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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: 8042-13610-030-077-I
                        
                        Project Type: Interagency Reimbursable Agreement Start Date: Aug 21, 2023
                        
                        End Date: Aug 21, 2028 **Objective:**
                        
                        This wetland assessment project is a part of the national CEAP network assessing effectiveness of wetland restorations in agricultural landscapes.  This project focuses on assessing ecosystem services provided by wetland restorations in the Mid-Atlantic Region.

[1] Characterize the risk to ecosystem service provisioning for wetland habitats.
[2] Assessment of pollination ecosystem service dynamics of protected conservation habitats in cropland.
[3] Assess agricultural nitrate mitigation extent by wetland ecosystems, spatially and temporally, using environmental tracers.
[4] Develop wetland hydroperiod maps in Delmarva. 
[5] Head-water ditch mapping and critical source area characterization in Delmarva.
[6] Quantifying hydrologic change with wetland restoration using time-series. **Approach:**
                        
                        This project will: 
1) Use the empirical field data to calibrate the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model to conduct ecosystem service analysis;
2)  Compare the function assessment of the Riparia database to that within the InVEST model; 
3) Compare the aptitude of different habitats as primary pollinator habitat in the intensive Midwest agricultural landscape;
4) Initiate the long-term goal of field calibration for our pollinator model; 
5) Develop a deep learning model to produce wetland maps for the entire Delmarva and explore hydroperiod maps using time series using SAR imagery; 
5) Use deep learning models to produce a map of head-water drainage ditches  for the entire Delmarva; 
6) Use deep learning approaches to demonstrate the potential for extracting micro ditches in low relief landscapes.


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