The Woda area in the upper Jinsha River has steep terrain and broken structures, causing landslide disasters frequently. Here, we used the distributed scatterer interferometric SAR (DS-InSAR) method to monitor and analyze the Woda landslide area. With the DS-InSAR method, we derived the deformation of the Woda landslide area from 106 Sentinel-1A ascending images acquired between 5 November 2014 and 4 September 2019 and 102 Sentinel-1A descending images acquired between 31 October 2014 and 11 September 2019. The obvious advantage of the DS-InSAR method compared to the persistent scatterer (PS) InSAR (PS-InSAR) method is that the densities of the monitoring points were increased by 25.1% and 22.9% in the ascending and descending images, respectively. The two-dimensional deformation of the landslide area shows that the maximum surface deformation rate in the normal direction was −80 mm/yr, and in the east–west direction, 118 mm/yr. According to the rescaled range (R/S) analysis, the Hurst index values of the deformation trends were all greater than 0.5, which means the deformation trend will continue for some time. In addition, we analyzed the influencing factors and the deformation mechanism of the Woda landslide area and found that the surface deformation is closely related to the geological structure and precipitation, among which precipitation is the main factor triggering the deformation. Our monitoring results will help the local government to conduct regular inspections and strengthen landslide disaster prevention in low-coherence mountainous areas.

When the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technology is used for surface deformation monitoring, the accuracy strongly depends on the quality of phase optimization. Especially, in the low-coherence region, how to conveniently and effectively improve the quality of phase optimization has been a hot and difficult research topic in recent years. This article proposes a hybrid phase optimization method for the DS-InSAR technology, which chooses either nonlocal (NL) or minimum mean square error (MMSE) method for each pixel according to the distribution of statistically homogeneous pixel in two windows of different sizes. This hybrid method (NL-MMSE) is not limited by the number of interferogram and is not influenced by multimaster images. The NL-MMSE method was applied to the deformation monitoring in the Jinsha River basin, Tibet, China, using 28 Sentinel-1A SAR images acquired between February and December 2020. Compared with the NL and MMSE methods, the NL-MMSE method provides better phase quality of the interferogram stack and is able to extract more temporal coherence points for subsequent deformation inversion. The deformation monitoring results showed there are three obvious large-scale landslide deformation and dozens of small-scale deformation in the study area. It is demonstrated that the NL-MMSE method based on DS-InSAR technology can accurately monitor the detailed deformation characteristics of the low-coherence surface, and can be applied and promoted as an effective means of identifying and monitoring geological hazards.

Statistically homogeneous pixel (SHP) selection and DS phase optimization are two critical steps in the distributed scatterer interferometric SAR (DS-InSAR) time series method. In this paper, a new algorithm named dynamic hypothesis test of confidence interval (D-HTCI) is proposed, which reduces the wrong selection rate of SHP and increases the number of SHP selections. Using adaptive spatial nonlocal filtering method for DS phase optimization, the phase standard deviation (PSD) and the sum of phase differences (SPD) show that compared with the traditional covariance matrix decomposition method, the optimization quality is improved by 2.1 and 1.8 times, respectively. Combining 24 scenes Sentinel-1A data from September 17, 2017 to July 14, 2018, the method is applied to monitor surface subsidence of the Daizhuang filling mining area (Jining, Shandong, China). The results show that the proposed method has mm-level accuracy for monitoring of surface subsidence in a filling mining area.

To meet the needs of the rapid development of new energy sources, China is currently accelerating the construction of pumped storage power stations (PSPS). However, the complex geological environment and large-scale construction can easily cause deformation areas such as collapse and sliding on the surface of PSPS. This paper adopts ground-based interferometric synthetic aperture radar (GB-InSAR) technology to monitor surface deformation using 206 ground-based radar (GBR) data collected from 2022/07/31 10:24 to 2022/08/06 14:57 with a PSPS in the construction stage in western China. In the selection of permanent scatterers (PSs) for GBR data processing, statistical homogeneous pixels (SHPs) information is considered in this paper. Based on the fast SHP selection (FaSHPS) algorithm, we introduce intensity deviations ( ${D_I}$ ) to exclude pixels with unstable intensity performance in the temporal dimension and propose the ${D_I} + FaSHPS$ algorithm, which is experimentally proven to have higher SHPs selection accuracy and computational efficiency. By combining with the traditional PSs selection algorithm, the distribution density of monitoring points is effectively improved. In addition, based on the deformation results and on-site survey, this paper analyzes and discusses the identified deformation areas in detail from the aspects of natural geography and man-made activities. The results show that the surface is relatively stable overall, except for the effects of man-made activities. This study has demonstrated the remarkable reliability and superiority of the GB-InSAR technology in identifying and analyzing deformation areas in the construction stage of PSPS.

The complex terrain and abundant ravines in the western mountainous areas of Beijing have led to dramatic changes in the geological environment. Monitoring and assessing the stability of landslide surfaces is of great significance for disaster prevention and ensuring the safety of the capital city. According to the spatial similarity characteristics of landslide surfaces, we propose a new interferometric synthetic aperture radar (InSAR) deformation enhancement method by taking into account the time-series deformation information of spatially adjacent homogeneous monitoring points. Taking Dongjiang Gully, Beijing as a typical study area, using multi-temporal InSAR technology, 80 scenes of RADARSAT-2 data from September 2016 to September 2022 were processed to obtain their time-series surface deformation to verify the advantages of the proposed method. The results show that the standard deviation of the deformation difference of all monitoring points is generally reduced after the deformation enhancement, and the mean value is reduced from 5.1 to 3.3, which is 35.2% lower in comparison. Then this study assesses the stability of the landslide surface based on the deformation enhancement results. Firstly, the optical image interpretation was combined with the angular distortions derived from deformation gradients to analyze the spatial location and boundaries of the landslide, and then to identify the infrastructure that is more susceptible to landslide impact. Secondly, through the principal component analysis (PCA) method, the correlation between each component and the distribution characteristics of surface deformation was analyzed. Finally, starting from geological factors and triggering conditions, the driving force for landslide surface deformation was discussed, and it was drawn that seasonal precipitation is a major influencing factor. The proposed method can provide a reference for landslide monitoring and assessment in similar areas.