Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2

被引:33
作者
Braun, Andreas [1 ]
Fakhri, Falah
Hochschild, Volker [1 ]
机构
[1] Univ Tubingen, Inst Geog, Rumelinstr 19-23, D-72072 Tubingen, Germany
关键词
synthetic aperture radar (SAR); deforestation; machine learning; humanitarian action; Sentinel-1; ALOS-2; RANDOM FOREST; TIME-SERIES; LAND-USE; DISASTER MANAGEMENT; HUMAN-RIGHTS; COVER; VEGETATION; IMPACT; CLASSIFICATION; SCALE;
D O I
10.3390/rs11172047
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:34
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