Work

Pixel-scale gully erosion susceptibility: Predictive modeling with R using gully inventory consistent with terrain variables

Year: 2025

Type: article

Abstract: This study develops a reproducible methodology for gully erosion susceptibility assessment using Multivariate Adaptive Regression Splines (MARS) in the Turkey Creek basin, Central Kansas (USA). MARS models were trained on two predictor sets (A and B) extracted from the Digital Elevation Model (DEM) and ten gully grids derived from a gully inventory. Set A included predictors independent of the catchment area (e.g., slope angle, plan curvature), while set B added catchment area-related variables (e.g., stream order, wetness index). Gully grids were created by snapping digitized gully pixels to DEM flow lines by varying snapping distances and catchment area thresholds. Cross-validation across 20 square zones revealed significant performance improvements with snapped gully data and set B predictors, as measured by AUC and Cohen’s kappa . The modeling framework, supported by open-source R code, offers a valuable tool for erosion susceptibility studies in regions where DEM and gully inventory data are available.

Source: CATENA

DOI: 10.1016/j.catena.2025.109091

Authors: Christian Conoscenti, Grazia Azzara, Aleksey Y. Sheshukov

Institutions: University of Palermo, Kansas State University

Language: English

Topic: Soil erosion and sediment transport

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Life on land

Open Access status: hybrid

Estimated APC paid: $3,680