Abstract
Sabina vulgaris is a keystone shrub species endemic to arid northwestern China, renowned for its exceptional drought tolerance, sand fixation capabilities, and critical role in desert ecosystem stability. This study investigates the impact of coal mining activities on the spatiotemporal dynamics of S. vulgaris shrublands in the ecologically fragile Mu Us Sandy Land, focusing on the Longde Coal Mine adjacent to the Shenmu S. vulgaris Nature Reserve. Utilizing seven periods (2013–2025) of 2 m resolution Gaofen-1 (GF-1) satellite imagery spanning 12 years of mining operations, we implemented a deep learning approach combining UAV-derived hyperspectral ground truth data and the SegU-Net semantic segmentation model to map shrub distribution via GF-1 data with high precision. Classification accuracy was rigorously validated through confusion matrix analysis (incorporating the Kappa coefficient and overall accuracy metrics). Results reveal contrasting trends: while the S. vulgaris Protection Area exhibited substantial expansion (e.g., Southern Section coverage grew from 2.6 km2 in 2013 to 7.88 km2 in 2025), mining panels experienced significant degradation. Within Panel 202, coverage declined by 15.4% (58.4 km2 to 49.5 km2), and Panel 203 showed a 18.5% decrease (3.16 km2 to 2.57 km2) over the study period. These losses correlate spatially and temporally with mining-induced groundwater depletion and land subsidence, disrupting the shrub’s shallow-root water access strategy. The study demonstrates that coal mining drives fragmentation and coverage reduction in S. vulgaris communities through mechanisms including (1) direct vegetation destruction, (2) aquifer disruption impairing drought adaptation, and (3) habitat fragmentation. These findings underscore the necessity for targeted ecological restoration strategies integrating groundwater management and progressive reclamation in mining-affected arid regions.