As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology is a powerful tool for monitoring surface deformation and has extensive applications in landslide monitoring. River gorge areas are typically characterized by steep terrain and dense vegetation, resulting in low coherence for shortwavelength SAR signals, such as those from Sentinel-1. This often leads to the minimal capability of conventional time-series InSAR methods, which poses challenges for effective ground deformation detection, particularly in low-coherence areas over long-term scenarios. In this study, we present an optimized time-series InSAR method called intermittent Stacking (IStacking), which allows rapid calculation of high-coverage deformation and detection of landslides in long-term and low-coherence scenarios. Using the Three Gorges Reservoir Area (TGRA) as a case study for deformation monitoring and landslide identification, we demonstrated the principles and application of the IStacking method. Analyzing 152 ascending Sentinel-1 SAR images from January 2018 to June 2023, we determined that conventional timeseries InSAR methods detected deformation in only 4% of the study area, the intermittent small baseline subset (ISBAS) method achieved a 70% coverage ratio, and the proposed IStacking method reached a coverage ratio of 96%. The identified landslides with the IStacking method were validated and assessed using highresolution Google imagery and ground-based GPS data. Because of its ability to fully exploit the intermittent coherence properties of pixels and its simple and efficient implementation, the IStacking method can exploit the potential of short-wavelength SAR data in low-coherence regions and provide a powerful tool for wide-area InSAR deformation monitoring.

Landslides represent a significant natural and geological hazard, resulting in considerable economic losses and casualties on an annual basis. The frequency of landslide disasters has increased markedly in recent times due to intensified human activity. The China-Pakistan Karakoram Highway (KKH) is a vital component of the China-Pakistan Economic Corridor (CPEC), representing the sole land route connecting China and Pakistan. Due to the topography, destructive landslides occur on occasion along the KKH, and the identification of landslide hazards along the KKH has become a matter of urgency. InSAR technology is a highly effective method for landslide detection, offering excellent deformation detection capabilities. However, the coherence of the region is severely compromised by the complex terrain, geometric distortion, presence of snow and strong weathering transport, which presents a significant challenge for the application of traditional time-series InSAR techniques in this area. In this paper, the intermittent Stacking-InSAR (IStacking) method is proposed to obtain deformation data over the mountainous region of northern Pakistan, with a deformation data coverage of 97% in a time period of 6.5 years and an average coherence of 0.2. Utilizing the LOS deformation data and a landslide screening model, this paper identifies more than 150 suspected landslides in northern Pakistan, including over 10 landslides larger than 1 km2 in area along the KKH. The subsequent validation of several large landslides was achieved through field visits and a comparison with Google images. Furthermore, the study identified that landslides along the KKH are characterized by high deformation velocity and large scale, which would cause significant damage to the highway and the residents living along it in the event of a collapse. In order to ensure the safety of these individuals and the continuity of the China-Pakistan Economic Corridor, it is necessary to assess the stability of the landslides.