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

Abstract

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.

Socioeconomic Status Changes of the Host Communities after the Rohingya Refugee Influx in the Southern Coastal Area of Bangladesh

“…In order to manage the Rohingya refugees and mitigate their impact on the host community, it is important to understand the host community's burden and suffering. In the case of hosting the Rohingya refugees, several empirical studies regarding different environmental degradation issues have been published, but there is a lack of empirical research regarding the impact on the host community [15][16][17][18]. This study aims to contribute to this knowledge gap by exploring the socioeconomic change on the host community after the Rohingya refugee influx.…”

“…Halbania,5 Jahajpura,6 Hajam Para,7 Mathabhanga,8 Marishbania,9 Baradail, 10 Kacchapia,11 Noakhali,12 Razarchhara,13 Habibchhara,14 Dargachhara,15 Mitta Panirchhara,16 Hatiarghona,17 Lambori,18 Tulatali,19 Lengurbil,20 Mahishkhalia Para,21 Khonkar Para,22 Hajam Para,23 Kachubania,24 Bara Habib Para,25 Moulvi Para,26 Choto Habib Para,27 Nazir Para,28 Hankar Para,29 Shilbania Para,30 Dail Para,31 Goder Bil,32 Natun Pallan Para,33 Jahalia Para,34 Kerantali,35 Baraitali.…”

Editorial for Special Issue “Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis”

“…In [9], the changes in the area of refugee camps and consequent environmental effects caused by the displacement of people from Myanmar to the Cox's Bazar District, around Kutupalong, in Bangladesh, close to the border of Myanmar, were monitored, including massive decreases of forest cover and agricultural lands, which were converted to areas for housing, sanitation, and infrastructure facilities. The paper commenced with details of reports and surveys on the historical development of the camp and its significant effects on natural resources, ecosystems, and the health of the refugees.…”

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment of Area, Accuracy, and Agreement at Thirty Refugee Settlements in Uganda

“…The diversity of spatio-temporal data on refugee settlement is beneficial for a targeted assessment of refugee settlement inclusion in broad-scale human settlement products, yet there has never been a formal assessment of the detection of refugee settlements. There are, by contrast, many examples of individual refugee settlements being analyzed using high or moderate resolution (e.g., Sentinel and Landsat) satellite imagery or derived products to estimate settlement area [31,32], record dwellings [33][34][35][36][37], model refugee settlement population [38], guide the delivery of aid and relief [39,40], assess environmental conditions [41][42][43][44], map land cover/use change [45][46][47], and quantify economic development [48]. However, there is a large gap between tailored local scale mapping and systematic global scale mapping of human settlements, and several characteristics of refugee settlements likely challenge broad-scale detection.…”

“…However, there is a large gap between tailored local scale mapping and systematic global scale mapping of human settlements, and several characteristics of refugee settlements likely challenge broad-scale detection. First, many refugee settlements consist of small-scale dwellings and structures that are diffusely distributed and interspersed with vegetation or bare earth [41,46,49], leading to mixed pixels in Sentinel-1 and Sentinel-2, Landsat, and other moderate resolution satellite imagery. Second, building materials used in refugee settlements may include plastic tarp, wood fiber, thatching, and mud, which have distinct spectral signatures from typical materials used outside of humanitarian settings [50] and may also offer less spectral separability from the immediate surroundings.…”