The vegetation of the Qinghai-Tibet Plateau (QTP) is fragile and vulnerable to global and regional climate change. This paper analyzed the temporal and spatial variability of NDVI and response to temperature and precipitation in the QTP from 1982 to 2015. The results showed that: (1) there was a nonsignificant increase temporally, while there was a significant spatial increase that accounted for 63.26% of the total area and was distributed in the north, west, and south of the QTP. A significant decrease was concentrated southeast of the QTP. (2) Average NDVI values for the main vegetation types in the QTP were broadleaf forest>coniferous forest>shrub>meadow>alpine vegetation>grassland>desert. NDVI values of grassland, alpine vegetation, and desert vegetation had a significant linear increase, while NDVI for shrub, coniferous, and broadleaf forest decreased non-significantly. (3) The correlation coefficients between NDVI and temperature were spatially distributed in a north-south direction with latitudinal zonal characteristics. The correlation coefficients between NDVI and precipitation were distributed in an east-west direction with dry-humidity zonal characteristics. The study concluded that with the lack of hydrothermal conditions in the northern part of the QTP, NDVI increased significantly, while there was a significant decrease in areas with sufficient hydrothermal conditions.

The Qinling Mountains are an important geographic boundary in central and eastern China. The region has diverse and complex mountain ecosystems that are ideal to study the response of terrestrial ecosystems in the context of global climate change. Based on GIMMS NDVI data, meteorological data, and DEM and vegetation type data, we used the Comprehensive and CASA (Carnegie Ames Stanford Approach) models simulate NPP (Net Primary Productivity) and the response to climate change in the Qinling Mountains from 1982 to 2015. The research includes three main aspects: (1) MOD17A3 NPP data was used to compare the accuracy of the NPP values obtained by different methods. The NPP values calculated using the CASA model and GIMMS NDVI were most accurate without considering the vegetation type. (2) Changes in NPP were analyzed. The change trend of inter-annual and seasonal NPP was not significant temporally, but the inter-annual and spring NPP increased significantly, reaching 35.49% and 57.84% of the total study area, respectively, while the area of winter NPP significantly reduced by 22.87%. (3) The relationship between NPP and air temperature and precipitation was analyzed. The proportion of significant positively correlated inter-annual and spring NPP and precipitation values were higher, reaching 31.20% and 21.20%, respectively, while the proportion of significant positively correlated spring and autumn NPP values were only 10.80% and 10.20%, respectively. The complexity of the Qinling mountainous system enhances the heterogeneity of spatial and temporal variations in NPP and the response to climate change.

The energy ecological footprint is an important theory and method for quantitative measurement of the coordinated development of the socio-economic-ecological environment. This research indicated the following: (1) the energy ecological footprints of China and the provinces showed significant linear increases (except Beijing). China’s economy developed rapidly over 1999-2015, but the economic development had a high dependence on energy consumption, which induced an increase in the energy ecological footprint. Optimizing industrial structure and reducing energy consumption are effective ways to reduce the energy ecological footprint. (2) Forest land was the most important land use type for per capita energy ecological footprint capacity, accounting for 50% or more of the total. The energy ecological footprints of China and the provinces were mainly in a state of deficit, and only Sichuan, Qinghai, Guangxi, Shaanxi, Inner Mongolia and Xinjiang were in surplus. In addition, the ecological pressure in the eastern developed provinces was higher than in the central provinces, and the lowest pressure was in the west. (3) The energy ecological footprint of China and the provinces was mainly affected by the secondary industry output value, followed by population, total investment in fixed assets, and then the tertiary and primary industry output values. The results showed that reduction of industrial energy consumption is still the main issue for addressing carbon emissions in China.

The Kherlen River Basin is a typical basin in the eastern Mongolian Plateau and is dominated by grassland. This study estimated the grassland yield in the Kherlen River Basin using the Carnegie–Ames–Stanford approach (CASA) model, combined with Theil–Sen median trend analysis and the Geodetector, to explore its spatiotemporal changes and driving factors. This integrated framework links temporal trend detection with spatial interaction analysis to better reveal ecological responses to climatic and anthropogenic influences. The results showed the following: (1) The root mean square error (RMSE) between the estimated grassland yield and the laboratory measurements was 37.88 g/m², with an estimation accuracy (EA) of 73.52%. (2) From 2000 to 2024, the grassland yield increased significantly at a rate of 1.98 g/(m²·a) (p < 0.05), with the fastest growth in the middle reaches. (3) Spatially, 79.78% of the basin exhibited significant increases, mainly in the central and western regions. The proportion of significant increase was highest in the upper reaches (40.36%), followed by the middle (32.89%) and lower reaches (6.53%). (4) Due to limited temporal resolution of socioeconomic data, the driving factor analysis covered the period 2000–2020, during which the overall grassland yield was primarily influenced by the interaction between precipitation and elevation (q = 0.6371). Specifically, the upper, middle, and lower reaches were mainly influenced by the interactions between temperature and precipitation (q = 0.6772), precipitation and elevation (q = 0.6377), and temperature and elevation (q = 0.4255), respectively. The study indicates that grassland yield in the Kherlen River Basin exhibited an overall increasing trend during 2000–2024, with climatic factors (precipitation and temperature) and the geographic factor (elevation) identified as the dominant drivers. The influence of human activities was not significant, although this result may be affected by uncertainties associated with data resolution limitations. Future work should incorporate higher-resolution remote sensing and socioeconomic datasets to better assess the impacts of human activities.

The Mongolian Plateau, a vital ecological barrier in northern China, is of great importance for studying vegetation dynamics in Mongolia against the background of climate warming. Such studies can enhance our understanding of regional vegetation responses to global warming and contribute to the establishment of a stronger ecological barrier in northern China. Here, we analyzed the spatial and temporal characteristics of the NDVI (normalized difference vegetation index) in Mongolia using 8 km resolution GIMMS NDVI3g data from 1990 to 2022, along with temperature, precipitation, and elevation data. Trend analysis and correlation methods were used to examine the relationships between the NDVI and temperature, as well as precipitation. The results showed four important aspects of these relationships. (1) The NDVI in Mongolia increased significantly from 1990 to 2022 at a rate of 0.0015 yr–1 (P<0.05). (2) Mongolia's NDVI increased from 1990 to 2022 in 60.73% of the country. Of this total, the area with a significant increase accounted for 31.67% and was concentrated on the eastern and western edges. The area experiencing a significant decrease accounted for 15.67% and was mainly located on the southwestern edges. (3) The NDVI analysis revealed significant increasing trends in all regions except for those at elevations of 1500–2000 m. The greatest rate of increase was observed between 500 and 1000 m, and the increasing trend weakened as elevation continued to increase before gradually becoming significant again. Additionally, the NDVI increased significantly across different slopes, and the rate of increase decreased as the slope increased. (4) From 1990 to 2022, Mongolia's NDVI was mostly negatively correlated with temperature. This occurred over 66.75% of the total land area, with 17.21% of the region exhibiting a significant negative correlation, mainly in the southwest. Conversely, the NDVI demonstrated a positive correlation with precipitation, encompassing 86.71% of the total land area. Approximately 40.44% of the region had a significant positive correlation, primarily in the southwest. In conclusion, throughout the experimental period, the vegetation state in Mongolia improved. However, due to the warming and drying climate, more attention should be paid to vegetation degradation in the south-central region.