Gridded Grazing Intensity Based on Geographically Weighted Random Forest and Its Drivers: A Case Study of Western Qinghai–Tibetan Plateau

作者：Zhihui Yang, Jie Gong, Xia Li, Yonghao Wang, Yixu Wang, Guobin Kan, Jing Shi · 发表于：Land Degradation and Development · 年份：2024 · DOI：10.1002/ldr.5297 · 被引用次数：6 · 研究领域：Rangeland Management and Livestock Ecology、Land Use and Ecosystem Services、Wildlife Ecology and Conservation

ABSTRACT Overgrazing affects the grass‐livestock balance and endangers grassland ecological security. Despite extensive studies conducted on identifying and quantifying grazing intensity, there is still room for improvement in the research on gridding grazing intensity, particularly in areas with limited data on the Qinghai–Tibet Plateau. Therefore, we proposed a grazing intensity spatialization method using geographically weighted random forest (GWRF) to gain further insights into the spatial heterogeneity of alpine grassland grazing intensity. This method incorporates multiple remote sensing data related to human activities and natural factors, as well as annual livestock statistics at the township level over several years, while adequately considering the spatial autocorrelation of grazing intensity. Additionally, we employed Lindeman Merenda Gold (LMG), the geographical detector model, and the structural equation model (SEM) to assess the contribution and influence path of driving factors to grazing intensity. We also utilize partial correlation analysis and dual‐phase mapping to examine the impact of natural and human activities on the spatial distribution of grazing intensity. The results demonstrate that the GWRF‐based grazing intensity spatial model accurately predicts grazing intensity by demonstrating its consistency with township‐scale livestock data ( R 2 = 0.92 ( p < 0.01), RMSE = 1.07). This provides valuable technical support for quantifying grazing intensit...