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Title: Monitoring forested wetland hydrology in the Choptank watershed using radar images

item Lang, Megan
item McCarty, Gregory
item Anderson, Martha
item Hively, Wells - Dean

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/1/2007
Publication Date: 3/14/2007
Citation: Lang, M.W., McCarty, G.W., Anderson, M.C., Hively, W.D. 2007. Monitoring forested wetland hydrology in the Choptank Watershed using radar images [abstract]. 4th Annual Science Meeting of the Chesapeake Marshlands National Wildlife Refuge Complex. 2007 CDROM.

Interpretive Summary:

Technical Abstract: Wetlands provide many beneficial services, including water quality improvement and floodwater reduction, but Mid-Atlantic wetlands are at high risk for loss, with forested wetlands being especially vulnerable. These services are performed through the interaction of multiple landscape components (e.g., agricultural fields, forests, urban areas, and wetlands) and therefore should be considered at the watershed scale. Hydrology (i.e., flooding and soil moisture) controls wetland function and must be better understood to conserve remaining wetlands, monitor wetland function, and improve water quality management. 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 capability to monitor forested wetland hydrology. The information derived from SAR can be easily updated through time to better inform adaptive, watershed scale management using water quality models and other decision support systems. However, the full potential of these data needs further investigation. Research is being conducted to support the use of SAR for monitoring forested wetland hydrology at a watershed scale. Forest hydrology has been mapped for the Choptank River Watershed, Maryland and a hydroperiod time series has been developed to better represent the dynamic nature of this ecosystem variable. Results are encouraging and opportunities are being explored to include this metric, as well as other biophysical parameters, in spatially explicit water quality models and other decision support systems.