Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

Abstract

In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

Using the GeoWEPP Model to Predict Water Erosion in Micro-Watersheds in the Brazilian Cerrado

“…GeoWEPP can be used to identify priority pasture areas in Brazil for improved soil conservation. Soil erosion is a significant challenge in Brazil, especially in agricultural areas with sandy soils, which can lead to noticeable erosion as rills and gullies even in areas with pasture [52], as well as increased suspended sediments loads in rivers such as the Teles Pires [53]. Brazil's pastures help support ~253 million head of cattle [49].…”

Testing the Reliability of Maximum Entropy Method for Mapping Gully Erosion Susceptibility in a Stream Catchment of Calabria Region (South Italy)

“…However, no universal guideline exists regarding the predisposing factors that should be used in the gully erosion prediction models. Several studies employed different geo-environmental variables based on the availability, reliability, and practicality of the data [13,20,24,25,47,48]. In this study, the predisposing factors were rationally selected, considering the data availability, previous knowledge of the study area, and concerning the relevant literature [23,24,26,46].…”

“…In the last decade, many statistical and machine learning methods combined with geographic information systems (GIS) and remote sensing technologies were proposed for evaluating and mapping gully erosion susceptibility in several areas worldwide [13,23,25,26]. The main bi-and multi-variate statistical methods employed in the previous research are frequency ratio [27], evidential belief function [28], certainty factors [29], information value [28], index of entropy [28], weights of evidence [30], logistic regression [25], and geographically weighted regression [13,31]. Instead, among the most known machine learning methods, there are random forest [32], artificial neural networks [22,33], support vector machine [33], boosted regression tree [32], and maximum entropy [20,34].…”

Gullying and landscape evolution: Lavaka in Lac Alaotra, Madagascar shed light on rates of change and non-anthropogenic controls

“…Common denominators for this kind of erosion are steep and often convex hillslopes with resistant laterite overlying deeply weathered saprolite and a monsoonal climate with alternating wet and dry seasons ( 23 , 35 ). Madagascar’s central highlands [which occupy more than 50% of the country’s area; ( 36 )] exhibit steep “demi-orange” topography and are weathered to several tens of meters depth with well-developed lateritic surface layers, making them particularly prone to this style of gullying ( 20 ).…”