Details

Autor(en) / Beteiligte

• Alcaras, Emanuele
• Amoroso, Pier Paolo
• Falchi, Ugo
• Figliomeni, Francesco Giuseppe
• Parente, Claudio

Titel

Using bi-temporal Sentinel-2 images to detect the effects of Hunga Tonga-Hunga Ha'apai eruption

Ist Teil von

• 2022 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), 2022, p.127-132

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEL

Beschreibungen/Notizen

• The eruption of the Hunga Tonga - Hunga Ha'apai volcano, which began on January 14,2022, is so far the largest eruption of the twenty-first century. The strong explosion it generated was able to change the physiognomy of the island, leaving only two islets. Thanks to Sentinel-2 satellite mission, it was possible to acquire images showing the island before and after the eruption. The aim of this work is to detect the changes that occurred on the island by comparing Sentinel-2 images acquired on different data (before and after the eruption). The work is developed over four basic phases: the first concerns the application of different indices to delineate the territorial boundaries of the island; next the Maximum Likelihood Classification (MLC) is applied to the indices to distinguish water and no-water classes; then the post-classification change detection technique is applied to measure the areas and the amount of soil sunk under water as a result of the eruption; finally, tests are carried out to evaluate the accuracy of the resulting coastlines as a marker of classification accuracy. The results show that the only effective index for determining the two aforementioned classes is the Modified Normalized Difference Water Index (MNDWI). That is due to the presence of lava soil which limits the effectiveness of the other indices available in the literature and normally adopted for the extraction of the coastline, e.g. the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI).