Analysis of Vegetative Cover Vulnerability in Rohingya Refugee Camps of Bangladesh Utilizing Landsat and Per Capita Greening Area (PCGA) Datasets

Abstract

The vegetative cover in and surrounding the Rohingya refugee camps in Ukhiya-Teknaf is highly vulnerable since millions of refugees moved into the area, which led to severe environmental degradation. In this research, we used a supervised image classification technique to quantify the vegetative cover changes both in Ukhiya-Teknaf and thirty-four refugee camps in three time-steps: one pre-refugee crisis (January 2017), and two post-refugee crisis (March 2018, and February 2019), in order to identify the factors behind the decline in vegetative cover. The vegetative cover vulnerability of the thirty-four refugee camps was assessed using the Per Capita Greening Area (PCGA) datasets and K-means classification techniques. The satellite-based monitoring result affirms a massive loss of vegetative cover, approximately 5482.2 hectares (14%), in Ukhiya-Teknaf and 1502.56 hectares (79.57%) among the thirty-four refugee camps, between 2017 and 2019. K-means classification revealed that the vegetative cover in about 82% of the refugee camps is highly vulnerable. In the end, a recommendation as to establishing the studied region as an ecological park is proposed and some guidelines discussed. This could protect and reserve forests from further deforestation in the area, and foster future discussion among policymakers and researchers.

The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning

“…The total territory of the camps is 24 km 2 , and about 905,776 refugees reside in 34 distinct camps (Jeffries et al, 2021). Ukhiya and Teknaf are currently the fastest-growing refugee camps, with 147 communities and two water routes, the Naf River and the Reju Canal (Karim & Zhang, 2021). The study area of this research work can be seen in Figure 1.…”

“…This region is mostly mountainous, with a history of landslides (M. A. Islam et al, 2017) and vulnerability to cyclones, storm surges, and tidal bores (Karim & Zhang, 2021). Because the annual average rainfall in this area is 4000 mm, it is classified as a monsoon area (Bappa et al, 2022).…”

Artificial Neural Network-Based Land Use-Specific Carbon Patterns and Their Effects on Land Surface Temperature as a Result of the Rohingya Refugee Influx

“…A set of studies have also linked Rohingya refugee influx on aboveground biomass [19], human-elephant confrontations [22], socioeconomic and environment status [23], and LST in the Rohingya camp area [17]. Works done by [17], [24], [25] have showed the relationship between vegetation and LST across the Rohingya camp. However, there is a noticeable lack of research that has examined the connection between LULC specific CEs and LST in Cox's Bazar after the massive influx of Rohingya refugees.…”

Dynamic space–time dark level correction approach for lunar radiometric calibration of the Lunar Observation Imaging Spectrometer

“…With the considerable waking developments in remote sensing technology, the capacity of satellites to consistently record surface observations has been greatly improved, which extends many applications in the fields of land use investigation [1,2], urban planning [3], change detection [4], disaster management [5], etc. Concomitantly, requirements for remote sensing products have escalated to quantitative applications, which involve from initial target imaging to qualitative analysis [6,7].…”