An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

Abstract

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

“…For a statistically significant negative z-score, the lower the zscore, the more concentrated the cluster (cool spot). The critical values of the z-score at clustering confidence coefficients of 90%, 95%, and 99% were ±1.65 (p-value = 0.10), ±1.96 (p-value = 0.05), and ±2.85 (p-value = 0.01), respectively [67]. In general, a higher confidence coefficient reflects a better clustering result.…”

Investigating Gravitational Slope Deformations with COSMO-SkyMed-Based Differential Interferometry: A Case Study of San Marco dei Cavoti

“…In the context of this study, the COSMO-SkyMed mission plays a pivotal role by providing the necessary SAR data for DInSAR analysis to monitor and assess surface deformations with great detail and accuracy. Additionally, an analysis based on the decomposition of the data obtained along the Line of Sight (LoS) in two components (horizontal and vertical) has been performed using methods already presented in the literature [30], which have shown good performance in the past [31][32][33]. Combining the results of these analyses made it possible to obtain valuable insights into the observed deformations, thus contributing to identifying areas that may require further investigation or intervention measures.…”

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China

“…In surface deformation monitoring, time-series InSAR technology has a greater monitoring range than other technologies and is not affected by the weather. As such technology is able to obtain images and earth surface data at any time of day or night [ 34 ], rendering it a one-of-a-kind remote sensing method for deformation monitoring based on surface data [ 35 ]. In complex mountainous regions with frequent landslide disasters, the combination of time-series SBAS-InSAR technology and high-resolution optical remote sensing images can achieve early landslide identification and inverse the slow deformation development process of landslide points in time series [ 36 , 37 , 38 ].…”