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 Hydrology and Remote Sensing Laboratory: Beltsville, MD
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ARS Home Northeast Area Beltsville, Maryland (BARC) Beltsville Agricultural Research Center Hydrology and Remote Sensing Laboratory Research





- McCarty, Gregory
- Water Availability and Watershed Management (211)
- Sustainable Agricultural Systems Research (216)

Research Project: **Location:
                            Hydrology and Remote Sensing Laboratory** Project Number: 8042-13610-030-048-S
                        
                        Project Type: Non-Assistance Cooperative Agreement Start Date: Jul 1, 2022
                        
                        End Date: Jul 31, 2024 **Objective:**
                        
                        It is well established that wetlands can provide important ecosystem services to impaired watersheds but there is uncertainty about the likelihood of ecosystem service provision.  This project is designed to reduce the uncertainty in assessment of ecosystem service provision by natural and restored wetlands, to collect and synthesize foundational information necessary to determine level of ecosystem functions in wetlands within NRCS easements at a national scale through geospatial analysis. **Approach:**
                        
                        Phase 1:  Review and synthesize multi-scale wetland functional assessment protocols and results across the coterminous US.
In general, most methods of functional assessment can be categorized into one of three levels, each with increasing data requirements and depth of analysis, with a concurrent decrease in levels of uncertainty. Determining which wetlands have the potential to deliver a function or service involves only a remote sensing inventory where wetland types and their landscape position are identified. Determining whether a function or service is probable for a specific wetland adds the dimensions of landscape analysis and listing likely stressors. At this level of investigation, there is increasing certainty that both the wetland type and a ranking of functional capacity are correct. Finally, actual occurrence of wetland functions or services for a subset of known sites within the population can be determined by deploying rapid and intensive field studies that measure a function or service directly or estimate its level of performance with surrogate metrics or indicators. One approach utilized in conjunction with the National Wetlands Inventory, termed “Enhanced Classification for Landscape Position, Landform, Water Flow Path, and Waterbody Type” (hereafter referred to as LLWW (Tiner 2011)) is an example of a remote sensing approach with potential applicability on a national scale, but it has not received extensive testing with site level data and therefore currently represents high levels of uncertainty. Before widespread implementation, therefore, it is prudent to both reassess its capabilities given that new sources of geospatial data have become available, as well as comparing this approach to other protocols that utilize the same geospatial data sets but may provide a more refined assessment of function with lower levels of uncertainty.  Phase 1 will collect the vast number of functional assessment protocols that have been developed across the US (including the LLWW), with explicit characterization of the level of uncertainty that they provide considering current geospatial data availability.  To present the results of the collection in an organized fashion, we will develop a classification or categorization scheme that recognizes important axes of variability between methods and that would impact their transferability to a national scale.  For example, methods may vary in their specificity to one type of landform, or ability to consider a wide range of surrounding land cover types.  We envision a classification tree that we could “map” functional assessment methods onto; Figure 1 presents a hypothetical example.  Utilizing this classification scheme and pieces of various protocols, we will create a blended approach to remote detection of wetland functional assessment.  For example, we have been developing a blended protocol in the Mid-Atlantic region between the LLWW approach and HGM Functional Assessment Models (intensive assessments that require field data).


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