In recent years, the vegetation cover in the Selinco region of the Qinghai-Tibet Plateau has undergone significant changes due to the influence of global warming and intensified human activity. Consequently, comprehending the distribution and change patterns of vegetation in this area has become a crucial scientific concern. To address this concern, the present study employed MODIS-NDVI and elevation data, integrating methodologies such as trend analysis, Hurst exponent analysis, and sequential cluster analysis to explore vegetation cover changes over the past 21 years and predict future trends, while examining their correlation with topographic factors. The study findings indicate a fluctuating upward trend in vegetation cover, with a notable decrease in 2015. Spatially, the overall fractional vegetation cover (FVC) in the study area showed a basic stability with a percentage of 78%. The analysis of future trends in vegetation cover revealed that the majority of areas (68.26%) exhibited an uncertain trend, followed by stable regions at 15.78%. The proportion of areas showing an increase and decrease in vegetation cover accounted for only 9.63% and 5.61%, respectively. Elevation and slope significantly influence vegetation cover, with a trend of decreasing vegetation cover as elevation increases, followed by an increase, and then another decrease. Likewise, as the slope increases, initially, there is a rise in vegetation cover, followed by a subsequent decline. Notably, significant abrupt changes in vegetation cover are observed within the 4800 m elevation band and the 4° slope band in the Selinco region. Moreover, aspect has no significant effect on vegetation cover. These findings offer comprehensive insights into the spatial and temporal variations of vegetation cover in the Selinco region and their association with topographic factors, thus serving as a crucial reference for future research.

Abandoned cropland is a great waste of land resources, which not only affects the stability of food production, but also wastes limited cropland resources. China has large areas of abandoned cropland, and accurately extracting the large-scale abandoned cropland is a prerequisite for the government to implement reclamation policies and estimate the food production potential of abandoned cropland. China has implemented the Grain for Green Project (GGP) since 1980, but most research has not considered its impact on the extraction of abandoned cropland. Therefore, a method was proposed to identify abandoned cropland excluding the regions of GGP, and to analyze the spatio-temporal characteristics and potential of abandoned cropland in China from 1991 to 2018. GGP potential and food production potential of abandoned cropland were discussed. The results showed that 26.47 million hectares of cropland has been abandoned in the past 28 years (including cropland that has been reclaimed). Under the positive influence of the government’s food security policy, the abandoned cropland has decreased in recent years, but the total area is still high. The abandonment mainly occurred in the Jiangnan Hills, the Hilly Regions of Fujian and Guangdong Province, the North China Mountains, and Eastern Inner Mongolia. The higher the elevation and the steeper the slope, the higher the abandonment rate. In addition, the urban-rural income gap significantly affected the abandonment rate. This study drew the spatial and temporal distribution map of abandoned cropland in China with a spatial resolution of 30 m considering GGP. It provides an important basis for formulating reclamation and GGP policy.

Estimating the spatiotemporal variations in natural grassland carrying capacity is crucial for maintaining the balance between grasslands and livestock. However, accurately assessing this capacity presents significant challenges due to the high costs of biomass measurement and the impact of human activities. In this study, we propose a novel method to estimate grassland carrying capacity based on potential net primary productivity (NPP), applied to the source area of the Nujiang River and Selinco Lake on the Tibetan Plateau. Initially, we utilize multisource remote sensing data—including soil, topography, and climate information—and employ the random forest regression algorithm to model potential NPP in areas where grazing is banned. The construction of the random forest model involves rigorous feature selection and hyperparameter optimization, enhancing the model’s accuracy. Next, we apply this trained model to areas with grazing, ensuring a more accurate estimation of grassland carrying capacity. Finally, we analyze the spatiotemporal variations in grassland carrying capacity. The main results showed that the model achieved a high level of precision, with a root mean square error (RMSE) of 4.89, indicating reliable predictions of grassland carrying capacity. From 2001 to 2020, the average carrying capacity was estimated at 9.44 SU/km², demonstrating a spatial distribution that decreases from southeast to northwest. A slight overall increase in carrying capacity was observed, with 65.7% of the area exhibiting an increasing trend, suggesting that climate change has a modest positive effect on the recovery of grassland carrying capacity. Most of the grassland carrying capacity is found in areas below 5000 m in altitude, with alpine meadows and alpine meadow steppes below 4750 m being particularly suitable for grazing. Given that the overall grassland carrying capacity remains low, it is crucial to strictly control local grazing intensity to mitigate the adverse impacts of human activities. This study provides a solid scientific foundation for developing targeted grassland management and protection policies.

A thorough understanding of the development process of grazing activities and an elucidation of their complex mechanisms are crucial for the formulation and adjustment of livestock management policies. In the Selinco region of the Qinghai–Tibet Plateau, we conducted a comprehensive analysis of year-end livestock numbers and densities over the past 30 years. The results indicate a gradual decline in overall livestock numbers in the Selinco region during this period, with a notable decrease between 2004 and 2014, followed by stabilization. Notably, the number of yaks has significantly increased, whereas the numbers of sheep, goats, and horses have markedly decreased. Regarding livestock density, there is a spatial pattern of decrease from northwest to southeast, with the density order being Seni District > Bange County > Anduo County > Shenzha County > Nima County > Shuanghu County. Between 2004 and 2014, most counties experienced a significant decline in livestock density, exhibiting a trough–peak pattern. However, after 2014, a complex spatiotemporal dynamic emerged. Concerning driving factors, from 1990 to 2004, rural population and economic development were the primary influences on livestock density. After 2004, forage–livestock balance management policies, snowstorms, and fluctuations in livestock prices likely became the main influencing factors. Further detailed analysis of these factors is essential for developing more effective management strategies.