Following a targeted campaign of violence by Myanmar military, police, and local militias, more than half a million Rohingya refugees have fled to neighboring Bangladesh since August 2017, joining thousands of others living in overcrowded settlement camps in Teknaf. To accommodate this mass influx of refugees, forestland is razed to build spontaneous settlements, resulting in an enormous threat to wildlife habitats, biodiversity, and entire ecosystems in the region. Although reports indicate that this rapid and vast expansion of refugee camps in Teknaf is causing large scale environmental destruction and degradation of forestlands, no study to date has quantified the camp expansion extent or forest cover loss. Using remotely sensed Sentinel-2A and-2B imagery and a random forest (RF) machine learning algorithm with ground observation data, we quantified the territorial expansion of refugee settlements and resulting degradation of the ecological resources surrounding the three largest concentrations of refugee camps-Kutupalong-Balukhali, Nayapara-Leda and Unchiprang-that developed between pre-and post-August of 2017. Employing RF as an image classification approach for this study with a cross-validation technique, we obtained a high overall classification accuracy of 94.53% and 95.14% for 2016 and 2017 land cover maps, respectively, with overall Kappa statistics of 0.93 and 0.94. The producer and user accuracy for forest cover ranged between 92.98-98.21% and 96.49-92.98%, respectively. Results derived from the thematic maps indicate a substantial expansion of refugee settlements in the three refugee camp study sites, with an increase of 175 to 1530 hectares between 2016 and 2017, and a net growth rate of 774%. The greatest camp expansion is observed in the Kutupalong-Balukhali site, growing from 146 ha to 1365 ha with a net increase of 1219 ha (total growth rate of 835%) in the same time period. While the refugee camps' occupancy expanded at a rapid rate, this gain mostly occurred by replacing the forested land, degrading the forest cover surrounding the three camps by 2283 ha. Such rapid degradation of forested land has already triggered ecological problems and disturbed wildlife habitats in the area since many of these makeshift resettlement camps were set up in or near corridors for wild elephants, causing the death of several Rohingyas by elephant trampling. Hence, the findings of this study may motivate the Bangladesh government and international humanitarian organizations to develop better plans to protect the ecologically sensitive forested land and wildlife habitats surrounding the refugee camps, enable more informed management of the settlements, and assist in more sustainable resource mobilization for the Rohingya refugees.

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Accurate information on, and human interpretation of, urban land cover using satellite-derived sensor imagery is critical given the intricate nature and niches of socioeconomic, demographic, and environmental factors occurring at multiple temporal and spatial scales. Detailed knowledge of urban land and their changing pattern over time periods associated with ecological risk is, however, required for the best use of critical land and its environmental resources. Interest in this topic has increased recently, driven by a surge in the use of open-source computing software, satellite-derived imagery, and improved classification algorithms. Using the machine learning algorithm Random Forest, combined with multi-date Landsat imagery, we classified eight periods of land cover maps with up-to-date spatial and temporal information of urban land between the period of 1972 and 2015 for the mega-urban region of greater Dhaka in Bangladesh. Random Forest-a non-parametric ensemble classifier-has shown a quantum increase in satellite-derived image classification accuracy due to its outperformance over traditional approaches, e.g., Maximum Likelihood. Employing Random Forest as an image classification approach for this study with independent cross-validation techniques, we obtained high classification accuracy, user and producer accuracy. Our overall classification accuracy ranges were between 85% and 97% with kappa values between 0.81 and 0.94. The area statistics derived from the thematic land cover map show that the built-up area in the 43-year study period expanded quickly, from 35 km 2 in 1972 to 378 km 2 in 2015, with a net increase rate of approximately 980% and an average annual growth rate of 6%. This growth rate, however, was higher in peripheral areas, with a 2903% increase and an annual expansion rate of 8%, compared to a 460% increase with an annual growth rate of 4% in the core city area (Dhaka City Corporation). This huge urban expansion took place in the north, northwest, and southwest regions of Dhaka, transforming areas that were previously agricultural land, vegetation cover, wetland, and water bodies. The main factors driving the city towards northern corridors include flood-free higher land, the availability of a transportation network, and the agglomeration of manufacturing-based employment centers. The resulting thematic map and spatial information produced from this study therefore serve to facilitate a detailed understanding of urban growth dynamics and land cover change patterns in the mega-urban region of Dhaka, Bangladesh.

As in many other developing countries, cities in Bangladesh have witnessed rapid urbanization, resulting in increasing amounts of land being taken over and therefore land cover changing at a faster rate. Until now, however, few efforts have been made to document the impact of land use and land cover changes on the climate, environment, and ecosystem of the country because of a lack of geospatial data and time-series information. By using open source Landsat data integrated with GIS technologies and other ancillary data, this study attempts to classify land use and create land cover maps, enabling postclassification change detection analysis. By this method, we document the spatial and temporal trajectory of urban expansion in Chittagong, the second largest city in Bangladesh, over a 36-year period. The findings suggest that, over the study period, 56 % of the land cover has undergone change, mainly because of the expansion of built-up areas and other human activities. During the 36-year period, the built-up area around Chittagong city has expanded by 618 %, with an average annual rate of increase of 17.5 %. As a result of rapid urbanization, the vegetated hills near urban development areas face serious threats of further encroachment and degradation, given that 2178 ha of hills have already been intruded over the study period. Because urbanization processes in Bangladesh have traditionally been viewed as the result of population growth and economic development, very little work has been done to track the potential growth trajectory in a physical or spatial context. This study, therefore, will contribute to the current understanding of urban development in Bangladesh from a temporal and spatial point of view. Findings will be able to assist planners, stakeholders, and policy makers in appreciating the dynamism of urban growth and therefore will facilitate better planning for the future to minimize environmental impacts.