Author:
Liu Meng,Yang Wentao,Yang Yuting,Guo Lanlan,Shi Peijun
Abstract
AbstractLandslides 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.
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Safety Research,Geography, Planning and Development,Global and Planetary Change