Spatiotemporal changes of vegetation and land surface temperature in the refugee camps and its surrounding areas of Bangladesh after the Rohingya influx from Myanmar

Kazi Jihadur Rashid, Md. Atikul Hoque, Tasnia Aysha Esha, Md. Atiqur Rahman () and Alak Paul
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Kazi Jihadur Rashid: University of Chittagong
Md. Atikul Hoque: University of Chittagong
Tasnia Aysha Esha: University of Chittagong
Md. Atiqur Rahman: University of Chittagong
Alak Paul: University of Chittagong

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 3, No 31, 3562-3577

Abstract: Abstract In August 24, 2017, a massive outbreak took place in the Rakhine state of Myanmar which triggered a huge refugee influx to the Teknaf Peninsula, Bangladesh. To settle the refugees, makeshift camps were built in large numbers destroying huge amount of forest areas near the existing Kutupalong and Nayapara camps. Refugees have been encroaching the nearby forest covers to collect fuelwood and other purposes. These forest destructions have put the wildlife and biodiversity of the system in a substantial pressure as well as altering the land surface temperature (LST). This paper has examined the extent of vegetation change and the changes of LST from 2017 to 2019 throughout Kutupalong and Balukhali camp and adjacent areas using Landsat 8 images. Random forest algorithm and Plank equation were applied on images to identify vegetation change and LST, respectively. The overall and kappa accuracies for the maps of 2017 are 96% and 92%, respectively, while it stands at 94% and 88% for the 2019 image. Results derived from the analysis suggest that an estimated 1876 hectares of forested lands have been decreased in the study area. LST of the study area increased spatially throughout the whole region with a maximum value of 34 °C which is significantly higher than the pre-influx period. If this trend of forest-clearing activities continues, the place will become barren land soon and the LST will also increase. All these factors will ultimately trigger the climate change impacts and biodiversity loss of the area.

Keywords: LST; Random forest classification; Remote sensing; Rohingya influx; Vegetation loss (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00733-x

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