The vegetation of the Qinghai-Tibet Plateau (QTP), China, is diverse and sensitive to climate change. Because of extensive grassland degradation in the QTP, several ecological restoration projects, which affect the livestock population, have been implemented in the QTP. Although many studies have reported the impacts of climate change on vegetation in the QTP, our knowledge on the impacts of both climate change and livestock on vegetation remains very limited. Here, we investigated the impacts of climate change and livestock population on vegetation growth by using the annual maximum normalized difference vegetation index (NDVI_max) and growing-season climate data from 1981 to 2019. We analyzed the relationship between NDVI_max and climate and livestock population using the modified Mann-Kendall trend Test and Pearson correlation analysis. For the entire QTP, NDVI_max had a two-phase trend, with a slow rise during 1981–2000 and a rapid rise during 2000–2019. Overall, NDVI_max in the QTP increased and decreased in 63.7% and 6.7% of the area in 2000–2019. In areas with significant changes in NDVI_max, it was strongly correlated with relative humidity and vapor pressure. The small positive trend in NDVI_max during 1981–2000 was influenced by warmer and wetter climate, and the overgrazing by a large population of livestock slowed down the rate of increase in NDVI_max. Livestock population for Qinghai and Tibet in recent years has been lower than in the 1980s.The warmer and wetter climate and substantial drops in the livestock population contributed to large recovery in vegetation during 2001–2019. Vegetation degradation in Qinghai during 1981–2000 and central-northern Tibet during 2000–2019 was driven mainly by drier and hotter climatic. Although 63.7% of the area in the QTP became greener, the vegetation degradation in central-northern Tibet should not be ignored and more measures should be taken to alleviate the impact of warming and drying climate. Our findings provide a better understanding of the factors that drove changes in vegetation in the QTP.

As a new green energy source, geothermal resource’s exploration, development, and utilization are an important direction in geophysical exploration at present. In this study, the actual land surface temperature was inferred based on the thermal infrared band of Landsat8 remote-sensing images, and the information about the surface anomalies and their spatial distribution was obtained through a multifactor analysis. In addition, three magnetotelluric sounding profiles were deployed in the study area, and the geo-electric sections in the study area were obtained through inversion of the measured data. Then, based on the inverse geo-electric information and the land surface temperature anomaly information, we analyzed and verified the geothermal resource genesis of the thermal anomaly area and inferred the favorable geothermal resource area in the study area. The results show that these two methods can be used to compare and analyze the possible distribution of the geothermal resources in the study area in two dimensions: the spatial distribution on the surface and the vertical distribution in the subsurface. Moreover, the results of the geothermal anomalies inferred from the thermal infrared remote sensing and the geo-electric results inferred from the magnetotelluric data are in good agreement. This study demonstrates that the integrated application of thermal infrared remote sensing and magnetotelluric technology is a promising tool for geothermal exploration.

Ground-level ozone (O₃) pollution is a problem when managing air quality in China, and biogenic volatile organic compounds (BVOCs) are key precursors of O₃ formation. Vegetation type and temperature influence BVOC emissions, yet the differences in emissions across vegetation types and their temperature responses still exhibit significant uncertainties. This study was focused on the Sichuan Basin in China. It used the G95 model to develop a high-resolution BVOC emission inventory, allowing the analysis of emission characteristics for different vegetation types. The study also used a temperature sensitivity algorithm to assess how temperature changes affect BVOC emissions. The impact of these emissions on regional O₃ formation potential (OFP) was then quantified using the OFP method. The results show significant differences in BVOC emissions across vegetation types. Forests at the basin edges (mixed, broad-leaved, and coniferous) have much higher emission intensity (10.5 t/km²) than agricultural areas in the center of the basin (0.15 t/km²). In terms of composition, monoterpenes (MON) mainly dominate mixed and coniferous forests (42.28% and 58.37%, respectively), while isoprene (ISOP) dominates broad-leaved forests (64.02%). The study found that temperature generally increases BVOC emissions, which vary by vegetation type. Broad-leaved forests have the highest temperature sensitivity (3.94%), much higher than agricultural vegetation (0.03%). BVOC emissions exhibit a seasonal pattern of “high in summer, low in winter” and a spatial pattern of “high at the edges, low at the center”. Temperature also influences emission intensity and composition, thus driving variations in the potential for O₃ formation. Seasonally, different vegetation types show structural changes in OFP contribution. Broad-leaved forests, dominated by ISOP, show a significant increase in summer contribution (+8.0%), becoming the main source of O₃ precursors. In contrast, mixed forests, dominated by MON, show a clear decrease in summer contribution (−6.3%).