|Hively, Wells - Dean|
Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/26/2007
Publication Date: 4/25/2007
Citation: Lang, M.W., Ritchie, J.C., Hively, W.D. 2007. Radar monitoring of wetland hydrology: Water quality implications for the Chesapeake Bay [abstract]. Abs. 21, BARC Poster Day. Interpretive Summary:
Technical Abstract: Wetlands are hydrologically dynamic ecosystems which have the potential to improve water quality. Unfortunately, many of the Chesapeake Bay’s wetlands, especially forested wetlands, have been lost or degraded due to human impacts primarily associated with agriculture and urban/suburban development. These anthropogenic impacts greatly reduce the ability of wetlands to remove agrochemicals and these impacts may be compounded by future climate change. Due to the large impact of agriculture on the ability of wetlands to function, the U.S. Department of Agriculture serves a vital role in wetland conservation and restoration. In order for the USDA to allocate funds to best manage wetlands, a better understanding of wetland functioning is necessary. Hydrology (flooding and soil moisture) controls wetland function and extent. Broad-scale forested wetland hydrology is difficult to monitor using ground-based and traditional remote sensing methods (i.e., aerial photography). Satellite borne synthetic aperture radar (SAR) data have the potential to improve the capability to monitor forested wetland hydrology. Research has been conducted which supports the use of C-band SAR to monitor hydrology in forested wetlands and forest hydrology is currently being mapped within the Choptank River Watershed, Maryland. Maps of forested wetland hydrology were compared with the U.S. Fish and Wildlife Service’s National Wetlands Inventory, the Natural Resources Conservation Service’s Soil Survey Geographic Database, and in situ data. Results are encouraging and opportunities are being explored to include this forest hydrology metric, as well as other biophysical parameters, in a decision support system.