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

Refugees, governance, and sustainable environment: PQARDL method

Although many variables that have adverse impacts on the sustainable environment are investigated from many aspects, some variables are missing. In this study, it will be simultaneously focused on the relation between refugees, governance, sustainable environment, economic growth, energy consumption, and supplementary explanatory variables, HDI, the trade deficit, and financial development, for Bangladesh, Ethiopia, Jordan, Lebanon, Pakistan, Sudan, and Uganda using the panel quantile autoregressive distributed lag (PQARDL) and causality methods for the 1996-2019 period. Long-run coefficients found by PQARDL method showed the evidence of the long-run relationship between the sustainable environment, refugee population, governance, economic growth, energy consumption, and explanatory variables. Both traditional and Dumitrescu and Hurlin (2012) causality tests determined the evidence of a unidirectional causality from political and economic governance to greenhouse gas (GHG) emissions and deforestation, as well as a unidirectional causality from refugees to GHG emissions and deforestation.

Investigation of near-surface chemical explosions effects using seismo-acoustic and synthetic aperture radar analyses

Chemical explosions are ground truth events that provide data, which, in turn, can enhance the understanding of wave propagation, damage assessment, and yield estimation. On 4 August 2020, Beirut, Lebanon was shocked by a catastrophic explosion, which caused devastating damage to the Mediterranean city. A second strong chemical explosion took place at the Xiangshui, China chemical plant on 21 March 2019. Both events generated shock waves that transitioned to infrasound waves, seismic waves, as well as hydroacoustic signals with accompanying T-phases in the case of the Beirut event. In this work, the seismo-acoustic signatures, yields, and associated damage of the two events are investigated. The differentiainterferometry synthetic aperture radar analysis quantified the surface damage and the estimated yield range, equivalent to 2,4,6-trinitrotoluene [C7H5(NO2)3] (TNT), through a "boom" relation of the peak overpressure was evaluated. Infrasound propagation modeling identified a strong duct in the stratosphere with the propagation to the west in the case of the Beirut-Port explosion. In the case of the Xiangshui explosion, the modeling supports the tropospheric propagation toward the Kochi University of Technology (KUT) sensor network in Japan. Although the Beirut yield (202-270 ± 100 tons) was slightly larger than the Xiangshui yield (201 ± 83.5 tons), the near-source damage areas are almost the same based on the distribution of damaged buildings surrounding the explosions.

Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network

Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced population (IDP)/refugee settlements. Manual information extraction pipelines are slow and resource inefficient. Advances in deep learning, especially convolutional neural networks (CNNs), are providing state-of-the-art possibilities for automation in information extraction. This study investigates a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP/refugee settlements. The study uses a time series of very high-resolution satellite images from WorldView-2 and WorldView-3. The model was trained with transfer learning through domain adaptation from nonremote sensing tasks. The capability of a model trained on historical images to detect dwelling features on completely unseen newly obtained images through temporal transfer was investigated. The results show that transfer learning provides better performance than training the model from scratch, with an MIoU range of 4.5 to 15.3%, and a range of 18.6 to 25.6% for the overall quality of the extracted dwellings, which varied on the bases of the source of the pretrained weight and the input image. Once it was trained on historical images, the model achieved 62.9, 89.3, and 77% for the object-based mean intersection over union (MIoU), completeness, and quality metrics, respectively, on completely unseen images.

Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach

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.

Modeling of Forest Ecosystem Degradation Due to Anthropogenic Stress: The Case of Rohingya Influx into the Cox’s Bazar–Teknaf Peninsula of Bangladesh

Overdependence and cumulative anthropogenic stresses have caused world forests to decrease at an unprecedented rate, especially in Southeast Asia. The Cox’s Bazar–Teknaf Peninsula of Bangladesh is not an exception and follows the global deforestation trend. Despite being one of the country’s richest forest ecosystems with multiple wildlife sanctuaries, reserve forests, and influential wildlife habitats, the peninsula is now providing shelter for nearly one million Rohingya refugees. With the global deforestation trend coupled with excessive anthropogenic stresses from the Rohingya population, the forests in the peninsula are continuously deteriorating in terms of quality and integrity. In response to deforestation, the government invested in conservation efforts through afforestation and restoration programs, although the peninsula faced a refugee crisis in August 2017. The impact of this sudden increase in population on the forest ecosystem is large and has raised questions and contradictions between the government’s conservation efforts and the humanitarian response. Relocation of the refugees seems to be a lengthy process and the forest ecosystem integrity needs to be preserved; therefore, the degree of stresses, level of impacts, and pattern of deforestation are crucial information for forest conservation and protection strategies. However, there are a lack of quantitative analyses on how the forest ecosystem is deteriorating and what future results would be in both space and time. In this study, the impact of the sudden humanitarian crisis (i.e., Rohingya refugees) as anthropogenic stress in Cox’s Bazar–Teknaf peninsula has been spatiotemporally modeled and assessed using Sentinel-2 satellite imagery and other collateral data. Using the density and accessibility of the Rohingya population along with the land cover and other physiographic data, a multi-criteria evaluation (MCE) technique was applied through the Markov cellular automata technique to model the forest vegetation status. The impact of deforestation differs in cost due to variability of the forest vegetation covers. The study, therefore, developed and adopted three indices for assessment of the forest ecosystem based on the variability and weight of the forest cover loss. The spatial severity of impact (SSI) index revealed that out of 5415 ha of total degraded forest lands, 650 ha area would have the highest cost from 2017 to 2027. In the case of the ecosystem integrity (EI) index, a rapid decline in ecosystem integrity in the peninsula was observed as the integrity value fell to 1190 ha (2019) from 1340 ha (2017). The integrity is expected to further decline to 740 ha by 2027, if the stress persists in a similar fashion. Finally, the findings of ecosystem integrity depletion (EID) elucidated areas of 540 and 544 hectares that had a severe EID score of (−5) between 2017 and 2019 and 2017 and 2027, respectively. The displacement and refugee crisis is a recurrent world event that, in many cases, compromises the integrity and quality of natural space. Therefore, the findings of this study are expected to have significant global and regional implications to help managers and policymakers of forest ecosystems make decisions that have minimal or no impact to facilitate humanitarian response.

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

Spatiotemporal pattern of forest degradation and loss of ecosystem function associated with Rohingya influx: A geospatial approach

Violence in Rakhine State of Myanmar forcibly displaced nearly one million Rohingya. They took refuge, from August 25, 2017 to the time of writing, in Cox's Bazar–Teknaf Peninsula of Bangladesh. Initially, nearly 2,000 ha of forested lands had to be cleared to accommodate them in one of the most ecologically critical areas (ECA) in the Peninsula. To support Rohingyas livelihoods, fuelwood collection and illegal logging have become widespread since their arrival, causing severe environmental degradation, including loss of a vast amount of forest cover. To devise conservation and protection strategies for a highly sensitive ecosystem, it is imperative to understand the degree of forest cover deterioration and associated impacts related to Rohingya emigration. This study employed satellite images and collateral data to monitor and model spatiotemporal patterns of forest cover degradation and loss of ecosystem function in Cox's Bazar–Teknaf Peninsula. Supervised classification method was used to derive multi‐date land use/cover data which was then utilized to monitor spatiotemporal pattern of forest cover change from 2017 to 2019. A projection of forest cover loss was also carried out using the Markov chain with cellular automata technique. Dynamic modeling was performed to predict changes in forest covers, assuming that displaced Rohingya continues to reside in this environmentally sensitive location. The result revealed that 3,130 ha of forested lands of different categories were transformed into either refugee camps or Rohingya influenced degraded forests between 2017 and 2019. Prediction showed that around 5,115 ha of forest cover may experience loss from 2019 to 2027. Furthermore, aboveground biomass and carbon stock estimation indicated a consistent and substantial loss during the study period, which is likely to swell if present deforestation rate continues. The findings have considerable implications in developing conservation decisions, priority interventions and public policies to save the ECA of Bangladesh.

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

The refugee influx from Myanmar, known as Rohingya refugees, is a serious concern for global refugee issues. Bangladesh currently hosts one million Rohingya refugees in the coastal district of Cox’s Bazar. Considering the number of the refugees, in addition to the humanitarian concerns, they are also creating pressure on the local host communities. This study explored the socioeconomic changes of the host communities after the refugee influx. In order to fulfill this study’s objectives, 35 villages near the Rohingya refugee camps from the coastal district of Bangladesh were surveyed. In the villages, 10% of households were surveyed in 2016 and also in 2020, covering 1924 and 2265 households, respectively. A temporal comparison of the host community’s socioeconomic status between 2016 and 2020 was conducted in order to determine the changes after the recent refugee influx. This study found that the local community’s socioeconomic status degraded. The annual income decreased by 24%, which is unusual for a country with over 6% gross domestic product (GDP) growth in recent times. The income decreased from all livelihood options except farming, which could be related to the availability of cheap labor and the high demand for commodities. The villages were clustered using k-means, and 20 villages were found to be affected after the refugee influx with degraded socioeconomic status. The host community’s general perception was initially positive, but later turned negative toward the refugees. This study will be important for the government and donor agencies to develop strategies to properly manage the refugee camps and adjacent host communities.