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

IF 5.4 1区农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Catena Pub Date : 2025-05-20 DOI:10.1016/j.catena.2025.109091

Christian Conoscenti , Grazia Azzara , Aleksey Y. Sheshukov

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引用次数: 0

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.

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像素尺度的沟壑侵蚀敏感性：使用与地形变量一致的沟壑库存的R预测模型

本研究利用多变量自适应回归样条（MARS）在美国堪萨斯州中部的土耳其河流域开发了一种可重复的沟壑侵蚀敏感性评估方法。从数字高程模型（DEM）中提取的两个预测集（A和B）和从沟壑清单中提取的十个沟壑网格对MARS模型进行了训练。集合A包含了与流域面积无关的预测因子（如坡度角、平面曲率），而集合B则增加了与流域面积相关的变量（如溪流顺序、湿度指数）。通过不同的捕获距离和集水区阈值，将数字化的沟壑像素捕获到DEM流线上，从而创建沟壑网格。通过AUC和Cohen’s kappa对20个方形区域进行交叉验证，结果显示，使用snap gully数据和集合B预测因子，性能有了显著提高。该建模框架由开放源代码R代码支持，为在有DEM和沟槽清单数据的地区进行侵蚀敏感性研究提供了一个有价值的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Catena 环境科学-地球科学综合

CiteScore

10.50

自引率

9.70%

发文量

816

审稿时长

54 days

期刊介绍： Catena publishes papers describing original field and laboratory investigations and reviews on geoecology and landscape evolution with emphasis on interdisciplinary aspects of soil science, hydrology and geomorphology. It aims to disseminate new knowledge and foster better understanding of the physical environment, of evolutionary sequences that have resulted in past and current landscapes, and of the natural processes that are likely to determine the fate of our terrestrial environment. Papers within any one of the above topics are welcome provided they are of sufficiently wide interest and relevance.