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.

中文翻译：

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从时间序列 InSAR 结果中识别滑坡前兆

摘要

山体滑坡在全球造成巨大的人员和经济损失。检测滑坡前兆对于预防灾害至关重要。小基线子集干涉合成孔径雷达（SBAS-InSAR）一直是检测滑坡前兆的流行方法。然而，SBAS-InSAR 结果中的非单调位移普遍存在，这使得挑选出真正的滑坡信号具有挑战性。通过利用 SBAS-InSAR 导出的时间序列位移，我们提出了一种识别移动滑坡的方法。该方法计算两个指数（全局/局部变化指数），以根据导出的位移对时间序列的单调性进行排序。使用所提出的指数的两个阈值，可以去除位移结果中超过 96% 的背景噪声。我们还发现，Sentinel-1 图像中东坡和西坡的滑坡比其他坡面更容易检测。通过抑制背景噪声，该方法可以作为检测山区滑坡前兆的便捷工具。