Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods

Kuanxing Zhu, Peihua Xu, Chen Cao, Lianjing Zheng, Yue Liu and Xiujun Dong
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Kuanxing Zhu: College of Construction Engineering, Jilin University, Changchun 130026, China
Peihua Xu: College of Construction Engineering, Jilin University, Changchun 130026, China
Chen Cao: College of Construction Engineering, Jilin University, Changchun 130026, China
Lianjing Zheng: College of Construction Engineering, Jilin University, Changchun 130026, China
Yue Liu: College of Construction Engineering, Jilin University, Changchun 130026, China
Xiujun Dong: College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610051, China

Sustainability, 2021, vol. 13, issue 3, 1-16

Abstract: Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.

Keywords: landslide and collapse identification; SBAS-InSAR; visibility analysis; kernel density analysis; field investigation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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