Abstract. The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows for monitoring areas prone to geohydrological disasters, providing relevant information for risk evaluation and management. In cases of major landslide events, for example, spaceborne radar data can provide an effective solution for the detection of slope failures, even in cases with persistent cloud cover. The information about the extension and location of the landslide-affected areas may support decision-making processes during emergency responses. In this paper, we present an automatic procedure based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed at facilitating the detection of landslides over wide areas. Specifically, the procedure evaluates changes of radar backscattered signals associated with land cover modifications that may be also caused by mass movements. After a one-time calibration of some parameters, the processing chain is able to automatically execute the download and preprocessing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a georeferenced map. This map should help decision makers and emergency managers to organize field investigations. The process of automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of open-source software. We tested the processing chain, in back analysis, on an area of about 3000 km 2 in central Papua New Guinea that was struck by a severe seismic sequence in February–March 2018. In the area, we simulated a periodic survey of about 7 months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidence of widespread land cover changes that occurred just after the most severe seismic events. Most of the detected changes can be interpreted as mass movements triggered by the seismic shaking.

中文翻译：

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一种基于星载 SAR 的程序来支持滑坡检测

摘要。越来越多的免费获取卫星数据代表了分析和评估自然灾害的相关机会。系统地获取星载图像可以监测易发生地质水文灾害的区域，为风险评估和管理提供相关信息。例如，在发生重大滑坡事件的情况下，星载雷达数据可以为检测边坡失稳提供有效的解决方案，即使在持续云层覆盖的情况下也是如此。有关滑坡影响区的扩展和位置的信息可以支持应急响应期间的决策过程。在本文中，我们提出了一种基于 Sentinel-1 合成孔径雷达 (SAR) 图像的自动程序，旨在促进大面积滑坡的检测。具体来说，该程序评估与土地覆盖变化相关的雷达反向散射信号的变化，这些变化也可能由大规模运动引起。在对一些参数进行一次校准后，处理链能够自动执行图像的下载和预处理、SAR 幅度变化的检测以及可能受滑坡影响的区域的识别，然后在地理配准地图中显示. 该地图应有助于决策者和应急管理人员组织现场调查。自动化过程是通过在 GNU/Linux 操作系统上运行的特定脚本并利用开源软件的模块来实现的。我们测试了处理链，在反向分析中，在巴布亚新几内亚中部约 3000 km 2 的区域上，该区域在 2018 年 2 月至 3 月遭受了强烈地震序列。在该区域，我们模拟了从 2017 年 11 月 12 日至 2018 年 6 月 6 日约 7 个月的定期调查，下载 36 个 Sentinel-1 图像并自动执行 17 个变化检测分析。该程序产生了在最严重的地震事件之后发生的广泛土地覆盖变化的统计和图形证据。大多数检测到的变化可以解释为地震震动引发的质量运动。该程序产生了在最严重的地震事件之后发生的广泛土地覆盖变化的统计和图形证据。大多数检测到的变化可以解释为地震震动引发的质量运动。该程序产生了在最严重的地震事件之后发生的广泛土地覆盖变化的统计和图形证据。大多数检测到的变化可以解释为地震震动引发的质量运动。