Mapping Benggang Erosion Susceptibility: An Analysis of Environmental Influencing Factors Based on the Maxent Model

Haidong Ou, Xiaolin Mu, Zaijian Yuan (), Xiankun Yang, Yishan Liao, Kim Loi Nguyen and Samran Sombatpanit
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Haidong Ou: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Xiaolin Mu: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Zaijian Yuan: Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
Xiankun Yang: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Yishan Liao: Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
Kim Loi Nguyen: Research Center for Climate Change, Nong Lam University, Ho Chi Minh City 70000, Vietnam
Samran Sombatpanit: Land Development Department, Bangkok 10900, Thailand

Sustainability, 2024, vol. 16, issue 17, 1-23

Abstract: Benggang erosion is one of the most severe geomorphological hazards occurring on deeply weathered crusts in the hilly regions of southern China. Unraveling the susceptibility and pinpointing the risk areas of Benggang erosion are essential for developing effective prevention and management strategies. This study introduced the Maxent model to investigate Benggang erosion susceptibility (BES) and compared the evaluation results with the widely used Random Forest (RF) model. The findings are as follows: (1) the incidence of Benggang erosion is rising initially with an increase in elevation, slope, topographic wetness index, rainfall erosivity, and fractional vegetation cover, followed by a subsequent decline, highlighting its distinct characteristics compared to typical types of gully erosion; (2) the AUC values from the ROC curves for the Maxent and RF models are 0.885 and 0.927, respectively. Both models converge on elevation, fractional vegetation cover, rainfall erosivity, Lithology, and topographic wetness index as the most impactful variables; (3) both models adeptly identified regions prone to potential Benggang erosion. However, the Maxent model demonstrated superior spatial correlation in its susceptibility assessment, contrasting with the RF model, which tended to overestimate the BES in certain regions; (4) the Maxent model’s advantages include no need for absence samples, direct handling of categorical data, and more convincing results, suggesting its potential for widespread application in the BES assessment. This research contributes empirical evidence to study the Benggang erosion developing conditions in the hilly regions of southern China and provides an important consideration for the sustainability of the regional ecological environment and human society.

Keywords: rainfall-triggered soil erosion; Benggang erosion; susceptibility and hazard mapping; Maxent model; remote sensing and GIS techniques (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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