Spatiotemporal Responses of Surface Vegetation and Landscape Pattern to Construction Disturbance: A Case Study of Zhen’an Pumped-Storage Power Station in Shaanxi Province, China

As an important infrastructure for the construction of a new power system, pumped-storage hydropower stations may exert certain impacts on the surrounding fractional vegetation cover (FVC) during their construction. Based on Landsat remote sensing imagery and China Land Cover Dataset (CLCD) land use data from 2013 to 2024, this study investigated the dynamic changes in FVC and the spatial extent of engineering disturbance associated with the Zhen’an Pumped-Storage Hydropower Station in Shaanxi Province, China. The analysis integrated the Pixel Dichotomy Model, Theil-Sen trend analysis, Mann–Kendall significance test, coefficient of variation, landscape pattern indices, and correlation analysis. The results showed that: (1) FVC in the study area exhibited distinct stage-dependent evolution characteristics that were highly consistent with the construction timeline of the project. (2) The spatial influence of engineering disturbance on FVC was mainly concentrated within 1250 m, with the 0–250 m zone identified as the core impact area. Landscape fragmentation in this zone was higher than in other distance ranges, and vegetation degradation gradually weakened with increasing distance from the project. (3) Land use change within the study area was primarily characterized by the conversion of forest to cropland and impervious surfaces, resulting in a reduction in high coverage vegetation areas. Landscape patterns exhibited pronounced buffer-gradient characteristics. Within 500 m, the Largest Patch Index (LPI) decreased while the Shannon Diversity Index (SHDI) increased, indicating weakened continuity of dominant landscape patches. Beyond 500 m, LPI generally increased and SHDI decreased, suggesting a trend toward a more stable landscape structure. (4) Both air temperature and precipitation exhibited interannual fluctuations, but neither showed a significant long-term trend. The correlations between climatic factors and FVC were relatively weak, and the multiple regression model demonstrated limited explanatory power for FVC variation.