Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine
Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine
复制标题
DOI：
10.5194/nhess-22-753-2022
复制
发表时间：
2022-03-09
影响因子：
4.6
通讯作者：
Kirschbaum, Dalia B.
中科院分区：
文献类型：
--
作者：
Handwerger, Alexander L.;Huang, Mong-Han;Kirschbaum, Dalia B.
快速检测山体滑坡对于应急响应、减灾和提高我们对山体滑坡动力学的了解至关重要。基于卫星的合成孔径雷达 (SAR) 可用于检测山体滑坡，通常在触发事件发生后几天内进行，因为它可以穿透云层，昼夜运行，并且可以在全球范围内定期获取。在这里，我们在基于云的 Google Earth Engine (GEE) 中提出了一种 SAR 反向散射变化方法，该方法使用来自 Copernicus Sentinel-1 卫星的免费可用数据的多时相堆栈来生成滑坡密度热图以进行快速检测。我们在近期多次降雨和地震引发的山体滑坡事件中测试了基于 GEE 的方法。通过结合上升和下降卫星采集几何形状的数据，并应用地形掩模去除不太可能发生山体滑坡的平坦区域，我们检测山体滑坡表面变化的能力通常会随着山体滑坡事件前后获取的 SAR 图像总数的增加而提高。重要的是，我们的 GEE 方法不需要将大量数据下载到本地系统或专门的处理软件，这使得更广泛的灾害和滑坡社区能够利用和推进这些最先进的遥感数据，以提高对滑坡灾害的态势感知。
Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.