Open-Source Sentinel-1 SBAS InSAR Monitoring of Ground Deformation in Pantnagar (Indian Terai) Using HyP3 and MintPy

Use of PSInSAR for Long-Term Surface Displacement Monitoring as a Complement to the Official Landslide Susceptibility Map in the Saguenay–Lac-Saint-Jean Region, Quebec

Monitoring ground surface movements is crucial for slope failure hazard assessment. A total of 168 Sentinel-1A SAR ascending orbit datasets were analyzed (January 2018 to May 2024) to identify slope failure-susceptible locations in the Saguenay–Lac-Saint-Jean (SLSJ) region in Quebec, Canada, using persistent scatterer interferometric synthetic aperture radar (PSInSAR). Validation against four regional Global Navigation Satellite System (GNSS) stations shows strong agreement, with correlation coefficients (r > 0.9) across stations and root-mean-square error (RMSE) values of 10.8 and 12.7 mm between the PSInSAR and GNSS cumulative displacement time series over the six-year period. Annual Line-of-Sight (LOS) surface displacement rates generally ranged from 0 to 20 mm/yr, and the median absolute deviation (MAD) method was subsequently applied to the PSInSAR time series to identify anomalous persistent scatterer (PS) points within PS-detectable areas as candidate displacement signals. Integrating them with historical slope failure records (HSFRs) highlights partial spatial correspondence with the official landslide susceptibility map (LSM). Results show that the official LSM covers about 45% of HSFRs and 50% of anomalous PS points, with the remainder located outside its mapped boundaries. Conversely, our approach achieved over 80% overlap between anomalous PS points and HSFRs within areas where PS observations are available, identifying candidate slope failure-susceptible areas beyond those currently mapped by the official LSM, thereby warranting further investigations. This is exemplified by a 2022 landslide detected beyond the LSM boundaries. The proposed approach could serve as a complementary framework for mapping slope failure-susceptible locations within the study region.

Remote sensing–based analysis of accelerated surface movements in Joshimath Town, Chamoli, India: first insights into deep-seated gravitational slope processes

This study presents a process-based integrated remote sensing framework to explain ground deformation crisis in Joshimath, Garhwal Himalayas (India), from deep-seated gravitational slope deformation (DSGSD) perspective. Integrated multisensor remote sensing, including high-resolution unmanned aerial vehicle (UAV) mapping and field validation, produced a new comprehensive landslide inventory, revealing Joshimath’s location on a large rock avalanche (RA) deposit hosting three active deep-seated landslides (DSLs) with head, core, and frontal domains. Sloping local base level (SLBL) modeling constrained failure depths (~ 350 m for RA deposit; ~ 100–250 m for DSLs), delineating first landslide geometries. Three-fold Sentinel-1 interferometric synthetic aperture radar (InSAR) framework (2020–2023) enabled new insights on inter–intra-landslide kinematic analysis, revealing spatial correlation with mapped DSLs, rainfall-driven seasonal displacement with minor snowmelt influence, first identification of 7 February 2021 debris flow–induced toe cutting on displacement acceleration and rainfall-driven localized pore pressure rise. SLBL failure-surface modeling reproduced observed InSAR displacement patterns, while stability modeling using new DSL geometries confirmed erosion and pore pressure as key instability drivers and lowest stability at landslide fronts consistent with higher InSAR displacement. Modeled strain–displacement patterns matched SLBL-derived failure surfaces. Together these results link landslide mapping, geometry, kinematics, and stability into a unified explanation of Joshimath deformation, providing a transferable framework for high-risk, data-limited Himalayan settings.

Assessments of Ground Subsidence and Stability Along G218 Highway at Xinjiang Uygur Autonomous Region, China, between 2022 and 2024 with Sentinel-1 Data

Highway G218, the world’s longest brick-paved road, serves as a vital transportation corridor connecting northern and southern Xinjiang Uygur Autonomous Region. However, it traverses permafrost regions and is exposed to a range of extreme climatic conditions. To ensure the safe and reliable operation of this highway, continuous deformation monitoring is essential. Multi-temporal InSAR (MT-InSAR) can monitor the ground deformation with high-resolution over a large area, thereby overcoming the spatiotemporal limitations of conventional in-situ measurements. In this paper, 82 Sentinel-1A images acquired from January 2022 to September 2024 were utilized, integrating persistent scatterers (PS) and distributed scatterers (DS) to perform MT-InSAR deformation monitoring along the Nalati–Baluntai section of G218. The results indicate that, during the study period, the surface deformation rates along the highway primarily ranged between −2 and 5 mm/yr, with the maximum rate of 16 mm/yr, indicating that the ground surface is relatively stable. Notably, the observed deformation is associated with two environmental factors: precipitation fluctuations and soil frost heave effects induced by changes in surface temperature.

High-Intensity Wildfires Increase the Risk of Severe Ground Subsidence in Permafrost Regions: Evidence from Long-Term InSAR Observations in the Da Xing’an Mountains Permafrost Region, China

Against the backdrop of global climate change, wildfires have emerged as key disturbance factors accelerating permafrost degradation. However, how wildfires affect ground-surface deformation, including spatial patterns and potential driving mechanisms, remains unclear. Therefore, in this study, the permafrost region in the northern Da Xing’an Mountains affected by the catastrophic Great Black Dragon Fire (1987) is taken as a case study. On the basis of Sentinel-1 SAR imagery acquired from 2016 to 2021, the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique was employed to derive surface deformation rates. These rates were combined with historical fire severity (dNBR) and topographic factors. Random forest and spatial autocorrelation analyses were used to evaluate the long-term association between wildfire disturbance and surface deformation and its potential controls. The results indicate that (1) thirty-five years after the wildfire, vegetation in the permafrost region had not fully recovered to prefire levels; (2) surface deformation from 2016 to 2021 was dominated by subsidence overall. When unburned patches within the same region were used as controls for climate-driven background subsidence, the proportion of areas experiencing severe subsidence (annual rate ≤ −50 mm yr−1) reached 12.86% in high-severity fire zones, compared with 10.21% in unburned areas, suggesting that high-severity fires may amplify regional background subsidence; and (3) the random forest model had low explanatory power (R2 = 0.03) and was therefore used for exploratory comparison of the selected predictors rather than for accurate prediction of surface deformation. Among the selected variables, dNBR had the greatest relative importance, followed by terrain ruggedness and slope, whereas the remaining spatial variability may reflect unmeasured hydrological and subsurface controls. This study provides a quantitative basis for understanding the wildfire-induced “abrupt degradation” of permafrost, defined here as disturbance-driven acceleration of thaw and subsidence beyond gradual climate-driven degradation, and contributes to understanding carbon–climate feedback mechanisms in permafrost regions.

Severity-Based Mapping of Land-Subsidence Hazard Zones and Critical Hotspots Using SBAS-InSAR and Spatial Statistics: The Konya Metropolitan Area, Turkey

Land subsidence induced by excessive groundwater withdrawal has become one of the most significant geohazards affecting the Konya Closed Basin, Turkey. Although previous studies have successfully monitored ground deformation using geodetic and remote sensing techniques, limited attention has been devoted to transforming deformation measurements into quantitative, spatially classified hazard information. This study presents a severity-based framework for delineating land-subsidence hazard zones and critical hotspots in the Konya metropolitan area by integrating SBAS-InSAR observations and spatial statistical analyses. A total of 82 Sentinel-1 SAR acquisitions (41 ascending and 41 descending images) acquired between January 2023 and May 2026 were processed using the Small Baseline Subset (SBAS) technique. Ascending and descending line-of-sight deformation measurements were combined to derive vertical deformation rates, which were integrated with spatial statistical indicators and a composite severity index to quantify deformation clustering and classify subsidence severity. Hazard zones and critical hotspot areas were delineated through severity-based classification and spatial connectivity analyses. The results reveal a continuous north–south-oriented subsidence deformation belt extending across the eastern Konya. Maximum vertical subsidence rates exceeded 230 mm/yr, while spatial statistical analyses confirmed strongly clustered and statistically significant deformation patterns. Severity-based hazard zonation identified four hazard classes and a continuous high-hazard corridor. Clustering analysis further identified a dominant hotspot belt covering approximately 160 km2, with mean subsidence rates of approximately 142 mm/yr. A sensitivity analysis of the composite severity index weighting scheme, the spatial statistical neighborhood distance, the DBSCAN clustering parameters, and the number of Jenks severity classes confirmed that the resulting hazard zones and critical hotspot belt are robust to reasonable parameter variations. The findings demonstrate that land subsidence in Konya is organized as a spatially continuous regional-scale deformation system rather than a collection of isolated subsidence centers. The proposed framework transforms InSAR-derived deformation measurements into quantitative, decision-support hazard information and provides a transferable methodology for land-subsidence hazard assessment in groundwater-stressed urban environments.

Geospatial assessment of ground deformation in the Greater Accra Region of Ghana using SBAS-InSAR and Sentinel-1 time series