Identify Landslide Precursors from Time Series InSAR Results

Meng Liu; Wentao Yang; Yuting Yang; Lanlan Guo; Peijun Shi

doi:10.1007/s13753-023-00532-8

Volume 14 Issue 6

Dec. 2023

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Meng Liu, Wentao Yang, Yuting Yang, Lanlan Guo, Peijun Shi. Identify Landslide Precursors from Time Series InSAR Results[J]. International Journal of Disaster Risk Science, 2023, 14(6): 963-978. doi: 10.1007/s13753-023-00532-8

Citation:

Meng Liu, Wentao Yang, Yuting Yang, Lanlan Guo, Peijun Shi. Identify Landslide Precursors from Time Series InSAR Results[J]. International Journal of Disaster Risk Science, 2023, 14(6): 963-978. doi: 10.1007/s13753-023-00532-8

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Identify Landslide Precursors from Time Series InSAR Results

doi: 10.1007/s13753-023-00532-8

Meng Liu¹,
Wentao Yang^{1,5
,
,},
Yuting Yang¹,
Lanlan Guo^2,3,4,
Peijun Shi^2,3,4

1. School of Soil and Water Conservation, Beijing Forestry University, Beijing, 100083, China;
2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China;
3. Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University, Beijing, 100875, China;
4. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China;
5. Academy of Plateau Science and Sustainability, People's Government of Qinghai Province and Beijing Normal University, Xining, 810016, China

Funds:

This work is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK0906).

Accepted Date: 2023-12-16
Publish Date: 2024-01-10

Abstract

Abstract

Landslides cause huge human and economic losses globally. Detecting landslide precursors is crucial for disaster prevention. The small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) has been a popular method for detecting landslide precursors. However, non-monotonic displacements in SBAS-InSAR results are pervasive, making it challenging to single out true landslide signals. By exploiting time series displacements derived by SBAS-InSAR, we proposed a method to identify moving landslides. The method calculates two indices (global/local change index) to rank monotonicity of the time series from the derived displacements. Using two thresholds of the proposed indices, more than 96% of background noises in displacement results can be removed. We also found that landslides on the east and west slopes are easier to detect than other slope aspects for the Sentinel-1 images. By repressing background noises, this method can serve as a convenient tool to detect landslide precursors in mountainous areas.
- Monotonously changing displacements,
- Moving landslides,
- SBAS-InSAR,
- Time series of deformation

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References(39)

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