Changes in vegetation cover serve as key indicators for understanding both climate change and human activities. Understanding vegetation change drivers is vital for regional ecological protection and sustainable development. In this study, the spatiotemporal variations in vegetation cover in Shanxi Province, China, from 1986 to 2023 were analysed, with the goal of identifying long-term normalized difference vegetation index (NDVI) trends and quantifying the relative contributions of climatic and anthropogenic factors. Multisource datasets—including NDVI time series, meteorological data (precipitation, temperature, and potential evapotranspiration), and socioeconomic indicators (GDP and population density)—were employed. Trend analysis, partial correlation, residual analysis and contribution analysis were conducted to disaggregate the impacts of climate variability and anthropogenic activities. The results indicated that (1) the vegetation NDVI significantly increased over the past 38 years, with notable spatial heterogeneity driven by climatic gradients and ecological restoration projects, with a slope of 0.0107, and more than 88% of the region exhibiting a positive vegetation trend; (2) precipitation and GDP were identified as the dominant driving factors, and vegetation dynamics were shaped by both natural and socioeconomic factors and (3) human influence was dominant in the northern semiarid regions, where human contributions exceeded climate contributions (e.g., Shuozhou: 0.401 vs. 0.262; Datong: 0.378 vs. 0.216), while climate factors exerted stronger effects in the southern semihumid prefectures (e.g., Linfen: 0.423 climate contribution). These findings provide scientific evidence to support targeted ecological management and sustainable development strategies in semiarid and semihumid transition zones. • Long-term vegetation dynamics — Analyzed NDVI variations in Shanxi Province from 1986 to 2023, capturing spatiotemporal change patterns in a semiarid–semihumid transitional zone. • Climate–human attribution — Integrated climate factors (temperature, precipitation, PET) and socioeconomic indicators (population density, GDP), applying residual analysis to disentangle natural and anthropogenic drivers. • Practical implications — Provided a scientific basis for ecological restoration, sustainable land use, and environmental policy-making in fragile transitional regions.
Sun et al. (Thu,) studied this question.