Identify Landslide Precursors from Time Series InSAR Results

• Published in: International journal of disaster risk science Vol. 14; no. 6; pp. 963 - 978
• Main Authors: Liu, Meng, Yang, Wentao, Yang, Yuting, Guo, Lanlan, Shi, Peijun
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01-12-2023; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Background noise; China; Climate Change; Disasters; Earth and Environmental Science; Earth Sciences; Economic impact; Emergency preparedness; Environment; Geology; Identification methods; Interferometric synthetic aperture radar; Interferometry; Landscape/Regional and Urban Planning; Landslides; Landslides & mudslides; Monotonously changing displacements; Mountain regions; Mountainous areas; Moving landslides; Natural Hazards; Precursors; Radar; Remote sensing; SBAS-InSAR; Sustainable Development; Synthetic aperture radar; Time series; Time series of deformation; Topography; Water conservation; China; Monotonously changing displacements; Time series of deformation; SBAS-InSAR; Moving landslides
• ISSN: 2095-0055; 2192-6395
• DOI: 10.1007/s13753-023-00532-8

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.