Approximately one million refugees of the Rohingya minority population in Myanmar crossed the border to Bangladesh on 25 August 2017, seeking shelter from systematic oppression and persecution. This led to a dramatic expansion of the Kutupalong refugee camp within a couple of months and a decrease of vegetation in the surrounding forests. As many humanitarian organizations demand frameworks for camp monitoring and environmental impact analysis, this study suggests a workflow based on spaceborne radar imagery to measure the expansion of settlements and the decrease of forests. Eleven image pairs of Sentinel-1 and ALOS-2, as well as a digital elevation model, were used for a supervised land cover classification. These were trained on automatically-derived reference areas retrieved from multispectral images to reduce required user input and increase transferability. Results show an overall decrease of vegetation of 1500 hectares, of which 20% were used to expand the camp and 80% were deforested, which matches findings from other studies of this case. The time-series analysis reduced the impact of seasonal variations on the results, and accuracies between 88% and 95% were achieved. The most important input variables for the classification were vegetation indices based on synthetic aperture radar (SAR) backscatter intensity, but topographic parameters also played a role.

Remotely sensed data have increasingly been used to improve flood mapping and modelling, providing much of the required information for delineating flood-affected areas and damage assessment. SAR satellite-based solutions have been proven to be among the most effective tools for flood extent detection because of their large spatial coverage, reasonable revisit time, and ability to penetrate through clouds and provide a full view of the Earth’s surface regardless of atmospheric or lighting conditions. This research proposes an innovative approach to applying an attention U-Net on SAR datasets to detect and extract flood extent maps. The approach was developed and validated using the datasets collected during a flooding event after extreme rainfall hit the eastern coast of Australia on 18 March 2021. Sentinel-1 (S1) ground range detected (GRD) and single look complex (SLC) descending track of the pre-and post-event on the 12th and 24th of March 2021, have been pre-processed, coincide with labels area of the flood extension have been carefully delineated to feed the model. The attention U-Net approach on S1 cross-polarization of VH provided promising results to identify the flood extent with precision, recall, and F1-score of 0.90, 0.88, 0.89 correspondingly. At the same time the result of the unseen frame achieved precision, recall, and F1-score, of 0.63, 0.59, and 0.61 respectively. The approach was also successfully employed to detect flood extent over the study area in July 2022, and the proposed model gave an outstanding accuracy of over 0.84 F1-score.