Integration of fuzzy logic and image analysis for the detection of gullies in the Calhoun Critical Zone Observatory using airborne LiDAR data

摘要: Abstract The entire Piedmont of the Southeastern United States, where Calhoun Critical Zone Observatory (CCZO) is located, experienced one most severe erosive events last two centuries. Forested areas were cleared to cultivate cotton, tobacco, and other crops during nineteenth early twentieth century these land use changes, together with intense rainfalls, initiated deep gullying. An accurate mapping landforms important since, despite some gully stabilization reforestation efforts, gullies are still major contributors sediment streams. Mapping in CCZO area hindered by presence dense canopy, which precludes identification through aerial photogrammetry traditional remote sensing methods. Moreover, wide spatial extent makes characterization field surveys a very large expensive proposition. This work aims develop methodology automatically detect map based on set algorithms morphological characteristics retrieved high resolution (VHR) imagery. A one-meter LiDAR Digital Elevation Model (DEM) used derive different morphometric indices, combined using analysis methods fuzzy logic rules, building up tool able identify gullies. model has been calibrated using, as reference, perimeters relatively that have measured recent survey. procedure provide estimates erosion patterns, characterize area, an objective method measure characteristic features (i.e., depth volume).