrict the development of land which is exposed to flood damage where appropriate, (2) guide the development of proposed construction away from locations which are threatened by flood hazards, (3) assist in reducing the damage caused by floods, and (4) otherwise improve the long-range land management and use of flood prone areas, and he shall work closely with and provide any necessary technical assistance to State, interstate, and local governmental agencies, to encourage the application of such criteria and the adoption and enforcement of such measures. See NFIA § 1315 (codified at 42 U.S.C. § 4022(a)(1)) [N]o new flood insurance coverage shall be provided under this chapter in any area (or subdivision thereof) unless an appropriate public body shall have adopted adequate land use and control measures (with effective enforcement provisions), which the Director finds, are consistent with the comprehensive criteria for land management and use under section 4102 of this title. See 44 C.F.R. § 60.3. See FEMA Form 81-31, July 2000, O.M.B. No. 3067-0077(visited Mar. 31, 2003) http://www.fema.gov/nfip/pdf/manual10_02/08ce1002.pdf, pages CERT 7-11. FEDERAL EMERGENCY MANAGEMENT AGENCY, COMMUNITY RATING SYSTEM MANUAL, Series 300, Section 311(a)(1999) (visited Mar. 31, 2003) . NFIA § 1315(b)(codified at 42 U.S.C. § 4022(b)). 42 U.S.C. § 4013(a). 42 U.S.C. § 4014(a). 42 U.S.C. § 4020. See 42 U.S.C. § 4015(b)(1). See FEMA, FLOOD INSURANCE MANUAL, Full Edition, pp. Rate 4-Rate 10 (showing the premium computation table for “elevation-rated” rates in a number of different flood zones). See Notes 3-11 and related text, infra. See Notes 18-21, infra. See Note 3, infra. See Notes 3 and 20, infra. Section 1363, 42 U.S.C. § 4104. 44 C.F.R. § 70.3(b)(4). See 44 C.F.R. §65.5; §65.6 and §65.8. See FEMA, FLOOD INSURANCE MANUAL, Full Edition, pp. Rate 4-Rate 10 (showing the premium computation table for “elevation-rated” rates in a number of different flood zones). 42 U.S.C. § 5121 et seq.. See 44 C.F.R. § 60.2. See FEMA Form 81-31, supra note 22. FEMA, COMMUNITY RATING SYSTEM MANUAL, SECTION 310, Page 310-2 (1999). Id., SECTION 311, Page 310-6 (1999). 42 U.S.C. § 4012(c); 44 C.F.R. § 59.22(a)(3). The Privacy Act of 1974, P.L. 93-579 (codified at 5 U.S.C. § 552a). The concept of fair information practices was first elucidated in a 1973 advisory committee report to the Secretary of Health, Education and Welfare. See UNITED STATES DEPARTMENT OF HEALTH, EDUCATION AND WELFARE, RECORDS, COMPUTERS AND THE RIGHTS OF CITIZENS: REPORT OF THE SECRETARY’S ADVISORY COMMITTEE ON AUTOMATED PERSONAL DATA SYSTEMS 41 (July 1973). 5 U.S.C. § 552a(a)(5). 5 U.S.C. § 552a(a)(4)(emphasis added). See 5 U.S.C. 552a(e)(4). Compare Reuber v. United States, 829 F.2d 133, 142 (D.C. Cir. 1987) (letter reprimanding individual sent to and disclosed by agency was “record” because it clearly identified individual by name and address), with Robinson v. United States Dep't of Educ., No. 87-2554, 1988 WL 22292, at *3 (E.D. Pa.) Mar. 8, 1988 (letter describing individual's administrative complaint was not “record” because it “did not identify plaintiff in any way.”); see also Albright v. United States, 631 F.2d 915, 920 (D.C. Cir. 1980) (case challenging agency’s maintenance of records concerning how plaintiff exercised First Amendment rights holding that a videotape of a meeting constituted a “record” and stating that “[a]s long as the tape contains a means of identifying an individual by picture or voice, it falls within the definition of a 'record' under the Privacy Act”). See 5 U.S.C. § 552a(a)(2). If the database were determined to be covered by the Privacy Act, and included records about any U.S. citizens, FEMA would be required to abide by the Act’s limits, at least with respect to those individuals. While any subjects who were not living permanent residents would not be entitled by law to Privacy Act rights, as a matter of public policy and administrative convenience, FEMA might choose to treat all of its records the same, rather than try to delineate covered individuals and covered records from those not covered. See 5 U.S.C. § 552a(a)(4). See id. DOD v. FLRA, 510 U.S. 487, 494. Id. at 502. No. 01 C 6289, 2002 WL 252459, at *2 (N.D. Ill. Feb. 21, 2002) Id. at *2 5 U.S.C. § 552a(a)(4). United States Office of Management and Budget, Privacy Act Guidelines, 40 Fed. Reg. 28948, 28951 (1975). OMB has statutory authority to issue guidance and oversee implementation of the Privacy Act. See 5 U.S.C. § 552a(v). 40 Fed. Reg. at 28952. See Bechhoefer v. United States Dep't of Justice Drug Enforcement Admin., 209 F.3d 57 (2d Cir. 2000); Quinn v. Stone, 978 F.2d 126 (3d Cir. 1992). Quinn v. Stone, 978 F.2d at 133 (holding out-of-date home address on roster and time card information were records covered by Privacy Act). Bechhoefer, 209 F.3d at 60. Id. Id. at 61-62 Id. at 62 (quoting Quinn and holding that a letter containing Bechhoefer's name and “several pieces of 'personal information' about him, including his address, his voice/fax telephone number, his employment, and his membership in [an association]” was a record covered by the Privacy Act). See, e.g., Williams v. VA, 104 F.3d 670, 673-74 (4th Cir. 1997) (citing Quinn, inter alia, and stating that “[w]hether the Tobey court's distinction [(discussed below)] be accepted, the legislative history of the Act makes it clear that a 'record' was meant to 'include as little as one descriptive item about an individual,'“ and finding that “draft” materials qualified as “records” because they “substantially pertain to Appellant,” “contain 'information about' [him], as well as his 'name' or 'identifying number,' “ and “do more than merely apply to him” (quoting legislative history, Source Book at 866)); Unt v. Aerospace Corp., 765 F.2d 1440, 1449-50 (9th Cir. 1985) (Ferguson, J., dissenting) (opining that majority's narrow interpretation of term “record” is “illogical, contrary to the legislative intent, and defies the case laws' consistent concern with the actual effect of a record on a person's employment when assessing that record's nature or subject”); cf. Doe v. Herman, No. 297CV00043, 1999 WL 1000212, at *9 (W.D. Va. Oct. 29, 1999) (unpublished magistrate's recommendation) (stating that in that litigation “no dispute exists as to whether the social security numbers at issue constitute records as defined by the Privacy Act”), adopted in part & rev'd in part on other grounds (W.D. Va. 2000), aff’d in part & rev’d in part on other grounds, sub nom. Doe v. Chao, 306 F.3d 170 (4th Cir. 2002). Boyd v. Sec'y of the Navy, 709 F.2d 684, 686 (11th Cir. 1983) (per curiam) (although stating narrow test, finding that memorandum reflecting “Boyd's failure to follow the chain of command and his relationship with management” qualified as Privacy Act record); accord Unt v. Aerospace Corp., 765 F.2d 1440, 1448-49 (9th Cir. 1985) (letter written by employee — containing allegations of mismanagement against corporation that led to his dismissal — held not his “record” because it was “about” the corporation and reflected “only indirectly on any quality or characteristic” of employee). See 5 U.S.C. § 552a(g)(5) (“An action to enforce any liability created under this section may be brought in the District court of the United States in the district in which the complainant resides, or has his principal place of business, or in which the agency records are situated, or in the District of Columbia”). Tobey v. NLRB, 40 F.3d 469, 471-73 (D.C. Cir. 1994). Id. Id. at 471. Tobey, 40 F.3d at 472. Id. at 471-73. Id. at 472-73. See Tripp v. DOD, 193 F. Supp. 2d 229, 236 (D.D.C. 2002) (citing Tobey and stating that salary information for position for which plaintiff had applied “is not 'about' plaintiff—the fact that she could receive that salary had she been chosen for the position does not convert this into information 'about' plaintiff”); Wolde-Giorgis v. United States, No. 94-254, slip op. at 5-6 (D. Ariz. Dec. 9, 1994) (citing Unt with approval and holding that Postal Service claim form and information concerning estimated value of item sent through mail was “not a 'record' within the meaning of the [Privacy Act]” because it “disclosed no information about the plaintiff” and did not reflect any “'quality or characteristic' concerning the plaintiff”), aff'd, 65 F.3d 177 (9th Cir. 1995) (unpublished table decision); Shewchun v. United States Customs Serv., No. 87-2967, 1989 WL 7351, at *1 (D.D.C. Jan. 11, 1989) (letter concerning agency's disposition of plaintiff's merchandise “lacks a sufficient informational nexus with [plaintiff] (himself, as opposed to his property) to bring it within the definition of 'record'“); Blair v. United States Forest Serv., No. A85-039, slip op. at 4-5 (D. Alaska Sept. 24, 1985) (“Plan of Operation” form completed by plaintiff held not his “record” as it “reveals nothing about his personal affairs”), appeal dismissed, No. 85-4220 (9th Cir. Apr. 1, 1986); Windsor v. A Fed. Executive Agency, 614 F. Supp. 1255, 1260-61 (M.D. Tenn. 1983) (record includes only sensitive information about individual's private affairs), aff'd, 767 F.2d 923 (6th Cir. 1985) (unpublished table decision); Cohen v. United States Dep't of Labor, 3 Gov't Disclosure Serv. (P-H) 83,157, at 83,791 (D. Mass. Mar. 21, 1983) (record includes only “personal” information); AFGE v. NASA, 482 F. Supp. 281, 282-83 (S.D. Tex. 1980) (determining that sign-in/sign-out sheet was not “record” because, standing alone, it did not reveal any “substantive information about the employees”); Houston v. United States Dep't of the Treasury, 494 F. Supp. 24, 28 (D.D.C. 1979) (same as Cohen); But cf. Williams v. VA, 104 F.3d 670, 673- 74 (4th Cir. 1997) (quoting legislative history and finding that materials qualified as “records” because they “substantially pertain to Appellant,” “contain 'information about' [him], as well as his 'name' or 'identifying number,'“ and “do more than merely apply to him”). Shewchun v. United States Customs Service, No. 87-2967, slip op. at 3 (D.D.C. Jan 11, 1989). Quinn v. Stone, 978 F.2d at 133. A “routine use” under 5 U.S.C. § 552a(b)(3) is a disclosure of a record outside of an agency for a “purpose which is compatible with the purpose for which it was collected.” 5 U.S.C. § 552a(a)(7). See 5 U.S.C. § 552a(e)(11). See United States Office of Management and Budget, Circular No. A-130, “Management of Federal Information Resources, Appendix I, 4.c.(1)(f), 65 Fed. Reg. 77677 (Dec. 12, 2000). See 5 U.S.C. § 552a(c)(1)-(2). See id. § 55a(c)(3). See id. § 552a(c)(4). Usually, agencies that have been able to justify such broad disclosure rely on specific statutory authority that requires it. For example, executive branch financial disclosure reports are a designated system of records and are required to be made available to any requester under section 205 of the Ethics in Government Act of 1978 (section 105 of the Ethics Act, as amended), which is administered by the Office of Government Ethics. However, if there is a program purpose that is “compatible with the purpose for which the information was collected,” it may be possible to justify broad public disclosure. See 5 U.S.C. § 552a(b). See 5 U.S.C. § 552a(a)(5). See Tobey v. NLRB (agency “could use data from [database] in combination with other information to draw inferences about [plaintiff’s] job performance. . . does not transform the [database] files into records. ) See Pub. L. No. 100-503 (codified in scattered sections of 5 U.S.C. § 552a). See 5 U.S.C. § 552a(a)(8). The discussion in this section responds to the following issue in our Task Order: Evaluate the legal impediments to making information in a National Elevation Registry available outside FEMA, either to the public at large, or, in the alternative, to selected NFIP stakeholders such as mortgage and insurance companies with a legitimate need to know.” 5 U.S.C. § 552. 5 U.S.C. § 552b. 5 U.C.C. App. See Chrysler Corp. v. Brown, 441 U.S. 281, 293 (1979); Bartholdi Cable Co. v. FCC, 114 F.3d 274, 282 (D.C. Cir. 1997) (“FOIA’s exemptions simply permit, but do not require, an agency to withhold exempted information.”). The core of the FOIA is the requirement that “upon any request for records which (i) reasonably describes such records and (ii) is made in accordance with published rules stating the time, place, fees (if any), and procedures to be followed, [an agency] shall make the records promptly available to any person.” 5 U.S.C. § 552(a)(3)(A). United States Department of Justice, Freedom of Information Act Guide, May 2002. See Campaign for Family Farms v. Glickman, 200 F.3d 1180, 1185 (D. Minn. 2000) (“agency has discretion to disclose information within a FOIA exemption, unless something independent of FOIA prohibits disclosure.”) See Chrysler Corp. v. Brown and Bartholdi Cable Co. v. FCC, supra note 94. See 42 U.S.C. § 4101. See FOIA, 5 U.S.C. § 552(a)(1) and (2), and U.S. Office of Management and Budget, Circular No. A-130, Transmittal No. 3, “Management of Federal Information Resources,” at 8(a)(5), 61 Fed. Reg. 6427, 6432 (Feb. 20, 1996), available at (visited Jan. 7, 2003). See 5 U.S.C. 552(f). NLRB v. Sears, Roebuck & Co., 421 U.S. 132, 143 n.10; see Parsons v. Freedom of Info. Act Officer, No. 96-4128, 1997 WL 461320, at *1 (6th Cir. Aug. 12, 1997)(holding that plaintiff’s argument of “legitimate need for the documents superior to that of the general public or the press” fails because identity of requester is irrelevant to determination of whether exemption applies). John Ashcroft, Attorney General, MEMORANDUM FOR HEADS OF ALL FEDERAL DEPARTMENTS AND AGENCIES, “The Freedom of Information Act,” Oct. 12, 2001. We concluded supra, at 20-30. that the Elevation Registry as contemplated by FEMA would not likely be held a “system of records” under the federal Privacy Act of 1974 and hence that disclosure of information in the Registry would not violate the Privacy Act. See Glickman, 200 F.3d at 1188 (without deciding whether there is a constitutional right to secret ballot, finding a “strong and clearly established privacy interest in a secret ballot,” and finding that the privacy interest in a secret ballot was “severely threatened,” and that disclosure of names and addresses of persons who signed a petition would “substantially invade that privacy interest”). Several such suits have recently been decided. See, e.g., Recticel Foam Corp. v. United States Dep't of Justice, No. 98-2523, slip op. at 9-10 (D.D.C. Jan. 31, 2002) (enjoining disclosure of FBI's criminal investigative files pertaining to plaintiffs), appeal dismissed, No. 02-5118 (D.C. Cir. Apr. 25, 2002); Tripp v. DOD, 193 F. Supp. 2d 229, 238-40 (D.D.C. 2002) (rejecting plaintiff’s challenge to disclosure of federal job-related information concerning herself after disclosure had already been made to the media); Glickman, 200 F.3d at 1182 (agreeing with submitter that Exemption 6 should have been invoked and ordering permanent injunction requiring agency to withhold requested information). Martin Marietta Corp. v. Dalton, 974 F. Supp. 37, 40 n.4 (D.D.C. 1997); accord Frazee v. United States Forest Serv., 97 F.3d 367, 371 (9th Cir. 1996) (“party seeking to withhold information under Exemption 4 has the burden of proving that the information is protected from disclosure”); Occidental Petroleum Corp. v. SEC, 873 F.2d 325, 342 (D.C. Cir. 1989) (explaining that “statutory policy favoring disclosure requires that the opponent of disclosure” bear burden of persuasion); TRIFID Corp. v. Nat'l Imagery & Mapping Agency, 10 F. Supp. 2d 1087, 1097 (E.D. Mo. 1998) (same). Martin Marietta, 974 F. Supp. at 40 (quoting United States Dep't of the Air Force v. Rose, 425 U.S. 352, 372 (1976)); see, e.g., TRIFID, 10 F. Supp. 2d at 1097 (reviewing submitter's claims in light of FOIA principle that "[i]nformation in the government's possession is presumptively disclosable unless it is clearly exempt"); Daisy Mfg. Co. v. Consumer Prod. Safety Comm'n, No. 96-5152, 1997 WL 578960, at *1 (W.D. Ark. Feb. 5, 1997) (examining submitter's claims in light of "the policy of the United States government to release records to the public except in the narrowest of exceptions" and observing that "[o]penness is a cherished aspect of our system of government"), aff'd, 133 F.3d 1081 (8th Cir. 1998). 441 U.S. 281, 293-94 (1979). See id; See also 5 U.S.C. §§ 701-06 (2000); see, e.g., CC Distribs. v. Kinzinger, No. 94-1330, 1995 WL 405445, at *2 (D.D.C. June 28, 1995) (“neither FOIA nor the Trade Secrets Act provides a cause of action to a party who challenges an agency decision to release information . . . [but] a party may challenge the agency's decision” under APA); Comdisco, Inc. v. General Svcs. Admin., 864 F. Supp. 510, 513 (E.D. Va. 1994) (“sole recourse” of ’party seeking to prevent an agency's disclosure of records under FOIA’ is review under APA); Atlantis Submarines Haw., Inc. v. United States Coast Guard, No. 93-00986, slip op. at 5 (D. Haw. Jan. 28, 1994) (in reverse FOIA suit, “an agency's decision to disclose documents over the objection of the submitter is reviewable only under” APA), dismissed per stipulation (D. Haw. Apr. 11, 1994); Envtl. Tech., Inc. v. EPA, 822 F. Supp. 1226, 1228 (E.D. Va. 1993) (same). See, e.g., McDonnell Douglas Corp. v. Widnall, 57 F.3d 1162, 1164 (D.C. Cir. 1995) (holding that the Trade Secrets Act "can be relied upon in challenging agency action that violates its terms as 'contrary to law' within the meaning of" APA); Acumenics Research & Tech. v. Dep't of Justice, 843 F.2d 800, 804 (4th Cir. 1988) (same); Gen. Elec. Co. v. NRC, 750 F.2d 1394, 1398 (7th Cir. 1984); Gen. Dynamics Corp. v. United States Dep't of the Air Force, 822 F. Supp. 804, 806 (D.D.C. 1992), vacated as moot, No. 92-5186 (D.C. Cir. Sept. 23, 1993); Raytheon Co. v. Dep't of the Navy, No. 89-2481, 1989 WL 550581, at *1 (D.D.C. Dec. 22, 1989). See Students Against Genocide v. Dep't of State, 257 F.3d 828, 836 (D.C. Cir. 2001); Cottone v. Reno, 193 F.3d 550, 555-56 (D.C. Cir. 1999) (finding waiver of government’s right to invoke exemption where plaintiff identified specific audio tapes played at trial, determining them to be in public domain). Id. Recall that in the CRS communities, the community must obtain and make publicly available the FEMA form of Elevation Certificate before issuing building permits in the floodplain. Further, And by federal regulation, all communities participating in the NFIP must obtain elevation data (not necessarily on the FEMA form) in connection with its permitting decisions. See page 15 supra. 489 U.S. 749 (1989). Id. at 767. Reporters Comm., 489 U.S. at 780; see also Wash. Post, 456 U.S. at 603 n.5; Abraham & Rose, P.L.C., v. United States, 138 F.3d 1075, 1083 (6th Cir. 1998) (noting that there may be privacy interest in personal information even if “available on publicly recorded filings”); Linn v. United States Dep't of Justice, No. 92-1406, 1995 WL 417810, at *31 (D.D.C. June 6, 1995) (declaring that even if “some of the names at issue were at one time released to the general public, individuals are entitled to maintaining the 'practical obscurity' of personal information that is developed through the passage of time”). See discussion of public record laws in Sources of Existing Elevation Data, at pp. 17-19 supra, cf. discussion, infra at pp. 89-92, of Strategy E, “Sharing of Community Tax Parcel Data and or other Community Data Bases.” See 5 U.S.C. § 552(b)(4). 18 U.S.C. § 1905. See Chrysler v. Brown, 441 U.S. 281, 295-96 (1979). See Allnet Communication Servs. v. FCC, 800 F. Supp. 984, 988 (D.D.C. 1992). It is conceivable that some of the remote sensing techniques or equipment used to obtain elevation data might be exempt as a “trade secret.” However, we do not believe that any such techniques would appear in the Elevation Registry. See 42 U.S.C. § 4001, et seq. 5 U.S.C. § 552(b)(6). 456 U.S. 595, 599-603 (quoting Washington Post Co., 456 U.S. at 601); accord Sherman v. United States Dep't of the Army, 244 F.3d 357, 361 (5th Cir. 2001). 894 F. Supp. 1397, 1413 (D. Haw. 1995) H.R. Rep. No. 1497, 89th Cong., 2d Sess. 11 (1966). See 489 U.S. at 772-75 See id. at 767. See Ripskis v. HUD, 746 F.2d 1, 3 (D.C. Cir. 1984); Holland v. CIA, No. 91-1233, 1992 WL 233820, at *16 (D.D.C. Aug. 31, 1992) (stating that information must be disclosed when there is no significant privacy interest, even if public interest is also de minimis). See, e.g., Sims v. CIA, 642 F.2d 562, 572 n.47 (D.C. Cir. 1980); Nat'l Parks & Conservation Ass'n v. K leppe, 547 F.2d 673, 685 n.44 (D.C. Cir. 1976); Ivanhoe Citrus Ass'n v. Handley, 612 F. Supp. 1560, 1567 (D.D.C. 1985). The right to privacy of deceased persons is not entirely settled, but the majority rule is that death extinguishes privacy rights. See, e.g., Na Iwi O Na Kupuna v. Dalton, 894 F. Supp. 1397, 1413 (D. Haw. 1995). The Department of Justice usually follows this rule as a matter of policy. See Department of Justice, FOIA Update, Vol. III, No. 4, at 5. See Dep't of the Air Force v. Rose, 425 U.S. 352, 380 n.19 (1976); Carter v. United States Dep't of Commerce, 830 F.2d 388, 391 (D.C. Cir. 1987); Arieff v. United States Dep't of the Navy, 712 F.2d 1462, 1467-68 (D.C. Cir. 1983). 879 F.2d 873, 878 (D.C. Cir. 1989) (emphasis added). See discussion of public record laws in Sources of Existing Elevation Data, at pp. 17-19 supra, cf. discussion infra at pp. 89-92, of Strategy E, “Sharing of Community Tax Parcel Data and or other Community Data Bases,” See 510 U.S. 487, 500 (1994). See, e.g., Professional Programs Group v. Dep't of Commerce, 29 F.3d 1349, 1353-55 (9th Cir. 1994) (withholding names and addresses of persons registered to take patent bar examination); Bibles v. Or. Natural Desert Ass'n, 519 U.S. at 355-56 (mailing list of recipients of Bureau of Land Management publication). See National Association of Retired Persons v. Horner, 879 F.2d at 878 (hereinafter “NARFE”). Id. at 876. See Holland v. CIA, No. 91-1233, 1992 WL 233829, at **15-16 (D.D.C. Aug. 31, 1992) (holding that researcher who sought assistance of presidential advisor in obtaining CIA files he had requested is comparable to FOIA requester whose identity is not protected by Exemption 6); Martinez v. FBI, No. 82-1547, slip op. at 7 (D.D.C. Dec. 19, 1985) (denying protection for identities of news reporters seeking information concerning criminal investigation) (Exemption 7(C)). See 29 F.3d 1349 (9th Cir. 1994). See 519 U.S. 355 (1997). NARFE, 879 F.2d at 876. NARFE, 879 F.2d at 877. Id. at 876. Id. (citations omitted). Exemption 1, 42 U.S.C. § 552(b)(1). The Act creating the Department of Homeland Security added an exemption from disclosure for information about critical infrastructure that is voluntarily submitted to the government as part of the government’s efforts to protect critical infrastructure from terrorist attack. Homeland Security Act of 2002, PL 107-296, Nov. 25, 2002, 116 Stat 2135, § 214. This exemption would not extend to information in on elevation certificate submitted in order to obtain flood insurance. Exemption 9, 42 U.S.C. § 552(b)(9). 5 U.S.C. §552(a)(3) provides: (B) In making any record available to a person under this paragraph, an agency shall provide the record in any form or format requested by the person if the record is readily reproducible by the agency in that form or format. Each agency shall make reasonable efforts to maintain its records in forms or formats that are reproducible for purposes of this section. (C) In responding under this paragraph to a request for records, an agency shall make reasonable efforts to search for the records in electronic form or format, except when such efforts would significantly interfere with the operation of the agency's automated information system. See 77 Am. Jur. 2d § 61 and cases cited therein. 42 U.S.C. § 4072. 42 U.S.C. § 4104(g). See 105 F. Supp. 2d 822 (S.D. Ohio 2000). Section 1331 of title 28 of the U.S. Code grants federal courts jurisdiction over “all civil actions arising under the Constitution, laws, or treaties of the United States.” This is not, however, “a general waiver of sovereign immunity, it merely establishes a subject matter that is within the competence of the federal courts to entertain.” Whittle v. United States, 7 F.3d 1259, 1262 (6th Cir. 1993). The Declaratory Judgment Act, 28 U.S.C. § 2201-02, “neither provides an independent basis for subject matter jurisdiction nor waives FEMA’s sovereign immunity. The Act merely grants the Court the power to issue declaratory judgments when jurisdiction otherwise exists.” Normandy Pointe Associates v. FEMA, 105 F. Supp. 2d 822, 827 (S.D. Ohio 2000)(citing Skelly Oil v. Phillips Petroleum Co., 339 U.S. 667, 671-72 (1950)). See 515 F. Supp. 1159 (M.D. Ala. 1981) See 875 F. Supp. 240 (S.D.N.Y. 1995). 875 F. Supp. at 241 (citations omitted). In Brown v. U.S., 599 F. Supp. 877 (D. Mass. 1984), the trial court was moved to find NOAA liable under the Federal Tort Claims Act (FTCA) for its failure to provide accurate information — a weather report — causing a fishing vessel to be at sea when a storm hit, sank the boat, and drowned three fishermen. In this case, however, the trial court’s decision did not survive appeal: the First Circuit held that the FTCA waiver of sovereign immunity did not apply due to its discretionary function exemption. Brown v. U.S., 790 F.2d 199 (1st Cir. 1986). See, e.g., Normandy Pointe Assocs. v. FEMA, 105 F. Supp. 2d 822 (S.D. Ohio 2000). The several subsections in 42 U.S.C. § 4101 contain some of FEMA’s broadest authorities for collecting and disseminating flood risk information. These authorities appear primarily to contemplate ‘horizontal’ map information; they use terms such as “flood plain areas . . . which have special flood hazards;” “updating flood maps;” estimates of “probable flood loss for the various flood risk zones for each of these areas,” all of which seem focused on horizontal maps. See 42 U.S.C. § 4104. The Administrative Procedure Act provides that a person “adversely affected” or “aggrieved” by agency action can seek judicial review of agency action, unless the relevant statute precludes review or the matter is committed to agency discretion by law. See 5 U.S.C. §§ 702, 706. The Federal Tort Claims Act is codified at 28 U.S.C. §§ 2671-80. Section 2674 provides that “[t]he United States shall be liable, respecting the provisions of this title relating to tort claims, in the same manner and to the same extent as a private individual under like circumstances, but shall not be liable for interest prior to judgment or for punitive damages.” Section 2680 states exceptions to the FTCA, including the “discretionary function” exception, § 2680(a), and an exception for certain torts arguably related to potential claims based on spreading false information about a property: libel, slander, misrepresentation, deceit, or interference with contract rights, see § 2680(h). Although we found no cases in which a plaintiff had successfully brought an FTCA action against FEMA for errors in maps or other floodplain information, a court could be tempted to do so in particular cases. See Brown v. U.S, infra at note 102 (liability for incorrect weather forecast dismissed only on appeal). Cf. Note, A Technological Dream Turned Legal Nightmare: Potential Liability of the United States under the Federal Tort Claims Act for Operating the Global Positioning System, 33 VAND. J. TRANSNAT’L L. 371 (2000). See, e.g., Kirchner v. Stief, 2001 WL 1555313 (Del. Com.Pl. 2001); Robertson v. George, 2001 WL 1173279, Tenn. Ct. App. 2001)(unpublished); Revitz v. Terrell, 572 So.2d 996 (Fla. App.s 3d.Dist. 1990); Garrison v. Barryman, 594 P.2d 159 (Kan. 1979); Chapman v. Hosek, 475 N.E.2d 593 (Ill. App. 1985). Potter v. First Real Estate Co., Inc., 2002 WL 31002850 (Sept. 6, 2002);see also, Chapman v. Hozek, 475 N.E.2d 593 (Ill. App. 1985). Nast v. State Farm Fire and Casualty Co., 82 S.W.3d 114 (Tex. Ct. App. 2002); McKinnon v. Batte, 485 So. 2d 295 (Miss.1986). McClung Surveying, Inc. v. Worl, 541 S.E.2d 703 (Ga. App. 2000)(dismissing buyer’s action against surveyor because closing attorney had hired surveyor for benefit of lender, not for benefit of buyer); Salmon v. Pearson & Associates, Inc, 446 S.E.2d 762 (Ga. 1994)(reversing summary judgment for surveyor hired by closing attorney for benefit of lender); Somers Mill Assoc. v. Fuss & O’Neill, 2002 WL 467910 (Conn. Super. Mar. 5, 2002)(dismissing action against engineering firm after finding no evidence of scope of engineer’s work). See, e.g., Dollar v. Nationsbank of Georgia, 534 S.E.2d 851 (Ga. App. 2000)(bank not liable); Small v. South Norwalk Savings Bank, 535 A.2d 1292 (Conn. 1992)(finding bank liable). See, e.g., Gibson v. Evansville Vanderburgh Bldg. Comm’n, 725 N.E.2d 949 (Ind. App. 2000); Quality by Father & Son, Ltd. v. Bruscella, 666 N.Y.S.2d 380 (N.Y. 1997); City of Tarpon Springs v. Garrigan, 510 So.2d 1198 (Fla. App. 2d Dist. 1987); Hanks v. Calcasieu Parish Police Jury, 479 So.2d 1010 (La. Ct. App. 3d Dist.1986). See, e.g., GRE Insurance Group v. Normandy Pointe Assoc., 2002 WL 360646 (Ohio App. 2d Dist. March 8, 2002). See, e.g., Somers Mill Assoc. v. Fuss & O’Neill, 2002 WL 467910 (Conn. Super. March 5, 2002)(where data from a FEMA flood study had been incorrectly transferred to a FIRM, and defendant engineer, who did not participate in the flood study or the transfer, relied on the FIRM without independently checking the flood study, case against the engineer dismissed after finding no evidence that the engineer had been asked to perform a more detailed review); Cf. Gibson v. Evansville Vanderburgh Bldg. Comm’n, 725 N.E.2d 822 (Ind. App. Mar. 29, 2000)(dismissing on immunity grounds action against community that misread a FIRM). See, e.g., Quality by Father & Son v. Bruscella, 666 N.Y.S.2d 380 (N.Y. 1997)( survey showing elevation of 15.7 feet later discovered during construction to actually have elevation of 7.61 feet);see also Segall v. Rapkin, 875 F. Supp. 240 (S.D.N.Y. 1995)(finding no private right of action against a FEMA contractor and dismissing action on jurisdictional grounds, where engineering firm’s flood study, conducted for FEMA, erroneously placed the BFE 3.5 feet too low). Cf., Morton Buildings, Inc. v. Redeeming Word of Life Church, 744 So.2d 5 (La. App. 1998), annulled by Morton Buildings, Inc. v. Redeeming Word of Life Church, 835 So.2d 685 (La. App. 2002). In this case, a new church gymnasium and education addition to a church was constructed with its first floor at the same elevation as the existing church — which was two feet below the elevation required by the building code adopted to comply with floodplain management requirements. Litigation ensued when the contractor failed to get waiver of the elevation requirement but constructed the addition at the lower elevation anyway. We note, however, that FEMA does not intend that communities use the Registry for floodplain management. Segall v. Rapkin, 875 F.Supp. 240 at 241 (S.D.N.Y. 1995), citing Arvai v. First Federal, 698 F.2d 683 (4th Cir. 1983); Till v. Unifirst Fed. Sav. & Loan, 653 F.2d 152, 155-56, 158-61 (5th Cir. 1981); Roberts v. Cameron-Brown Co., 556 F.2d 356, 360-62 (5th Cir. 1977). See, e.g., Nast v. State Farm Fire & Casualty, 825 S.W.3d 114 (Tex. Ct. App. 2002)(reversing summary judgment for insurance agent and holding that representations by insurance agent that NFIP coverage was not available, and that neighbor’s insurance policy had been sold by a shyster, were affirmative misrepresentations supporting a claim under the Texas Deceptive Trade Practices Act.) See Chevron U.S.A., Inc. v. NRDC, 467 U.S. 837 (1984)(holding that a federal agency charged by statute with administering a program should be given great deference with respect to decisions interpreting the statute and carrying out the program). See Dollar v. Nationsbank of Georgia, 534 S.E. 2d 851 (Ga. App. 2000). See Salmon v. Pearson & Associates, 446 S.E. 2d 762 (Ga. App. 1994)(absence of privity with surveyor does not bar a negligence action). We have not researched whether an engineer or surveyor could have exposure to persons whose businesses were damaged by the collapse of a purchase transaction caused by erroneous survey data (such as a construction contractor). We believe such liability, if any, would be very limited. See, e.g., Gibson v. Evansville Vanderburgh Building Commission, 725 N.E.2d 949 (Ind. App. 2000); City of Tarpon Springs v. Garrigan, 510 So. 2d 1198 (Fla. Ct. App. 2d Dist. 1987). See, e.g., Hanks v. Calcasieu Parish Police Jury, 479 So. 2d 1010 (La. Ct. App. 1986). Lenders are usually not liable if the notice they provide home borrowers is incorrect. See, Lukosus v. First Tennessee Bank Nat’l Assoc., 2003 WestLaw 21658263 (W.D.Vir. July 9, 2003) 42 U.S.C. § 4104b (d). For example, in Kentucky, the doctrine of caveat emptor “(the buyer beware)” applies to purchase of house subject to exceptions for fraud and, in the case of new home from builder, implied warranty of merchantability. Craig v. Keene, 32 S.W. 3d 90 (Ky. App. 2000). For example, sellers in California must fill out a Seller Real Estate Disclosure form, which includes specific reference to conditions of flooding. See Cal. Civ. Code § 1102 (2001). This section on the legal effect of Electronic Signatures and Verification was prepared by Mr. Terry Banks of the EOP Foundation. See FEDERAL EMERGENCY MANAGEMENT AGENCY, FLOOD INSURANCE MANUAL, at Applications 6 (2000, rev. Oct. 1, 2002). This requirement applies to properties that are Post-FIRM construction, as well as pre-FIRM construction using optional post-FIRM rating, and are located in Zones A1- A30, AE, AH, A, V1-V30, VE, and V. Id. Pub. Law 106-229, codified at 15 U.S.C. §§ 7001-31. 15 U.S.C. § 7001. Congress’ intent is embodied in sections on “Required Actions,” and “Principles.” Required actions. The Secretary of Commerce shall promote the acceptance and use, on an international basis, of electronic signatures in accordance with the principles specified in paragraph (2) and in a manner consistent with section 101 of this Act [15 U.S.C.S. § 7001]. The Secretary of Commerce shall take all actions necessary in a manner consistent with such principles to eliminate or reduce, to the maximum extent possible, the impediments to commerce in electronic signatures, for the purpose of facilitating the development of interstate and foreign commerce. Principles. The principles specified in this paragraph are the following: (A) Remove paper-based obstacles to electronic transactions by adopting relevant principles from the Model Law on Electronic Commerce adopted in 1996 by the United Nations Commission on International Trade Law. (B) Permit parties to a transaction to determine the appropriate authentication technologies and implementation models for their transactions, with assurance that those technologies and implementation models will be recognized and enforced. (C) Permit parties to a transaction to have the opportunity to prove in court or other proceedings that their authentication approaches and their transactions are valid. (D) Take a nondiscriminatory approach to electronic signatures and authentication methods from other jurisdictions. 156 U.S.C. § 7031(a). 15 U.S.C. §7001(a). 15 U.S.C. § 7006(4). 15 U.S.C. § 7006(5). Note that “electronic signature” is a general term referring to a manifestation of intent to create an agreement, while “digital signature” refers to a specific technology based on encryption which may be used to sign a document electronically and which can be authenticated. See 15 U.S.C. § 7001(g), which states in pertinent part: If a statute, regulation, or other rule of law requires a signature or record relating to a transaction in or affecting interstate or foreign commerce to be notarized, acknowledged, verified, or made under oath, that requirement is satisfied if the electronic signature of the person authorized to perform those acts, together with all other information required to be included by other applicable statute, regulation, or rule of law, is attached to or logically associated with the signature or record. 15 U.S.C. § 7001(i)(“It is the specific intent of the Congress that this title . . . apply to the business of insurance”). See E-SIGN, § 104, codified at 15 U.S.C. § 7004. See Uniform Electronic Transactions Act, 7A U.L.A. 17 (Supp. 2000), available at Uniform Electronic Transactions Act (visited Mar. 17, 2003) (hereinafter UETA). See 15 U.S.C. § 7002(a)(1)(permitting preemption of E-SIGN if a state passes UETA). Id. See Uniform Electronic Transactions Act, 1999 Cal. Stat. 428, Cal. Civ. Code §§ 1633.1-17; Uniform Electronic Transactions Act, 2000 Fla. Laws ch. 668.50; La. Rev. Stat. Ann. §§ 9:2601- 20; N.C. Gen. Stat. §§ 66-308 to 308.17. Section 7 of the model UETA provides, “Legal Recognition of Electronic Records, Electronic Signatures, and Electronic Contracts”: (a) A record or signature may not be denied legal effect or enforceability solely because it is in electronic form. (b) A contract may not be denied legal effect or enforceability solely because an electronic record was used in its formation. (c) If a law requires a record to be in writing, an electronic record satisfies the law. (d) If a law requires a signature, an electronic signature satisfies the law. See UETA, § 11. Section 18 of UETA sets out rules for “Acceptance and Distribution of Electronic Records by Governmental Agencies”: (a) Except as otherwise provided [separately for each governmental agency] [the designated state officer] of this State shall determine whether, and the extent to which, [the state agencies] will send and accept electronic records and electronic signatures to and from other persons and otherwise create, generate, communicate, store, process, use, and rely upon electronic records and electronic signatures. (b) To the extent that a governmental agency uses electronic records and electronic signatures under subsection (a), the [governmental agency] [designated state officer], giving due consideration to security, may specify: (1) the manner and format in which the electronic records must be created, generated, sent, communicated, received, and stored and the systems established for those purposes; (2) if electronic records must be signed by electronic means, the type of electronic signature required, the manner and format in which the electronic signature must be affixed to the electronic record, and the identity of, or criteria that must be met by, any third party used by a person filing a document to facilitate the process; (3) control processes and procedures as appropriate to ensure adequate preservation, disposition, integrity, security, confidentiality, and auditability of electronic records; and (4) any other required attributes for electronic records which are specified for corresponding non-electronic records or reasonably necessary under the circumstances. (c) Except as otherwise provided in Section 12(f), this [Act] does not require a governmental agency of this State to use or permit the use of electronic records or electronic signatures. See supra note 196. Cal. Gov’t Code § 16.5(a). Cal. Gov’t Code § 16.5(b). See 15 U.S.C § 7001(a)(1). See Fla. Stat. § 668.50. See La. Rev. Stat. Ann. § 9:2601 et seq. See N.C. Gen. Stat. § 66-311 et seq. See N.C. Gen. Stat. §§ 66-58.1 to 58.11. See UETA, § 11. See discussion in “Background,” supra p.10ff., for an extensive review of FEMA’s authority to collect, use, and publish elevation data. 42 U.S.C. § 4081(a). See 44 C.F.R. § 62.23(h)(4) & (j)(3); see also 44 C.F.R. § 62.23 (j)(6). In preparing this Report, we have not reviewed the Transaction Record Reporting and Processing Plan. 44 C.F.R. Part 62, App. A, Article VI (emphasis added). 42 U.S.C. § 4101(a). 42 U.S.C. § 4014(a). See Cal. Civ. Code § 6250 et seq.; Fla. Stat. Ann. § 199.01 et seq.; La. Rev. Stat. Ann. § 44:1 et seq.; N.C. Gen. Stat. § 132-6. See 44 U.S.C. § 1320.l, et seq. See 5 C.F.R. § 1320, “Controlling Paperwork Burdens on the Public,” 60 Fed. Reg. 44978-96 (Aug. 29, 1996). CRS is a voluntary program expressly authorized by 42 U.S.C. § 4022(b). See discussion of the CRS program, supra p. 17. FEDERAL EMERGENCY MANAGEMENT AGENCY, COMMUNITY RATING SYSTEM MANUAL, Series 300, § 311(a). For example, as of June 2002, there were 210 CRS communities in Florida, 57 in California, 80 in North Carolina, and 38 in Louisiana. See ”Community Rating System, Eligible Communities,” available at , (visited Mar. 19, 2003). Federal Emergency Management Agency, Community Rating System Manual, § 311, p. 310- 6 (1999). Compare 42 U.S.C. § 4082 (“may enter into contracts”) and 42 U.S.C. § 4101(a) (“is authorized to . . . enter into agreements with”), with 42 U.S.C. § 5196(i) (“may procure by condemnation or otherwise”). 42 U.S.C. § 4081(a). 42 U.S.C. §§ 4102 and 4015(c)(2). To be subject to the requirements of the Unfunded Mandates Reform Act, a rule must impose, in aggregate, a cost of $100 million per year on State, local, or tribal governments. See 2 U.S.C. § 1501 et seq. We do not know whether such a change is likely to reach that threshold. The variety of potential fact patterns — and the difficulty of ascertaining rights of “ownership” in these fact patterns — is illustrated by McClung Surveying v. Worl, 541 S.E. 2d 703 (Ga. App. 2000). In Worl, an escrow agent ordered a flood zone determination (not an elevation certificate) from an engineer at the request of the lender, so that the lender would know if the property was subject to a mandatory flood insurance requirement. The borrower/property owner paid for the determination as part of closing costs. Nonetheless, the property owner/borrower was held not to be in privity with the engineer and could not sue the engineer for damages caused by an erroneous certification that the property was not in the flood plain. See 476 U.S. 227, 232 (1986). A mortgage lender is permitted to obtain elevation determinations from a third party only if the third party “guarantees the accuracy of the information.” 42 U.S.C. § 4104b(d). Flood zone determination companies make and guarantee the accuracy of flood zone determinations for a fee. See Olmstead v. United States, 277 U.S. 438, 457, 466 (1928) (holding that wiretaps inserted into telephone wires from the street without any physical trespass on the defendant's property, physical entry into his house, or seizure of any tangible item, did not constitute an unlawful search under the Fourth Amendment). See 389 U.S. 347 (1967). See 389 U.S. at 361 (1967) (Harlan, J., concurring). Katz involved the wiretapping by police of a conversation in a public telephone booth without any physical intrusion by a device inside the telephone booth. The Court saw that the advances in technology made its previous physical intrusion test insufficient to deal with new kinds of privacy invasions and abandoned that rule in favor of the two-part test. See Oliver v. United States, 466 U.S. 170, 180 (1984). Id. at 180 n.11 (explaining that an “open field” need not be actually “open” or a “field” in the common use of those terms). Id. at 180. Id. at 181. Id. at 180 (internal quotations deleted). Id. at 183. 476 U.S. 207 (1986). 476 U.S. 227, 229. The majority described the camera as a “conventional, albeit precise, commercial camera commonly used in mapmaking.” Id. at 238. The dissenters quoted the District Court’s findings with respect to the camera: The camera used “cost in excess of $22,000.00 and is described by the company as the 'finest precision aerial camera available.' . . . The camera was mounted to the floor inside the aircraft and was capable of taking several photographs in precise and rapid succession.” This technique facilitates stereoscopic examination, a type of examination that permits depth perception. Id. at 242 n.4 (Powell, J., dissenting)(citations omitted). Id. Id. at 238. Id. at 237-38. Id. at 238. Dow, at 233-34. See 488 U.S. 445, 449 (1989) (finding aerial observation by police officer with his naked eye in helicopter 400 feet above defendant's partially covered greenhouse in the backyard of his home was not a violation of Fourth Amendment). See Oliver, 466 U.S. at 180. See Dow, 476 U.S. at 237-38. See Ciraolo, 476 U.S. at 213-14. Id. at 213. In this section, we are not making any observation about the law of trespass. As the Court said in Oliver, The law of trespass, however, forbids intrusions upon land that the Fourth Amendment would not proscribe. For trespass law extends to instances where the exercise of the right to exclude vindicates no legitimate privacy interest. Thus, in the case of open fields, the general rights of property protected by the common law of trespass have little or no relevance to the applicability of the Fourth Amendment. 466 U.S. at 183-84. See Dow, 476 U.S. at 236. See Dow, 476 U.S. at 238 (“EPA was not employing some unique sensory device that, for example, could penetrate the walls of buildings and record conversations in Dow's plants, offices, or laboratories, but rather a conventional, albeit precise, commercial camera commonly used in mapmaking”). See id. at 233 (“Regulatory or enforcement authority generally carries with it all the modes of inquiry and investigation traditionally employed or useful to execute the authority granted.”). See Ciraolo, 476 U.S. at 213-14. See Dow, 476 U.S. at 238-39. See Ciraolo, 476 U.S. at 212. See discussion of trespassing under Strategy D. See Oliver, 466 U.S. at 179 (referring to ” those intimate activities that the Amendment is intended to shelter from government interference or surveillance”). California, for example, has chosen not to follow the Supreme Court’s rulings but to afford greater protection to its residents under the California Constitution: We were not persuaded [in People v. Cook, 710 P.2d 299 (Cal. 1985)] that police officers who examine a residence from the air are simply observing what is in "plain view" from a lawful public vantage point. Such reasoning, we explained, ignores the essential difference between ground and aerial surveillance. One can take reasonable steps to ensure his yard's privacy from the street, sidewalk, or neighborhood, and police on the ground may not broach such barriers to gain a view of the enclosed area. But there is no practical defense against aerial spying, and precious constitutional privacy rights would mean little if the government could defeat them so easily. Even if members of the public may casually see into his yard when a routine flight happens over the property, we concluded, a householder does not thereby consent to focused examination of the curtilage by airborne police officers looking for evidence of crime. No law enforcement interest justifies such intensive warrantless government intrusion into a zone of heightened constitutional privacy. People v. Mahoff, 729 P.2d 166 (Cal. 1986). Conversely, in Texas, police surveillance with a helicopter hovering just 100 feet above a residential garden is not considered a search subject to the Fourth Amendment. See Moss v. State, 878 S.W.2d 632 (Tex. App. 1994). See, e.g., Board of Cty. Commrs. v. Sundheim, 926 P.2d 545 (Co. 1996). See Dow, 476 U.S. at 238 (“It may well be, as the Government concedes, that surveillance of private property by using highly sophisticated surveillance equipment not generally available to the public, such as satellite technology, might be constitutionally proscribed absent a warrant”) Id. Dow, 476 U.S. at 229, 231. Terry Banks of the EOP Foundation provided significant research and analysis to this “Strategy D” section of the report. However, the ultimate conclusions are those of FEMA Law Associates, PLLC. Cal. Pen. Code § 602 (2001). San Diego Gas & Elec. Co. v. Superior Court of Orange Cty, 920 P.2d 669, 695 (Cal. 1996) (finding no trespass cause of action existed for property damage caused by electric and magnetic fields arising from power lines operated by defendant public utility, where intrusions from power lines were wholly intangible, and plaintiffs did not allege any physical damage to their property); Wilson v. Interlake Steel Co., 649 P.2d. 922, 924 (Cal. 1982) (holding noise alone, without damage to the property, will not support a tort action for trespass). Cal Civ. Code § 3479 (2001). 106 P. 581 (1910). Id. at 583 (citations omitted). Fla. Stat. § 810.09(a) and (b)(2002). 716 So. 2d 850 (Fla. App. 1998). Id. at 851, citing Pearson v. Ford Motor Co., 694 So.2d. 61, 69 (Fla. App. 1997). 578 So. 2d 831 (Fla.App. 1991). Id. at 832. La. Rev. Stat. § 14:63(B) (2002). La. Rev. Stat. § 14:63(H) (2002). Louisiana law provides in pertinent part: Affirmative defenses to a prosecution pursuant to Subsection B of this Section shall be: (1) That the entry was by a registered land surveyor, and his personnel, engaged in the ‘Practice of Land Surveying,’ as defined in R.S. 37:682, or a person employed by a public utility acting in the course and scope of his employment relating to operation, repair, or maintenance of a public utility facility. La. Rev. Stat. § 14:63(G)(1) (2002). La. Civ. Code Ann. art. 2315. Dickie’s Sportsman’s Centers, Inc. v. Department of Transp. and Dev.,477 So. 2d 744, 750 (La. App), writ denied, 478 So. 2d 530 (La. 1985). Williams v. J.B. Levert Land Co., 162 So. 2d 53, 58 (La. App.), writ refused, 245 La. 1031, 162 So. 2d 574 ( La.1964). Williams v. City of Baton Rouge, 715 So. 2d 15, 24 (La. App. 1998), aff’d in part, rev’d in part, 631 So. 2d 240 (La. 1999); Britt Builders, Inc. v. Brister, 618 So. 2d 899 (La. App. 1993). Williams v. City of Baton Rouge, 631 So.2d 240 246-47(La. 1999); Reymond v. State, 231 So. 2d 375, 383 (La. 1970). N.C. Gen. Stat. § 14-159.13(a)(1)(2002). N.C. Gen. Stat. § 14-159.13(b)(2002). Whiteside Estates, Inc. v. Highlands Cove, L.L.C., 553 S.E.2d 431, 438 (N.C. App. 2001); Jordan v. Foust Oil Co., Inc., 447 S.E.2d 491, 498 (N.C. App. 1994)(citing Matthews v. Forrest, 69 S.E.2d 553, 555 (1952)). See Whiteside Estates, 553 S.E.2d at 436; Jordan v. Foust, 447 S.E. 2d at 498. See Whiteside Estates, 553 S.E.2d at 436; Morgan v. High Penn Oil Co., 77 S.E.2d 682, 689 (N.C. 1953). See Whiteside Estates, 553 S.E.2d at 436; Watts v. Pama Mfg. Co., 124 S.E.2d 809, 814 (N.C. 1962). See Whiteside Estates, 553 S.E.2d at 437(citing W. PAGE KEETON ET AL., PROSSER AND KEETON ON THE LAW OF TORTS § 88 (5th ed. 1984) (emphasis supplied). See Whiteside Estates, 553 S.E.2d at 437; Watts, 124 S.E.2d at 814 (N.C. 1962); Rudd v. Electrolux Corp., 982 F. Supp. 355 (M.D.N.C. 1997). See, 42 U.S.C. 4013(a): “The Director shall from time to time [after various required consultations] provide by regulation for general terms and conditions of insurability which shall be applicable to properties eligible for flood insurance coverage … including (1) the types, classes, and locations of any such properties which shall be eligible for flood insurance; …(3) the classification, limitation, and rejection of any risks which may be advisable; … and (6) any other terms and conditions relating to insurance coverage or exclusion which may be necessary to carry out the purposes of this chapter.” See, e.g., 44 C.F.R. Part 61, App A(1)(Standard Flood Insurance Policy, Dwelling Form), J.6. and K.1(a). See, e.g., 44 C.F.R. Part 60 App A(1)(Standard Flood Insurance Policy, Dwelling Form), H. 4. 44 C.F.R. Part 60 App. A(4)-A(6). See also 44 C.F.R. 59.30. See 64 Fed. Reg. 24256 (May 5, 1999)(NPRM); 65 Fed. Reg. 39726 (June 27, 2000)(Final Rule). There may be some uninsured properties for which owners have provided consent for government agents to enter onto the property to evaluate flood risk. For example, we understand that applications for property acquisition or elevation under the Hazard Mitigation Grant Program of the Robert T. Stafford Disaster Assistance and Emergency Relief Act, 42 U.S.C. § 5170c contain consent language. We have not performed a detailed analysis of all possible inspection/property appraisal authorities in each state; this analysis is intended to illustrate the types of authorities encountered in the various states. See Fla. Stat. Ann. § 400.434. Fla. Stat. Ann. § 933.21 (Emphasis added). See 64 Fed. Reg. 24256 at 24258 (May 5, 1999). (FEMA statement that in Florida, inspection pursuant to a search warrant (upon showing of probable cause) is available but “extremely difficult to obtain” for floodplain compliance inspections.) Cal. Civ. Pro. § 1822.56. La Rev. Stat. § 38:301 (D). La. Rev. Stat. § 38:325. If measuring elevation and observing the type of basement in a structure were a permissible purpose, we note that the Governor appears to have express power to enter into agreements with FEMA on behalf of any political subdivision in the state to “carry out, effect, secure the benefits and obligations of any state or federal law. La. Rev. Stat. 38: 81. See N.C. Stat. § 130A-17. See N.C. Stat. § 131E-80. See N.C. Stat. § 131E-105. See N.C. Stat. § 15-27.2. Md. Real Prop. § 12-111(a). 31 Cal. 538, 555 (1867). Id. at 555. N.C. Rev. Stat. § 40A-11. Md. Real Prop. § 12-111(a). See La Rev. Stat. § 38:301 (D). Section 701c-1 of Title 33 of the U.S. Code provides as follows: In case of any dam and reservoir project, or channel improvement or channel rectification project for flood control . . . title to all lands, easements, and rights-of- way for such project shall be acquired by the United States or by States, political subdivisions thereof or other responsible local agencies and conveyed to the United States . . . [T}he Secretary of War [Secretary of the Army] is hereby authorized and directed to acquire in the name of the United States title to all lands, easements, and rights-of-way necessary for any dam and reservoir project or channel improvement or channel rectification project for flood control, with funds heretofore or hereafter appropriated or made available for such projects, and States, political subdivisions thereof, or other responsible local agencies, shall be granted and reimbursed, from such funds, sums equivalent to actual expenditures deemed reasonable by the Secretary of War [Secretary of the Army] and the Chief of Engineers and made by them in acquiring lands, easements, and rights-of-way for any dam and reservoir project, or any channel improvement or channel rectification project for flood control heretofore or herein authorized. Telephone conversation with Real Estate Attorney in the Directorate for Real Estate of the Army Corps of Engineers, May 22, 2003. Britt v. U.S., 515 F. Supp. 1159 (D. Ala. 1981). See Fla. Stat. § 810.09. The Federal Government has waived sovereign immunity for actions in tort based on trespass. See generally Hatahley v. United States, 351 U.S. 173, 181 (1956) (holding that the Federal Tort Claims Act allows the United States to be sued for trespass); Black v. Sheraton Corp. of Am., 564 F.2d 531, 539-41 (D.C.Cir.1977) (holding that the FTCA allows the United States to be sued for trespass based on illegal eavesdropping). However, the government will prevail in an action of this type if its entry onto the property us authorized by law. See, e.g., Lawmaster v. Ward, 125 F.3d 1341, 1352 (10th Cir. 1997)(no trespass under Oklahoma law by officers entering property pursuant to warrant). Restatement 2d, Torts § 652A(1). 62A Am. Jur. 2d Privacy § 40. The only form of invasion of privacy that does not require a showing of highly offensive conduct is the wrongful appropriation of one’s name or likeness. Restatement 2d, Torts § 652(B), (C), (D) and (E). 62 Am. Jur. 2d Privacy § 100. 62 Am. Jur. 2d Privacy § 103. 721 F.2d 506 (5th Cir. 1983). Id. at 509. See 13 U.S.C. § 9. See id; see also Baldridge v. Shapiro, 455 U.S. 345 (1982) (master address register was part of the raw census data intended by Congress to be protected from disclosure under Census Act, therefore such information was exempted from disclosure under the Freedom of Information Act, and the confidentiality provisions of Census Act constituted a "privilege" within meaning of discovery provisions of Federal Rules of Civil Procedure). See United States Census Bureau, “TIGER® Overview,” available at (visited Jan. 5, 2003). Telephone conversation with Dan Sweeney, Math Operations Branch, U.S. Census Bureau, Jan. 2, 2003. Id. Telephone conversation with Gerald Gates, Chief, Office of Policy, U.S. Census Bureau, Jan. 7, 2003. Telephone conversation with Andrew Flora, Linear Features and GPS Programs, U.S. Census Bureau, Jan. 3, 2003. Id. See Cal. Civ. Code § 6250 et seq.; Fla. Stat. Ann. § 199.01 et seq.; La. Rev. Stat. Ann. § 44:1 to 44:6; and N.C. Gen. Stat. § 132 to 132-6. See Cal. Rev. & Tax Code § 408.1. See Cal. Rev. & Tax Code § 408.2. See Cal. Rev. & Tax Code § 408.3. Id. See Cal. Gov’t Code § 60253. See Fla. Stat. Ann. § 28.222(1). Id. § 28.222(3)(a). See id. § 28.222(5). See id. § 28.222(6). La. Rev. Stat. Ann. § 33:421. See 1946 Op. Atty. Gen. 48, at 774 (assessment rolls for State Tax Collector for City of New Orleans are public records and any elector or taxpayer may examine or take photographs of them); 1987 Op. Atty. Gen. 301 (May 4, 1987; 1990 Op. Atty. Gen. 330 (computer information generated by the office of the assessor is subject to the Public Records Law); 1987 Op. Atty. Gen. 301-A (June 11, 1987). See N.C. Gen. Stat. § 132-1 (defining public records and stating North Carolina’s policy that public records are property of the people, and therefore “the people may obtain copies of their public records and public information free or at minimal cost unless otherwise specifically provided by law”). See N.C. Gen. Stat. § 132-6.2. See N.C. Gen. Stat. § 132-1.5. See N.C. Gen. Stat. § 132-10. See id. See id. See Freedom of Information Act, 5 U.S.C. § 552(a)(prohibiting a federal agency from inquiring as to the purpose of a request for information except for the purpose of calculating fees); OMB Circular No. A-130, supra, at 8.a. (requiring dissemination of information to the public to be on “equitable” terms). x Evaluation of Alternatives in Obtaining Structural Elevation Data Dewberry PURPOSE PURPOSE Evaluation of Alternatives in Obtaining Structural Elevation Data Dewberry iv TABLE OF CONTENTS TABLE OF CONTENTS BACKGROUND 21 Evaluation of Alternatives in Obtaining Structural Elevation Data Dewberry ACCURACY OF ELEVATION CERTIFICATES LESSONS LEARNED IN NATIONWIDE SURVEYS STRATEGY ASSESSMENTS STRATEGY ASSESSMENTS — STRATEGY A STRATEGY ASSESSMENTS — STRATEGY B STRATEGY ASSESSMENTS — STRATEGY C STRATEGY ASSESSMENTS — STRATEGY D STRATEGY ASSESSMENTS — STRATEGY E SUMMARY OF TECHNOLOGY CAPABILITIES COST-EFFECTIVENESS (CE) ANALYSES STRATEGY SUMMARIES STRATEGY RECOMMENDATIONS PART II — PROVIDING STRUCTURAL ELEVATION DATA APPENDIX A — REPORT ON LEGAL ISSUES APPENDIX B — GEOSPATIAL ACCURACY STANDARDS APPENDIX C — ELEVATION SURVEYS APPENDIX D — ISO ELEVATION CERTIFICATE DATA APPENDIX E — ELEVATION CERTIFICATE HOLDINGS OF DEWBERRY AND URS APPENDIX F — COMPARISON OF COMMERCIAL GEOCODING SERVICES APPENDIX G — PHOTOGRAMMETRY ACCURACY ANALYSES APPENDIX H — PICTOMETRY EXPLANATION APPENDIX I — PICTOMETRY ACCURACY ANALYSES APPENDIX J — LIDAR AUTOMATED DATA EXTRACTION REPORT APPENDIX K — LIDAR ACCURACY ANALYSES APPENDIX L — IFSAR ACCURACY ANALYSES APPENDIX M — VISAT ACCURACY ANALYSES APPENDIX N — SideSwipe — VEHICLE MOUNTED SIDE SCAN LIDAR APPENDIX O — LEGAL COMMENTS ON WEB-BASED ELEVATION CERTIFICATES APPENDIX P — ELEVATION REGISTRY SYSTEM DESCRIPTION APPENDIX Q — CE SPREADSHEET FOR BASE SCENARIO APPENDIX R — PROPOSED DATA DICTIONARY APPENDIX S — WEB-BASED REGISTRY APPENDIX T — COMMUNITY RATING SYSTEM