Gully erosion assessment and prediction on non-agricultural lands using logistic regression

Abstract

Gully erosion is a serious problem on military training lands resulting in not only soil erosion and environmental degradation, but also increased soldier injuries and equipment damage. Assessment of gully erosion occurring on Fort Riley was conducted in order to evaluate different gully location methods and to develop a gully prediction model based on logistic regression. Of the 360 sites visited, fifty two gullies were identified with the majority found using LiDAR based data. Logistic regression model was developed using topographic, landuse/landcover, and soil variables. Tests for multicollinearity were used to reduce the input variables such that each model input had a unique effect on the model output. The logistic regression determined that available water content was one of the most important factors affecting the formation of gullies. Additional important factors included particle size classification, runoff class, erosion class, and drainage class. Of the 1577 watersheds evaluated for the Fort Riley area, 192 watersheds were predicted to have gullies. Model accuracy was approximately 79% with an error of omission or false positive value of 10% and an error of commission or false negative value of 11%; which is a large improvement compared to previous methods used to locate gully erosion.