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The problem of grassland degradation induced by livestock production at the expense of local grasses has become a serious ecological problem worldwide. To maintain livestock production and sustainable grassland development, the extent and intensity of grassland utilization needs to be better understood. In this study, a model was developed to monitor the intensity of grazing in a typical grazing area (Tibet Selinco basin) of the Qinghai‐Tibet Plateau. Combining the number of livestock at the township scale with their matching livestock habitat area and location, the biomass consumed by livestock was assigned to a productivity supply map (NPP) using a mathematical iterative algorithm from the perspective of different foraging habits of different livestock. The objective was to accurately measure the overall grazing pressure on the pasture as well as to estimate livestock pasture utilization. The model confirmed distinct spatial differences in the intensity of grassland utilization in the Tibet Selinco basin, in which the overall intensity was significantly low except in the southwestern region. The overall grazing area was found to be 150,000 km2, of which moderate grazing area occupied 130,000 km2 and overgrazing area occupied 20,000 km2, accounting for 87% and 13% of the total grazing area, respectively. The proposed model can quantify human activities spatially and provide a reliable and accurate scientific basis for livestock production development and ecological environment management.

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Land degradation and desertification (LDD) has become one of the most urgent global environmental issues. The complexity of LDD make it difficult to quantify, how to monitor quickly and accurately has become the key to realize the sustainability for land resources. To achieve this target, firstly, a comprehensive index—land degradation and desertification status index (LDDSI) is built, which integrates the information in fractional vegetation cover (FVC), net primary productivity (NPP), albedo and modified temperature vegetation drought index (MTVDI) based on the spatial principal component analysis (SPCA). Then, identifies LDD from dynamics of land degradation and desertification status (LDDS) in 2001-2018. Based on this, we analyze the spatio-temporal process and driving mechanism of LDD in Northern China. The result indicates that: (a) LDDSI has a better monitoring performance, (b) LDD has been effectively alleviated, but the spatial distribution of LDDS maintains a high clustering pattern, which is difficult to be broken, (c) LDD in local regions is further expanded (1.75%) affected by many factors, which deserves our attention, and (d) the differences in climate, environmental backgrounds and human activities play a key role in LDDS and LDD. In addition, we assess the effectiveness of ecological projects implemented by the Chinese government. The current understanding in the change pattern and influencing mechanism for LDDS and LDD can provide a scientific basis for formulating ecological policies based on local conditions.