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

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-access datasets for grazing distribution on the QTP, the GDGI has the highest accuracy, with the determinant coefficient (R2) exceeding 0.8. Given its high resolution, recentness, and robustness, we believe that the GDGI dataset can significantly enhance our understanding of the substantial threats to grasslands emanating from overgrazing activities. Furthermore, the GDGI product holds considerable potential as a foundational source for other research, facilitating the rational utilization of grasslands, refined environmental impact assessments, and the sustainable development of animal husbandry. The GDGI product developed in this study is available at https://doi.org/10.5281/zenodo.10851119 (Zhou et al., 2024).