The Qilian Mountains, located on the northeastern edge of the Qinghai–Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai–Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze–thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze–thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east–west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai–Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration.