{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T22:15:56Z","timestamp":1771798556802,"version":"3.50.1"},"reference-count":140,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,30]],"date-time":"2019-08-30T00:00:00Z","timestamp":1567123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs11172047","type":"journal-article","created":{"date-parts":[[2019,8,30]],"date-time":"2019-08-30T10:31:17Z","timestamp":1567161077000},"page":"2047","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8630-1389","authenticated-orcid":false,"given":"Andreas","family":"Braun","sequence":"first","affiliation":[{"name":"Institute of Geography, University of T\u00fcbingen, R\u00fcmelinstra\u00dfe 19-23, 72072 T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3950-2078","authenticated-orcid":false,"given":"Falah","family":"Fakhri","sequence":"additional","affiliation":[{"name":"Independent Researcher"}]},{"given":"Volker","family":"Hochschild","sequence":"additional","affiliation":[{"name":"Institute of Geography, University of T\u00fcbingen, R\u00fcmelinstra\u00dfe 19-23, 72072 T\u00fcbingen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,30]]},"reference":[{"key":"ref_1","first-page":"483","article-title":"Humanitarian emergencies: Causes, traits and impacts as observed by remote sensing","volume":"3","author":"Lang","year":"2015","journal-title":"Remote Sens. 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