Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach
Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach
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DOI：
10.3390/rs13245056
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
M. Sakamoto;S. M. A. Ullah;Masakazu Tani
中科院分区：
文献类型：
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作者：
M. Sakamoto;S. M. A. Ullah;Masakazu Tani
2017年涌入孟加拉国的罗辛亚难民是一个历史性事件；难民人数如此之多，对当地社区的重大影响是不可避免的。孟加拉国政府在保护区内提供土地，用于为难民建造临时营地。以前的研究揭示了难民涌入营地周围的土地覆盖变化和影响，特别是对当地森林资源的影响。我们的目标是建立一种方便的方法，提供最新信息，以监测全面的当地情况。我们使用了一种经典的非监督技术--k-均值聚类和最大似然估计的组合--使用了最新的丰富的哨兵1号和哨兵2号时间序列卫星图像。VV和归一化差分水指数(NDWI)图像的组合成功地识别了建成区/扰动区，VH和NDWI图像的组合成功地区分了湿地/盐碱地、农业/露地、退化的森林/灌木和林区。通过这样做，我们为整个特克纳夫半岛提供了流入前和流入后时期的年度土地覆盖分类图，质量还不错，而且没有事先的培训数据。我们的分析表明，到2021年5月，仍然可以观察到持续的影响。作为对干预后果的简单估计，建成区/扰动区增加了6825公顷(与2015-17年期间相比)。然而，虽然对原始森林的影响不是很大，但退化的森林/灌木区大部分退化了4606公顷。这些耕地将用于农业活动。这与报道的农民收入增加是一致的，尽管当地从事其他职业的人都同样面临收入下降的问题。我们的无监督分类方法的便利性将有助于不断积累时间序列的土地覆盖分类，这对于监测对当地社区的影响非常重要。
The Rohingya refugee influx to Bangladesh in 2017 was a historical incident; the number of refugees was so massive that significant impacts to local communities was inevitable. The Bangladesh government provided land in a preserved area for constructing makeshift camps for the refugees. Previous studies have revealed the land cover changes and impacts of the refugee influx around campsites, especially with regard to local forest resources. Our aim is to establish a convenient approach of providing up-to-date information to monitor holistic local situations. We employed a classic unsupervised technique—a combination of k-means clustering and maximum likelihood estimation—with the latest rich time-series satellite images of Sentinal-1 and Sentinal-2. A combination of VV and normalized difference water index (NDWI) images was successful in identifying built-up/disturbed areas, and a combination of VH and NDWI images was successful in differentiating wetland/saltpan, agriculture /open field, degraded forest/bush, and forest areas. By doing this, we provided annual land cover classification maps for the entire Teknaf peninsula for the pre- and post-influx periods with both fair quality and without prior training data. Our analyses revealed that on-going impacts were still observed by May 2021. As a simple estimation of the intervention consequence, the built-up/disturbed areas increased 6825 ha (compared with the 2015–17 period). However, while the impacts on the original forest were not found to be significant, the degraded forest/bush areas were largely degraded by 4606 ha. These cultivated lands would be used for agricultural activities. This is in line with the reported farmers’ increased income, despite local people with other occupations that are all equally facing the decreases in income. The convenience of our unsupervised classification approach would help keep accumulating a time-series land cover classification, which is important in monitoring impacts on local communities.