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Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique

Time: Wed 2024-06-05 10.00

Location: 1515, Teknikringen 74D, Stockholm

Language: English

Subject area: Geodesy and Geoinformatics, Geoinformatics

Doctoral student: Manuel Nhangumbe , Geoinformatik

Opponent: Professor Alfonso Vitti, University of Trento, Italy

Supervisor: Professor Yifang Ban, Geoinformatik; Associate Professor Andrea Nascetti, Geoinformatik, Royal Institute of Technology

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QC 20240521


Floods are one of the most frequent natural disasters worldwide. Althoughthe vulnerability varies from region to region, all countries are susceptible toflooding. Mozambique was hit by several tropical cyclones (TCs) in the lastfew decades, and in 2019, after TCs Idai and Kenneth, the country becamethe first one in southern Africa to be hit by two cyclones in the same rainyseason. In 2023, Mozambique was slammed twice by the same cyclone (TCFreddy) which was also recorded as the longest one. Aiming to provide thelocal authorities with tools to yield better responses before and after any disasterevent, and to mitigate the impact and support in decision making forsustainable development, it is fundamental to continue investigating reliablemethods for disaster management. In this thesis, two approaches for floodmapping (FM) are proposed. The first is a fully automated method for FM innear real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar(SAR) data acquired in the Beira municipality and the Macomia district.The second approach relies on supervised and unsupervised machine learning(ML) methods as we investigate a dataset provided by DrivenData Labsbased on Sentinel-1 (S1) imagery (VH, VV imagery and labels from 13 countriesworldwide). By exploiting the processing capability of the Google EarthEngine (GEE) platform, both approaches are presented as an alternative todeep learning (DL) methods due to cost effectiveness and low computationalpower requirement. The first approach is implemented by finding the differencesof images acquired before and after the flooding and then use Otsu’sthresholding method to automatically extract the flooded area from the differenceimage, while the second one is based on ML methods such as SVMand K-Means. To validate and compute the accuracy of the proposed techniques,we compare our results with the Copernicus Emergency ManagementService (Copernicus EMS) data available in the study areas. Furthermore, weinvestigated the use of a Sentinel-2 (S2) multi-spectral instrument (MSI) toproduce a land cover (LC) map of the study area and estimate the percentageof flooded areas in each LC class. The results show that the combinationof S1 and S2 data is reliable for near real-time flood mapping and damageassessment. We automatically mapped flooded areas with an overall accuracyof about 87–88% and kappa of 0.73–0.75 for the first approach. The secondapproach produced satisfactory results, and showed to be better than usingVV imagery; in Cambodia and Bolivia with VH polarization yielded IoUs valuesranging from 0.819 to 0.856. The predictions in Beira using VH imageryyielded IoU of 0.568, which is a reasonable outcome. The LC classification isvalidated by randomly collecting over 600 points for each LC, and the overallaccuracy is 90–95% with a kappa of 0.80–0.94. With these results we wereable to detect areas that are prone to flooding and where floods recede fasterfor improving the planning; we were also able to determine the percentageof flooded LC such as Agriculture, Mangrove and Built as their destructionnegatively impacts on food security and socio-economic development plans.