Delineation of agricultural ditch network and critical source area using LiDAR data

Critical source areas (CSAs) are landscape features such as gullies, swales, or depressions that accumulate high volumes of runoff and may negatively impact waterways by mobilization of sediment and nutrients from agricultural lands. Delineating CSAs and mapping drainage networks are essential for cropland management. Availability of Light Detection and Ranging (LiDAR) data with high spatial resolutions enables efficient delineation of CSAs by providing accurate geomorphic data. In this study, we reconstruct ditch network and delineate CSAs based on topographic metrics extracted from LiDAR-derived digital elevation model (DEM) in the Choptank watershed, Maryland. The flow accumulation metric is a foundational index for drainage network delineation, but tends to exaggerate the ditch sinuosity since its centerlines meander within ditches. We propose a method combining the flow accumulation metric with a convergence index and topographic openness and test a range of thresholds for the combined variables to derive the drainage network in the croplands of Choptank watershed. Results indicate that the best variable combination reconstructed about 70% of the drainage network after eliminating small details caused by background noise. Topographic openness is identified as a sensitive index in identifying CSAs due to its ability in detecting the degree of dominance or enclosure of a landscape point and delineate flow divides. Future investigation will be focused on validating delineation results and constraining uncertainties associated with connectivity of CSAs. Methods developed in this study are expected to provide scientific support for better water and soil conservation and agricultural land management. Results of this study will also benefit watershed modeling which is focused on the impacts of CSAs on hydrology and biogeochemistry.