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Environmental Cost of Refugee Crisis: Case Study of Kutupalong Balukhali Rohingya Camp Site A Remote Sensing Approach

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This paper explored the environmental cost of existing Rohingya Refugee crisis between Myanmar and Bangladesh, utilizing remote sensing techniques. The focus was to determine the loss of vegetation in setting refugee camp sites at Kutupalong-Balukhali expansion area. Change Vector Analysis has been applied to determine the forest cover loss using LandSat 7 images. Then using carbon sequestration capacity of these forest land, total loss has been estimated. It has been found that around 572 hectares areas deforested to set up camp and that account for approximate loss of 365,288 GBP per year.
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Environmental Cost of Refugee Crisis: Case Study of Kutupalong-
Balukhali Rohingya Camp Site A Remote Sensing Approach.
S M Labib
*
1, Nazia Hossain
2 and Shahadath Hossain Patwary
3
1School of Environment, Education and Development (SEED), University of Manchester.
2Rajshahi University of Engineering and Technology
3Sheltech (Pvt.) Ltd
January 11, 2018
Summary
This paper explored the environmental cost of existing Rohingya Refugee crisis between Myanmar and
Bangladesh, utilizing remote sensing techniques. The focus was to determine the loss of vegetation in
setting refugee camp sites at Kutupalong-Balukhali expansion area. Change Vector Analysis has been
applied to determine the forest cover loss using LandSat 7 images. Then using carbon sequestration
capacity of these forest land, total loss has been estimated. It has been found that around 572 hectares
areas deforested to set up camp and that account for approximate loss of 365,288 GBP per year.
KEYWORDS: Rohingya, Refugee, Deforestation, Environmental Cost, Remote Sensing.
1. Introduction
Geo-politics, wars, regional conflicts and extreme climatic events have triggered pressure on different
population groups, result in population displacement and created refugee crisis around the globe at
present. Apart from humanitarian crisis and threat to lives of millions, exodus of people from their
homeland is causing long term environmental damage and creating pressure on natural resources.
Existing literature suggest need for environmental monitoring using different methods, but majority of
them are time consuming, such as environmental impact assessments (EIAs). In contrast, Remote
Sensing tools and methods are often considered as an easy proxy to rapidly monitor of environmental
conditions and to determine possible cost for such population displacement (Price, 2017).
Sudden influx of Rohingya (a Muslim ethnic minority group in Myanmar), caused by recent event (on
25th August, 2017) of Arakan Rohingya Salvation Army (ARSA) attack against Military-police at
Rakhine state, triggered an ethnic cleansing of Rohingyas by Myanmar Military operations (Gee, 2017).
A total of 6,700 Rohingyas killed, UNHCR estimated 1.2 millions Rohingyas become refugees, and
among them 947,000 are in Bangladesh (BBC News, 2018). This sudden influx of Rohingyas cased
formation of many refugee camps (more than 10) in Teknaf cox's bazar areas (Figure 1) by cleaning
forest areas and cutting hills. Among these camps, IMO mentioned Kutupalong-Balukhali expansion
(KBE) is the largest camp accommodating 547,000 Rohingyas. In this study KBE site and surrounding
areas are considered to evaluate how much vegetation loss took place to provide shelter, the related
cost due to carbon sequestration loss.
*
labib.sm@postgrad.manchester.ac.uk
naziaurp@ ruet.ac.bd
shahadath3.1416@gmail.com
2. Methods And Materials
Change Vector Analysis (CVA) is a radiometric difference based multivariate method, this method
identifies changes among multi-temporal images, by calculating difference vectors analysis unites, and
estimate direction as well as magnitude. CVA has the capacity to detect changes in “n” number of
bands, in contrast some other methods concentrated on selected bands only (e.g. Red, NIR). Hence, it
has abilities to overcome the cumulative errors in classification. Additionally, CVA has the capability
to provide better understanding about types of change happening within the area of interest (Johnson
and Kasischke, 1998). In this study, CVA has been applied due to better representation of all changes,
and as the author has no prior knowledge about study area.
Figure 1: Existing Rohingya Refugee Camps within Cox’s Bazar Bangladesh (Source: BBC News,
2018)
2.1 Change Detection Analysis
Step 1: Data Collection; LandSat Surface Reflectance (L7 ETM+) images of 04th December 2017 (Date
2), and 23th March 2017 (Date 1) have been downloaded from USGS website.
Step 2: Pre-processing; Several bands of the image were stacked together and then resized to focus on
study area. Radiometric correction of gain and offset value of 0.0001 applied to transform the Digital
numbers to reflectance values.
Step 3: Tasseled Cap (TC) transformation; CVA can be used of any number of bands, but as this
research is focused on vegetation change monitoring, using TC would allow for better interpretation of
change in vegetation based on brightness and greenness of TC (Fig 2). TC help reduce the redundant
information from all spectral bands, act as biophysical parameter, which can have >=95% variance
expressed within first two bands of LandSat TM; brightness and greenness (Siwe and Koch, 2008).
Step 4: CVA-Magnitude; The magnitude of change is Euclidean distance of pixel spectra between date
1 TC and date 2 TC bands of brightness and greenness. Equation 1 shows the CVA-magnitude
estimation process (Johnson and Kasischke, 1998).
      
(1)
Cm is Change Magnitude and brightness and greenness are for time 2 and 1.
Step 5: CVA-Direction; The direction of change indicate the type of change happed to the
corresponding pixels. This allow labeling land cover change to land type change. Direction angle (θ)is
estimated based on Equation 2 (Johnson and Kasischke, 1998).
θ=actangent ( greenness2-greenness1
brightness2-brightness1 )
(2)
Angel is initially estimated in radian, later on transformed into degrees (0-360). Essentially, angle
ranges (in four quadrants) indicate increase or decrease of brightness or greenness values between two
dates for the same pixel.
Using the knowledge of TC attributes, and range of angles associated with different changes, in this
case four land cover types were labeled after several trial and error process; to identify the change;
forest to forest (F To F), Forest to No Forest (F To NF), No water to water (NW To W) or Water To
Water (W To W), and finally No Forest to Forest (NF To F).
Step 6: Threshold Selection and apply threshold; In this study, mean (M) and standard deviation (SD)
of magnitude values been utilized to determine threshold, to indicate actual change and no change.
After experimenting with Mean+1SD, and Mean+2SD, the author selected Mean+1SD values of all
magnitude values as threshold value, Karnieli et al., (2014) showed use of 1SD provide realistic change
and no-change pixels.
2.2 Carbon Sequestration Loss Estimation
Using carbon sequestration capacities of forest cover per unit area, and per unit price of Carbon-tonne,
the estimation of overall loss in terms of carbon storing has been conducted using equation 3.
Closs = FA* S* PC
(3)
Closs= Carbon loss; FA is Forest Area in Hectare, S denotes Sequestration capacity (hectare/year), PC
is Per unit cost of Carbon-tonne in USD.
According to Bangladesh Forest Department (BFD), Sequestration capacity of the forest areas in
Teknaf, is 43.08Carbon-tonne/Hectare/Year (Bforest.gov.bd., 2018). Additionally, Luckow (2014),
indicated per unit cost of carbon-tonne under mid scenario would be 20 USD up-to year 2020, this price
has been considered in this study.
3. Results And Discussions
Figure 2 (a, c) shows the standard FCC of the study area in two different periods, and Figure 2 (b,d)
shows the TC images, and both indicate, the increase in white or brightness. CVA analysis provided
the magnitude of changes (Figure 3a), and after applying the threshold value, the real change pixels
where the intensity of change are significant have been found (Figure 3b), the mask image created from
this threshold would apply on the classification image obtained based on the classification conducted
on the direction image (Figure 4a). The final changed areas have been illustrated in Figure 4(b), the red
areas shows the areas loss major vegetation, and these are the areas where the deforestation took place
due to cutting trees and hills to set the shelter.
Figure 2: (a) FCC (NIR, R, G) of Data 1, (b) Tassel Cap (TC) bands for Date 1 (c) FCC (NIR, R, G) of
Data 1 (d) TC bands for Date 2.
Figure 5a illustrates, within 0-90 range, no change in forest cover observed, but between 90-180 range
major deforestation took place. Increase in brightness values, and decrease in greenness in the 2nd
quadrant indicate more exposed land, and loss of vegetation. Quantitatively the among 5332.32 hectares
of study area, 572.85 hectares were previously forest cover areas been transformed into temporary
shelter for refugees in KBE areas. This account for total 10.74% loss in forest cover (Figure 5b).
Previously Khatun (2017) showed the deforestation in the study area 382.42 hectares, according to
BFD, which was clearly an underestimation. When visually checked (due to no access of field level
accuracy assessment data) with high-resolution images, areas indicated deforestation in this study,
matched more accurately with the camp sites.
Figure 3: (a) Magnitude image showing intensities of change between data 2 and date 1 (c) Mask image,
using M+1SD threshold indicating the real change pixels.
Figure 4: (a) Direction Image, After classification using Analyst’s expertise and knowledge of TC. (b)
Masked classified direction changes, showing changes over true colour image of Date 1.
Figure 5: (a) Change detection from Direction image (b) Percentage of change in different classes.
The loss of 572.85 hectare account for total loss of 24,678.35 tonne of carbon sequestration per year,
and if this camp exists for 3 years, and reforestation initiatives need 5 years to revive the loss vegetation,
it would account 197,427 tonne-carbon sequestration loss. As mentioned earlier with account for
20USD per tonne-carbon, the loss would be 493,567 USD/year (365,288.94 GBP, 1 USD = 0.74 GBP),
and total loss in eight years would be 1,579,416 USD (1,167,204.22 GBP).
4. Conclusion and Future Works
Rohingya exodus has created massive humanitarian crisis, and the value of human lives are
unmeasurable in this context. Bangladesh has done a great job in saving lives of millions, and the
environmental cost associated with this is low, compared to the total value of saving the lives. However,
it is critical to understand that, displacement of such larger population causing immediate and also long
term negative impacts on the environment, and this need careful examination to create pressure on the
authorities to resolve such situation. In this regard, use of remote sensing could be very effective and
timely, despite the fact this study provided a crude estimation. Further studies may improve the cost
estimation by integrating ground water resource depletion, using temporary waste generation and
further validate the results applying proper accuracy assessment.
5. Biography
S M Labib is a first year PhD student at SEED, University of Manchester. His research interest include
environmental management, use of GIS and RS in monitoring ecosystem services, and Urban Green
Infrastructure.
Nazia Hossian is a Lecturer, at Dept. of Urban & Regional Planning in RUET. Her research interest
include disaster management, conflict and crisis analysis, and Urban Planning.
Shahadath Hossain Patwary is an Assistant Urban Planner. His research interests are urban housing,
and environmental management.
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

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1-14.
... As of April 2019, more than 610,372 refugees were accommodated in that camp, and the number is growing each day (see Figure 1). The vegetative cover of Ukhiya-Teknaf is under severe pressure due to the unregulated expansion of refugee settlements [2,4,9,15]. The rapid conversion of lands with vegetative cover into refugee settlements is the principal agent of environmental degradation and has caused the most significant large-scale land cover changes in Ukhiya-Teknaf in recent times [14]. ...
... With the intention of calculating the vegetative cover loss and territorial expansion of the Rohingya refugee settlement in Teknaf, Hasan et al. (2018) created multiple ring buffer zones centered on the three pre-existing refugee camps to narrow down their analysis [2]. However, previous studies did not attempt to define the actual vegetative cover changes among all thirty-four refugee camps, which is The vegetative cover of Ukhiya-Teknaf is under severe pressure due to the unregulated expansion of refugee settlements [2,4,9,15]. The rapid conversion of lands with vegetative cover into refugee settlements is the principal agent of environmental degradation and has caused the most significant large-scale land cover changes in Ukhiya-Teknaf in recent times [14]. ...
... Imtiaz (2018) assessed the impact on the vegetative cover by displaced refugees on the Teknaf peninsula, estimating a total vegetative cover loss of 1284.48 hectares between 2014 and 2017 [4]. Labib et al. (2018) determined vegetative cover and estimated carbon emission losses in the Kutupalong-Balukhali expansion site: 572 hectares of vegetative cover were deforested to set up camps between 2016 and 2017, which accounted for an approximate loss of 365,288 Great British Pounds (GBP) per year [15]. ...
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The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple consequences. Two biophysical variables, NDVI, positively correlated with vegetation cover, and albedo, positively correlated with cover of exposed sands, were computed from a time series of merged NOAA-AVHRR and MODIS images (1981 to 2010). Generally, throughout the study period, NDVI increased and albedo decreased. Improved understanding of spatial and temporal dynamics of these environmental processes was achieved by using the Change Vector Analysis (CVA) technique applied to NDVI and albedo data extracted from four sets of consecutive Landsat images, several years apart. Changes were detected for each time step, as well as over the entire period (1978 to 2007). Four categories of land cover were created-vegetation, exposed sands, water bodies and wetlands. The CVA's direction and magnitude enable detecting and quantifying finer changes compared to separate NDVI or albedo difference/ratio images and result in pixel-based maps of the change. Each of the four categories has a biophysical meaning that was validated in selected hot-spots, employing very high spatial resolution images (e. g., Ikonos). Selection of images, taking into account inter and intra annual variability of rainfall, enables differentiating between short-term conservancies (e. g., drought) and long-term alterations. NDVI and albedo, although comparable to tasseled cap's brightness and greenness indices, have the advantage of being computed using reflectance values extracted from various Landsat platforms since the early 1970s. It is shown that, over the entire study period, the majority of the Mu Us Sandy Land area remained unchanged. Part of the area (6%), mainly in the east, was under human-induced rehabilitation processes, in terms of increasing vegetation cover. In other areas (5.1%), bare sands were found to expand to the central-north and the southwest of the area.
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Change vector analysis (CVA) is a robust approach for detecting and characterizing radiometric change in multispectral remote sensing data sets. CVA is reviewed as a useful technique to: (1) process the full dimensionality of multispectral/multi-layer data so as to ensure detection of all change present in the data; (2) extract and exploit the 'components' of change in multispectral data; and (3) facilitate composition and analysis of change images. Examples drawn from various projects are included throughout this methodological discussion, in order to illustrate the CVA approach and suggest its potential utility.
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