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An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets

Lookup NU author(s): Chen Yu

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Abstract

© 2022, Springer-Verlag GmbH Germany, part of Springer Nature. The Jinsha River flows through one of the most geologically complex regions in western China with extremely high altitudes and capricious climates. Frequent landslides (e.g., the Baige landslides on 11 October and 3 November 2018) occurred along its stretch which posed severe damage to bridges, dams, and roads, and put the safety of local residents at risk. Systematic and comprehensive landslide detection has been rarely carried out in the Jinsha River corridor because it is a wide and hard-to-reach region. With 30 years of development, InSAR has become an effective way to map ground motion, which in turn makes it feasible to detect active landslides over wide regions. However, several challenges remain in applying InSAR in the Jinsha River corridor, such as low coherence due to dense vegetation and/or large gradient surface movements, and atmospheric effects due to the spatio-temporal variations of the atmosphere (especially the part due to tropospheric water vapor). In this paper, we propose a framework to overcome the limitations of conventional InSAR through the integration of GACOS-assisted InSAR, advanced SBAS InSAR, and SAR pixel offset tracking techniques. This integrated framework enables us to detect active landslides under complex topographic and climate conditions. Our results show that the detected active landslides are largely controlled by active faults, most of them are found at elevations of 2500–4000 m, their slopes fall between 25 and 45 ∘ facing southwest and northwest, and 83.3% of the active landslides have a NDVI value of less than 0.3. Furthermore, precipitation is one critical triggering factor of active landslides, evidenced by the high temporal correlation between average precipitation and surface displacement time series.


Publication metadata

Author(s): Zhang C, Li Z, Yu C, Chen B, Ding M, Zhu W, Yang J, Liu Z, Peng J

Publication type: Article

Publication status: Published

Journal: Landslides

Year: 2022

Pages: Epub ahead of print

Online publication date: 23/08/2022

Acceptance date: 03/08/2022

ISSN (print): 1612-510X

ISSN (electronic): 1612-5118

Publisher: Springer Nature

URL: https://doi.org/10.1007/s10346-022-01954-z

DOI: 10.1007/s10346-022-01954-z


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