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

This work aims to develop a methodology to automatically detect and map gullies based on a set of algorithms and morphological characteristics retrieved by very high resolution (VHR) imagery. A one-meter resolution LiDAR Digital Elevation Model (DEM) is used to derive different morphometric indices, which are combined by using spatial analysis methods and fuzzy logic rules, building up a tool able to automatically identify gullies. This spatial model has been calibrated using, as reference, the perimeters of two relatively large gullies that have been measured during a recent field survey. The entire procedure aims to provide estimates of gully erosion patterns, which characterize the entire CCZO area, and to develop an objective method to measure characteristic features of gullies (i.e., depth and volume).