[P21] Accuracy of Global Datasets for Long-Term Tsunami Exposure Evolution Analysis: A Case Study in Banda Aceh Two Decades After The 2004 Indian Ocean Tsunami.
Conference Bldg 2F - Sakura Hall
| Affiliation | Osaka University |
|---|---|
| Author | Amra Rajuli |
| Co-Author | Susumu Araki（Osaka University） |
Keywords
- Global datasets
- Long-term exposure analysis
- 2004 Indian Ocean Tsunami
Outline
Following the development of the earth observation system, several global scale exposure datasets are publicly accessible. This research evaluated the accuracy of these global datasets in assessing the exposure evolution between 2004 and 2024 in Banda Aceh, Indonesia, which has undergone significant development after the 2004 Indian Ocean Tsunami (IOT). We analyzed 17 multitemporal global datasets (7 gridded population and 10 built-up area layers) with resolutions ranging from 10 m to 1 km, using Exposure Agreement Index (EAI) and Population Agreement Index (PAI). These indices were calculated by comparing the exposure estimates from global data against high-resolution (5 m) local exposure data developed by Amra et al. (2024). Additionally, using the 2004 IOT satellite-derived inundation limit, we examined the bias introduced by global datasets in evaluating long-term evolution of built-up and population distribution within the 2004 IOT affected-areas. This research provides crucial insights into the applicability and limitations of global datasets for long-term disaster risk assessment at local-scale, especially for data-scarce regions.
Reference:
Amra, R., Araki, S., Geiß, C., Davies, G., 2024. Error-reduced digital elevation models and high-resolution land cover roughness in mapping tsunami exposure for low elevation coastal zones. Remote Sensing Applications: Society and Environment 37, 101438. https://doi.org/10.1016/j.rsase.2024.101438
