Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
Sensors (Basel). 2022 Dec 19;22(24):9998. doi: 10.3390/s22249998.
The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes or tsunamis. However, research focusing on the level of damage or its distribution in rural areas is lacking. This study presents a methodology for mapping, characterizing, and assessing the damage in rural environments following natural disasters, both in built-up and vegetation areas, by combining synthetic-aperture radar (SAR) and optical remote sensing data. As a case study, we applied the methodology to characterize the rural areas affected by the Sulawesi earthquake and the subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images obtained pre- and post-event, alongside Sentinel-2 images, were used as inputs. This study's results emphasize that remote sensing data from rural areas must be treated differently from that of urban areas following a disaster. Additionally, the analysis must include the surrounding features, not only the damaged structures. Furthermore, the results highlight the applicability of the methodology for a variety of disaster events, as well as multiple hazards, and can be adapted using a combination of different optical and SAR sensors.
农村地区自然灾害造成的破坏在性质、范围和结构上与城市环境中的破坏不同。以往和当前的研究主要集中在对地震或海啸等灾难性事件后城市地区受损结构的测绘上。然而，缺乏针对农村地区破坏程度或其分布的研究。本研究提出了一种将合成孔径雷达（SAR）和光学遥感数据相结合，对农村地区自然灾害（包括建成区和植被区）后的破坏进行测绘、特征描述和评估的方法。作为一个案例研究，我们应用该方法对 2018 年 9 月 28 日印度尼西亚苏拉威西地震和随后海啸事件影响的农村地区进行了特征描述。使用了事件前后获取的高分辨率 COSMO-SkyMed 图像和 Sentinel-2 图像作为输入。本研究的结果强调，灾害发生后，必须对农村地区的遥感数据进行不同于城市地区的数据处理。此外，分析必须包括周围的特征，而不仅仅是受损的结构。此外，研究结果还强调了该方法对于各种灾害事件以及多种灾害的适用性，并可以通过结合使用不同的光学和 SAR 传感器进行调整。
