Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS−InSAR

Peilian Ran, Shaoda Li (), Guanchen Zhuo, Xiao Wang, Mingjie Meng, Liang Liu, Youdong Chen, Huina Huang, Yu Ye and Xiangqi Lei
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Peilian Ran: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Shaoda Li: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Guanchen Zhuo: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Xiao Wang: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
Mingjie Meng: Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610081, China
Liang Liu: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Youdong Chen: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Huina Huang: Disaster Reduction Center of Fujian, Fuzhou 350013, China
Yu Ye: Chengdu Qingbaijiang District Land Reserve Center, Chengdu 610399, China
Xiangqi Lei: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2023, vol. 15, issue 5, 1-18

Abstract: Potential landslides in the mountainous areas of southwest China pose a serious threat to the lives and property of local residents. Synthetic aperture radar interferometry (InSAR) technology has the advantages of wide coverage, all weather applicability, and low cost and can quickly and accurately identify large range of active landslides, making it a useful geodetic tool for the early identification and prevention of landslides. This paper employed small baseline subset InSAR (SBAS−InSAR) technology and ascending and descending Sentinel−1 data from January 2019 to December 2021 to early identify active landslides in the Maoxian County to Li County National Highway (G317 and G213). The InSAR deformation results were verified by geometric distortion analysis, optical remote sensing interpretation, and field investigation, and 115 active landslides were successfully determined, among which 23 active landslides were identified by ascending and descending Sentinel−1 data together. In addition, InSAR deformation results show that fault, stratigraphic lithology, and rainfall are the three main factors that accelerate the deformation of active landslides and can trigger new active landslides. This study can provide an important reference for the early identification and prevention of landslides in mountainous areas.

Keywords: SBAS?InSAR; Sentinel?1; southwest China; landslide identification; visibility; influencing factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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