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绘制地图和建模半干旱地区土地退化脆弱性:土耳其巴塔尔加兹区的案例研究

Miraç Kılıç¹

¹Department of Soil Science and Plant Nutrition, Malatya Turgut Özal Üniversitesi, Malatya, Battalgazi, Turkey.

|February 2, 2026

概括

本研究介绍了HyStoRSM,这是一个结合机器学习和遥感的新型框架,用于绘制半干旱地区土地退化脆弱性 (LDV) 的地图. 该框架准确地预测了LDV,确定了面向有针对性的土地管理高度脆弱的地区.

科学领域:

环境科学环境科学
地质科学是地球科学.
遥感遥感遥感遥感

背景情况:

土地退化对生态系统服务构成全球威胁.
现有的土地退化脆弱性 (LDV) 评估缺乏空间细节和预测准确性.
对于LDV评估的光谱指数和机器学习的整合尚未得到充分探索.

研究的目的:

开发和验证一个新的框架 (HyStoRSM) 以高精度地绘制LDV的空间地图.
整合土地调查数据,遥感和机器学习,以提高LDV预测.
为积极和精确的土地退化风险管理提供一个工具.

主要方法:

在HyStoRSM框架中集成了土地测量数据,Landsat 8光谱指数和SRTM地形数据.
谷歌地球引擎用于数据处理,XGBoost回归用于LDV预测,SHAP用于特征重要性.
使用STORIE指数评级 (SIR) 来生成当地的LDV分数.

主要成果:

该XGBoost模型实现了高预测准确性 (R2 = 0.74).
水域地形变量,特别是长度斜率因子,比光谱指数更有影响力.
一个30米分辨率的LDV地图显示了高和非常高脆弱性的区域的21.7%和20.3%.

结论:

该HyStoRSM框架提供了一个可解释的方法,集成多源数据和机器学习.
它允许精确地管理土地退化风险,特别是在半干旱地区.
地形和水文因素是这些地区侵蚀脆弱性的关键驱动因素.

关键词:

可解释的人工智能土地退化土地退化机器学习是机器学习.遥感是一种远程传感.半干旱地区半干旱地区土壤侵蚀导致土壤侵蚀.地形索引地形索引脆弱性映射脆弱性映射脆弱性映射

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