Refined landslide inventory and susceptibility of Weining County, China, inferred from machine learning and Sentinel‐1 InSAR analysis

作者：Xuguo Shi, Dianqiang Chen, Jianing Wang, Pan Wang, Yunlong Wu, Shaocheng Zhang, Yi Zhang, Chen Yang, Lunche Wang · 发表于：Transactions in GIS · 年份：2024 · DOI：10.1111/tgis.13202 · 被引用次数：15 · 研究领域：Landslides and related hazards、Cryospheric studies and observations、Rock Mechanics and Modeling

Abstract Landslides are widely distributed mountainous geological hazards that threaten economic development and people's daily lives. Interferometric synthetic aperture radar (InSAR) with comprehensive coverage and high‐precision ground displacement monitoring abilities are frequently utilized for regional‐scale active slope detection. Moreover, InSAR measurements that characterize ground dynamics are integrated with conventional topographic, hydrological, and geological landslide conditioning factors (LCFs) for landslide susceptibility mapping (LSM). Weining County in southwest China, with complex geological conditions, steep terrain, and frequent tectonic activities, is prone to catastrophic landslide failures. In this study, we refined the landslide inventory of Weining County using one ascending and one descending Sentinel‐1 dataset acquired during 2015–2021 through a small baseline subset InSAR (SBAS InSAR) analysis. We then combine the LOS measurements from both datasets using multidimensional SBAS to obtain time series two‐dimensional (2D) displacements to characterize the kinematics of active slopes. Hot spot and cluster analysis (HCA) was carried out on 2D displacement rate maps to highlight clustered deformed areas and suppress noisy signals that occurred on single pixels. Two hundred fifty‐eight landslides (including 71 active identified in this study) are used to construct 76,412 positive samples for LSM. In our study, the HCA maps, instead of the 2D displacement...