Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020

作者：Jia Zhou, Jin Niu, Ning Wu, Tao Lu · 发表于：Earth system science data · 年份：2024 · DOI：10.5194/essd-16-5171-2024 · 被引用次数：13 · 研究领域：Rangeland Management and Livestock Ecology、Remote Sensing and Land Use、Environmental Changes in China

Abstract. Grazing activities constitute the paramount challenge to grassland conservation over the Qinghai–Tibet Plateau (QTP), underscoring the urgency of obtaining detailed information regarding the extent, patterns, and trends of grazing to enable efficient grassland management and sustainable development. Here, to inform stakeholders about these issues, we provided the first annual Gridded Dataset of Grazing Intensity (GDGI), with a resolution of 100 m, from 1990 to 2020 for the QTP. The five most commonly used machine learning algorithms were leveraged to develop a livestock spatialization model, which spatially disaggregates the livestock census data at the county level into a detailed 100 m × 100 m grid based on seven key predictors from terrain, climate, vegetation, and socio-economic factors. Among these algorithms, the extreme-tree (ET) model performed the best in representing the complex nonlinear relationship between various environmental factors and livestock intensity, with an average absolute error of just 0.081 SU ha−2 (where SU denotes sheep units), a rate outperforming the other models by 21.58 %–414.60 %. By using the ET model, we further generated the GDGI for the QTP to reveal the spatio-temporal heterogeneity and variations in grazing intensities. The GDGI indicates that grazing intensity remained high and largely stable from 1990 to 1997, followed by a sharp decline from 1997 to 2001 and fluctuations thereafter. Encouragingly, compared to other open-acc...