SBAS-InSAR-Based Landslide Susceptibility Mapping Along the North Lancang River, Tibetan Plateau

Abstract

Landslides pose huge challenges to the economic activities in mountainous areas at present, while large numbers of landslide disasters have developed in the Hengduan Mountains area in the eastern part of the Tibetan Plateau. Accurate landslide susceptibility mapping (LSM) serves as a critical measure to predict the serious risks that may be encountered in engineering activities. However, previous landslide susceptibility assessment can only play a limited role in the real-time analysis of current activities of slopes. In this study, the deformation rates of the slopes along the Lancang River were determined using the SBAS-InSAR technique. Meanwhile, the landslide susceptibility along the north Lancang River was assessed using the frequency ratio (FR), random forest and FR-RF models, and the precision of the assessment results was verified according to receiver operating characteristic curves (ROCs). Finally, a refined landslide susceptibility map was developed by integrating the deformation rates and landslide susceptibility indexes (LSIs) using a contingency matrix. As indicated by the deformation rates calculated using the SBAS-InSAR technique according to ascending and descending data show that the RADARSAT-2 descending data yielded more precise deformation results. The area-under-the-curve (AUC) values of the three assessment models were 0.866, 0.897, and 0.916, respectively, indicating that the assessment results obtained with the FR-RF model are the most precise. In the upgraded landslide susceptibility map, the areas with high and very high landslide susceptibility increased by 2.97%. Meanwhile, a total of 563,430 grid cells showed an increase in landslide susceptibility, accounting for 11.15% of all the grid cells. Most especially, the Xueru and Ritong areas exhibited a significant increase in landslide susceptibility, and it has been verified by remote sensing images and field surveys that both areas are subject to landslide risks. Therefore, the upgraded landslide susceptibility map has a better prediction performance and can provide valuable support for the decision making in the construction of major engineering facilities and the prevention and remediation of landslides.

A quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism

“…Rott and Nagler (2006) and Dong et al (2018) used In-SAR data to analyze landslides and have demonstrated that the In-SAR technique is a reliable approach to improve landslide identification and monitoring. Especially the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) proposed by Italian scholar Berardino et al (2002), the precision of its surface deformation monitoring can reach millimeter level, which has a significant superiority in landslide deformation monitoring research ( (Lanari et al, 2007;Zhao et al, 2019;Yang, 2020;Zhang et al, 2022). It has been proved that such observation data can well reflect the real state of landslide, but how to further analyze the deformation trend is still a problem.…”

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

“…SAR satellites have various advantages, such as being able to penetrate vegetation, continuously observe the earth surface with all-day and all-weather capability, and collect high-resolution SAR images with a short time interval [34]. With continuous SAR images, Interferometric SAR (InSAR) techniques, including Differential InSAR (D-InSAR) [35,36], Permanent Scatterer (PS) InSAR [37,38], and Small Baselines Subset (SBAS) InSAR [39,40], can be applied to analyze ground deformation and identify potential landslides with high precision, especially in alpine forest regions [41,42]. For example, Bianchini et al [43] identified 1012 active landslides and 64 new potential landslides within an area of 4470 km 2 in the Calabria region, south Italy, using ENVISAT data.…”