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INTERNATIONAL JOURNAL OF ADVANCED SCIENTIFIC RESEARCH AND TECHNOLOGY
ISSUE 2, VOLUME 2 (APRIL 2012) ISSN: 2249-9954
LAND USE/LAND COVER CHANGES DETECTION AND URBAN SPRAWL
ANALYSIS
M. HARIKA1, SK. ASPIYA BEGUM1, S. YAMINI1,
K. BALAKRISHNA1 1B.Tech, Department of Civil Engineering, K L University, Guntur, A.P, India
*Corresponding author e-mail:[email protected]
_____________________________________________________________________________________________
ABSTRACT
Land use and land cover change has become a central component in current strategies for managing natural
resources and monitoring environmental changes. Urban expansion has brought serious losses of agriculture land,
shrub , barren land and water bodies. Urban sprawl is responsible for a variety of urban environmental issues like
decreased air quality, increased runoff and subsequent flooding, increased local temperature, deterioration of water
quality, etc. In this work we have taken Vijayawada , Hyderabad and Visakhapatnam cities as a study. The urban
expansion and land cover change that took place in a span of 19,20 and 21 years from 1990 to 2009,1989 to 2009
and 1988 to 2009. Remote sensing methodology is adopted to study the geographical land use changes occurred
during the study period. Landsat images of TM and Liss3 of three City areas are collected from the USGS Earth
Explorer, Liss Bhuvan website. After image pre-processing, un-supervised and supervised image classification has
been performed to classify the images in to different land use categories. Five land use classes have been identified
as Urban (Built-up), Water body, Agricultural land, Barren land and Shrub. Classification accuracy is also estimated
using the field knowledge obtained from field surveys. The obtained accuracy is between 80 to87 percent for all the
classes. Change detection analysis shows that Built-up area has been increased by 15.14%, agricultural area has been
increased by 0.69% and barren area reduced by 14.45% similarly change detection for remaining areas has been
done. Future prediction is done by using the Markov chain analysis; it is a statistical and probability method. The
future areas of Built-up, water, barren land, shrub, agriculture are computed using this technique.Information on
urban growth, land use and land cover change study is very useful to local government and urban planners for the
betterment of future plans of sustainable development of the city.
Keywords: Land use / Land cover, Urban Sprawl, Remote sensing, Landsat data, Liss data, Markov.
____________________________________________________________________________________________
1.INTRODUCTION
Land-use and land-cover change, as one of the main driving forces of global environmental change, is central to the
sustainable development debate. Land use/land-cover changes have impacts on a wide range of environmental. The
land use/land cover pattern of a region is an outcome of natural, socio-economic factors of their utilization by man
in time and space. Land is becoming a scarce resource due to immense agricultural and demographic pressure. The
change in land cover occurs even in the absence of human activities through natural processes where as land use
change is the manipulation of land cover by human being for multiple purposes- food, fuel wood, timber, fodder,
leaf, litter, medicine, raw materials and recreation. Land use /land cover change has been reviewed from different
perspectives in order to identify the drivers of land use/land cover change, their process and consequences. Urban
growth, particularly the movement of residential and commercial land to rural areas at the periphery of metropolitan
areas, has long been considered a sign of regional economic vitality. Geographical information systems (GIS),
remote sensing are well-established information Technologies, whose applications in land and natural resources
management are widely recognized. Current technologies such as geographical information systems (GIS), remote sensing provides a cost effective and accurate alternative to understanding landscape dynamics. Digital change
detection techniques based on multi-temporal and multi- spectral remotely sensed data have demonstrated a great
potential as a means to understanding landscape dynamics to detect, identify, map, and monitor differences in land
use/land cover patterns over time, irrespective of the causal factors. Recent improvements in satellite image quality
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ISSUE 2, VOLUME 2 (APRIL 2012) ISSN: 2249-9954
and availability have made it possible to perform image analysis at much larger scale than in the past. Markov
process models are a class of probability models used to study the evolution of a system over time. Transition
probabilities are used to identify how a system evolves from one time period to the next. A Markov chain is the
behaviour of the system over time, as described by the transition probabilities and the probability of the system
being in various states. In this paper combined the remotely sensed data to investigate urban growth dynamics of
three areas from 1990 to 2009, 1989 to 2009,1988 to 2009. The main objective of this study is to trace out the land use/land cover changes of city. To predict land use/land cover for future using Markov model.
Satellite imagery has been well utilized in the natural science communities for measuring qualitative and
quantitative terrestrial land-cover changes. Landsat data are most widely used for studying the Land use /Land cover
changes. H.S.Shdhira, T.V Ramachandra and K.S. Jagadeesh(2004), Urban sprawl: metrics, dynamics and
modelling using GIS, International Journal of Applied Earth Observation and Geoinformation,5,29-39[1].
S.Tamilenthi1, J. Punithavathi1, R. Baskaran1 and K. ChandraMohan(2011), Dynamics of urban sprawl, changing
direction and mapping: A case study of Salem city, Tamilnadu, India, Achieves of Applied Science Research, 3(1):
277-286[2]. Bassam Saleh and Samih Al Rawashdeh(2007),Study of Urban Expansion in Jordanian Cities Using GIS
and Remote Sensing, International Journal of Applied Science and Engineering,5,1:41-52[3].Mohamed Ait BELAID,
Bahrain(2003) Urban-Rural Land Use Change Detection and Analysis Using GIS & RS Technologies, 2nd FIG
Regional Conference Marrakech, Morocco, December 2-5, 2003[4].Bhagawat Rimal,(2005) Application of Remote sensing and gis, land use/land cover change in Kathmandu metropolitan city, Nepal. Journal of Theoretical and
Applied Information Technology © 2005 - 2011 JATIT & LLS[5]. Samereh Falahatkar, Ali Reza Soffianian (2011)
Integration of Remote Sensing data and GIS for prediction of land cover map, International Journal Of Geomatics
And Geosciences, Volume 1, No 4, 2011[6]. Zubair ayodeji opeyemi (2008), monitoring the growth of settlements in
ilorin, nigeria (a gis and remote sensing approach), The International archives of the photogrammetric, Remote
sensing and spatial information sciences. vol. xxxvii. part b6b[7] . K. Sundarakumar, M. Harika, Sk. Aspiya begum,
S. Yamini, K. Balakrishna(2012), Land Use And Land Cover Change Detection And Urban Sprawl Analysis Of
Vijayawada City Using Multitemporal Landsat Data. International Journal of Engineering and Sciences,Vol:4 NO.1
ISSN: 0975-5462[8]. All the researchers identified that urban environments are most dynamic in nature. Information
on urban growth, land use and land cover change study is very useful to local government and urban planners for the
betterment of future plans of sustainable development of any area.
2. STUDY AREA
Greater Hyderabad (78o 28’29.53” E and17o25’18.30” N), administered by the Greater Hyderabad Municipal
Corporation was created in April 2007 following an order from the Government of Andhra Pradesh. It covers an area of 650 square kilometers (250 sq mi) and has a population of 6,809,970, making it the fourth largest city in
India, while the population of the urban agglomeration is 7,749,334. A proposal to expand the area of Greater
Hyderabad further to 721 square kilometers (278 sq mi) has been hinted following the Telangana agitation. This
would be done by merging 30 villages to the present area of the city. Is the principal administrative, cultural,
commercial, industrial and knowledge capital of the state of Andhra Pradesh. Hyderabad is the fifth largest city of
India. The mean maximum temperature ranges between 39 °C and 43 °C in May. After the withdrawal of the
monsoon, the maximum temperature rises slightly due to increased insulation. The mean minimum temperature may
rise to 26 °C to 29 °C in May.. The heaviest rainfall recorded in a 24-hour period is 241.5 milli metres on 24 August
2000 . The maximum (day) temperature ever recorded was 45.5 °C (114 °F) on 2 June 1966, while the minimum
recorded temperature was 6.1 °C (43 °F) on 8 January 1946. With rapid urbanization Hyderabad is experiencing
various urban environmental problems. Elevation of urban land surface temperature is one of the upcoming issues. For sustainability of urban eco systems a balanced land use land cover is to be planned.
Visakhapatnam also called as Vizag is a major port city on the south east coast of India. With a population of
1,435,099,[1] it is the second largest city in the state of Andhra Pradesh and the third largest city on the east coast
of India after Kolkata and Chennai. It is located 625 kilometres (388 mi) east of the state capital Hyderabad.
Visakhapatnam is home to several state-owned heavy industries, a major steel plant, and has one of India's largest
sea ports and its oldest shipyard. It has the only natural harbour on the east coast of India. Visakhapatnam
experiences a tropical savanna climate (Köppen climate classification Aw) with little variation in temperature through the year. May is the hottest month with average temperatures around 32 °C (90 °F), while January is the
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coolest month with average temperatures near 23 °C (73 °F). As the city is located on the Bay of Bengal, the
humidity remains high throughout the year. The total annual rainfall is around 945 mm (38 inches), the bulk of
which is received during the south-west monsoon. October is the wettest month with around 204 mm (8 inches) of
rainfall. From a small fishing village in the 20th century, Visakhapatnam has grown into an mega-industrial hub. Its
saga began when the British needed a suitable port that could serve move the rich mineral wealth from the central
India. Unlike the western coast of India, the east coast is devoid of any natural harbours .According to the 2001 India census, Visakhapatnam had a population of 2,569,608 and that of urban area 3,329,472. After the state
government approved the formation of Greater Visakhapatnam with the merger of Gajuwaka municipality and 32
villages in the vicinity in the Visakhapatnam Municipal Corporation, the population of the city and the metro area
swelled present population may be between 2.6 to 3.8 million.
Vijayawada is a historical city situated at the geographical centre of Andhra Pradesh state in India on the banks of
Krishna River with latitude 16003’11” N and longitude 800 03’91” E. The climate is tropical, with hot summers and
moderate winters. The peak temperature reaches 47 °C in May-June, while the winter temperature is 20-270 C. The
average humidity is 78% and the average annual rainfall is 103 cm. Vijayawada gets its rainfall from both the south-
west monsoon and north-east monsoon. The topography of Vijayawada is flat, with a few small to medium sized
hills. It is also a major railway junction connecting all states in the country. The Vijayawada city is the commercial
capital of the state of Andhra Pradesh. The population growth has been rapidly registering almost three fold increase in 3 decades ending 2001 with a population account of 8.45lakhs The overall gross density as of 2001 was 13600 per
sq km. Vijayawada municipality was set up in 1888 with an area around 30 sq.km has now spread to 61.88 sq.km
and inclusive of the contiguous suburbs like Gollapudi, Nunna, Kanuru, Poranki, Tadigadapa, Enamalakuduru and
Ramarvappadu commands a total development area of 87.32 sq.km. Being the third largest city in the state of
Andhrapradesh and largest city in the Krishna district, Vijayawada has a lot of scope for development and urban
growth.The city’s population is expected to increase to 16.5 lakh by 2021.With ever increasing population and
unprecedented growth of urban area the city’s landscape is undergoing unwanted changes. The increased runoff is
inundating the low lying areas of the many parts of the city even from the normal spell of rainfall. This is mainly due
the impervious nature imparted to the land surface because of the urbanization. Urban Heat Island is one of the
upcoming urban climatologically problems developing in the city.
Fig 1 Location of study area of Visakhapatnam, Vijayawada and Greater Hyderabad
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3. METHODOLOGY
The present study involves the collection of Topo-sheets from Survey of India and city map from relevant
authorities. The required satellite imagery for the study area is to be downloaded from the USGS Earth Explorer and
Liss Bhuvan. Processing the imagery and image interpretation for development of Land use/ Land cover maps is
n done in ERDAS Imagine software. The obtained maps are studied and analyzed to detect the change in urban
sprawl. Future prediction is done based on past data. The methodology adopted in detail is shown in the Fig.2
Fig 2 Methodology adopted in this work
3.1 Data collection
Cloud Free Landsat and Liss data of four dates available in the past four decades has been downloaded from USGS Earth Explorer,Liss Bhuvan website. All the data are preprocessed and projected to the Universal Transverse
Mercator (UTM) projection system. The satellite data collected are shown in the Table.1.
Table 1 Details of satellite images
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3.2 Image preprocessing
The downloaded images contain scenes mosaicing is done to get required area and all the images contain different types of
bands and stacking is performed to get the composite image. Other image enhancement techniques like histogram equalization are also performed on each image for improving the quality of the image. With the help of Survey of India
Topo-sheets of 1:50000 and city plan map obtained from Vijayawada, Hyderabad and Visakhapatnam cities Municipal Corporation, the study area has been delineated. The base layer so formed is used to subset the Landsat, Liss images
3.3Classification of images
The pre-processed images are then classified by both un-supervised , supervised classification methods. In un-supervised classification method the ISODATA clustering algorithm which is built in the ERDAS Imagine will classify according the
number of classes required and the digital number of the pixels available. In the supervised classification technique the maximum likely hood algorithm will classify the image based on the training sets (signatures) provided by the user based
on his field knowledge. The training data given by the user tells the software, that what types of pixels are to be selected for certain land cover type. The un-supervised classified image has been used for reference and for understanding about
the distribution of pixels with different digital numbers. The classification finally gives the land use/land cover image of the area. Five land cover classes namely built-up area, agricultural land, water bodies, barren area and shrubs are identified
in the study area.
3.4 Land use and land cover (LU/LC)
There is no doubt that human activities have profoundly changed land cover in the three areas during the past years. Land is one of the most important natural resources. All agricultural, animal and forestry productions depend on the productivity
of the land. The entire eco-system of the land, which comprises of soil, water and plant, meets the community demand for
food, energy and other needs of livelihood. Viewing the Earth from space is now crucial to the understanding of the influence of man’s activities on his natural resource base over time. In situations of rapid and often undocumented and
unrecorded land use change, observations of the earth from space provide objective information of human activities and utilization of the landscape. The classified images provide all the information to understand the land use and land cover of
the study area.
3.5 Change detection analysis
Change detection analyses describes and quantify differences between images of the same scene at different times.The classified images of the four dates can be used to calculate the area of different land cover and observe the changes that are
taking place in the span of data. This analysis is very much helpful to identify various changes occurring in different classes of land use like increase in urban built-up area or decrease in shrub and so on.
4 RESULTS AND DISCUSSIONS
4.1 Land use/Land cover images
The classified images obtained after preprocessing and supervised classification which are showing the land
use and land cover of the Hyderabad city, Visakhapatnam and Vijayawada city are given in the following figures
viz., Fig 3,4,5,6,7and 8. These images provide the information about the land use pattern of the study area. The red
color represents the urban built-up area, dark green color shows the agricultural area, blue color shows the water
bodies tan color shows the barren land and light green color shows the vegetation like shrubs and grassland.
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Fig 3 LU/LC classified image of 1989 Fig 4 LU/LC classified image of 2009
(Hyderabad city) (Hyderabad city)
Fig 5 LU/LC classified image of 1988 Fig 6 LU/LC classified image of 2009 (Visakhapatnam city) (Visakhapatnam city)
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Fig 7 LU/LC classified image of 1990 Fig 8 LU/LC classified image of 2009
(Vijayawada city) (Vijayawada city)
4.2 Classification accuracy assessment
Each of the land use and land cover map was compared to the reference data to assess the accuracy of the
classification. The reference data was prepared by considering random sample points, the field knowledge and
Google earth. During the field visits a hand held GPS (Global Positioning System) is used to identify the exact
position of the place under consideration with Latitude and Longitude and its type by visual observation. The ground
truth data so obtained was used to verify the classification accuracy. Over all classification accuracy of Hyderabad
city for the years 1989&2009 is 86.67&84%,simillary Visakhapatnam city(1988,2009) and Vijayawada
city(1990,2009)are 80%,86.67% and.86.67%,85% respectively. The Kappa coefficient for Hyderabad city
(1989&2009) is 0.81%,0.81%, Visakhapatnam city(1988,2009) is 0.77%,0.85% and Vijayawada city (1990,2009) is 0.80% , 0.78%. The results of the accuracy assessment are presented in Table 2,3 and 4
Table 2 Classification Accuracy Assessment Report for Hyderabad
Table 3 lassification Accuracy Assessment Report for Visakhapatnam
Table 4 Classification Accuracy Assessment Report for Vijayawada city
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4.3 Change detection analysis
The urban change analysis presented in this work was based on the statistics extracted from the four land use
and land cover maps of the Hyderabad city, Visakhapatnam city& Vijayawada city. The changes in land cover
during the study period (four dates) can be observed clearly from the pie diagrams shown in Fig 9,10,11,12,13&14.
Fig 9 Land cover in 1989 Hyderabad city Fig 10 Land cover in 2009 Hyderabad city
Fig 11 Land cover in 1988 Visakhapatnam city Fig 12 Land cover in 2009 Visakhapatnam city
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Fig 13 Land cover in 1990 Vijayawada city Fig 14 Land cover in 2009 Vijayawada city
The built-up area has been changed drastically from 1989 to 2009. Built-up area has been increased by
15.14%, agricultural area as also increased by 0.69%, vegetation and barren land area has been reduced by 3.2%and
14.45%. The increase in built-up area has many reasons. Hyderabad is famous for industrial, educational
institutions, large numbers of institutions are coming in to existence and corresponding infrastructure development
leads to the increase of built-up area.
Similarly in Visakhapatnam city and Vijayawada city the built up area has been increased by 15.7% &5.39%,
agriculture area and vegetation area has been reduced to 0.68%,9.51%, 14.71%,4.81%.Increase in merchant
establishments and industrial areas are contributing o the loss of agriculture area. There is an increase in vegetation
by 15.65% over the study period in Vijayawada city. It was observed that Eutrophication phenomena is taking place
in all the lakes and small water bodies are disappeared because of the deposition of sediments and indiscriminate dumping of solid waste in Vijayawada city. The change in percentage for three cities can been observed in
tables5,6,&7.
Table 5 Statistics and magnitude of land use and land cover change for Hyderabad
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Table 6 Statistics and magnitude of land use and land cover change for Visakhapatnam
1988-2009(ha)
Table 7 Statistics and magnitude of land use and land cover change for Vijayawada city
1990-2009(ha)
Table 8 Post classification matrix of study area Hyderabad from 1989-2009 (ha)
4.4 Markov results
4.4.1 Post classification matrix
Cross tabulation is a means to determine quantities of conversions from a particular land cover to another land
cover category at a later date. The change matrices based on post classification comparison were obtained and are shown in Tables 8,9&10.
The nature of the changes of different the land cover classes can be examined in Tables 5,6&7 . For example,
built up area covered 22193.9 ha in 1989 and 32633.92 ha in 2009, while the vegetation class covered an area of
21892.2 ha in 1989 and 3747.47 ha in 2009. 19690.78 ha of the area which was vegetation in 1990 was still
vegetation cover in 2009, but 1729.35 ha had been converted to built up area use by 2009. During the same time
period, 8710.67 ha of barren land were restored to Built up area cover. 15.14% was changed to Built up area.
Similarly change of different land class into another land class for three different cities can been observed in tables
8,9&10.
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\
Table 9 Post classification matrix of study area Visakhapatnam from 1989-2009 (ha)
Table 10 Post classification matrix of study area Vijayawada from 1990-2009 (ha)
4.4.2 Transitional probability matrix and Future Land use statistic
A Markov model result is a transition matrix which shows the probability of changes from each class of land
cover or land use to each other class in the future. Tables 4.10, 4.11&4.12 are the probability transition matrix of
different land cover types of Hyderabad Visakhapatnam and Vijayawada cities during the periods 1989-2009, 1988-
2009 and 1990-2009 respectively. In this project, we use the 1989-2009, 1988-2009&1990-2009 land cover maps to
predict the 2029.2030 and 2029 land cover areas. Future Land use statistic can been observed in tables 11,12&13
Table 11 Transitional probability matrix table derived from the land use/land cover map of 1990-2009(Hyderabad)
Table 12 Transitional probability matrix table derived from the land use/land cover map of 1988- 2009
(Visakhapatnam)
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Table 13 Transitional probability matrix table derived from the land use/land cover map of 1990-2009(Vijayawada)
Table 14 Land use statistic of Hyderabad city, 2009-2029
Fig 15 Land cover in 2029 Hyderabad city
56%
6%
26%
5% 7%
2029
Built up Agriculture shrub water Barren land
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Table 15 Land use statistic of Visakhapatnam city, 2009-2030
Fig 16 Land cover in 2030 Visakhapatnam city
Table 16 Land use statistic of Vijayawada city, 2009-2028
52%
9%11%
9%19%
2030
Built up Agriculture shrub water Barren land
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Fig 17 Land cover in 2028 Vijayawada city
CONCLUSION
In this work we are mainly highlighting the urban sprawl analysis of Vijayawada, Hyderabad and Vishakhapatnam
cities. The following are conclusions were obtained from our study. Vijayawada, Hyderabad and Vishakhapatnam
are the three urbanized and rapidly growing cities of Andhra Pradesh. The land cover / land use maps are generated
for the three cities using the ERDAS software. The urbanization growth between the three cities is observed. From
our study it was observed that the urbanization in Vijayawada has increased about 5.39% from 1990 to 2009. From our study it was observed that the urbanization in Hyderabad has increased about 15.14 % from 1989 to 2009. From
our study it was observed that the urbanization in Vishakhapatnam has increased about 15.7% from 1988 to 2009.
Future prediction has been done by using Markova chain analysis. It was observed that the future area may increase
of about1.21 % in Vijayawada, 8.65 % in Hyderabad and10.84 % in Vishakhapatnam. The increased urbanization
may have several impacts on infrastructure , energy use and economy of the country.
REFERENCES
[1].H.S.Shdhira, T.V Ramachandra and K.S. Jagadeesh(2004), Urban sprawl: metrics, dynamics and modelling
using GIS, International Journal of Applied Earth Observation and Geoinformation,5,29-39.
[2] S.Tamilenthi1, J. Punithavathi1, R. Baskaran1 and K. ChandraMohan(2011), Dynamics of urban sprawl,
changing direction and mapping: A case study of Salem city, Tamilnadu, India, Achieves of Applied Science
Research, 3(1): 277-286.
[3] Bassam Saleh and Samih Al Rawashdeh(2007),Study of Urban Expansion in Jordanian Cities Using GIS and
Remote Sensing, International Journal of Applied Science and Engineering,5,1:41-52.
[4]. Mohamed Ait BELAID, Bahrain(2003) Urban-Rural Land Use Change Detection and Analysis Using GIS & RS
Technologies, 2nd FIG Regional Conference Marrakech, Morocco, December 2-5, 2003. [5]. Bhagawat Rimal,(2005) Application of Remote sensing and gis, land use/land cover change in Kathmandu
metropolitan city, Nepal. Journal of Theoretical and Applied Information Technology © 2005 - 2011 JATIT & LLS.
[6]. Samereh Falahatkar, Ali Reza Soffianian (2011) Integration of Remote Sensing data and GIS for prediction of
land cover map, International Journal Of Geomatics And Geosciences, Volume 1, No 4, 2011.
[8]. zubair ayodeji opeyemi (2008), monitoring the growth of settlements in ilorin, nigeria (a gis and remote sensing
approach), The International archives of the photogrammetric, Remote sensing and spatial information sciences. vol.
xxxvii. part b6b.
[7]. K. Sundarakumar, M. Harika, Sk. Aspiya begum, S. Yamini, K. Balakrishna(2012), Land Use And Land Cover
Change Detection And Urban Sprawl Analysis Of Vijayawada City Using Multitemporal Landsat Data.
International Journal of Engineering and Sciences,Vol:4 NO.1 ISSN: 0975-5462.
43%
1%
27%1%
28%
2028
Built up Agriculture shrub water Barren land