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Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

机译:基于光学和极化SAR数据的变化检测在植被覆盖区进行滑坡测绘

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摘要

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.
机译:快速提供有关受灾地区的范围以及破坏的类型和程度的信息的滑坡测绘对于实现快速的危机响应(即支持救援和人道主义行动)至关重要。文献中报道的大多数基于合成孔径雷达(SAR)数据的滑坡检测方法都使用变化检测技术,要求在滑坡事件发生前不久获得非常高分辨率(VHR)的SAR图像,这通常是不可用的。由于机载磁盘空间和下行链路传输速率的限制,现代VHR SAR任务(例如Radarsat-2,TerraSAR-X或COSMO-SkyMed)无法系统地覆盖整个世界。在这里,我们基于事件前光学影像与事件后VHR极化SAR数据得出的极化熵之间的变化检测,提出了一种快速且可转移的滑坡测绘程序。赛前信息来自Landsat-8或Sentinel-2的高分辨率光学图像,这些图像可免费获得并在整个地球陆地上系统地获取。滑坡贴图根据来自双静态TanDEM-X影像的数字高程模型的坡度信息进行细化。该方法已成功应用于两个具有不同特征的滑坡事件:美国西弗吉尼亚州查尔斯顿附近的旋转滑坡和俄罗斯博尔沙亚塔尔达附近的采矿废土流。

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