ANALYSIS OF LAND USE AND LAND COVER CHANGE OF BANGALORE URBAN USING REMOTE SENSING AND GIS TECHNIQUES

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InternationalJournalofInformative& FuturisticResearch ISSN: 2347-1697
Volume 4 Issue 3 November 2016
www.ijifr.com
Abstract
Land use and land cover of any area is essential to access the urban growth,
vegetation, water quality issues, land suitability, etc. Furthermore, studying
the land cover change over time gives the trend of transformation of area
and enables to predict the possible effects of it in the future. With this, timely
implication of necessary steps to protect the area can be taken and a
sustainable development can be achieved. In order to obtain the land cover
data, Remote sensing and GIS systems are often used. A classification
method called as supervised classification is very popular in classifying the
land cover of any area. This study uses the same technique to study the land
cover of Bangalore from 1992 to 2016. The area was divided into four
classes: Water, Built up, Vegetation and Others. Water coverage in the
study area showed a decreasing trend with a decrease by 72%. Vegetation
coverage also showed a decreasing trend with a decrease by 35%. Built up
coverage showed an exponential increase by 152%. Other areas reduced by
a total of 23%. The conversion of vegetation land and open spaces into built
up areas in the periphery of the city shows an unsustainable growth of the
city which must be addressed.
ANALYSIS OF LAND USE AND LAND COVER
CHANGE OF BANGALORE URBAN USING
REMOTE SENSING AND GIS TECHNIQUES
Paper ID IJIFR/V4/ E3/ 037 Page No. 5545-5555 Subject Area Civil Engineering
Keywords Land Use, Land Cover, Remote Sensing, GIS, Supervised Image Classification
1st Vinod Kumar H A
Assistant Professor
Department of Civil Engineering
M S Ramaiah Institute of Technology
Bangalore, Karnataka
2nd Aakash Bikram Rana
B.E. Student
Department of Civil Engineering
M S Ramaiah Institute of Technology
Bangalore, Karnataka

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ISSN: 2347-1697
International Journal of Informative & Futuristic Research (IJIFR)
Volume - 4, Issue -3, November 2016
Continuous 39th Edition, Page No: 5545-5555
Vinod Kumar H A., Aakash Bikram Rana :: Analysis Of Land Use
And Land Cover Change Of Bangalore Urban Using Remote Sensing
And GIS Techniques
I. INTRODUCTION
Land Cover is the term used to define what covers the land of any area. Some examples of
land cover are vegetation, built up, water, sub-tropical trees, desert, etc. Land use is the term
used to define how the land is being used. Some examples of land use are multi-storeyed
building, residential home, malls, parks, reservoirs, nurseries, etc. Land cover and land use
has an effect on ecosystem and climate system of any area so it needs to be managed
properly. However, in many parts of the world, the expansion of city and construction
projects are being carried out in a haphazard and unmanaged manner thereby causing the
land cover and land use scenario to change in an undesirable manner.Thus, this change
needs to be studied and accessed and this can be done using land cover and land use
maps.Thematic maps of land use and land cover provides a comprehensive picture of a
particular area. This data is a fundamental component in the planning and decision-making
processes for many communities because it helps them to understand better which areas can
be planned for growth and which areas needs to be preserved. It also helps them to
understand the correlation between various components of the society. The effects of any
past decisions can be analysed using these maps and at the same time, possible effects of
current decisions and projects can be estimated.These maps are also an essential component
for coastal management. Coastal management is the science of defending community
against flooding and erosion.Furthermore, urban flooding, urban sprawl, vegetation issues,
water quality issues, etc are many other aspects which can be accessed and dealt with land
cover and land use data effectively.
II. REMOTE SENSING
Remote sensing is the science of making inferences about any object without coming in
direct contact with the object. These may be done through electrical methods, magnetic
methods, visual interpretation, acoustic methods, etc. However, most part of remote sensing
deals with the study of aerial and satellite imageries. Remote sensing is used in numerous
fields such as hydrology, ecology, oceanology, military, economics, planning, intelligence,
etc. The two basic processes involved in remote sensing are data acquisition and data
analysis.
Data acquisition: For active remote sensors, they themselves generate signals whereas for
passive remote sensors, they use the energy of the sun. This energy is transmitted to objects
via atmosphere. The objects absorb a certain amount of energy of different wavelengths and
reflect it back to the atmosphere. These reflected waves are then detected by remote sensors.
In this way, data regarding the object is obtained.
Data analysis & Data Interpretation.These data are then converted in pictorial form and
then analysed using relevant software in computer. Analysis of Remote sensing data is
primarily based upon an Inverse problem approach. This means measuring the phenomenon
or object of interest by not measuring it directly but instead measuring some other variable
that has a high correlation with the object or phenomenon of interest. For example: In this
study, land cover is measured not by surveying the ground but by analysing the reflectance

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spectra of the image of ground. This process is called as data analysis. The results are then
interpreted to find out the possible effects of the phenomena and their causes. If problems
are detected, precautions are suggested. If possible hazards are detected, warnings are given.
Geographic Information System (GIS):
The complete system of software, hardware, application and personnel required capturing,
storing, manipulating, analysing, managing, and present all types of spatial or geographical
data is Geographic Information System. Spatial data means data related to space which can
include elevation data, rainfall data, land cover data, population data, consumer’s data, etc.
Various types of software are available for these types of analyses like ArcGIS, QGIS,
MapInfo, etc. First of all, data, such as of remote sensing data, are imported then pre-
processed. Then, thematic maps are generated. A number of thematic maps are displayed in
terms of layers simultaneously such that each layer displays a different type of data of the
same area. All these data and information can be further analysed and compared using
relevant geoprocessing tools. Finally, a wide variety of individual maps can be produced
using GIS by displaying a selected number of layers and exporting them. GIS is thus a
powerful modern tool for handling spatial data and answering spatial questions.
LANDSAT: LANDSAT is the abbreviation for Land Satellite. The Landsat programme is
the longest running enterprise for acquisition of satellite imagery of Earth. On July 23, 1972,
the Earth resource technology satellite was launched. This was eventually renamed to
Landsat. Landsat data are extensively used in research and mapping works throughout the
world. Various Landsat satellites have been launched till date, landsat 8 being the most
recently launched satellite. It was launched on February 11, 2013. Many researchers depend
upon these images for their work.
It is to be noted that the Scan Line Corrector (SLC) of Landsat 7 failed permanently on
2003. On account of this failure, any landsat 7 data products beyond 2003 till 2013(until
Landsat 8 satellite was launched) contain data gaps in them.
Image Classification: Image classification refers to the task of classifying a multi band
raster image into a number of classes to produce thematic land cover maps. Different types
of object have different types of spectral reflectance curves (i.e.: They absorb and reflect
different wavelengths differently). Using this principle, the multiband raster can be
classified into a number of classes. There are basically two methods in which image
classification is carried out.
1) Unsupervised classification Method: In unsupervised classification, the image is
classified based upon natural groupings of spectral classes into a number of classes as
specified by the analyst.
2) Supervised classification method: In supervised classification, a number of training
samples is created by the analyst and classification is carried out based upon those
training samples. Thus, the accuracy of results of supervised classification depends
highly on the quality of training data and analyst’s experience. Supervised classification
may use different algorithms for classification which may be maximum likelihood rule,
parallelepiped rule, minimum distance rule, etc. Out of these, maximum likelihood rule

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Figure 1: Location map of Bangalore Urban
gives the most accurate results.Several attempts have been made to study land use and
land cover of different areas using image classification techniques [5]. However, as stated
by Anderson [1], there is no one ideal classification of land use and land cover, and it is
unlikely that one could ever be developed.
III. RESEARCH DESIGN
3.1 Study Area
The area of study for is Bangalore Urban
district. Bangalore is an active metropolitan
city of South India and is the capital of
Karnataka state. It lies at latitude of 77.36E
to 77.87E and longitude of 12.65N to
13.30N. It has an area of 741sq.km and an
average elevation of 920m. The temperature
of Bengaluru is mild and equable throughout
the year. It is one of the fastest developing
cities of India and is well known as IT
capital of India.
3.2 Data used
This study primarily uses Landsat images for the analysis. The study comprises of analysing
land cover and land use change, thus a number of landsat images of time period between
1992 and 2016 was used. The images were obtained from earthexplorer.usgs.gov. The
details of the images are given in table 1.
Table 1: Landsat data used for this study
SN Landsat image Date Year Resolution (m)
1 LT51440511992014ISP00 14 January 1992 1992 30
2 LT51440511995022ISP00 22 January 1995 1995 30
3 LT51440511997043ISP01 12 February 1997 1997 30
4 LT51440511999033AAA02 02 February 1999 1999 30
5 LE71440512001014SGS00 14 January 2001 2001 30
6 LE71440512003068SGS00 09 March 2003 2003 30
7 LE71440512006012PFS00 12 January 2006 2006 30
8 LE71440512008018ASN00 18 January 2008 2008 30
9 LE71440512010023SGS00 23 January 2010 2010 30
10 LE71440512012061PFS00 01 March 2012 2012 30
11 LC81440512014010LGN00 11 February 2014 2014 30
12 LC81440512016080LGN00 20 March 2016 2016 30
3.3 Software used (ArcGIS for desktop 10.3)
ArcGIS® is a desktop mapping and spatial data analysis application produced by ESRI. The

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version of ArcGIS 10.3 was released on December 2014. This software gives a desktop user
the ability to create, modify and analyse any form of spatial data.
3.4 Methodology
First of all, “composite bands” tool was used to combine the various bands of Landsat
image into a single multi-band raster image. The study is based only on Urban Bangalore so
the Urban Bangalore area was extracted using “Extract by mask” tool using Urban
Bangalore polygon as mask.
Using image classification window, multiple polygons were drawn on screen as training
samples for supervised classification technique. Training samples were taken for four
categories: built up, vegetation, water body and others. After taking adequate training
samples, the signature was saved and “Maximum likelihood classification” was carried out.
The result obtained in the first turn was generally inaccurate so more training samples were
created and “Maximum likelihood classification” was carried out again. This process was
continued until a satisfactory result was obtained.
After this, a new field of float data type and name “percentage_coverage” was added in the
attribute table of our land cover raster and percentage_coverage was calculated using
“Calculate Field” tool.
3.5 Building The Automating Model
One advantage of using GIS is that any workflow can be easily automated using “model-
builder tool”. The model so created for the above procedure is given in figure 2. Using this
model, the land cover raster of other years was found out and percentage_coverage value
was calculated.
Figure 2: Model to generate land cover image and calculate percentage_coverage

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IV. DATA GAPS IN SOME IMAGES
There were some data gaps in images obtained from Landsat 7 between 2003 and 2013.
These data gaps can be filled by various techniques [9]; however, for this study, the data
gaps were not filled up and results were taken as a percentage of true data only. The
percentage area coverage obtained from this data is inaccurate to some degree but the degree
of error is negligible. This was verified using Landsat image of 2001. Here, in landsat 2001
image, there is no data gap. But, some gaps were intentionally introduced to it using the data
gaps of landsat 2006 image. Then, land cover analysis was carried out on both the correct
image and the gapped image of 2001. The percentage land coverage as obtained from both
of these images had a difference of less than 2 percent.
Table 2: Comparison of LC results obtained from gapless 2001 image and gap introduced 2001
image
V. RESULTS
The satellite image along with its corresponding land cover image and land cover
breakdown is shown in figure 5 through figure 16. The changes and trend is discussed in
following paragraph and is shown in figure 4.
Changes in water cover:
Water cover includes lakes and river bodies. A large variation was observed in the water
cover of Bangalore over the years. However, a decreasing trend of water cover is seen. This
decreasing trend of water cover shows that the water bodies of Bangalore have been
encroached upon. In overall, from 1992to 2016, water coverage has shown a decrease by
72%. The trend of reduction in water cover can be represented by the following formula.
However, the standard deviation is as high as 2.70.
y = -0.0649x + 132.03 where y = percentage water coverage and x= years in AD
Changes in vegetation cover:
The vegetation cover includes trees, forests and densely cultivated lands. The vegetation
cover of Bangalore Urban is showing a decreasing trend. Overall, from 1992 to 2016, a total
of 35% decrease in vegetation cover is observed. The trend line equation is given below.
Results From Images Water Vegetation Built Up Others
Gapless 2001 3.39 29.30 17.01 50.30
Gap Introduced 2001 3.33 29.01 16.88 50.77
Percentage Change -1.48 -0.99 -0.76 0.91
Figure 3: True Lc And Gap Introduced Lc 2001

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The standard deviation of values from this equation is 8.17.
y = -.41x+840.5 where y = percentage vegetation coverage and x= years in AD
Changes in built up cover:
Built up area includes residential homes, malls, roads, pavements and all such man made
constructions. The analysis shows that the built up coverage is increasing. Overall, built up
coverage showed an exponential increase by 152% from 1992 to 2016. The equation of the
trend line is shown below. A standard deviation of 6.58 was obtained between the obtained
data and the trend line data.
y = 3E-34e0.0401x
where y = percentage built up coverage and x= years in AD
Changes in other areas:
Any data besides water, vegetation and built up are included in “others”. This mostly
includes fallow lands and open spaces along with less cultivated areas. The analysis shows a
decreasing trend of others coverage. Overall, from 1973 to 2016, other areas reduced by a
total of 23%.This can be roughly represented by the trend equation as shown below. The
obtained data has a standard variation of 10.76 from the trend line.
y = -.551x + 1152 where y = percentage others coverage and x= years in AD
Figure 4: Graph comparing the result of the analysis

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VI. FINDINGS
1) Exponential increase in built up by 152%. Migration from other cities is the major cause
of it. Such an increase of built up areas in the periphery of the city is an indication of
unplanned growth of city, also known as urban sprawl , which must be minimized
otherwise there will be problem of urban flooding, insufficient commodities for residents,
pollution, water shortage, etc. This study highlights the problem of urban sprawl in
Bangalore, but this is in fact a general problem for whole of India. The population of
India is increasing at an alarming rate such that it is expected to be the most populous
country by 2022. Also, it has been concluded that about 40% of India will be living in
cities by 2030. This also reflects the seriousness of unsustainable urban growth in India.
One possible solution would be by building high rise building.
2) Decrease in vegetation by 35%. The decreasing vegetation has effects on the ecosystem
of the area. It also has a negative effect on the rainfall pattern and average temperature of
Bangalore. In order to control this, vegetation should be protected by constructing parks
and better policies should be brought about against cutting down of trees.
3) Decrease in water cover by 72%. The decreasing trend of water cover shows the
depletion of water availability of the area. This is mostly due to drying up of Bangalore
lakes for commercial usage and their encroachment. Since there are no perennial rivers in
Bangalore, the lakes have a paramount importance in Bangalore and this issue has been
highlighted in various papers. A recent study has even concluded that 98% of lakes of
Bangalore are encroached and 90% are polluted. The water body of Bangalore thus needs
an immediate attention.
4) Decrease in other areas by 23%. Open spaces are essential for recreational activities and
socializing. It also acts as a good medium for ground water recharge. As long as open
spaces are converted to vegetated area or parks, it would not affect the sustainability of
the area.. However, the data shows that the conversion of most open spaces is being done
into built up areas only. Such unplanned development should be restricted.
5) The results show a clear need of protection of lakes and vegetated area of the city and a
need for better initiative and policies for city expansion.

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VII. REFERENCES
[1] Anderson, James R., Hardy, Ernest E., and Roach, John T., 1972, A land-use classification
system for use with remote-sensor data: U.S. Geol. Survey Cire. 671, 16 p., refs.July
[2] AshokaVanjare, S.N. Omkar, J.Senthilnath (2014) ,”Satellite Image Processing for Land Use
and Land Cover Mapping” I.J. Image, Graphics and Signal Processing, 2014, 10, 18-28
[3] C.B.Jagadeesh, Dr.H.S.ShivakumarNaiklal& Dr. NagarajSitaram (2015),”Dynamics of Rapid
Urbanization of Bangalore and Its Impact on Land Use/Land-Cover – A case study of
Vrishabhavathi sub-watershed “e-ISSN: 2395-0056 Volume: 02 Issue: 03
[4] D K Prabhuraj& Surya Deb Chakraborty (2013) ,”Land Use/Land Cover Change Analysis of
Bangalore Urban District and Its Impact on Land Surface Temperature” National Symposium
On remote Sensing And Gis for environment with Special Emphasis on Marine And Coastal
Dynamics
[5] Dr. Malini A.Shetty, et al (2014),”“Land Cover Classification of Bangalore Metro Using
Satellite Imageries - A Comparative Study”.VolVolume-3, Issue-7, July-2014 • ISSN No 2277
- 8160ume-3, Issue-
[6] Ellefsen, R., Swaiu, P. H., and Wray, J. R., 1973, Urban land use mapping by machine
processing of ERTS-1 multispectral data: A San Francisco Bay area example: West Lafayette,
Ind., Purdue Univ. Lab.for Applications of Remote Sensing Inf. Note 101573.
[7] Hawley, Arthur J., 1973, The present and future status of Eastern North Carolina wetlands:
Chapel Hill,Univ. of North Carolina, Water Resources Res. Iust., Rept. no. 87.7
[8] James R. Anderson, Ernest E. Hardy, John T. Roach & Richard E. Witmer (1976),” A Land
Use And Land Cover Classification System For Use With Remote Sensor Data “Geological
Survey Professional Paper 964
[9] M.J. Pringle , M. Schmidt & J.S. Muir (2009) ,”Geostatistical Interpolation of SLC-off Landsat
ETM+ images” ,ISPRS Journal of Photogrammetry and Remote Sensing ,Volume 64, Issue 6
[10] Ramachandra T.V. & Uttam Kumar (2009) Geo informatics for urbanization and urban sprawl
pattern
[11] Sudhira H. S. & T. V. Ramachandra (2007),”Characterising Urban Sprawl from Remote
Sensing Data and Using Landscape Metrics “,International conference on Urban Planning and
Urban Management
[12] T. V. Ramachandra, K.S. Asulabha&V.Sincy (2016) ,Wetlands - Treasure of Bangalore
analysis. In: Joshi P. K. (Eds). Geoinformatics for Natural Resource Management, Nova
Science Publishers, Chap 19.
[13] Zandbergen, P. A. (1998),” Urban watershed ecological risk assessment using GIS: a case study
of the Brunette River watershed in British Columbia”, Canada, Journalof Hazardous Materials,
61(1-3), 163-1732014 • ISSN No 2277 - 8160
To Cite This Article
Kumar Vinod, H. A., Rana, B.A. (2016):“Analysis Of Land Use And Land Cover Change
Of Bangalore Urban Using Remote Sensing And GIS Techniques” International Journal of
Informative & Futuristic Research (ISSN: 2347-1697), Vol. 4 No. (3), November 2016, pp.
5545-5555, Paper ID: IJIFR/V4/E3/037.

ANALYSIS OF LAND USE AND LAND COVER CHANGE OF BANGALORE URBAN USING REMOTE SENSING AND GIS TECHNIQUES

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