Under the influence of extreme climatic conditions, landslide disasters occur frequently in the Loess Plateau due to complex geological structures, loose soil, and frequent intense rainfall. These events are often concealed, posing significant challenges for disaster prevention. High-resolution optical remote sensing combined with field surveys can improve identification accuracy; however, concerns persist regarding issues such as omission and misidentification during hazard identification and monitoring processes. To address these challenges, this study proposes an integrated remote-sensing identification approach, focusing specifically on the central region of Tianshui, a typical landslide-prone area within the Loess Plateau. Utilizing Sentinel-1 and JL1LF01A remote-sensing imagery collected from 2022 to 2023, we conducted ground deformation monitoring through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. By integrating deformation results with optical imagery features indicative of potential landslide sites, a comprehensive identification method was developed to precisely detect potential landslide hazards. Verification of the identified sites was subsequently performed using the Google Earth platform, resulting in the establishment of a final dataset of potential landslide hazards within the study area. This outcome clearly demonstrates the high applicability and accuracy of the integrated remote-sensing identification method in the context of landslide hazard assessment. Furthermore, this research provides a solid scientific foundation for geological hazard identification efforts and plays a critical guiding role in disaster prevention and mitigation in Tianshui City, thereby enhancing the region’s capacity to withstand disaster risks and effectively safeguarding local lives and property.

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.

Surface deformation induced by underground coal mining is characterized by strong nonlinearity and spatial heterogeneity, which complicates early warning and ecological assessment. While InSAR-driven data assimilation models and remote sensing-based ecological indices are widely used for long-term monitoring, three fundamental limitations remain unresolved: (1) severe spatial imbalance in deformation samples biases data-driven models toward mean-reverting predictions, (2) recursive multi-step forecasting accumulates errors, leading to instability in long-horizon extrapolation, and (3) in ecological monitoring, vegetation resilience further induces a multi-year observation lag, resulting in a “pseudo-stable” bias in optical indicators. To address these issues, this study proposes an unified framework integrating multi-step deformation prediction and ecological time-lag analysis. Taking the Datong Coalfield as the study area, we utilized 231 Sentinel-1A images from March 2017 to December 2024 for SBAS-InSAR deformation inversion. A spatial stratified sampling strategy is used to extract 5894 representative points. A 24-step backward and 15-step forward windows were reconstructed to systematically compare six predictive models. Simultaneously, the Remote Sensing Ecological Index (RSEI) derived from Landsat data is used for cross-lagged analysis. The results demonstrate that: (1) The maximum deformation rate reached −276.75 mm/year, with cumulative subsidence exceeding −2000 mm. (2) At 3-step short-term forecasting, all models proved robust, with LSTM performing best (RMSE = 5.78 mm). At 15-step extreme extrapolation, however, traditional recursive models diverged significantly (Kalman, RMSE = 45.70 mm), whereas N-BEATS maintained stability and effectively mitigated temporal error cascades with an RMSE of 17.98 mm. (3) The core collapse zone exhibited concurrent ecological degradation (Lag 0), while the marginal basin presented a hidden degradation period of one to two years. It provides reliable scientific support for precise tracking and proactive safety management in complex mining areas.