Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis
The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/1/178 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588233640247296 |
|---|---|
| author | Youshuang Hu Aggeliki Barberopoulou Magaly Koch |
| author_facet | Youshuang Hu Aggeliki Barberopoulou Magaly Koch |
| author_sort | Youshuang Hu |
| collection | DOAJ |
| description | The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions. |
| format | Article |
| id | doaj-art-cb243e18fa134fb7836272b1ea96dc50 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-cb243e18fa134fb7836272b1ea96dc502025-01-24T13:37:09ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113117810.3390/jmse13010178Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing AnalysisYoushuang Hu0Aggeliki Barberopoulou1Magaly Koch2Department of Geography, Sustainability, Community, and Urban Studies, University of Connecticut, Storrs, CT 06269, USAPrometheus Space Technologies 2, Kifissias Av. & D. Gounari 95, 15124 Marousi, GreeceDepartment of Earth & Environment, Boston University, Boston, MA 02215, USAThe 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions.https://www.mdpi.com/2077-1312/13/1/178remote sensingnatural hazardsenvironmental indicesland cover changevulnerability assessmentIndonesia |
| spellingShingle | Youshuang Hu Aggeliki Barberopoulou Magaly Koch Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis Journal of Marine Science and Engineering remote sensing natural hazards environmental indices land cover change vulnerability assessment Indonesia |
| title | Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis |
| title_full | Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis |
| title_fullStr | Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis |
| title_full_unstemmed | Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis |
| title_short | Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis |
| title_sort | tracing the 2018 sulawesi earthquake and tsunami s impact on palu indonesia a remote sensing analysis |
| topic | remote sensing natural hazards environmental indices land cover change vulnerability assessment Indonesia |
| url | https://www.mdpi.com/2077-1312/13/1/178 |
| work_keys_str_mv | AT youshuanghu tracingthe2018sulawesiearthquakeandtsunamisimpactonpaluindonesiaaremotesensinganalysis AT aggelikibarberopoulou tracingthe2018sulawesiearthquakeandtsunamisimpactonpaluindonesiaaremotesensinganalysis AT magalykoch tracingthe2018sulawesiearthquakeandtsunamisimpactonpaluindonesiaaremotesensinganalysis |