National Enhanced Elevation Assessment (NEEA)Part 3: The Cost-Benefit Analysis Process

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The US Geological Survey’s extremely successful 3D Elevation Program (3DEP) is based on the National Enhanced Elevation Assessment (NEEA) for which I served as Dewberry’s project manager. In Part 1 of this series, I summarized the USGS/Dewberry planning that went into the NEEA, and in Part 2, I summarized the steps we went through to document and validate DEM user requirements and benefits.

Master Geodatabase
All of the DEM user requirements and benefits were entered by Dewberry into a master geodatabase for 104 Functional Activities (FA’s) from federal agencies, 329 FA’s from states and U.S. territories, 144 FA’s from local and tribal governments within each state, and 25 FA’s from other organizations (not-for-profit and private companies). When populated, the geodatabase included over 100,000 polygons, each linked to user elevation data requirements and benefits for 602 FA’s within 27 major business uses.

Dewberry aggregated and analyzed all elevation data requirements and benefits for each FA and Business Use. Each FA was summarized for its mission-critical elevation data requirements by Quality Level and update frequency; and its tangible and intangible benefits to include annual dollar benefits for use in the CostBenefit Analyses (CBA). For about half of the FA’s, users reported major dollar benefits but could not quantify those benefits, and many of the other benefits appeared to be ultra-conservative, i.e., financial benefits were understated for reasons explained in Appendix E of the NEEA report. Although Dewberry also estimated higher potential benefits documented in Table 1, the CBA and Return on Investment (ROI) calculations were performed only with the most conservative benefits as validated for each of the 602 FA’s.

As shown at Table 1, the conservative benefits total $1.4 billion/year and the potential benefits total $13.3 billion/year. However, not all of these benefits would be achieved if users received poorer Quality Level data or update frequencies than optimally required for each FA. Table 1 compares the conservativelyestimated benefits that seemed to be understated, with potential future benefits that are much higher but may still be understated. Table 1, sorted by conservative benefits, contrasts conservative benefits and potential benefits. State requirements and benefits vary widely in terms of data quality and benefits. For example, four states specified requirements for QL1 LiDAR and other states specified QL2 or QL3 LiDAR. In addition, North Carolina reported significantly higher benefits for coastal flood risk management than did other coastal states, and some states significantly underestimated or were unable to assign any benefits at all for flood risk management.

Estimated Costs per Square Mile
For the 48 conterminous states, USGS provided average cost estimates by Quality Level from its Geospatial Products and Services Contract (GPSC2) contractors for Quality Levels 1 through 4. These estimates, in 2011 dollars, are in column B in Table 2 below. Columns C and D include the 15 percent estimated costs of QA/QC to include the survey of QA/QC checkpoints. Column E assumes 5 percent for USGS to manage the acquisition and processing of data. Column F includes the total cost per square mile used in the CBA. Dewberry provided cost estimates for QL5 IFSAR (Interferometric Synthetic Aperture Radar) in Alaska ($94.50/mi2) and reduced costs for QL5 IFSAR in the other 49 states ($80/mi2) where acquisition costs were estimated to be about 18 percent lower because of improved access to suitable airports and facilities. All costs assumed that the same Quality Level of elevation data is acquired in the most efficient manner for entire 1-degree cells (1 latitude by 1 longitude). As of 2017, LiDAR costs are much lower than estimated in 2011, so that the ROI today would be significantly higher than totaled in these tables.

Reduced Benefits Value Multipliers
Recognizing that benefits are degraded if users do not receive the Quality Level and update frequency required, Dewberry developed a procedure for degrading annual dollar benefits with reduced value multipliers. Table 3 shows how the benefits value multiplier is decreased for a Functional Activity that has the most demanding requirement (QL1 LiDAR with annual updates) and receives something less than that (shown in the other 24 alternatives). For other less-demanding requirements, the value multiplier is 1.0 (full benefit value) if the Quality Level and update frequency is equal to or better than required, but decreased by half for every column to the right for Quality Level and for every row beneath for update frequency. For example, if a FA required QL1 LiDAR updated every 4-5 years to achieve a $100,000 annual benefit, but received QL2 LiDAR updated every 6-10 years, the benefit would be reduced to a $25,000 annual benefit.

Two widely used methods for performing Benefit Cost Analyses are: (1) Net Benefits (NB) where costs are subtracted from the benefits (NB = benefits minus costs); and (2) Benefit/ Cost Ratio (B/C Ratio) where the benefits are divided by the costs (B/C Ratio = benefits/costs). Dewberry used the master geodatabase to optimize Net Benefits, but also computed the B/C Ratio for multiple options.

Cost-Benefit Analyses
The CBA demonstrate the synergy achieved if sectors work together to meet their common needs. Table 4 shows that if the federal government, state governments, and nongovernmental organizations work as independent groups, their subtotal aggregate annual costs would be higher ($289M), their aggregate benefits would be lower ($891M), and the annual net benefits ($602M) would be lower (yellow), than if the groups worked collaboratively to optimize the overall benefit-cost model (green). At this stage of the NEEA study, we were considering potentially different Quality Levels and update frequencies for each individual 1-degree cell in all 50 states and U.S. territories.

Dewberry used the power of the geodatabase to evaluate all 25 options (five update frequencies for each Quality Level) for collecting consistent elevation data (Table 5.) Each option would result in a uniform Quality Level and a uniform update frequency for the 48 conterminous states, excluding Alaska, Hawaii and U.S. territories where costs were uncertain. For each option, Table 5 shows annual total data costs, annual total benefits, annual net benefits (negative net benefits for red numbers in parentheses) and B/C Ratios. The five colors in this table match those used in all maps in the NEEA report that show Quality Levels.

Although Option 3 (QL1 LiDAR, 4-5 year update frequency) has the highest Net Benefits, Option 9 (QL2 LiDAR, 6-10 year update frequency) provides the best B/C Ratio (5.356) with annual Net Benefits of $548M. Therefore Option 9 would provide the "biggest bang for the buck" if a uniform Quality Level and update frequency option is desired for the 48 conterminous states.

Table 5 does not imply that alternatives are limited to uniform data Quality Levels for the 48 conterminous states, and certainly not for all 50 states and U.S. territories. For example, Alaska has many requirements for LiDAR, but IFSAR provides a more-realistic and achievable statewide solution for Alaska because IFSAR maps through clouds and fog that would make LiDAR unachievable or unaffordable throughout much of that state.

3D Elevation Program (3DEP) and Future 3D Nation
After evaluating these options and multiple nationwide implementation scenarios prepared by Dewberry, USGS developed the 3DEP based on QL2 LiDAR nationwide, except for QL5 IfSAR in Alaska, with updates on an 8-year cycle if possible. USGS called the NEEA report "the most comprehensive benefit/cost analysis ever performed for any layer of The National Map," and similar cost-benefit analysis processes are planned for the ongoing NEEA Update and Coastal Nearshore/ Offshore Bathymetry Requirements and Benefits Study now in progress, with a goal to obtain a seamless, consistent, high-accuracy, high-resolution 3D Nation, from the tops of the mountains to the depths of the sea, that is cost-effective and up-to-date. The NEEA was a vital part of this vision for the future.

Dr. David Maune is an Associate Vice President at Dewberry Consultants LLC where he is an elevation specialist and manages photogrammetric, LiDAR, IFSAR and acoustic mapping projects for USGS, NOAA, FEMA, USACE, and other federal, state and county governments. He authored the National Enhanced Elevation Assessment (NEEA) report referenced in this article. He specializes in independent QA/QC of LiDAR data produced by others and is perhaps best known as the editor and primary author of the 1st and 2nd editions of Digital Elevation Model Technologies and Applications: The DEM Users Manual published by ASPRS. He is a retired Army Colonel, last serving as Commander and Director of the U.S. Army Topographic Engineering Center (TEC), now the Army Geospatial Center (AGC).

A 710Kb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE