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

机译:基于Sentinel-1和ALOS-2雷达图像的孟加拉国Kutupalong难民营监测和环境变化评估

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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.
机译:2017年8月25日,缅甸罗兴亚少数民族人口中约有100万难民越过边界前往孟加拉国,寻求庇护以免遭到有计划的压迫和迫害。这导致库图帕隆难民营在几个月内急剧增加,周围森林的植被减少。由于许多人道主义组织都要求对营地进行监测和环境影响分析,因此本研究提出了一种基于星载雷达图像的工作流,以测量定居点的扩大和森林的减少。将11个Sentinel-1和ALOS-2图像对以及一个数字高程模型用于有监督的土地覆盖分类。在从多光谱图像检索的自动派生参考区域上对这些区域进行了培训,以减少所需的用户输入并提高可传递性。结果表明,总体植被减少了1500公顷,其中20%用于扩大营地,80%被砍伐森林,这与该案例的其他研究结果相符。时间序列分析减少了季节性变化对结果的影响,并且达到了88%至95%的准确度。对于分类而言,最重要的输入变量是基于合成孔径雷达(SAR)背向散射强度的植被指数,但地形参数也起作用。

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