DAI Keren, ZHUO Guanchen, XU Qiang, LI Zhenhong, LI Weile, GUAN Wei. Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12): 1778-1786, 1796. DOI: 10.13203/j.whugis20190092
Citation: DAI Keren, ZHUO Guanchen, XU Qiang, LI Zhenhong, LI Weile, GUAN Wei. Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12): 1778-1786, 1796. DOI: 10.13203/j.whugis20190092

Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry

Funds: 

The National Natural Science Foundation of China 41801391

Science and Technology Plan of Sichuan Province 2019YJ0404

Independent Research Topic of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection SKLGP2018Z019

More Information
  • Author Bio:

    DAI Keren, PhD, professor, specializes in geological hazard monitoring and prevention based on InSAR and multi-spectral remote sensing technology. E-mail:daikren17@cdut.edu.cn

  • Corresponding author:

    XU Qiang, PhD, professor. E-mail:xq@cdut.edu.cn

  • Received Date: February 14, 2019
  • Published Date: December 04, 2019
  • The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to analyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency's Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future.
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