The Island of Tongatapu, Tonga, was struck by a tsunami on January 15, 2022. Internet was cut off from the island, which made remote sensing a valuable tool for the assessment of damages. Through land cover classification, change vector analysis and log-ratio image differencing, damages caused by the tsunami were assessed remotely in this thesis. Damage assessment is a vital part of both assessing the need for humanitarian aid after a tsunami, but also lays the foundation for preventative measurements and reconstruction. The objective of this thesis was to assess damage in terms of square kilometers and create damage maps. It was also vital to assess the different methods and evaluate their accuracy. Results from this study could theoretically be combined with other damage assessments to evaluate different aspects of damage. It was also important to evaluate which methods would be good to use in a similar event. In this study Sentinel-1, Sentinel-2 and high-resolution Planet Imagery were used to conduct a damage assessment. Evaluating both moderate and high-resolution imagery in combination with SAR yielded plausible, but flawed results. Land cover was computed for moderate and high-resolution imagery using three types of classifiers. It was found that the Random Forest classifier outperforms both CART and Support Vector Machine classification for this study area. Land cover composite image differencing for pre-and-post tsunami Sentinel-2 images achieved an accuracy of around 85%. Damage was estimated to be about 10.5 km^2. Land cover classification with high-resolution images gave higher accuracy. The total estimated damaged area was about 18 km^2. The high-resolution image classification was deemed to be the better method of urban damage assessment, with moderate-resolution imagery working well for regional damage assessment. Change vector analysis provided plausible results when using Sentinel-2 with NDVI, NDMI, SAVI and BSI. NDVI was found to be the most comprehensive change indicator when compared to the other tested indices. The total estimated damage using all tested indices was roughly 7.6 km^2. Using the same method for Sentinel-1's VV and VH bands, the total damage was estimated to be 0.4 and 2.6 km^2 respectively. Log ratio for Sentinel-1 did not work well compared to change vector analysis. Issues with false positives occurred. Both log-ratios of VV and VH gave a similar total estimated damage of roughly 5.2 km^2. Problems were caused by cloud cover and ash deposits. The analysis could have been improved by being consistent with the choice of dates for satellite images. Also, balancing classification samples and using high-resolution land cover classification on specific areas of interest indicated by regional methods. This would circumvent problems with ash, as reducing the study area would make more high-resolution imagery available.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-315774 |
| Date | January 2022 |
| Creators | Larsson, Milton |
| Publisher | KTH, Geoinformatik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-ABE-MBT ; 22567 |
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