Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas.

Edgecumbe, a dormant volcano located on Kruzof Island in the southeastern part of Alaska, USA, west of the Sitka Strait, has exhibited increased volcanic activity since 2018. To assess the historical and current intensity of this activity and explore its relationship with seismic events in the surrounding region, this study utilized data from the ERS-1/2, ALOS-1, and Sentinel-1 satellites. The Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques were employed to obtain surface deformation data spanning nearly 30 years. Based on the acquired deformation field, the point-source Mogi model was applied to invert the position and temporal volume changes in the volcanic source. Then, by integrating seismic activity data from the surrounding area, the correlation between volcanic activity and earthquake occurrences was analyzed. The results indicate the following: (1) the coherence of interferograms is influenced by seasonal variations, with snow accumulation during the winter months negatively impacting interferometric coherence. (2) Between 1992 and 2000, the surface of the volcano remained relatively stable. From 2007 to 2010, the frequency of seismic events increased, leading to significant surface deformation, with the maximum Line-of-Sight (LOS) deformation rate during this period reaching −26 mm/yr. Between 2015 and 2023, the volcano entered a phase of accelerated uplift, with surface deformation rates increasing to 68 mm/yr after August 2018. (3) The inversion results for the period from 2015 to 2023 show that the volcanic source, located at a depth of 5.4 km, experienced expansion in its magma chamber, with a volumetric increase of 57.8 × 10⁶ m³. These inversion results are consistent with surface deformation fields obtained from both ascending and descending orbits, with cumulative LOS displacement reaching approximately 210 mm and 250 mm in the ascending and descending tracks, respectively. (4) Long-term volcanic surface deformation, changes in magma source volume, and seismic activity suggest that the earthquakes occurring after 2018 have facilitated the expansion of the volcanic magma source and intensified surface deformation. The uplift rate around the volcano has significantly increased.

With its high spatiotemporal resolution and wide-area coverage, InSAR technology has become an essential tool for monitoring mining-induced surface subsidence. However, the large-gradient deformation caused by the traditional caving method has hindered this technology’s widespread application in subsidence monitoring. With the increasing adoption of backfill mining techniques, both the magnitude and the gradient of surface deformation have been significantly reduced, creating new opportunities for applying InSAR to subsidence monitoring in mining areas. Nevertheless, current research on InSAR under backfill mining conditions remains relatively limited, particularly with respect to the applicability of time-series InSAR techniques in such settings. In this study, the Wu’an mining area, characterized by the traditional caving method, and the Fengfeng mining area, which employs overburden isolated grouting backfill mining, were selected as representative cases. By integrating Small Baseline Subset InSAR (SBAS-InSAR) time-series deformation results with ground-based leveling measurements, we comparatively analyzed the interferometric coherence characteristics and deformation monitoring performance associated with the two mining methods. We then evaluated the monitoring applicability and engineering feasibility of InSAR under overburden isolated grouting backfill mining conditions. The results indicate that, under similar surface land cover conditions, the gradient of surface deformation exerts a strong influence on interferometric coherence. Comparison with leveling measurements shows that the RMSE in the Fengfeng mining area under overburden isolated grouting backfill mining is at least 68.2% lower than that in the Wu’an mining area under caving mining. Moreover, overburden isolated grouting backfill mining can effectively mitigate mining-induced surface deformation, limiting the maximum subsidence in the Fengfeng mining area to less than 300 mm and to less than 200 mm in village areas. Using Sentinel-1 data and a conventional SBAS-InSAR processing workflow, InSAR demonstrates higher reliability and applicability for surface subsidence monitoring in mining areas under overburden isolated grouting backfilling conditions than in those under caving mining conditions. This study aims to provide a useful reference for mining subsidence monitoring based on Sentinel-1 C-band data and a conventional SBAS-InSAR processing workflow.