Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data
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 tsuna...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/24/9998 |
| _version_ | 1797455341901840384 |
|---|---|
| author | Shiran Havivi Stanley R. Rotman Dan G. Blumberg Shimrit Maman |
| author_facet | Shiran Havivi Stanley R. Rotman Dan G. Blumberg Shimrit Maman |
| author_sort | Shiran Havivi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:52:02Z |
| format | Article |
| id | doaj.art-e48aae4e7a92496889593de4ba022804 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:02Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e48aae4e7a92496889593de4ba0228042023-11-24T17:58:50ZengMDPI AGSensors1424-82202022-12-012224999810.3390/s22249998Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing DataShiran Havivi0Stanley R. Rotman1Dan G. Blumberg2Shimrit Maman3Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, IsraelGeography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, IsraelHomeland Security Institute, Ben-Gurion University of the Negev, Beer Sheva 8410501, IsraelThe 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.https://www.mdpi.com/1424-8220/22/24/9998damage assessmentInSARmulti-hazardmulti-sensorruralurban |
| spellingShingle | Shiran Havivi Stanley R. Rotman Dan G. Blumberg Shimrit Maman Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data Sensors damage assessment InSAR multi-hazard multi-sensor rural urban |
| title | Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data |
| title_full | Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data |
| title_fullStr | Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data |
| title_full_unstemmed | Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data |
| title_short | Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data |
| title_sort | damage assessment in rural environments following natural disasters using multi sensor remote sensing data |
| topic | damage assessment InSAR multi-hazard multi-sensor rural urban |
| url | https://www.mdpi.com/1424-8220/22/24/9998 |
| work_keys_str_mv | AT shiranhavivi damageassessmentinruralenvironmentsfollowingnaturaldisastersusingmultisensorremotesensingdata AT stanleyrrotman damageassessmentinruralenvironmentsfollowingnaturaldisastersusingmultisensorremotesensingdata AT dangblumberg damageassessmentinruralenvironmentsfollowingnaturaldisastersusingmultisensorremotesensingdata AT shimritmaman damageassessmentinruralenvironmentsfollowingnaturaldisastersusingmultisensorremotesensingdata |