This document analyzes urban sprawl in Pune, India over a 13-year period using remote sensing data. It finds that built up area increased 149.5% from 2000 to 2013, while agricultural land decreased 564% and vegetation decreased 630%. Spatial metrics like number of patches and mean patch size increased over time indicating greater fragmentation on the urban periphery. The core area was more concentrated in the center in 2000 compared to 2013. Total edge and edge density positively correlated with distance from the center, showing dispersed growth over time. In summary, this study finds rapid urban expansion in Pune from 2000 to 2013 primarily through conversion of agricultural land and vegetation to built up area on the urban periphery.

1. Analyzing Urban sprawl using Geoinformatics: A case study of
Pune
Emtiaj Hoque, Sohini Kar, Varsha Yadhav, Shamita Kumar
Institute of Environment Education & Research, Bharati Vidyapeeth University, Pune 411043, India
Abstract: Rapid industrialization and associated migration led Pune to become rapidly urbanized.
Monitoring, modeling and mapping urban sprawl is essential for better sustainable development. This
paper focuses on identifying the trend of urban sprawl in Pune city over the past one decade using
temporal remote sensing data (Landsat TM 2000 & Landsat-8 2013), using a buffer of 50km from city
center to the outskirt in five concentric circles and combining gradient analysis with spatial class metrics
tools using FRAGSTATS. A detailed quantitative analysis has been done showing the spatial changes.
This study performs spatio-temporal analysis along with the extensive use of spatial class metrics to
identify the physical drivers of urban sprawl of Pune city.
Keywords: Urban sprawl, Spatial metrics, FRAGSTATS, Pune.
1. Introduction
Urbanization is a very important issue in India. Urban sprawl is also referred as irresponsible, and often
poorly planned development that destroys green space, increases traffic, contributes to air pollution, leads
to congestion with crowding and does not contribute significantly to revenue, a major concern.
Increasingly, the impact of population growth on urban sprawl has become a topic of discussion and
debate. Typically conditions in environmental systems with gross measures of urbanisation are correlated
such as population density with built-up area. The relation of population growth and urban sprawl is that
the population growth is a key driver of urban sprawl. The study on urban sprawl was attempted in the
developed countries and recently in developing countries such as China.
In India alone currently 25.73% of the population (Census of India, 2001) lives in the urban centres, while
it is projected that in the next fifteen years about 33 % would be living in the urban centers. Pune is the
second largest city in Maharashtra and 8th in the country. Pune is one of the fastest growing cities in India
which is growing at an alarming rate. The growth of the city is peripheral. The growth rate in the core part
of the city is about 2 – 2.5% per year and the annual growth rate in peripheral wards is about 4.4%. The
driving force for growth is mainly the development of IT industry as well as the economic boom in the
automobile sector which forms a major portion of the industries in and around Pune. The peripheral growth
has resulted into the increased residential areas and area under transportation network and facilities. Hence,
spatial pattern of its growth is an important issue for analytical study using remote sensing and GIS
applications.
2. Aim and Objective
The aim and objectives of the current study are:
 To map and monitor the urban pattern using temporal spatial remote sensing data.
 To understand and assess the urban spatial growth at class level through spatial metrics.
3. Study area
The study has been carried out for a rapidly urbanizing region in India. Pune is the cultural, commercial,
industrial, and knowledge city of the state of Maharashtra, India with an area of 741 sq. km and lies
between the latitude 18◦52’04’’and longitude 73°86’00’’. To account periurban growth we have

2. considered 50km. circular buffer from the Pune administrative boundary by considering the City Business
District (CBD) as center (Figure 1). The predominant land cover primarily consists of grassland, cropland,
and bare land with forests, urban zones and scattered water bodies.
Pune district is among the highest in the state with over 57.39 lakh people living in cities according to the
Census of India. Population is one of the main factor of the urban growth as well as migration from rural to
urban areas. According to the Census of India 2001 and 2011, the actual population of Pune in 2001 was
7,232,555 which had an increase in urban growth to 9,429,408. The population growth per sq. km reduced
from 30.73% in 2001 to 30.37% in 2011. The density per sq.km in 2001 was 462 which increased to 603 in
2011. This depicts that there has been a huge growth in a span of 10 years contributing to urban growth.
Invest in manufacturing, IT and overall growth in economic activity has led to an influx of people into
Pune. Pune has established itself as the ‘Academic Corridor’ of India and is also the emerging InfoTech
Hub. It is the place of huge IT investments. Hence migration due to employment opportunities as well as
educational purpose has been major ‘pull factors’. Due to close proximity to the economic region of the
country i.e Mumbai, rapid growing infrastructure and enchanting climate makes Pune a favorable place to
settle. Urban sprawl is the scattering of new development of land use pattern causing loss of productive
agricultural land, forest covers and other forms of greenery, loss in surface water bodies, depletion in
ground water aquifers and increasing levels of air and water pollution causing environmental problems.
The process of urbanization is affected by population growth and migration.
4. Data source and type
Different remote sensing and GIS data from different sources has been used in this research. Landsat TM
images of 2000 and Landsat 8 20113 were used to detect urban land cover change pattern of this study
area. These images were obtained from the United States Geological Survey (USGS) website as standard
product i.e. geometrically and radio metrically corrected. Most GIS data such as administrative boundaries,
CBD, major roads are obtained from SOI maps. All dataset used in this study are geometrically referenced
to the WGS 1984, UTM 43N projection system

3. Table 1: List of Satellite images collected for the study area
Satellite data Resolution Year Source
Landsat TM
image
30m 2000
http://glcf.umi
acs.umd.edu)
Landsat 8 image
Band 1 to 7 – 30 m
Band 8 – 15 m
Band 9 – 30
Band 10 and 11 – 100 m
2013
http://glcf.umi
acs.umd.edu)
.
5. Methodology
The methodology in this study involves remote sensing classification technique as well as spatio temporal
analysis of spatial metrics. Creation of base layers: base layers like district boundary layers and road layers
are created from SOI maps of scale 1:25000 and 1:50000. Image preprocessing included georeferencing of
the remote sensing data of the two years and then creating a 50 km buffer around the Pune CBD.
Supervised classification using Anderson classification scheme level-1 is done for finding out the variation
of urban growth that took place in Pune district. Land cover analysis and change detection is necessary for
analyzing the difference of the two years. Built up area extraction is done in two parts: Class level pattern
analysis using spatial metrics (FRAGSTATS) and analyze urban expansion map.
Figure 2: General methodology of the study
2000
2013
Image pre-processing
(Create a 50 km buffer)
Multi-Temporal Images
(Landsat TM & Landsat 8)
Signature extraction
Supervised Classification
Accuracy assessment
Land use & Land cover map
Built up area extraction
Class level
pattern analysis
Analyze urban
expansion map

4. 6. Results and Discussions
6.1. Land use analysis
Table 2: Land use change of different categories between 2000 and 2013
Land use
Area in sq.km
Change in %
2000 2013
Built up area 6.4756 9.91497 - 149.573
Agricultural land 58.2096 71.17905 - 564.034
Barren land 6.95808 11.23788 - 186.126
Scrub land 152.3367 142.9713 407.2958
Vegetation 30.47514 15.97941 630.4108
Water bodies 6.23268 7.10586 - 37.9741
The region with rich vegetation of 12% (2000), gradually loses vegetation to 6% (2013) at the cost of
increase in built-up from 3% (2000) to 4% (2013). 2000 and 2013, the major change was detected in the
scrub land use category and significant change in agricultural and commercial use. In the 2000 land use
map of Pune, agricultural land use was 22% of the total area of the city and in 2013 it became 32%. The
increase in agricultural land use in the year 2013 was, mainly due to some area, which was not covered in
2000 shown as agricultural use in 2013. In 2000, barren land was 3% and in the land use map of 2013, it
was shown as 4%. In 2000, water bodies were 2% shown and land use for water bodies appeared in 2013
as 3%.
Figure 3: Land use & Land cover change map
The classified images clearly illustrates the percentage of urban land is increasing in all the directions due
to setting up the new industries(IT&BT), economic development and high-rise buildings coming up in the
periphery. Concentric pattern of urban growth is observed with the aggregations at the center and dispersed
growth in the periphery. Open spaces and vegetation have been converted to built-up. At some locations
linear pattern is observed along the national highways and the local roads leading to the formation of the
typical ‘urban corridors’ mainly consisting of commerce and small industrial activities

5. Figure 4: Areas acquired by various land-use features of 2000
Figure 5: Areas acquired by various land-use features of 2013
6.2 Urban expansion in Pune city over 13 year
The total urban area of Pune increased from 6.47568sq.km to 9.91497sq.km during 2000-2013 (Figure
4.5), with an annual urban expansion area of 0.26sq.km per year, which also means the expanded area was
6.67 times of the original urban area in 2000. The rate of urban expansion, however, was not homogeneous
spatially and temporally. Generally speaking, Shanghai’s urban expansion experienced continuously
increases if every ten years is taken into consideration as interval. This rule is consistent with the overall
one in the whole India (Liu et al. 2005a; Liu et al. 2005b).
As to the trajectory of urban land in Pune, it was closely related to the national and regional policies and
development strategies.

6. Figure 6: Land use change in sq.km
Specifically, after the economic reform starting 2000, Pune’s urban development started obviously. Not
only the total urban area increased, but also the annual urban expansion area increased from 2000 to 2013,
which is the representative of powerful economic engines.
Figure 7: Urban Expansion
6.3 Spatio temporal analysis of urban sprawl using spatial metrics
The classification of multi-temporal satellite images into built up, non built up and water body for two
different time period of 2000 and 2013 has resulted in highly simplified abstracted representation of the

7. study area. These study area shows a clear pattern of increased urban expansion prolonging both from
urban center to adjoining non-built up areas along major transportation corridors.The spatial metric are
used to describe trend and changing pattern of actual built up extracted Landsat images.
6.3.1 Number of Urban Patches
Table 4.3: NP and it significance
Formula
NPU = n
NP equals the number of patches in the
landscape.
Range NPU>0, Without limit.
Significance/ Description
It is a fragmentation Index. Higher the
value more the fragmentation
Number of patches (NP) of a particular habitat type may affect a variety of ecological processes depending
on the landscape context. For example, the number of patches may determine the number of
subpopulations in a spatially-dispersed population. In the following figure 4.7, the concentric zones of
10km each represent the X axis and the Y axis represents the number of patches. There is a positive
correlation in both the years, the number of patches in 2013 increases with increase from the center C1
towards the fifth zone C5 in almost a straight line, while C3 from where it increases but slightly less than
in 2013.
Figure 8: No of Patches
6.3.2 Mean patch size
Table 4.4: MPS and it significance
Formula
MPS =
∑
,
i = patch; a = area of patch I; n = total number of
patches
Range MPS>0,without limit
Significance/ Description
MPS is widely used to describe landscape structure. MPS
is a measure of subdivision of the class or landscape.
Mean patch size index on a raster map calculated, using a
4 neighboring algorithm.
The range in mean patch size is ultimately constrained by the grain and extent of the image and minimum
patch size; relationships cannot be detected beyond these lower and upper limits of resolution. Mean patch
size at the class level is a function of the number of patches in the class and total class area. In the figure

8. 4.8, there is a huge difference between the two years, though the trend of the line for both the years shows
a strong negative correlation. In 2000, the mean patch size decreases in a slightly bend curve from 3
hectares in C1 to 1 hectare in C5 much below the size of 2013 where patch size decreases from 6 hectares
in C1 to 2.5 hectares in C3 to 1.5 hectare in C5. The patch size is more towards the center and reduces as it
reaches the peripheral boundary.
Figure 9: Mean patch size
6.3.3 Total core area index
Table 4.5: TCAI and it significance
Formula TCAI = ∑ ∑
,
= ( ) ℎ ℎ ( )
Range TCAI ≥ 0, ℎ
Significance/ Description TCA equals the sum of the core areas of each patch (m2), divided by 10,000.
Figure 10: Total core area index
In the following figure 4.9 65% of the core area is found to be in the first class or zone C1 of 2013 while it
reduces in a straight line to nearly 52% in C5 and in 2000 the total core area was 50% in C1 which reduced
to about 40% in C5. Same is the case of mean core area, except that in 2013, there was an abrupt decrease
of the core area in C3 which slightly increased to C4 and then reduced in C5.

9. 6.3.4 Core area density
Table 4.6: CAD and it significance
Formula
CAD =
∑
(10,000)(100)
= number of disjunct core areas in patch ij based on
specified edge depth (m)
A= total landscape area
Range
CAD > 0, without limit.
Significance/
Description
CAD equals the sum of number of disjunct core areas
contained within each patch of the corresponding patch
type, divided by total landscape area (m2), multiplied by
10,000 and100.
The core area density (CAD) does a much better job of characterizing the differences in landscape
structure among landscapes. The core area density was more in 2000 in C1 as compared to C1 in 2013.
The core area density of both the years merged in C2, C4 and C5, while C3 of 2000 has a dense core as
compared to the C3 of 2013.
Figure 11: Core area density
6.3.5 Total edge
Table 4.7: TE and it significance
Formula
TE = ∑ e
e = total length (m)of edge in landscape involving patch type ( class)
i; includes landscape boundary and background segments involving patch
type i
Range TE≥ 0, ℎ
Significance/
Description
TE equals the sum of the lengths (m) of all edge segments involving the
corresponding patch type. If a landscape border is present, TE includes
landscape boundary segments involving the corresponding patch type and
representing ‘true’ edge only (i.e., abutting patches of different classes).
Total amount of edge in a landscape is important to many ecological phenomena. In the following figure
4.10, the total edge positively increases for both the years till 30 km zone after which it increases more for
2013 and in 2000 the trend remains the same but the number is lesser than 2013.

10. Figure 12: Total edge
6.3.6 Edge density
Table 4.8: ED and it significance
Formula
ED = (10,000)
E = total length (m) of edge in landscape.
A = total landscape area (m2).
Range ED = 0, without limit.
Significance/
Description
ED equals the sum of the lengths (m) of all edge segments
in the landscape, divided by the total landscape area (m2),
multiplied by 10,000 (to convert to hectares).
In the figure 4.11, there is a negative correlation in both the years. The edge density for both the years is
more towards the center and reduces towards the boundary of the study area. The difference between the
two years is very less.
Figure 13: Edge metrics
6.3.7 Mean Shape index
Table 4.9: MSI and it significance
Formula SHAPE =
.
P = perimeter (m) of patch ij
a = area of patch ij
Range SHAPE > 1, without limit.

11. Significance/
Description
SHAPE equals patch perimeter (m) divided by the square root
of patch area (m2), adjusted by a constant to adjust for a square
standard.
Shape is a difficult parameter to quantify concisely in a metric. FRAGSTATS computes 2 types of shape
indices; both are based on perimeter-area relationships. Mean shape index (SHAPE) measures the
complexity of patch shape compared to a standard shape. Mean shape index is minimum for circular
patches and increases as patches become increasingly noncircular. Mean shape index (MSI) measures the
average patch shape, or the average perimeter-to-area ratio, for a particular patch type (class) or for all
patches in the landscape. The trend of the figure shows a negative correlation in both the years, In 2000,the
mean shape index was higher i.e. nearly 1.27 in C1 which indicates noncircular patches while in 2013 it
reduced to 1.25 in C1. The mean shape index of both the years intersected in C2 and C3 while it reduces to
C5 steadily but the index being still higher in 2000 than in 2013 indicating lower shape index has circular
patches. Hence patches near the boundary indicate almost circular patches.
Figure 14: Mean shape index
6.3.8 Mean nearest neighbour distance
Table 4.10: MNND and it significance
Formula
ENN = ℎ
ℎ = distance (m) from patch ij to nearest
neighboring patch of the same type (class), based on
patch edge-to-edge distance, computed from cell
center to cell center.
Range MNN > 0, without limit
Significance/
Description
MNN equals the distance (m) to the nearest
neighboring patch of the same type, based on
shortest edge-to-edge distance.
Nearest-neighbour distance is defined as the distance from a patch to the nearest neighbouring patch of the
same type, based on edge-to-edge distance. Nearest-neighbour metrics quantify landscape configuration.
FRAGSTATS computes the nearest-neighbour distance (NEAR) and proximity index (PROXIM) for each
patch. The index distinguishes sparse distributions of small habitat patches from configurations where the
habitat forms a complex cluster of larger patches. Mean nearest neighbour distance is the same for both the
years in C1 which increases gradually to C5 in both the years but more in 2013 than in 2000

12. Figure 15: Mean nearest neighbor distance
6.3.9 Proximity index
Table 4.11: PROX and it significance
Formula
PROX = ∑
= area of patch ijs within specified neighborhood of patch ij
ℎ = distance between patch ijs and patch ijs based on patch edge to edge distance,
computed from cell center to cell center
Range PROX > 0.
Significance/
Description
PROX equals the sum of patch area (m2) divided by the nearest edge-to-edge distance
squared (m2) between the patch and the focal patch of all patches of the corresponding
patch type whose edges are within a specified distance (m) of the focal patch.
The proximity index for both years show a negative correlation trend, more in 2013 than in 2000. In 2013
C1, there are 25000 patches which abruptly fall to 14000 patches in C2, bends and gradually reduce to
5000 patches in C5. The proximity index in 2000 shows a gradual decrease in C1 5000 patches to nearly
1000 patches in C5.
Figure 16: Proximity index
7. Conclusion
This paper demonstrates urban expansion of Pune, and identifies the temporal and spatial development
patterns by using multitemporal remote sensing images and GIS tool. Various analysts have made
considerable progress in quantifying the urban sprawl pattern. The pattern of urban sprawl of Madurai is
identified as linear along major roads especially on State Highway and National Highways (Mumbai-Pune
Highway and Pune-Kholapur Highway).

13. The urban sprawl is one of the potential threats to sustainable development where urban planning with
effective resource utilization and allocation of infrastructure initiatives are key concerns. Thus
identification and analysis of the patterns of sprawl would help in effective landuse planning in urban area.
It is important to study and understand the trend of urban sprawls, which ultimately focus for urban
landscape planning and environmental management.
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