Project Number: 1930-13630-001-01
Project Type: Grant

Start Date: Sep 01, 2006
End Date: Aug 31, 2010

Objective:
Develop and validate methods to identify critical source areas of soil and nutrient loss in agricultural catchments of the Chesapeake Bay drainage basin using remote sensing techniques, particularly LiDAR-developed topographic maps. A. Develop technology for detection of runoff and erosion prone critical source areas in steeply sloped landscapes of the Chesapeake Bay drainage area. B. Develop technology for detection of runoff and nutrient loss critical source areas in nearly-level landscapes of the Chesapeake Bay drainage area.

Approach:
This collaborative research will involve USDA-ARS and Canaan Valley Institute (CVI) scientists, in the context of ongoing research initiatives, particularly the Conservation Effects Assessment Project. Research will be conducted at sties representative of landscapes and agricultural practices within the Chesapeake Bay drainage basin, including the Mid-Atlantic Highlands of Appalachia, Allegheny Plateau, Valley and Ridge, and Atlantic Coastal Plain. Research in steeply sloping landscapes will center on identification of areas of high frequency runoff generation and erosion and on improving site assessment index application using high resolution elevation models. Research in nearly level landscapes will focus on characterization of overland flow and subsurface recharge areas, drainage ditch networks and critical control points favorable to different management practices. Testing of remote sensing applications will take advantage of ongoing USDA-ARS watershed studies (Choptank, Manokin, Mahantango, Town Brook) to assess the potential to remotely identify critical source areas of nutrient loss. Research in the Choptank watershed will involve direct collaboration with scientists at the Hydrology and Remote Sensing Laboratory in Beltsville, MD. Existing soil and water quality monitoring data from these established experimental watersheds, as well as direct sampling and new experimentation, will be used to support testing. CVI scientist will acquire and process remotely sensed data (e.g., LiDAR and hyperspectral). With these data, USDA-ARS and CVI will collaboratively develop of novel inference techniques to identify areas of flow favorable to nutrient transport. Critical flow areas will be used to target areas where investment in additional conservation practices can be expected to achieve maximum water quality benefit and to evaluate the effectiveness of existing conservation practices. USDA-ARS and CVI will evaluate the potential for widespread application of newly identified inference techniques and participate in the development of strategies for their use in watershed management.