Skadebedömning av översvämning i Moçambique med hjälp av Sentinel

In the past 40 years, floods have become a bane of Mozambique’s inhabitants and economy. The latest of them, caused by the cyclone Idai, has devastated the area resulting in loss of life and property. It was estimated that around 715 000 hectares of farmland was destroyed as a result of the cyclone. The main goal of this thesis was to assess the extent of the flooding and to determine the types of land cover that were affected. This was done in Google Earth Engine, using SAR change detection on Sentinel 1 data to create a mask for the flooded areas, followed by a supervised image classification on Sentinel 2 data to identify the types of land cover that were flooded. Two classifications were done, using imagery from early periods of the country’s plant growing season and later periods of the same season, respectively. The results of both classifications were below standard, with the main problems stemming from difficulties with differentiating between agriculture and roads along with agriculture and vegetation. Multiple ways to improve the results and avoid the errors in future similar projects were discussed, including using multi temporal data and utilizing a road map for the area to create a large amount of training points for the classification. In conclusion, while the results were not as good as was envisioned, the thesis provided ample opportunity to analyze errors and to theorize methods for improving future work