Abstract

Landslide susceptibility maps (LSMs) are generally prepared by integrating multiple prominent thematic layers, including DEM derived products (elevation, slope, and aspect), and other parameters such as lithology, geomorphology, LULC, etc. These parameters can be assigned optimum weights using the analytic hierarchy process (AHP) method, followed by a GIS-based weighted overlay analysis. In recent years, multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been rigorously explored, for land deformation detection and monitoring, by extracting highly stable measurement pixels using tens of SAR acquisitions simultaneously. In this research work, a GIS-based multi-criteria decision analysis to prepare LSMs is proposed, with MT-InSAR derived displacement estimates used as a critical input parameter. An LSM is generated by processing 20 ERS-1/2 and Envisat ASAR images, acquired over ∼120 sq. km wide river basin, located in Uttarakhand, India. The generated LSM is found to be congruent with the susceptible maps made available by the Geological Survey of India (GSI) under the National Landslide Susceptibility Mapping (NLSM) program. Preliminary results indicate that the majority of the unstable zones along the Alaknanda River are correctly identified. The approach is further implemented to generate an updated susceptibility map using 60 scenes of freely available Sentinel-1A dataset, followed by validation through actual field survey. This resulted in the generation of an updated susceptibility map, which helped in the identification of 44.5% new landslide susceptible zones (LSZs). Furthermore, the status of previously identified zones is also quantified. The performance of the proposed approach suggests its usability in generating and updating near-real-time LSMs.

摘要

滑坡敏感性图（LSM）通常是通过整合多个突出的主题层（包括DEM派生产品（高程，坡度和坡向）以及其他参数（如岩性，地貌，LULC等）而准备的。可以使用以下参数为这些参数分配最佳权重：层次分析法（AHP）方法，然后进行基于GIS的加权覆盖分析。近年来，通过同时使用数十次SAR采集来提取高度稳定的测量像素，已对多时相干涉合成孔径雷达（MT-InSAR）技术进行了严格的探索，以用于土地变形检测和监测。在这项研究工作中，提出了一种基于GIS的多标准决策分析方法来制备LSM，并将MT-InSAR导出的位移估算值用作关键输入参数。通过处理20个ERS-1 / 2和Envisat ASAR图像生成LSM，这些图像是在印度北阿坎德邦约120平方公里的流域上采集的。发现生成的LSM与国家地质滑坡敏感性地图（NLSM）计划下的印度地质调查局（GSI）提供的敏感地图一致。初步结果表明，沿着Alaknanda河的大部分不稳定地区都得到了正确识别。该方法被进一步实施以使用60个免费可用的Sentinel-1A数据集场景生成更新的磁化率图，然后通过实际的现场调查进行验证。这导致了更新的磁化率图的生成，这有助于确定44.5％的新滑坡易感区（LSZ）。此外，先前确定的区域的状态也将被量化。所提出方法的性能表明它在生成和更新近实时LSM中的可用性。