Spatiotemporal Landslide Monitoring in Complex Environments Using Radiative Transfer Model and SBAS-InSAR Technology

Bing Wang, Li He (), Zhengwei He, Yongze Song, Rui Qu, Jiao Hu, Zhifei Wang and Zehua Zhang
Additional contact information
Bing Wang: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Li He: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Zhengwei He: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Yongze Song: School of Design and the Built Environment, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
Rui Qu: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Jiao Hu: College of Earth and Planet Science, Chengdu University of Technology, Chengdu 610059, China
Zhifei Wang: College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
Zehua Zhang: School of Design and the Built Environment, Curtin University, GPO Box U1987, Perth, WA 6845, Australia

Land, 2025, vol. 14, issue 5, 1-21

Abstract: Landslides are among the most frequent geological hazards, often resulting in casualties and economic losses, particularly in alpine valley areas characterized by complex topography and dense vegetation. Landslides in these regions are distinguished by their high altitude, concealment, and sudden onset, which render traditional monitoring methods inefficient. This study proposes a landslide monitoring method for complex environments that leverages multi-source remote sensing data, incorporating the radiative transfer model and Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. The proposed method was implemented to monitor the instability of the Baige landslide in Tibet, China. The results show that the vegetation Canopy Water Content (CWC) estimated using the radiative transfer model indirectly reflects landslide susceptibility. Specifically, excessive soil moisture from rainfall reduces oxygen in plant roots, affecting growth and lowering canopy water content. The region with lower Canopy Water Content (CWC < 0.04) exhibited an increasing trend in the number of pixels, rising from 271 to 549 before the landslide event, indicating poorer vegetation conditions in the area. Additionally, the SBAS-InSAR technique was utilized to extract surface displacement, achieving a maximum displacement of 112 mm during the monitoring period. Ultimately, the spatial changes of the two monitoring signals exhibited a high consistency. This study enhances the reliability of landslide displacement monitoring in complex environments and provides substantial scientific support for future large-scale monitoring efforts.

Keywords: complex environment; Baige landslide; radiative transfer model; canopy water content; SBAS-InSAR (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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