National parks face ecological threats from climate change and human activities. Sanjiangyuan National Park (SNP), a major ecological area in China, lacks a systematic evaluation of its ecological environmental quality changes and their driving factors. This study explores these dynamics to provide a scientific basis for regional ecological management. By constructing the remote sensing ecological index (RSEI) and using the optimal multivariate-stratification geographical detector (OMGD) model, we assessed ecological changes from 2014 to 2024. The results showed the RSEI remained stable at approximately 0.66, peaking at 0.732 in 2022, indicating a general improvement in ecological quality. The vegetation coverage rate (NDVI) increased from 0.591 to 0.680. Driving factor analysis revealed considerable regional variation, with temperature and human activities as the primary drivers. Higher RSEI values were associated with conditions where precipitation was moderate (~100 mm), evapotranspiration levels were high (>50 mm), temperatures were above average (>4 °C), and nighttime light indices were low (<0.6). These findings suggest that specific combinations of these factor thresholds may enhance ecological quality, informing protection strategies for SNP and providing a reference for similar plateau ecosystems.

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland–wetland–forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982–2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)—a metric quantifying greening and browning speeds via NDVI temporal variation—employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr⁻¹ (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency. The VNDVI revealed an accelerated spring greening rate of +0.8% yr⁻¹ (p < 0.05) and a slowed autumn browning rate of −0.6% yr⁻¹ (p < 0.05), reflecting an extended growing season. Spatial correlation analysis showed that the temperature dominated spring greening (r = 0.52), precipitation governed summer growth (r = 0.64), and solar radiation modulated autumn senescence (r = 0.38). Compared with the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics. These findings provide quantitative insights into the mechanisms of vegetation change in the HLB and offer scientific support for ecological conservation in North China’s grassland–forest ecotone.

As a key indicator of terrestrial carbon cycle, carbon use efficiency (CUE) represents the efficiency of carbon fixation and carbon allocation strategy, sh