Abstract
Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai-Tibet Plateau in 1982-2015. The proposed method (R(2) = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R(2) of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R(2) of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982-2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.