GeoJournal (2023) 88:425–448
https://doi.org/10.1007/s10708-022-10609-4
Simulating future intra‑urban land use patterns
of a developing city: a case study of Jashore, Bangladesh
Syed Riad Morshed · Md. Abdul Fattah · Md. Mojammel Hoque · Md. Razzakul Islam ·
Fahmida Sultana · Kaniz Fatema · Md. Fazle Rabbi · Asma Amin Rimi · Fahmida Yeasmin Sami ·
F. M. Rezvi Amin · Musfiqur Rahman Chowdhury Seam · Mehedi Hasan Sizan · Mahamudul Hasan ·
Md. Nazmul Haque
Accepted: 9 February 2022 / Published online: 5 March 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022, corrected publication 2022
Abstract Increasing urban growth at an unprecedented rate entails adverse implications for societal,
economic, and environmental sustainability. In the
cities of Bangladesh, the land covers are experiencing
rapid construction-associated land expansion, population growth, and socioeconomic development. Comprehensive assessment and understanding of the prospects for rapid land use/land cover (LULC) changes
are essential for managing land surface resources and
ensuring sustainable development. Therefore, this
study aims to assess the historical land use/land cover
(LULC) changes and simulate future potential intraurban LULC growth patterns of Jashore City up to
2050. We used (i) Landsat images to analyze LULC
change using maximum likelihood supervised image
classification method; (ii) Markov-CA model to illustrate the LULC transition matrix during 2000–2020,
(iii) Multilayer Perception Neural Network Markov
Chain (MPNNMC) Model to simulate future LULC
patterns. The result shows that built-up area expanded
quickly, while cropland and water areas have had a
large loss of coverage. The LULC change analysis
S. R. Morshed · M. A. Fattah (*) · M. R. Islam ·
F. Sultana · K. Fatema · M. F. Rabbi · A. A. Rimi ·
F. Y. Sami · F. M. Rezvi Amin · M. R. C. Seam ·
M. H. Sizan · M. Hasan · M. N. Haque
Department of Urban and Regional Planning, Khulna
University of Engineering and Technology, Khulna,
Bangladesh
e-mail: mafattah.kuet@gmail.com
F. Y. Sami
e-mail: fahmida.s32@gmail.com
S. R. Morshed
e-mail: riad.kuet.urp16@gmail.com
M. R. Islam
e-mail: razzakul152@gmail.com
F. Sultana
e-mail: fahmiiidatasnim@gmail.com
K. Fatema
e-mail: kanizfatema1013@gmail.com
M. F. Rabbi
e-mail: azlerabbi.fearless@gmail.com
F. M. Rezvi Amin
e-mail: rezviamin.kuet@gmail.com
M. R. C. Seam
e-mail: musfiqseam@yahoo.com
M. H. Sizan
e-mail: mehedihasansizan@yahoo.com
M. Hasan
e-mail: offpvl16@gmail.com
M. N. Haque
e-mail: nhaque.kuet13@gmail.com
M. M. Hoque
Department of Civil Engineering, Chittagong University
of Engineering and Technology, Chattogram, Bangladesh
e-mail: hmojammel40@gmail.com
A. A. Rimi
e-mail: asmaamin97@gmail.com
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derived from prior LULC was utilized for future
simulations, where natural and anthropogenic factors
were chosen as the driving variables in the MPNNMC model. The future LULC modeling shows
that compared to 2020, the urban area is expected to
increase by 23.64%, whereas cropland, vegetation,
unused land, and water areas are expected to reduce
by 1.16%, 5.47%, 9.55%, and 7.73% respectively, by
2050. The change analysis shows that urban areas will
increase the fastest during 2020–2030. The findings
demonstrate that the rapid and unplanned urbanization and the rise of the population due to migration
resulted in the fastest LULC transformation. The
study findings contribute to the long-term ecological
development of Jashore City and potentially enhance
environmental decision making.
Keywords Urban land cover growth pattern · Land
cover modeler · Jashore city · LULC transition
Introduction
The enormous urbanization of the cities around the
world is a global concern that creates challenges and
opportunities for long-term sustainability (Gómez
et al., 2020). In the last century, continuous urbanization was accomplished under the distinctive influence
of population growth, migration, and continued development. The sustainable development of the cities of
Bangladesh has mainly been hindered by uncontrolled
and unplanned rapid population growth and urbanization (Kafy et al., 2021a). Over the last few decades,
the cities in Bangladesh have experienced one of the
fastest urbanization rates in the world (Faisal et al.
2021a). Migration of people to urban areas accelerates urban growth and creates problems such as environmental degradation (Kafy et al., 2020), poverty
traps (Grant, 2010), informal settlements, air, and
noise pollution, fast-spreading disease (Moore et al.,
2003), and the economic costs of increasing public
infrastructure (Gómez et al., 2020). These phenomena
have been observed in the major cities of the country
which are emerging as unplanned cities, mostly dominated by overcrowded and haphazard settlements. For
sustainable urban planning, environmental and ecological resource management, it is necessary to consider the spatiotemporal changes in urban land use
and land cover (LULC) and especially the prediction
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of future potential urban growth (Aburasa et al.,
2016; Al-Darwish et al., 2018; Bihamta et al., 2015;
Feng et al., 2018). Moreover, the complex structure
of the LULC pattern must be understood to accurately
simulate future urban growth.
City provide many opportunities, such as access
to basic service facilities, health, financial, entertainment, educational, and employment opportunities with huge wages (Ahrend et al., 2014; Moore
et al., 2003). This has significantly accelerated global
urbanization over the past few centuries. The urban
growth prediction model helps local authorities and
policymakers to determine strategies for controlling
urban growth and environmental changes and also
estimates the investment needed for public infrastructure and sustainable urban development (Maarseveen et al., 2018; Weng, 2012). Many researchers
around the globe have indicated the study of urban
growth for many developed and developing countries
(Aithal et al., 2018; Al-shalabi et al., 2013; Gómez
et al., 2020; Herold et al., 2003; Rimal et al., 2018).
Cities consume the surrounding lands and areas as
they grow. Therefore, urban planners, policymakers,
and engineers need to develop methods to assess and
simulate future urban growth, regarding the future
consequences of LULC, to prevent communities from
building in protected or conserved areas or areas vulnerable to hazards.
In recent years, several studies have witnessed the
rapid urban expansion in both urban and rural areas
of Bangladesh. About 809 ­km2 of cropland in Bangladesh is declining (Gazi et al., 2020). According to
Rai et al. (2017) the forest land cover area percentage
in Bangladesh declined from 15.7% to 9.5% during
1930–2014, beel and haor area increased from 1.66%
to 1.72% during 1976–2010, urban area increased
from 23.6% to 28.4% during 2000–2011 (Hasan
et al. 2013). The average vegetation cover loss was
calculated 66.87 k­ m2 between 1989 and 2009. Several studies of LULC change analysis revealed that
the waterbody area percentage in the urban areas of
Bangladesh declined rapidly (Gazi et al., 2020; Kafy
et al., 2020; Morshed et al., 2021) while increased in
the rural areas, especially in the southwestern regions
due to the shifting of rice field to shrimp farms (Fattah et al., 2021b; Islam et al., 2015; Khan et al.,
2015).
Bangladesh is a developing country. According to
the General Economics Division (2020), Bangladesh
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will experience a rapid and transformational change
in agriculture, education, trade and industry, transportation, healthcare, and communication. The current government aims to reach the title of developed
country by 2041, and in this regard, "Perspective Plan
2041" has been adopted. Various infrastructures, such
as roads, bridges, offices, buildings, factories, and
industries, are being built in the cities and towns of
the country, and this has accelerated the urbanization rate throughout the country (Hassan & Nazem,
2016; Shubho & Islam, 2020). Topics related to
LULC change, such as simulation of future potential
LULC patterns and their consequences, have recently
attracted interest from a wide range of literature
(Araya & Cabral, 2010; Han et al., 2015; Kafy et al.,
2020; Maduako et al., 2016). Though there have
been many studies on LULC change and its impact
on the contest of the cities of Bangladesh, there have
not been sufficient studies on urban growth prediction. Previous studies, related to LULC simulation,
have focused on urban growth in Dhaka (Ahmed &
Bramley, 2015; Pramanik & Stathakis, 2015; Shubho
& Islam, 2020) and Chittagong (Hassan & Nazem,
2016), but the assessment of urban growth patterns
and prediction of other cities has not been studied in
the previous literature.
In this study, we identified the historical urban
growth pattern, transformation and direction of the
built-up land expansion of Jashore City during 2000
– 2020 to model the future intra-urban land use pattern (FILUP) of 2030, 2040, and 2050. The FILUP
modelling is performed by using the natural and
anthropogenic variables in the MPNNMC model. The
study contributes to the planning and implementation
of many short- and long-term development plans for
Jashore City and its environs. The simulated spatial
complex variation in LULC can assist policymakers
and planners in designing sustainable plans for the
cities of Bangladesh by providing a holistic understanding of the current scenario in relation to future
expansion possibilities and environmental safety.
Literature review
Urban population increase has had a massive impact
on the entire world to date, resulting in massive
changes in LULC, which has become a key concern
in natural resource management and sustainable
427
development in cities (Al-Hameedi et al. 2021). A
large amount of land covers is being transformed
and substituted into built-up land for fulfilling the
demands of residential, commercial, industrial and
other demands (Somvanshi et al., 2020). The term
"LULC change" is used to describe how human
activities alter the Earth’s terrestrial surface (Gazi
et al., 2020). While humans have been altering land
for thousands of years to obtain livelihoods and other
necessities, the rate, intensity, and extent of LULC
transformation are much greater now than they were
earlier. These changes are causing unprecedented
changes in ecosystems and environmental processes
at the local, regional, and global levels (Fattah et. al.
2021b). The study of LULC change is regarded as a
significant aspect of observing parameters that are
accountable for overall changes (Li et al., 2017; Nath
et al., 2018; Tendaupenyu et al., 2016), landscape
changes (Li & Liu, 2017; Ying et al., 2017), ecosystem change, landscape fragmentation (Nagendra
et al., 2004; Nurwanda et al., 2015), urbanization
(Ahmed, 2011), climate change (Morshed & Fattah,
2021), sustainable development (Boadi, et al., 2005),
and risk assessment at the city level. Therefore, the
effective use and management of land resources is
important for sustainable development from environmental and socioeconomic aspects.
Currently, GIS and remote sensing technologies
can provide advanced, more powerful and accurate
tools to aid in better monitoring and understanding
the spectral, spatial and temporal characteristics of
LULC changes at regional, national and global scales
(Hasan and Nazem, 2015). The LULC changes are
identified through the image classification process.
Image classification is a broad category of digital
image processing techniques that involve the automatic grouping of pixels into specified categories
(Kafy et al., 2020). The images have been classified
by (i) supervised image classification method (SICM)
and (ii) unsupervised image classification method
(UICM). In SICM, some training pixels are selected
from each of the classes in the first step, which is
called the training stage. Image classification can be
achieved by identifying the characteristics of training pixels and then looking for other pixels with the
same features (Hassan & Southworth, 2017). There
is no need for a training stage in UICM, but different algorithms are used for clustering. The supervised
image classification method is the most popular due
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Fig. 1 Study area map of Jashore City, Bangladesh
to more accuracy of image classified data (Hassan
et al., 2016; Simwanda et al., 2021). The classification results are influenced by several factors, including the classification goal, the spatial and spectral
characteristics of the data, the natural variability of
terrain conditions in the geographic area, and the digital classification technique used. The availability of
high-quality remotely sensed imagery and ancillary
data, the design of a proper classification procedure,
and the analysts’ skills and experience are all important factors in the performance of image classification
(Gazi, et al., 2020; Hu, et al., 2019).
Several models have been introduced to predict
LULC change in the GIS environment, such as Cellular Automata (CA) model (Clarke & Hoppen, 1997),
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Land Transformation Model (LTM) (Pijanowski et al.,
2014), Conversion of Land Use and its Effects (CLUE)
model (Verburg et al., 2006), Weights of Evidence
(WE) model (Amato et al., 2014), Logistic Regression Model (Arsanjani et al., 2013), Frequency Ratio
(FR) model (Park et al., 2011), Urban Growth Potential
(UGP) model (Kong et al., 2012), Fuzzy Logic model
(Grekousis et al., 2013), etc. The Artificial Neural Network (ANN) based CA model is the first theoretical
model to simulate potential urban growth and develop
and increase (i) spatiality, (ii) dynamics, (iii) simplicity, (iv) visualization accuracy, (v) linked macro to
micro approaches, and (vi) integrated GIS and remotesensing techniques (Aburasa et al., 2016; Kafy et al.,
2021b). The CA model has a high computational
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Table 1 Information about
the used Landsat images in
the study. Source: USGS,
2020
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Satellite/sensor Path/row Spectral resolution
Resolution Date
Cloud coverage
LANDSAT 5
LANDSAT 5
LANDSAT 8
30 m
< 10%
138/44
138/44
137/44
efficiency and spatial resolution since models of this
kind are capable of simulating with the assumption that
past urban growth will influence future urbanization
patterns based on local land-use interactions (Santé
et al., 2010). The main advantages of the CA model are
the open scalability of the model structure, its integration with other models, and its ability to simulate both
temporal and spatial patterns (Clarke & Hoppen, 1997;
He et al., 2008, 2018). In this study, the Multilayer Perception Neural Network Markov Chain (MPNNMC)
model was used to simulate future potential urban
land use patterns. The ANN consists of a neural network approach to multilayer perceptron (MLP) that
decides automatically about network parameters and
their changes for an accurate future model (Kafy et al.,
2021; Kafy et al., 2021; Kafy, Rakib, et al., 2021). Subedi et al. (2013) and Simwanda et al. (2021) explained
the MPNNMC model as the most effective model for
simulating future spatiotemporal changes with highprecision LULC transformation. This model is also
used to predict future potential environmental and ecological change (Kafy, Rakib, et al., 2021; Zhang et al.,
2019). Researchers have explained the LULC prediction model as the most reliable and accurate for urban
growth prediction in the previous literature as spatial,
environmental, and ecological factors and GIS and
remote-sensing techniques can be considered in this
model (Aburasa et al., 2016; Al-Darwish et al., 2018;
Bihamta et al., 2015; He et al., 2018; Kafy et al., 2020).
A few researches have conducted the intra-urban
LULC simulations in Bangladesh including (Kafy
et al., 2020, 2021b; Hoque et al., 2020; Dey et al.
2021) and most them are based on the four major
Table 2 Description of
selected five LULC types in
this study
Multispectral (6 Bands)
Multispectral (11 Bands)
17/03/2000
06/03/2010
30/03/2020
cities of Bangladesh, likely Dhaka, Chattogram,
Khulna and Rajshahi. Previous research in this context has focused on either evaluating the drivers of
unplanned growth or monitoring and mapping urban
growth. We have considered the study of monitoring and mapping urban growth, urban growth direction analysis, analyzed the drivers of urban growth,
identified the future potential urban LULC scenario
and future intra-urban LULC growth direction for
2030, 2040, and 2050. Our study will be useful in
future master plan projects for Jashore City.
Methodology
Study area
Jashore City, an increasing center of southwestern region of Bangladesh, under Khulna Division
(Fig. 1). The city located on the bank of Bhairob river
and a major industrialized and commercial center of
Jashore District. Jashore district now covers a total
area of 2578.20 sq km, with Jashore City covering a
total area of 25.72 sq km, and the population was estimated to be around 237,478 which is half of the entire
district’s population. The annual rainfall is 1537 mm
and the temperature varies from a minimum of 1­ 10C
to a maximum of ­370C. Jessore is well-connected to
both Bangladesh and India via highways. Jessore is a
station on the Western Bangla Railway’s broad-gauge
network. The Bangladesh Air Force uses the Jessore airport, which is located near Jessore city. This
LULC types
Description
Built-up/ Urban area
Constructive lands, industrial, residential and commercial
areas, infrastructures, road network etc
Bare soils, playground, unused land, open space, landfill sites
Agricultural/ crop land, grasslands
Trees, vegetation and forest areas
Wetlands, river, canals, ponds and lakes
Unused land
Crop land
Greeneries
Water area
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city’s road transportation system includes rickshaws,
automobiles, buses, trucks, and other vehicles (BBS,
2013)(BBS, 2013). Over the past few decades, rise of
temperature and steepness in the northwestern part of
the country have greatly reduced the crop production.
In the last three decades the district’s intercommunication system has been enormously developed. The
development project and steepness has been influencing the massive LULC change in the study area.
Data collection
In this study, remotely sensed Landsat data for a specific period were used to analyze the LULC changes.
Three Multi-spectral Landsat images were collected
from the United States Geological Survey (USGS)
for the year 2000, 2010, and 2020 to illustrate the
LULC dynamics. These three satellite images collected were taken in the same month (March) to
avoid the seasonal effects. Due to less possibility
of rainfall in Jashore during March, the accuracy or
acceptability of the LST result is higher than in other
months. The maximum cloud coverage was set to less
than 10% during collecting the images. The detailed
information of the images is shown in Table 1.
LULC classification
LULC classification is one of the key remote sensing applications to identify the characteristics of
land use by using multispectral satellite imagery.
Remotely sensed images are classified by supervised
and unsupervised methods (Koko, 2020). In this
study, we have applied the maximum likelihood classifier (MLC) method, which is mostly used for image
classification due to its availability, simplicity and
could produce LULC maps with high classification
accuracies (Li et al. 2014). Before image classification, the preparation of satellite images is essential to
avoid these errors and to create a closer link between
the data obtained and biophysical characteristics on
the ground (Alawamy, 2020). Radiometric, atmospheric and geometric corrections have been done to
fill the image gap and enhancement. In this study,
the generation of composite band combinations such
as natural color, true color, and false color composite, etc. is used to identify the land use type in the
study area (Kafy et al., 2020). During data processing, bands such as red, green, blue, and near-infrared
bands were utilized for Landsat images to find true
color in ERDAS Imagine 2014. The LULC type was
classified into five classes (Urban area, unused land,
crop land, water areas, and greeneries) by applying the MLC method (Bishta, 2018). Table 2 represents the land use types and criteria which was
considered for classifying the land cover types in
this study. Bands were used 4–3-2 (Color Infrared)
and 3–2-1 (True Color) for Landsat 5 images, 4–3-2
(True Color), and 5–4-3 (Color Infrared) for Landsat
8 images. Then the land cover type change direction
during the study period 2000 to 2020 has been analyzed in ArcMap 10.6 version.
To check the accuracy of the classification, more
acceptable and best quantitative image classification
accuracy measurement technics named Kappa statistics and confusing matrix were calculated (Janssen &
Wel, 1994; Saputra & Lee, 2019; Yadav & Congalton, 2019). This study also calculated user accuracy,
producer accuracy, and overall accuracy to check the
accuracy of the LULC model. In this regard around
50 training sites have been randomly chosen for each
image to ensure that each LULC type is covered by
all spectral groups. Equation 1–4 is utilized for a different type of accuracy assessment.
∑
Totalsamplenumber × Totalcorrectedsamplenumber − (col.tot × rowtot)
× 100%
Kappacoefficient =
∑
Totalsamplenumber2 − col.tot × rowtot
Useraccuracy =
Num. of correctly classified pixels in each category
× 100%
Total numb of reference pixels in each category (row total)
Produceraccuracy =
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Numofcorrectlyclassifiedpixels(diagonal)
× 100%
Totalnumbofreferencepixelsineachcategory(column)
(1)
(2)
(3)
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Overallaccuracy =
431
Total num of corrected classified pixels (diagonal)
× 100%
Total num of reference pixels
The value of the overall accuracy was found
93.33%, 93.33%, and 96.67% for the year 2000,
2010, and 2020 respectively and the Kappa coefficient values are 0.9143, 0.915, and 0.9577 which
indicates the good accuracy of the classified data
and suitable for urban expansion detection. Also,
the results meet the recommended Kappa Statistics
values recommended in the literature (Jansse & van
der Wel, 1994; Saputra & Lee, 2019; Yadav & Congalton, 2019).
LULC change analysis using LCM
LULC change detection indicates the process of the
identification and determination of the nature and
extent of the LULC change over a period of time
using remote sensing imagery (Meng et al., 2017). In
this study, the LULC map of Jashore was extracted for
2000, 2010 and 2020 to detect the changes of LULC
over two periods of 2000–2010 and 2010–2020 and
also calculated the increase, decreases in each LULC
types. The LCM is the most used models for LULC
change detection and prediction that is accessible in
the TerrSet software. The LCM is a software-based
solution, developed to identify the growing challenges
of LULC and analytic requirements for the conservation of biodiversity (Kumar et al., 2015). A review of
the recent literatures suggests that the LCM model of
TerrSet that integrates the Markov Chain-based neural network (MCNN), is very effective in predicting
future LULC (Shahi et al., 2020). Based on the LULC
change analysis, the transition of one LULC type to
another was identified, along with gains of losses by
LULC types. The spatial pattern of LULC change
was determined through Eq. 5 (Mannan et al., 2019).
LULCCi =
LULCi,j� × LULCAi
LULCi,j
×
1
× 100%
j × j�
(5)
where LULCCi = LULC change, LULCi,j = LULC
area at an earlier date, LULCi,j′ = LULC area at a later
date, LULCAi = Areas not changed.
The urban growth rate of the study area has been
calculated through Eq. 6 (Zhou et al., 2019).
Urban growth rate(% ) =
(4)
UAj − UAi
UAi
× 100%
(6)
where UAi denotes the urban area in the initial year,
UAj denotes the urban area in the final year.
Urban growth prediction using CA based MPNNMC
Model
The LULC change analysis, simulation and future
potential urban growth prediction have been conducted by using Land Change Modeler, an integrated
software presented in IDRISI. The LCM model
includes a set of tools for analyzing and modeling
future LULC changes (Tewolde & Cabral, 2011). In
this study, the LCM is used to implement the Neural Network Markov (NNM) model. The LCM is one
of the most widely used simulating systems and is
widely used in studies related to modeling and simulating future potential urban expansion (Mishra &
Rai, 2016; Iizuka et al. 2017; Ranagalage et al., 2019;
Gong et al., 2017). The NNM model requires four
steps, or phases, to follow for urban growth prediction. To begin, (i) factors or model driver variables
are chosen, (ii) transition-potential modeling and
simulation is performed, and (iii) model validation
and calibration of the model and actual pattern with
the Kappa Index is performed (Aburasa et al., 2016;
Al-sharif & Pradhan, 2014).and (iv) the creation of
scenarios and simulations of potential future urban
expansion (Simwanda et al., 2021).
Researchers use LULC change models to detect
and predict future potential LULC scenarios. The
Land-use Change Modeler (LCM) is the most commonly used model to illustrate the LULC change
analysis (Shahi et al., 2020). The artificial neural
network (ANN) method, whose most popular model
is the multilayer perceptron algorithm, is a wellknown method for analyzing satellite data (Fathollahi Roudary et al. 2018). The Multilayer Perception
Neural Network Markov Chain (MPNNMC) Model
can manage nonlinear functions, perform model-free
function estimation, learn from previously unknown
data relationships, and generalize unknown circumstances. As a result, ANNs are useful tools for
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Fig. 2 GIS-based land cover distribution in Jashore in A 2000, B 2010, and C 2020
Table 3 Landcover area
(in acres) in Jashore City
during 2000–2020
CL crop land, UA urban
areas, UL unused land, GE
greeneries, WA water area,
UG urban growth
LULC types
CL
UA
UL
GE
WA
Total
2000
2010
13
Change rate in %
Area
%
Area
%
Area
%
2000–2010
2010–2020
1143.04
1829.23
534.53
1636.88
1021.58
6165.26
18.54
29.67
8.67
26.55
16.57
100%
662.15
2353.28
855.12
1912.46
382.25
6165.26
10.74
38.17
13.87
31.02
6.2
100%
391.41
2555.17
1055.14
1592.01
571.53
6165.26
6.35
41.44
17.11
25.81
9.27
100%
-42.07
28.65
59.98
16.84
-62.58
-40.89
8.58
23.39
-16.76
49.52
modeling intra-urban LULCs, especially when the
underlying complex relationships in the data are
unknown (Arsanjani et al., 2013). Markov models, on
the other hand, are commonly used as excellent projectors of potential LULCs. The Markov model is a
stochastic model that describes the likelihood of one
state transforming into another. The Markov model
not only describes how to quantify conversion states
between land-use forms but can also show the transition rate between them (Sang et al., 2011). As a
result, combining the ANN and Markov models is a
reliable approach for modeling and simulating potential intra-urban expansion (Simwanda et al., 2021).
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LULC change depends on several factors. For the
illustration of each LULC transition, choosing the
best set of explanatory variables is most important
in LULC modelling to ensure the best fit between
the empirical data and the observable reality (Simwanda et al., 2021). As there are no fixed universal
variables, several researchers have shown that the
selection of LULC modeling drivers varies for different landscapes and for different study areas (Thappa
and Murayama, 2012; Zhang et al., 2018; Iizuka
et al. 2017; Simwanda et al., 2021). Although in most
of the studies, similar variables have been used for
which the results differed (Basu et al., 2021). Therefore, the driver variables are selected on the basis of
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the characteristics of the study area, observed LULC
changes, and based on expert knowledge about the
study areas. In this study, factors such as slope, DEM,
LULC patterns of the observed period, latitudes, and
433
longitudes were considered to simulate future urban
growth. For the prediction of the future LULC pattern, the LULC maps of the year 2000, 2010, and
2020 were used as independent variables based on
Fig. 3 LULC Change
analysis during 2000–2020
Fig. 4 Spatiotemporal urban areas’ transition pattern during: A 2000–2010, B 2010–2020
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Fig. 5 Spatial illustration of LULC conversion in Jessore during 2000–2020
the recent studies done in different cities in Bangladesh (Ahmed, 2012; Kafy et al., 2020; Hasan et al.
2017). The slope and elevation data used in this study
were derived from Google Earth. The urban disturbance, the distance of locations from the road were
calculated from the GIS data, collected from Khulna
City Corporation’s authorities. The data used for the
other factors in this study was derived from land-use
classification.
We have used Neural Network Markov Chain
(MPNNMC) model to estimate future potential urban
growth of Jashore City. The MPNNMC is a form of
ANN that uses back-propagation algorithm and is
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the most commonly used simulating model to simulate potential LULC transition (Ahmed, 2012). The
MPNNMC includes 3 layers, one input layer, one
or several hidden layers, and one output layer. The
input layer represents input data to the network during training of the model. The data are received by
the hidden layers, which extract and express information as weights from the input layers. The output layer
results depend on the back-and-forward interactions
between the three layers. In order to create the neural network–Markov modeling framework, we combined the MPNNMC and Markov chain models provided by LCM (Zhang et al., 2018). In the LCM, the
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Table 4 LULC conversion
matrix of Jessore during
2000–2020
435
LULC types
CL
UA
2000–2010 (Area in percentage)
CL
4.05%
5.04%
UA
2.48%
31.06%
UL
1.08%
1.26%
GE
0.81%
1.42%
WA
0.62%
6.86%
Total
9.05%
45.64%
2010–2020
CL
1.41%
3.03%
UA
1.03%
32.32%
UL
2.18%
2.33%
GE
1.43%
2.66%
WA
0.28%
1.10%
Total
6.34%
41.45%
2000–2020
CL
2.52%
4.17%
UA
0.74%
31.48%
UL
1.64%
1.14%
GE
0.94%
0.71%
WA
0.50%
3.95%
Total
6.34%
41.45%
multivariable function MPNNMC includes the driver
variables to predict the transition potential of LUCs
in any location between two points. The intra-urban
LULC changes of Jashore during 2000–2020 have
been used in the MPNNMC model to simulate future
potential urban growth.
To check the accuracy of the simulated model,
percentage error, accuracy percentage were estimated and also calculated correlation coefficient (R)
and RMSE by Eqs. 8 and 7, respectively. For model
validation, a statistical similarity was performed
by comparing the predicted and observed results of
LULC for the year 2020. To check the accuracy of
the prediction model, the spatiotemporal matched
LULC classes and non-matched LULC classes were
also identified by comparing the raster data set of the
actual and predicted LULC maps for the year 2020.
The higher the value of accuracy and lower the value
of percentage error, the RMSE value indicates the
UL
GE
WA
Overall
4.96%
1.68%
4.73%
0.59%
0.26%
12.21%
5.87%
1.55%
1.52%
15.06%
3.33%
27.32%
0.24%
1.05%
0.08%
0.09%
4.32%
5.77%
20.16%
37.81%
8.67%
17.97%
15.39%
100.00%
2.45%
4.99%
4.88%
3.73%
0.67%
16.72%
1.74%
5.43%
2.69%
15.36%
0.59%
25.82%
0.41%
1.86%
0.12%
4.13%
3.13%
9.66%
9.05%
45.64%
12.21%
27.32%
5.77%
100.00%
6.66%
3.21%
3.93%
1.51%
1.41%
16.72%
6.43%
1.66%
1.89%
12.35%
3.50%
25.82%
0.37%
0.73%
0.07%
2.47%
6.03%
9.66%
20.16%
37.81%
8.67%
17.97%
15.39%
100.00%
best-fitted prediction model (Shamshirband et al.,
2020; Shatnawi & Qdais, 2019).
�
�2
∑�
Tactual − Tsimulated
(6)
RMSE =
n
� �
�
∑�
Tactual − T � actual × Tsimulated − T � simulated
R= �
�2 �∑ �
�
�2
Tactual − T � actual ×
Tsimulated − T � simulated
(7)
R and RMSE are the most commonly used metrics in
geosciences to evaluate model performance (Mansour
et al., 2020; Saputra & Lee, 2019). The RMSE value
close to zero indicates the best performance of the
model, while the + 1 value of R represents the perfect
positive correlation between the observed and simulated variables.
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Fig. 6 Spatiotemporal LULC urban growth direction analysis during: A 2000–2010, B 2010–2020, C 2000–2020
Results and discussion
Spatial distribution of LULC types—2000 to 2020
The key causes of LULCC are rapid population
growth in cities, migration from rural to urban areas,
conversion of suburban areas to urban areas, a lack
of evaluation of ecological services, ignorance, biophysical limitations, and the use of ecologically mismatched technologies (Hyandye et al., 2015). We
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extracted the LULC maps for 2000, 2010, and 2020
by using ERDAS Imagine and ArcGIS software. Figure 2 shows strong evidence of changing landscape
patterns over the last two decades in Jashore City
for each LUCC type. The land cover areas covered
by different LULC types such as water areas, urban
areas, greeneries, cropland and unused land cover are
expressed in acres. Table 3 represents the statistics of
different LULC cover areas in the study area during
2000–2020.
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437
Fig. 7 Simulated future LULC pattern of Jashore city by A 2030, B 2040, and C 2050
In accordance with the LULC analysis in Table 3
Table 5 Validation of the
urban growth prediction
model
LULC types
CL
UA
UL
GE
WA
Total
LULC type in 2000. About 29.67% (1829.23 acres)
LULC area in 2020
(acres)
Actual
Predicted
Difference
391.41
2555.17
1055.14
1592.01
571.53
6165.26
388.22
2569.61
1072.44
1583.94
551.04
6165.26
3.19
− 14.44
− 17.30
8.07
20.49
–
shows that urban areas were the most prominent
Table 6 Simulated future
LULC statistics of Jashore
during 2030–2050
LULC types
CL
UA
UL
GE
WA
Total
RMSE
R2
Accuracy (%)
0.01
0.03
0.03
0.02
0.04
–
0.92
0.86
0.83
0.89
0.82
–
96
86
84
90
82
87
of total areas were extracted under urban areas.
Area in percentage
Change in %
UG rate in %
2030
2040
2050
2020–2050
2000–2050
2020–2050
5.64%
59.84%
8.80%
23.19%
2.54%
100%
5.36%
62.99%
8.02%
21.59%
2.04%
100%
5.19%
65.08%
7.56%
20.34%
1.84%
100%
− 1.16%
+ 23.64%
− 9.55%
− 5.47%
− 7.43%
-
− 14.79%
26.52%
− 0.79%
+ 2.31%
− 13.22%
-
− 20.57%
39.51%
− 108.52%
− 23.59%
− 292.52%
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438
Sequentially about 26.55% (1636.88 acres), 10.74%
(1143.04 acres), 16.57% (1021.58 acres) and 8.67%
(534.53 acres) of the total areas were for greeneries, cropland, water, and unused land in the year
2000. A moderate change in different LULC has
been observed during the years 2000–2010. The
percentage of areas for LULC types such as cropland and water were reduced. In 2020, croplands
were observed at 6.35% (391.41 acres), urban areas
were 41.44% (2555.17 acres), vegetative areas were
25.81% (1592.01 acres), water areas were 9.27%
(571.53 acres), and unused lands were 17.11%
(1055.14 acres) of the total study area. The highest
declination rate was found (32.9% per decade) for
crop land area over the past two decades, while in
the case of water areas, this rate was 22% per decade. The area of urban areas is increasing by an
average of 48.7% per decade as compared to the
past decade. This indicates the huge urban growth
trends in the study region for the past two decades.
Urban growth analysis—2000 to 2020
Two changing trends in LULC classes were identified (Fig. 2); firstly, the increase of urban areas,
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greeneries, and unused areas; and secondly, the declination of cropland and water areas at a tremendous
rate over the study period (Fig. 3). Figure 4 represents
the spatiotemporal urban growth transition, and Fig. 5
represents the spatiotemporal gain loss maps of all
LULC classes over the study period. The Markov-CA
LULC transition matrix is presented in Table 4.
The result shows that the percentage of the urban
area increased by 11.77% (from 1829.23 to 2555.17
acres) and unused land by 8.44% (from 534.53 to
1055.14 acres) during 2000–2020 (Fig. 3). Correspondingly, the cropland area decreased by 12.19%
(from 1143.04 to 391.41 acres), the greeneries areas
by 0.74% (from 1636.88 to 1592.01 acres), and the
water areas by 7.30% (from 1021.58 to 571.53 acres).
Figure 4 shows that during 2000–2010, about 8.50%
and during 2010–2020, about 3.27% of the cropland
was converted to urban areas. About 10.37% and
3.07% of water areas were converted to urban areas
during 2000–2010 and 2010–2020, respectively. The
urban growth rate was 28.65% between the years
2000 and 2010 and 8.58% from 2010 to 2020.
Table 4 shows that the LULC transition in Jashore
City has undergone a significant change since 2000.
During 2000–2020, about 15% of water areas have
Fig. 8 The transition map showing the spatiotemporal urban growth during: A 2020–2030, B 2030–2040, C 2040–2050
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Table 7 Predicted LULC
transition probability matrix
for 2020–2050 at Jashore
439
LULC types
2020–2030
CL
UA
UL
GE
WA
Total
2030–2040
CL
UA
UL
GE
WA
Total
2040–2050
CL
UA
UL
GE
WA
Total
2020–2050
CL
UA
UL
GE
WA
Total
CL
UA
UL
GE
WA
Overall
1.24%
0.70%
1.88%
1.50%
0.32%
5.64%
1.92%
38.11%
8.38%
8.04%
3.45%
59.91%
1.66%
1.33%
3.50%
2.18%
0.13%
8.80%
1.38%
1.32%
2.73%
13.68%
4.00%
23.11%
0.13%
0.06%
0.25%
0.31%
1.78%
2.54%
5.64%
59.83%
8.80%
23.19%
2.54%
100.00%
5.14%
0.04%
0.01%
0.01%
0.00%
5.19%
0.22%
62.17%
0.49%
1.34%
0.04%
64.26%
0.00%
0.05%
7.49%
0.01%
0.00%
7.56%
0.00%
0.09%
0.03%
20.22%
0.00%
20.34%
0.00%
0.13%
0.00%
0.00%
2.51%
2.65%
5.36%
62.48%
8.02%
21.59%
2.55%
100.00%
1.19%
0.56%
1.70%
1.44%
0.31%
5.19%
2.31%
38.96%
9.85%
9.64%
3.60%
64.35%
0.00%
0.05%
7.49%
0.01%
0.00%
7.56%
0.00%
0.09%
0.03%
20.22%
0.00%
20.34%
0.00%
0.13%
0.00%
0.00%
2.51%
2.65%
5.36%
62.48%
8.02%
21.59%
2.55%
100.00%
1.19%
0.56%
1.70%
1.44%
0.31%
5.19%
2.31%
38.96%
9.85%
9.64%
3.60%
64.35%
1.44%
1.18%
2.89%
1.92%
0.12%
7.55%
1.26%
0.77%
2.03%
12.39%
3.80%
20.26%
0.14%
0.07%
0.27%
0.33%
1.84%
2.65%
6.34%
41.54%
16.74%
25.71%
9.67%
100.00%
been transformed, of which 3.95% in urban areas,
1.41% in unused land, and 3.50% in greeneries.
About 6.66% of croplands were converted to unused
land, 4.17% to urban areas, and 6.43% to greeneries.
While the transformation of greeneries in urban areas
was found to be less, only 0.71% of greeneries was
transformed into urban areas during the study period.
The LULC transition matrix shows that the croplands
and water areas are experiencing the highest declination rates due to urban expansion in Jashore City.
Several factors are responsible for this increasing trend of urban areas, greeneries and unused land
cover. Rapid urbanization and the large influx of
migrants seeking to better educate themselves and
city services account for the construction of more
infrastructures. This increases the amount of urban
areas. Raufe (2011) found a significant transformation
of the cropland pattern in Jashore, which resulted in
the declination of crop area from 0.45 ha/household
in 1981–1990 to 0.29 ha/household in 2001–2010.
This output is consistent with the cropland declination pattern of this study. The cropland of Jashore
City declined over the past two decades due to flooding and also for use of lands for commercial and
housing purposes.
Weather data analysis shows that the annual rainfall in Jashore City has decreased significantly over
the last few decades and the average temperature has
risen (Mondal et al., 2017). Due to inadequate rainfall, many reservoirs have dried up and converted to
unused or vacant land. This adverse climate change
has also affected farming activities and croplands
have become urban areas, unused land and greeneries.
About 77% of ground water has been used in cropping
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Fig. 9 Predicted spatial locations of different LULC gain–loss during 2020–2050
in Bangladesh. Due to less rainfall, the ground water
level of this Jashore has fallen drastically (7.95 m
from the surface), for which residents face difficulties in collecting water for cropping. This resulted in
the declination of croplands (DPHE, Jessore, 2012)
and the conversion of croplands to unused lands at
Jashore (Mondal & Hossain, 2009). This study also
found that a large percentage (6.66%) of cropland
has been transformed on unused lands during the
2000–2020 period. Besides many infrastructural and
economic activities are influencing this LULC change
and these are causing an adverse impact on the environment and ecology of the study area.
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This study also performed the spatiotemporal
urban growth direction analysis during 2000–2010
and 2010–2020 and presented in Fig. 6. The analysis shows that urban areas expanded mostly in the
eastern direction and then in the NW-NNW direction. The growth rate in the eastern and NNE-NE
directions was observed 1.32 sq.km and 1.28 sq.km
respectively during 2000–2020. While a negative
growth rate has been observed in the WSW-W and
SW-WSW directions. In the NW-NNW direction,
the urban growth rate varied between 0.82 sq.km
and 1.05 sq.km over the study period. This indicates that urbanization is not taking place in any
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A
0.6
441
B
CL
C
0.4
0.3
0.2
0.1
0
0.4
0.2
WA
UA
WA
0
GE
2020 - 2030
UL
GE
1
CL
UA
0.5
WA
UL
CL
UA
0
GE
UL
2030 - 2040
2040 - 2050
Fig. 10 Predicted LULC change direction during 2020–2050: A water area, B cropland, C greeneries
structured manner and urban areas are increasing in
an unplanned and uncontrolled way.
Simulated urban LULC pattern—2030 to 2050
During 2000–2020, the LULC classes underwent
a remarkable transformation, which is presented in
Figs. 3, 4, 5 and Table 4. Therefore, a simulation to
illustrate future LULC dynamics is essential. The aim
is to forecast urban growth trends, which can provide the foundation of long-term sustainable urban
planning. Based on the LULC change from 2000 to
2020, the MLP-MC model is used to simulate future
potential urban growth patterns of Jashore City. The
simulated urban LULC pattern of the study area is
illustrated in Fig. 7 and LULC statistics in Table 6. To
check the validity of the prediction model, the LULC
pattern of the year 2020 was simulated and compared
with the actual LULC pattern of 2020. Table 5 shows
that, with a large number of iterations, all the potential LULC types showed an overall accuracy of 87%
with an ­R2 value of more than 0.82 and all the land
cover types with more than 82%.
From the MPNNMC based CA model simulation, a significant increase was estimated in the urban
areas by 18.40%, 21.55%, and 23.64% for the year
2030, 2040, and 2050 compared to 2020. Considering the forecasted years, in 50 years (2000–2050) the
percentage of the urban area (26.52%) and greeneries (2.31%) will increase enormously. On the
contrary, the water area (− 13.22%) and cropland
(− 14.49%) area will decrease mostly while unused
land (− 0.79%) will decrease slightly in the study area
(Table 6).
Table 6 shows that the urban growth rate will
observe 39.51% during the next three decades in
the study area while the water area will possess the
highest percentage of declination. Potential expected
urban growth may have an adverse impact on the
environment, ecosystem, atmosphere, and biodiversity of the study region. The increase of urban areas
moves the additional land to high-temperature areas,
carbon emitted zones and if this continues, the urban
area in the coming years will face significant environmental degradation. Proper implementation of land
use planning, natural resource management, conservation of water areas, and increase of urban green
area would make Jashore a sustainable city in the
future.
Urban growth direction simulation—2030 to 2050
The CA Markov transition matrix was determined to
show the future spatial urban area expansion or transition from 2030 to 2050 over the study region and is
presented in Fig. 8 and Table 7. The spatial locations
of gains and losses of different LULC classes during
2020–2050 are also prepared and presented in Fig. 9.
Usually, urban growth took place around the
city centers. The predicted LULC transition matrix
shows that LULC transformation will take place
in about 74.79% of total areas during 2020–2030,
62.72% areas during 2030–2040 and 64.51% areas
during 2040–2050. During 2020–2030 10.47%
of unused land and 10.04% of greeneries will be
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Fig. 11 Predicted urban area expansion direction during: A 2020–2030, B 2030–2040, and C 2040–2050 in the study region
transformed into urban areas, while it will be 0.50%
and 1.34% respectively during 2040–2050. Figure 8
shows that over time, other LULC conversions into
urban areas will reduce gradually because due to
rapid urbanization, the share of these LULC class
areas will reduce dramatically. This will transform
the entire study area into urban areas in the future
and the percentage of eco-friendly LULC will be
zero and the city will become uninhabitable.
Table 7 shows the predicted LULC transformation matrix for the years 2020 to 2050. Analysis
shows that during 2020 – 2030, about 8.04% of
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greeneries will be transformed into urban areas,
2.18% to unused lands. About 1.66% of croplands
will be converted to unused land. By 2050, the conversion to urban land cover will be 3.60% for water
areas, 2.31% for croplands, and 9.85% for unused
lands. The extent of urban expansion relative to
other LUCC varied significantly by period, according to the previous LUCC trend analysis.
The simulated LULC class change rate in Fig. 10
shows that the water area and cropland area will
transform mostly in urban areas rather than greeneries. Croplands will transform into urban areas and
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greeneries; the declination rate was found to be 0.38
and 0.4 respectively. Due to climate change, such as
the increase in temperature and drought tendency, and
the reduction of total rainfall, the percentage of wetlands and cropland will continue to decrease.
The simulated intra-urban land expansion showed
that if the urban areas continued to expand without
proper regulatory measures, the increase in urban
areas would be found in NW-NNW direction, with
a growth rate of 1.8 sq.km. The growth rate in the
eastern direction will be the same 1.4 sq.km as the
growth rate observed during 2000–2020. The growth
rate in the south and SSE-S direction will be within
the range of 1 to 1.3 sq.km during 2020–2050. As
long as the percentage of cropland is moderately high
in the ND-NNW direction (Fig. 11), urbanization will
be more in that direction. The density of urban areas
will be highest in the north and eastern directions
of the city. This growth direction analysis indicates
that future urban growth will be in an unplanned and
uncontrolled manner like the period from 2000–2020.
This will be an obstacle to sustainable environmental
and economic development in the future. The overall
analysis suggest that Jashore City had experienced
and will experience rapid intra-urban land expansion.
Our results are consistent with the findings of several
researches in the context of land use prediction in
the other major cities of Bangladesh, such as Khulna
City (Fattah et al., 2021a), Rajshahi City (Kafy et al.,
2020), Cumilla City (Kafy et al., 2021a), Dhaka City
(Islam and Ahmed, 2021). The increase in population and housing demand, along with other service
facilities such as commercial and industrial activities and road infrastructure, are the main drivers of
rapid urban expansion in the urban and rural areas of
Bangladesh (Hasan et al. 2017). If this expansion rate
and population growth rate cannot be accommodated,
urban sprawl might occur. Moreover, the declination
of vegetative land cover will substantially change the
climatic factors (Morshed & Fattah, 2021) and these
will make the cities uninhabitable.
Conclusion
The developing cities around the world are going
through enormous urban expansion, and this is affecting the environment as well as the ecosystem. Simulating future potential urban growth patterns helps
443
in effective and sustainable urban planning of infrastructure, the environment, and economy in urban
areas. This study adopted Landsat satellite images
from Landsat 5TM and Landsat 8 OLI to document
the historical urban growth pattern of a developing
city, Jashore, and predicted the urban LULC pattern using the MPNNMC model for the year 2030
to 2050. For the MPNNMC, seven variables such as
slope, DEM, LULC pattern, latitudes and longitudes,
road network, and distance of locations from the road
are considered to simulate future potential intra-urban
land use change patterns. The LULC change pattern showed the increase of urban areas by 11.77%
and unused land by 8.44%, due to the declination of
cropland by 12.19% and water areas by 7.30% during 2000–2020. The increasing pressure of unplanned
urbanization will be reflected on eco-friendly lands,
causing the declination of croplands, greeneries, and
waterbodies that surround the city. These non-urban
areas have started to disappear gradually, and the
cropland is expected to decline from 6.35% in 2020
to 5.19% in 2050 and other LULC classes, such as
unused land from 17.11% to 7.56%, greeneries from
25.81% to 20.34%, and water areas from 9.27% to
1.84% in 2050. Urban areas are expected to increase
from 41.44% in 2020 to 65.08% in 2050. The predicted urban growth reveals an alarmingly rapid
transformation of cropland and water areas into urban
areas. The study demonstrates that the natural landscape of Jashore will be insufficient to achieve urban
sustainability, as the maximum portion of greeneries
and water-areas will be depleted in the future.
The urban growth simulation model demonstrates
that the city of Jashore will go through enormous,
spontaneous, and unplanned urban expansion in the
future. This will affect the climate, environment, and
ecosystem of the city. So, the city urgently needs the
implementation of an eco-friendly and sustainable
urban development plan with the preservation and
management of existing natural resources and nonurban areas to ensure a sustainable urban environment. This research provides useful perspectives for
policymakers, responsible authors, environmental
engineers, and urban planners in sustainable decisionmaking considering future aspects.
Funding
There was no funding for this research.
Availability of data and material
Data will be available.
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Declarations
Conflict of interest The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
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