Identification of topographic factors for gully erosion susceptibility and their spatial modelling using machine learning in the black soil region of Northeast China

• Published in: Ecological indicators Vol. 143; p. 109376
• Main Authors: Huang, Donghao, Su, Lin, Fan, Haoming, Zhou, Lili, Tian, Yulu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022; Elsevier
• Subjects: agricultural development; algorithms; Catchment areas; China; forestry equipment; gully erosion; Gully erosion susceptibility; Northeast China; Random forest; Remote sensing; risk; sediments; soil; streams; surface roughness; Topographic attribute; topography; watersheds; China; Northeast China; Random forest; Gully erosion susceptibility; Topographic attribute; Catchment areas; Remote sensing

[Display omitted] •Gully erosion susceptibility (GES) of black soil region in China was assessed.•Ten topographic factors were used to predict gully erosion using random forest model.•Results revealed considerable potential for gully erosion (35.36–42.69 %)•Landforms close to channel network systems...