Abstract

The entire Piedmont of the Southeastern United States, where the Calhoun Critical Zone Observatory (CCZO) is located, experienced one of the most severe erosive events of the last two centuries. Forested areas were cleared to cultivate cotton, tobacco, and other crops during the nineteenth and early twentieth century and these land use changes, together with intense rainfalls, initiated deep gullying. An accurate mapping of these landforms is important since, despite some gully stabilization and reforestation efforts, gullies are still major contributors of sediment to streams. Mapping gullies in the CCZO area is hindered by the presence of dense canopy, which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of the gullies makes the identification and characterization of entire gullies through field surveys a very large and expensive proposition. 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).

Citation

Noto, L. V., S. Bastola, Y. G. Dialynas, E. Arnone, and R. L. Bras (2017): Integration of fuzzy logic and image analysis for the detection of gullies in the Calhoun Critical Zone Observatory using airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing 126: 209-224. DOI: 10.1016/j.isprsjprs.2017.02.013

This Paper/Book acknowledges NSF CZO grant support.