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Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu
Rajchandar Padmanaban 1,*, Pedro Cabral 1,*, Avit K. Bhowmik 2, Alexander Zamyatin 3
and Oraib Almegdadi 4
1 NOVA IMS, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal
2 Stockholm Resilience Centre, Stockholm University, Kraftriket 2B, SE-104 05 Stockholm, Sweden;
avit.bhowmik@su.se
3 Department of Applied Informatics, Tomsk State University, Lenin Avenue 36, 634050 Tomsk, Russia;
zamyatin@mail.tsu.ru
4 IFGI, Westfälische Wilhelms-Universität, Heisenbergstraße 2, 48149 Münster, Germany;
Almegdadi.oraib@gmail.com
* Correspondence: rajchandar07@gmail.com (R.P.); pcabral@novaims.unl.pt (P.C.)
Abstract
Urban sprawl propelled by rapid population growth leads to the shrinkage of productive
agricultural lands and pristine forests in the suburban areas and, in turn, substantially alters
ecosystem services. Hence, the quantification of urban sprawl is crucial for effective urban
planning, and environmental and ecosystem management. Like many megacities in fast growing
developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has
experienced extensive urban sprawl triggered by the doubling of total population over the past
three decades. We employed the Random Forest (RF) classification on Landsat imageries from
1991, 2003, and 2016, and computed spatial metrics to quantify the extent of urban sprawl within
a 10km suburban buffer of Chennai. The rate of urban sprawl was quantified using Renyi’s
entropy, and the urban extent was predicted for 2027 using land-use and land-cover change
modeling. A 70.35% increase in urban areas was observed for the suburban periphery of Chennai
between 1991 and 2016. The Renyi’s entropy value for year 2016 was ≥ 0.9, exhibiting a two-fold
rate of urban sprawl. The spatial metrics values indicate that the existing urban areas of Chennai
became denser and the suburban agricultural, forests and barren lands were transformed into
fragmented urban settlements. The forecasted urban growth for 2027 predicts a conversion of
13670.33ha (16.57 % of the total landscape) of existing forests and agricultural lands into urban
areas with an associated increase in the entropy value of 1.7. Our findings are relevant for urban
planning and environmental management in Chennai and provide quantitative measures for
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addressing the social-ecological consequences of urban sprawl and the protection of ecosystem
services.
Key-words: Random forest classification; urban sprawl; spatial metrics; Renyi’s entropy;
sustainability; land change modelling; remote sensing; urban growth model; Chennai
1. Introduction
Human activities have been gradually transforming ecosystems in urban, near urban, and rural land
[1]. The most significant increase in population has been observed in the developing countries [2].
By 2030 the world’s population is expected to increase by 72%. In cities of more than 100,000
inhabitants population may surge by 175% [3]. The expansion of cities causes urban sprawl,
characterized by the extension of settlements with the decrease of agriculture, water bodies, and
forest land. Studies on urban sprawl are necessary as they support different fields of science such
as transport planning, landscape architecture studies, urban planning, land-use planning,
economics, and ecosystem services [4].
In developing countries like India, people are moving from the villages toward the cities for better
social life, education, and income [5]. The increasing population of cities directly affects the land-
use and land-cover and land-changes, causing land transformation, land scarcity, and deforestation
[6,7]. The development of cities often causes the movement of urban and rural poor into the
bordering lands of cities [8]. The great movement of urban poor and extension of cities with the
sudden development of industries and IT sectors cuts the bordering productive agriculture land,
encroaches on water bodies, and transforms wild lands [9].
Much attention has been paid to urban growth of emerging cities in India [10]. The uncontrolled
urban sprawl in Chennai city is having negative consequences regarding air pollution, housing,
overcrowding, encroachment, slums, disposal of waste, increasing settlements, water availability,
water pollution, and sewage [11]. The uncontrolled urban sprawl also leads to various
environmental impacts, such as higher energy utilization, disturbance of species diversity, flood
risk, and ecosystem fragmentation [4]. Predicting urban growth and associated impacts on
ecosystem services will contribute to better urban planning with the design of eco-friendly cities
that avoid the major negative consequences of urban sprawl [1,12].
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The spatial and physical characteristics of urban features, urban patterns and its forms may be
quantified using spatial metrics [13]. These indices can be obtained directly from thematic maps
derived from remote sensing data [14]. The availability of remotely sensed data from multiple
dates enables us to carry out studies on urban modeling [8], urban landscape pattern analysis [15],
and urban growth studies [13,16]. Globally, different studies on urban growth and model analysis
have been carried out [8,9,12,15,17]. However, with a few exceptions, such studies are scarce for
India [16,18–20]. The city of Chennai, India has been one of the fastest growing urban areas in the
country in the last three decades. This has resulted in traffic congestion, air and water pollution,
uncontrolled increase of population, encroachment, water and land scarcity, the growth of slums,
and the degradation of vegetation within and in the peripheral areas of the city [21]. Thus, such a
study would benefit urban planners that need to understand the spatiotemporal changes of urban
areas to better address these environmental problems and, at the same time, to ensure the provision
of basic infrastructures and facilities without disturbing ecosystems. This study (1) studies land-
use and land-cover changes from 1991 to 2016; (2) examines the spatiotemporal urban growth
pattern using entropy and spatial metrics; and (3) predicts the urban growth and the urban sprawl
for the year 2027.
2. Study area
Chennai is the capital city of Tamilnadu, India, and gateway of south India (Figure 1). The
geographical location of Chennai is 13.04°N 80.17°E with an elevation between 6m and 60m
above the mean sea level. It covers an area of about 42,600ha. Due to the high urbanization in the
Chennai district, most of the residential amenities, industries and factories, universities, and
educational institutes are moving toward the periphery of Tiruvallur (North Chennai) and
Kanchipuram (South Chennai) districts [21]. For this reason, this study also covers the 10km
adjacent area of Chennai city (82,488.16ha) from Tiruvallur and Kanchipuram district for
analyzing urban sprawl.
Chennai city is currently home to 8,233,084 people [22] is India’s 4th largest city, and is the main
business hub in the country. Chennai has witnessed a remarkable development of IT sectors,
educational institutes, health care, and retail and manufacturing industries in recent decades [18].
Chennai is one of the main Indian cities for tourism and includes several UNESCO world heritage
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sites, such as monuments and ancient temples. The city is well engaged with trade, employment,
and business. Chennai is also rich in natural resources [23]. As a result, the population has
increased rapidly in the last two decades [18].
The city coastline is about 25.60km and has a lengthy natural urban beach (Marina beach) [24].
The city also has a dense forest, which is the only habitat of the Great Indian Horned Owls and
home to spotted deer, mongoose, bonnet monkeys, and many species of insects, snakes, and birds
[25]. The mangroves along the coastline, iluppai, caper, and vanni trees and trinicomalee teak are
part of the city vegetation [11].
Figure 1. Location of the study area.
3. Data and methods
3.1 Data
Two decades of Landsat Thematic Mapper TM satellite images with 30m resolution for years
1991, 2003, and 2016 were used (Table 1). These Landsat data were downloaded without charge
from the Unites States Geological Survey (USGS) portal [26] using a World Geodetic System
(WGS) 1984 projection.
Table 1. Data description.
Date Sensor Path/Row Resolution Format
August 25
th
, 1991 Landsat-5 142/51 30m GeoTIFF
May 9
th
, 2003 Landsat-7 142/51 30m GeoTIFF
July 4
th
, 2016 Landsat-7 142/51 30m GeoTIFF
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3.2 Satellite image pre-processing and land-use and land-cover mapping
The availability of multi-temporal data obtained from the remote sensing techniques supports the
development of change mapping and detection for land-use and land-cover (LULC) [9]. The
LULC is mainly useful for environmental studies, studies on urban dynamics and urban sprawl
modelling, and studies on sustainable development [27]. Therefore, the urban sprawl studies, as is
the case with this research, should rely on updated terrain land-use and land-cover classification.
Figure 2 shows the flow chart for image pre-processing and classification, and validation.
Figure 2. Methodology followed for image pre-processing, classification, and validation.
3.2.1 Image pre-processing
The set of Landsat ETM data were initially geo-corrected and rectified. The whole scene sizes of
the satellite imagery of ETM data were cropped to the study area. The study area includes Chennai
city administrative area and a 10km buffer from the city boundary. The final subset imagery of the
study area was obtained using ENVI Imagine [28] for further classification.
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3.2.2 Image classification in R programming
Object-based classification in R programming is becoming more advantageous for classifying
high-resolution satellite image at object level instead of pixel level [29]. Random forest (RF)
classification is a machine learning algorithm adequate for object-based classification, which
produces automatic and accurate results compared to other conventional classification methods
[30]. We used RF package in R programming for land-use and land-cover classification [31]. Five
land-use classes were extracted from the resulting image classification [32]. The categories and
their corresponding classes are described in Table 2.
Table 2. Land-use and land-cover nomenclature.
No LULC classes Land uses included in the class
1 Water bodies Rivers, reservoir, lake, streams, open water, and ponds
2 Urban Residential, industries, IT sectors, private and government
buildings, roads, airport, and other related built-up areas
3 Agriculture Agriculture land and plantation
4 Bare land Dry land, non-irrigated lands, ready for construction, and real estate
plots
5 Vegetation Forest and shrubs
The spatially and spatially-cohesive features on the ground are divided by running the
segmentation algorithm in the ENVI feature extraction tool [33]. This segmentation process is used
to locate several regions with maximum homogeneity. The feature in the region can be vegetation
covers of parallel structure, appearance, and color. The segmentation algorithm reduces the
heterogeneity of image features or objects for a given resolution [34]. The segmentation image
from classified raster data is obtained by providing minimum and maximum threshold values of
pixel and population of pixels that can be contained in a region.
3.2.3 Accuracy assessment
Accuracy assessment evaluates the performance of the image classification procedure [35]. This
study used kappa coefficients [36] to analyze the accuracy of three-decade classified images. The
producer data were obtained from Google Maps; thus, user and producer accuracies were
calculated through a confusion matrix. The kappa coefficient index was calculated for the three
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different date land-use classified images in eCognition Developer [37].
3.3 Spatiotemporal urban sprawl analysis
3.3.1 Urbanization analysis using spatial metrics
Different landscape metrics were calculated in FRAGSTATS software [38] to understand the
pattern of urban growth from 1991 to 2016 (Table 3). These metrics were used to quantify and
compute the spatial characteristics at different levels: patch level, class area level, and the complete
landscape level. The selected landscape metrics were CA, NP, LPI, CLUMPY, AI, FRAC_AM,
and CONTAG [39].
Table 3. Spatial metrics used in the study.
Landscape
metrics
Formula Description Range
Class Area
Metrics = 1
10000
aij = area in m2 of patch ij.
Total amount of
class area in the
landscape
CA>0,without
limit
Patch Size =
ni=total number of patches in the area of patch type i (class).
Number of
patches of
landscape classes
(Built up and
non-built-up)
NP≥1,without
limit
Largest
patch Index =
(100)
aij = area in m2 of patch ij and A= landscape area in total(m2)
Percentage of the
landscape
included by the
largest patch
0<LIP≤100
Clumpiness
Index = [(−
)/
<
&
< 5, else
−
/1− ]
gii= number of like joins among pixels of patch type, i based
double-count process and gik= number of like joins among
pixels of patch type, k based double-count process
Pi= amount of the landscape occupied by patch type
Measure the
clumpiness of
patches in urban
areas
-1≤ CLUMPY
≤ 1
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Aggregation
Index
=[
/](100)
gii= number of like joins among pixels of patch type, i based
double-count process and maxgii=maximum number of like
adjacencies among pixels of patch type
Calculates the
adjacency
between similar
patch types
0 ≤ AI ≤ 100
Fractal
Index
Distribution
=[(
/
)]
aij = area in m2 of patch ij.
To measure area
weighted mean
patch fractal
dimension
1 ≤
FRAC_AM≤2
Contagion = [1 +[(){/}{ln()
[
/]/2ln()]100
pi= amount of the landscape employed by patch type (i) class
and gik= number of like joins among pixels of patch type, i
and k based double-count process
m= number of patch classes (types) existing in the landscape
Defines the
heterogeneity of
a landscape
Percent<Cont
agion≤100
3.3.2 Urbanization analysis using Renyi’s entropy
Renyi’s entropy was calculated to estimate urban sprawl between 1991 and 2016. This concept is
used in information theory and image processing analysis such as information entropy [40]. To
estimate the urban sprawl of Chennai city over three decades, the land-use and land-cover map
was separated into two built-up and non-built-up classes and their spatial and temporal variations
were taken into consideration.
Renyi´s entropy is based on knowledge theory and acts as an indicator of spatial dispersion or
intensity to examine any geographic components [40]. We calculated Renyi’s entropy for each
period based on the total number of patches (N) in the LULC and their length (Pi). The extended
version of Shannon’s information defining a comprehensive entropy [41] of order H
α
, where α ≥
0 and α ≠ 0 is described as (1):
=
∑
(1)
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The Renyi’s entropy values vary from 0, indicating very dense distribution in a selected region, to
1, indicating unequal distribution / dispersed across space [41].
3.4 Urban growth model
Land change modeler in IDRISI/TERRSET [42] was used to predict the urban growth of the study
area in 2027. The land change modeling process consists of four steps: (1) urban change analysis,
(2) transition modelling, (3) change prediction, and (4) validation. Figure 3 shows the methodology
followed for change analysis and modelling.
Figure 3. Methodology applied for spatial and temporal analysis and modelling, and validation.
4. Results and Discussion
4.1 Land-use and land-cover classification and accuracy assessment
Figure 4 shows the LULC maps obtained for the different time periods using Random Forest
classification in R programming. The accuracy of the derived maps was examined in eCognition
software [43] with training samples collected from Google maps. The overall accuracy for the
LULC maps of 1991, 2003, and 2016 were 0.92, 0.97, and 0.92 respectively. These values were
above the satisfactory level for land-use and land-cover classification. The accuracy assessment
values of several classes for the different corresponding years is given in Table 4.
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Table 4. Summary of confusion matrix for the classified images 1991, 2003 and 2016.
Classes 1991 2003 2016
Producer
Accuracy
User
Accuracy
Producer
Accuracy
User
Accuracy
Producer
Accuracy
User
Accuracy
Urban 91.05 92.32 82.02 98.09 78.12 97.25
Vegetation 98.45 93.58 93.04 97.12 84.56 89.78
Agriculture 96.68 91.56 98.56 86.45 96.04 97.31
Water bodies 93.65 92.05 84.51 89.47 92.78 92.23
Bare land 92.01 94.02 94.03 88.12 76.05 95.77
Overall accuracy 92 97 91
Kappa 0.92 0.97 0.92
4.2 LULC changes and analysis
The LULC maps of three different periods were quantified and the results are in Table 5. The
quantified result clearly shows that the study area has experienced a remarkable change among
the land classes. From 1991 to 2016 the growth of built-up more than tripled, i.e. an increase of
about 37,919.81ha. This transformation influenced several classes, especially the agriculture and
vegetation land, which decreased about 3,802.70ha and 9,923.32ha respectively. The bare land
also decreased around 30.3%, i.e. nearly 100%. The water bodies increased about 1,451.79ha
between 1991 and 2003, but decreased slightly between 2003 and 2016 (about 651.65ha).
Table 5. Land-use and land-cover in percentage and hectares during the three periods.
Land class 1991 2003 2016
Area (%) Area (ha) Area (%) Area (ha) Area (%) Area (ha)
Built-
up/urban
24.38
20,110.61 48.45
39,965.51
70.35
58,030.42
Agriculture 11.38
9,387.15
27.71
22,857.47
6.77
5,584.44
Vegetation 26.28
21,677.89
11.27
9,296.41
14.25
11,754.56
Water
bodies
6.45
5,320.48
8.21
6,772.27
7.42
6,120.62
Bare land 31.51
25,992.02
4.36
3,596.48
1.21
998.10
Total 82,488.16
82,488.16
82,488.16
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4.3 Analysis of landscape metrics
The spatial metrics were calculated with FRAGSTATS tool [38]. The values obtained and changes
in percentage are in Table 6. The spatial variations and metrics were estimated for the built-up
areas, indicating that the urban area increased by 98.72% between 1991 and 2003. Likewise, the
built-up area between 2003 and 2016 also increased by 45.20%. When compared to 2003, the
growth rate of urban area in 2016 is becoming slower (about 3%). The urban areas increased by
188.55% during the last three decades.
Patch size (NP) calculation helps to quantify the level of fragmentation, i.e. aggregation or
disaggregation in the built-up landscape [16]. The number of patches has significantly increased
(22.49%) between 1991 and 2003. Similarly, the NP increased about 37.74% between 2003 and
2016. The number of built-up patches noticeably increased in number during the last two decades.
The patches at the center of the urban area were clumped after 2003 and more fragmented during
2003 and 2016.
The proportion of the landscape area covered by the largest urban patch is quantified by the Largest
Patch Index (LPI) landscape metrics [18]. The LPI increased by 127% between 1991 and 2003.
The higher value in the number of urban patches illustrates a denser urban growth, but it decreases
moving away from the center, showing the existence of semi-urban or peri-urban fragmented urban
patches. However, LPI increased by 39.21% from 2003 to 2016, indicating a compact urban
growth and presence of new dispersed settlements.
In order to identify and examine the patch aggregation level, the clumpiness index was used. This
landscape metric measures the clumpiness of urban patches [13]. The clumpiness in 1991 is -1,
showing that the urban patch is maximally disaggregated. However, these values were 0.6 and 0.8
in 2003 and 2016, respectively, indicating that the urban patches are becoming aggregated or
clumped. The patch clumpiness increased by 180% from 1991 to 2016, indicating that the urban
patch became more clumped.
The aggregation index is computed from an adjacency matrix to measure the aggregation amount
of the urban patches. The value of the aggregation index in 1991 shows the aggregation level as 9,
indicating that the urban center is more clumped, and that the outskirts or suburbs have few
patches. However in the following years (2003 and 2016) the suburbs and peripheral zone had a
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highly fragmented progression and were more clumped in the city region. The patch aggregation
in the urban structure clearly indicates that the city is becoming a more homogenous urban patch
and that the suburbs are also developing with clumped patches as well as uneven patches. The
growth of homogeneous and heterogeneous urban patches is destroying other classes such as
vegetation, agriculture, and bare land, and transforming these landscapes into a single large urban
patch.
FRAC_AM increased between 1991 and 2003 as a result of contained urban growth with
reasonable shape complexity (the values for this metric are marginally greater than 1).
Nevertheless, the FRAC_AM value between 2003 and 2016 was 1.83, indicating that the landscape
had a higher range of urban growth and more dispersed urban sprawl. The value between 2003 and
2016 has increased by 44.09%. The fall in the CONTAG value between 1991 and 2003 resulted
from a higher fragmentation. However, this value increased slightly between 2003 and 2016
(4.10%) showing that the fragmented urban area is becoming denser. Figures 5 shows different
temporal urban settlement growth signatures of spatial metrics.
Table 6. Landscape indices and changes in percentage.
Metrics
Year Changes in urban structure
1991 2003 2016 Δ%=1991-2003 Δ%=2003-2016
CA 20,110.61 39,965.51 58,030.42 98.72 45.20
NP 289 354 477 22.49 34.74
LPI 2.14 4.87 6.78 127.57 39.21
Clumpy -1 0.6 0.8 160 33.33
AI 9 42 69 366.66 64.28
FRAC_AM 1.12 1.27 1.83 13.39 44.09
CONTAG 72.32 68.43 71.24 6.66 4.10
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Figure 5. Different temporal urban settlement growth signatures of spatial metrics.
4.4 Urban sprawl measurement using entropy
In order to estimate the sprawl and urbanization pattern during the last three decades, the LULC
maps of Chennai and its peripheral district area were classified into non-built-up and built-up areas.
The classified result shows that the Chennai area covered by the built-up area proportion was only
24.38% until 1991. This value has grown immensely, reaching 70.35% in 2016. The built-up and
non-built-up area proportion has been switched during the last 25 years. The Renyi’s entropy
calculation indicates that the Chennai city experienced high growth of the urban area between 1991
and 2016. The entropy value of the urban area in 1991 was 0.4, showing a moderate urban sprawl.
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In 2003 the entropy value was 0.5, which is above the threshold value of 0.5, denoting a higher
development of urban sprawl. In 2016 the entropy value was closer to 1, indicating a high urban
sprawl level. Table 7 shows the built-up and non-built-up area in the Chennai city in 1991, 2003,
and 2016.
Table 7. Built-up and non-built-up area in the Chennai city in 1991, 2003, and 2016.
Land class 1991 2003 2016
Area (%) Area (ha) Area (%) Area (ha) Area (%) Area (ha)
Built-up 24.38
20,110.61 48.45
39,965.51
70.35
58,030.42
Non-built-up 75.62
62,377.55
51.55
42,522.65
29.65
24,457.74
Total 82,488.16
82,488.16
82,488.16
5.5 LULC changes and urban sprawl detection for year 2027
The land-use and land-cover prediction for the year 2027 was carried out after model validation,
by confronting the 2016 LULC map with the kappa variations. The kappa value had an acceptable
level of accuracy to predict the 2027 LULC map. The kappa values obtained were more than
satisfactory (Kno = 82%, K location = 84% and Kquantity = 81%). The simulated 2027 land-use
map and LULC transition between 1991 and 2016 and predicted LULC for the year 2027 are
shown in Figures 6.
The quantification of the LULC map of 2027 shows the projected built-up area, which will be
increased by about 70,836.76ha. To the contrary, the vegetation and agriculture are expected to
decrease by 3,961.66ha and 701.35ha respectively. The increases of bare land (1,570.12ha) will
occur as a consequence of transitions in vegetation, water bodies, and agriculture land. Table 8
compares the quantities of the LULC maps of 2016 and the predicted map of Chennai for year
2027.
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Figure 6. Simulated LULC map for the year 2027.
Table 8. Comparison of the LULC map of 2016 and predicted LULC map of Chennai 2027.
Land-use Class 2016 2027 Forecasted changes in 2027
in hectares in percentage
Built-up/urban 58,030.42
70,836.76 12,805.58 22.06
Agriculture 5,584.44
701.35 -4,883.09 -87.44
Vegetation 11,754.56
3,961.66 -7,792.9 -66.29
Water bodies 6,120.62
5,420.62 -700 -11.43
Bare land 998.10
1,570.12 572.02 57.31
Total 82,488.16
82,488.16
In order to examine and visualize the spatial extension of the urban area in the predicted year
(2027), the LULC of 2027 was classified into the built-up and non-built-up areas (Figure 7).
The
built-up areas amounted to 70.35% until 2016. This proportion increased to 85.87% in 2027. The
Renyi’s entropy value of the predicted urban area in 2027 was 1.7, which is above the range value
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of 1, indicating a complex growth of urban sprawl.
Figure 7. Classified LULC map for the year 2027.
5 Conclusions
The purpose of computing urban models for a period of time help us to understand the growth of
the urban area and regulate the land transformation and sustainability [44]. The application of GIS,
RS, and geospatial modelling tools enables the assessment and modelling of land-cover changes
and the study of urban growth [45].
Land-use and land-cover maps of the Chennai city area for the years 1991, 2003, and 2016 were
obtained using Random Forest classification with R programming. The computed maps provided
a new perspective of the spatiotemporal distribution of the landscape in the study region. We found
a fragmented urban growth in the outskirts of Chennai city, with the transformation of vegetation
cover and agriculture land into settlements. The dense urbanization in the city region, quantified
and analyzed using urban sprawl measurement and landscape metrics, raises challenges for
sustainable urban planning and suitable land resource provision.
The urban system in Chennai is extending toward the peripheral region of Kanchipuram and to the
Thiruvalluvar district area. This growth has greatly impacted the landscapes with the loss of
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valuable urban vegetation and agriculture land and outward sprawling. The land changes around
the urban center have to become denser and a reasonable amount of the urban expansion in
Tiruvallur (North Chennai) and Kanchipuram (South Chennai) is also observed. This development
is characterized by the decreasing largest patch index (LPI) and by the increase in the amount of
urban patches (NP) in 2027.
The Chennai coastline had a remarkable urban growth between 1991 and 2016. Almost all the
coastline of the study region is covered by urban settlements that negatively impact the mangrove
forest [46] and the Savukku plantations [47]. This vegetation provides important ecosystem
services, such as a natural shield from Tsunami, cyclones, and other ecological disasters [48].
Between 1991 and 2003 16.3% of agriculture and 15.01% of vegetation was transformed into
urban/built-up, and 20.44% of agriculture land was lost between 2003 and 2016, while 30.3% of
bare land became urban settlement during 1991 and 2003. The absence of involvement in
agriculture by the population, the urban clumpiness in the city center, and the increasing population
are the main reasons for such massive land transformation. The decreasing amount of vegetation
and agriculture land will have negative environmental impacts, such as habitat loss for native
species including Great Indian Horned Owls, spotted deer, mongoose, bonnet monkeys, and
golden jackals [11]. Also, the degradation of forest and farm land leads directly to increases in city
temperature and air pollution levels [49,50]. Recent studies already reveal that Chennai is one of
the most highly air polluted cities in India [22].
The Entropy sprawl measurement indicates that the urban landscape had a high progress rate of
urban sprawl and dispersion of urban growth between 1991 and 2016. This is directly and
significantly influencing the urban fringe. The predicted entropy value for 2027 is 1.7, indicating
a very high growth of urban sprawl.
Aggregated build-up, population growth, and land scarcity in the city are the reasons behind urban
sprawl in the districts neighboring Chennai. Furthermore, increasing urban settlements also adds
strain to sewage water disposal and waste management, both of which openly cause water and soil
pollution [51]. Recent studies reveal that the threat of hydrological pollution of the Cooum river
[52] and the coastal water in Chennai have been increasing due to the discharge of uncontrolled
and non-treated sewage waters from domestic and commercial activities [53]. The rapid growth of
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19
urban area also poses a waste management problem, as everyday the city produces about 0.71kg
of garbage per capita [54]. The recent investigation on waste disposal and management of Chennai
city found that burning waste in Chennai's dump yards at Perungudi and Kodungaiyur creates
severe soil and air pollution, which cause several health hazards, including respiratory disorders
and the threat of cancer for nearby residents and animals [55].
The projected 2027 land-use and land-cover maps estimate an urban transition of 87% from
agriculture land and 66% from vegetation, between 2016 and 2027. This projection and associated
rapid transformation will most likely affect existing ecosystem services and the environment. At
the same time, the bare land will increase at the expense of vegetation and agriculture classes. The
growth and transformation of non-built-up area into the built-up area in the coming 11 years is
expected to continue growing at an alarming rate.
The computation and modelling of urban growth patterns emphasize the need for well-judged land-
use distribution and transformation, as well as the preparation of urban development policies
giving importance to the sustainable deployment of natural resources. The land allocation strategy
should conform to the land availability and capability classification of Chennai city to achieve
sustainable urban expansion and development.
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