Submitted to: Society of Wetland Scientists
Publication Type: Abstract Only
Publication Acceptance Date: May 26, 2008
Publication Date: May 26, 2008
Citation: Lang, M.W., McCarty, G.W., Ritchie, J.C. 2008. Remote sensing of wetland hydrology: Implications for water quality management in agricultural landscapes [abstract]. Society of Wetland Scientists. p. 203. Technical Abstract: Due to the substantial effect of agriculture on the ability of wetlands to function, the U.S. Department of Agriculture (USDA) serves a key 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. Hydroperiod is the most important abiotic factor controlling wetland functioning and extent. Broad-scale forested wetland hydrology is difficult to monitor using ground-based and traditional remote sensing methods (i.e., aerial photography). C-band synthetic aperture radar (SAR) data can improve the ability to map forested wetlands via the monitoring of forested wetland hydrology, but further research is necessary to fully quantify the benefits and limitations of this approach, especially at the watershed scale. Research has been conducted which supports the use of C-band SAR to map and monitor Mid-Atlantic forested wetlands. A forested wetland time series demonstrating characteristic variations in hydrology throughout the leaf-off season has been developed for the Tuckahoe Watershed, Maryland to better represent the dynamic nature of this ecosystem. Management applicability was increased though the use of images with multiple incidence angle. Forested wetland maps were compared with U.S. Fish and Wildlife Service’s National Wetlands Inventory (NWI), USDA Natural Resources Conservation Service’s Soil Survey Geographic Database (SSURGO), and in situ data. Results are encouraging and opportunities are being explored to include the radar derived wetland maps, as well as other biophysical parameters and digital elevation maps derived from lidar, in a watershed-scale decision support tool to assist USDA managers.