International Journal of Engineering Trends and Technology (IJETT) – Volume 3 Issue 3 No 4 – June 2012
Assessment of Landuse/landcover change and Vegetation response using
Normalized difference vegetation index (NDVI): A case study of Neyveli
Township, Cuddalore district, Using Remote Sensing and GIS techniques
*K. Ilayaraja, Peterguspher, Vennilavan and Niranjan
Department of Civil Engineering
Bharath University
Selaiyur, Chennai- 600073.
ABSTRACT
Landuse and landcover mapping determines the land resources throughout the world also it helps to
determine the changes in land uses throughout the world. The landuse/land cover pattern in Neyveli Township was
studied by using Survey of India toposheet (1977) and with the available satellite data sets 1991 and 2006. The
Landuse/landcover patterns were visually interpreted by using Arc GIS 9.3 software. The study has determined the
changes that have occurred over a time period of three decades and those changes impact the surrounding
environment, affect the availability of natural resources such as water, and alter the landscape and how it’s used.
From the study it is found that the barren lands is decreased by 36.38% (1991), cashew plantation has decreased by
2.63% (1991), water bodies decreased by 14.57% (1991), mines increased by 3.73% (1991) and the area of the
mines increased by 1.22% in (2006) and the area occupied by the barren lands, cashew plantation, water bodies,
decreased by 3.71%, 2.24%, 14.0% in 2006 respectively. This information, in turn, can help people anticipate and
plan for future changes. Remote sensing can present an efficient and reliable means of collecting the information
and with the multispectral sensors can provide information of about the health of the vegetation. The spectral
reflectance of an area will vary with respect to changes in the crop type, health and growth. The study deals to
calculate the NDVI (Normalized difference vegetation index) for the Chennai City using remote sensing technique
by using open source Quantum GIS software. For this purpose Landsat ETM+ images of the year 1991 and 2006
was obtained for the study area.
Keywords: LULC, GIS, Planning and Management
1.
INTRODUCTION
Landuse and land cover changes have impacts on a wide range of environmental and landscape attributes
including the quality of water, land and air resources, ecosystem processes and function, and the climate system
itself through greenhouse gas fluxes and surface albedo effect (Lambin 2000). Bhagawat Rimal (2005) has predicted
the land-use pattern for the Kathmandu metropolitan city, Nepal by using remote sensing and GIS Techniques. The
knowledge of land use and land cover is important for many Planning and Management Activities and considered as
essential element for modeling and understanding the earth as a system Land cover maps have presently developed
from local to national to global scales. Land cover is the physical material at the surface of the earth. Land cover
includes grass, asphalt, trees, bares ground, water etc. It is a description of how people utilize the land. When
considering land cover in a very pure and strict sense, it should be confined to describe vegetation and man-made
features. Consequently, areas where the surface consists of bare rock or bare soil are describing land itself rather
than land cover. Land use is the human use of land and it is the description of how people utilize the land. Land use
involves the management and medication of natural environment or wilderness into built environment such as
fields, pastures, and settlements . It has also been defined as “the arrangements , activities and input people
undertake in a certain land cover type to produce , change or maintain it (Turner et al., (1990) and Lambin et al.,
(1999). Land use change maps are also used for predicting the effects on Climate change (Hu et al., 2003 and Klein
et al., 2004). According to Kachhwaha (1985) application of remotely sensed data used to study the changes of land
cover in less time, at low cost and with better accuracy. GIS is a suitable platform for data analysis, update and
retrieval (Star et al. 1997; Chilar 2000). Andreas Dittrich et al., (2010) has assessment the land use and land cover
changes in oases and surrounding rangelands of Xinjiang, NW China for the last five decades and inferred that the
vegetated areas of the surrounding rangelands are decreased, mainly due to drought events. Now-a-days remote
sensing and GIS Techniques are widely used to assessment of land use and land cover changes. Therefore, the study
aims to assess the landuse and landcover change in and around Neyveli by using remote sensing and GIS
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International Journal of Engineering Trends and Technology (IJETT) – Volume 3 Issue 3 No 4 – June 2012
Techniques. The objectives of the study is undertaken in order to achieve the aim are (i) To prepare base map and
various thematic maps by using SOI toposheet. (ii) To classify visually and categorizing all pixels in an image in to
land use /land cover classification scheme. (iii) To access the cause and effect of the changes responsible for the
alterations of geo environmental condition. (iv) To create a digital database of the study area.
2.
MATERIALS AND METHODS
For the present study, the available data sets like SOI toposheets, multispectral, multi-temporal LANDSAT
satellite data were collected for years namely 1977, 1991 and 2006 respectively. LANDSAT image have been taken
from Global Land Cover Facility (GLCF), a NASA-funded member of the Earth Science Information Partnership at
the University of Maryland. The GLF develops and distribute remotely sensed satellite data and products are
available free of cost by the GLCF. The details of the materials used in the study are given in the Table 1 with their
date of production, resolution and source. The thematic mapper image of 1991 and 2006 has a resolution of 30
meters. Visual interpolation techniques were adopted to classify the pixels in the images into different classes such
barren land, vegetation, settlements, water bodies, mining area and cashew plantation. The key elements such as
color, tone, texture, shape, size, pattern, association, shadow are used in detection of the classes. The base map was
geo-referenced and was projected to standard projection called the Universal Transverse Mercator (UTM) projection
with the zone 44N by using ArcGIS 9.3 software. The satellite data which was downloaded composed of seven
bands and the standard band combination was carried out for the generation of false composite color (FCC) image.
The satellite images were subset by using clip analysis for the limitation of the study area. Therefore all the pixels
within the study area are classified into various classes or themes. The generalized flowchart adopted for the present
study is shown in figure 1. According to Wardlow et al., 2007 remotely sensed data are applicable, for practical
purposes it is the temporal information been most useful for monitoring of major crop types. NDVI has been used
extensively to measure vegetation cover characteristics, crop assessment studies (Bausch, 1993, Benedetti &
Rossini, 1993, Hatfield et al., 1985). According to Rouse el al., 1974 the normalized difference vegetation index
(NDVI) is a representative of the various spectral vegetation indices. According to Tucker, 1979 NDVI is the
customary vegetation index used by many researchers for extracting vegetation abundance from remotely sensed
data. According to Lillesand & Kiefer, 2004 and Wang et al, 2003, NDVI is a good indicator of the ability for
vegetation to absorb photosynthetically active radiation and has been widely used by researchers to estimate green
biomass. The generalized flow chart of the present study is given in the figure 1.
3
STUDY AREA
The Neyveli power station has founded in 1956. Neyveli Lignite Corporation limited is a government
owned lignite mining and power generating company in India. NLC operates the largest open pit lignite mines in
India presently mining 24MT of lignite and has an installed capacity of 2740MW of electricity the union
government holds 93% shares of NLC and is administered through Ministry of coal NLC has well developed town
ship in Neyveli district of Cuddalore Tamilnadu. It is located at 11.30 degree North 79.29 degree East. The township
covers 53 Km2 provide around 18000 houses for the employees. The town is surrounded by several water bodies on
its southern portion. The national high way from Chennai to Thanjavore NH45A runs north to south of the town and
other state highways which runs from Cuddalore to Trichy runs in the west direction of the town. The northern part
of the town is mainly occupied by the settlements and southern part is occupied by water bodies. Mines which are
present in central part of the township play a very important role in our study. The area occupied by mines is
increasing year by year shows the major change in landuse and landcover pattern. The major railway networks are
connected in this area.
SL.
DATA TYPE
DATA
PRODUCTION
SCALE
SOURCE
LANDSAT
25-08-1991
30m
GLCF(global land cover facility)
NO.
1
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International Journal of Engineering Trends and Technology (IJETT) – Volume 3 Issue 3 No 4 – June 2012
Image(TM)
www.glcf.umiacs.umd.edu
2
LANDSAT
Image(TM)
18-08-2006
30m
GLCF(global land cover facility)
www.glcf.umiacs.umd.edu
3
Toposheet
1977
1:2,50,000
SOI (Survey of India)
Table 1 Details of the data types
4. RESULT AND DISCUSSIONS
The landuse and landcover analysis of multi-temporal satellite images using ArcGIS was carried out and
results are shown in Table 2. The area occupied by the barren lands, cashew plantation, settlements, water bodies,
mines and vegetation in the base 1977 are 469.6, 128.21, 106.28, 33.24, 2.12 Km2 respectively of the percentage
63.49, 17.34, 14.39, 4.50, 29, respectively (Figure 2-4). The area occupied by the barren lands ,cashew plantation,
settlements, water bodies, mines and vegetation in the base map 1991 are 200.39, 147.65, 173.21,18.67, 29.75,
169.74 Km2 respectively of the percentage of 27.10,19.97,23.43,2.52,.4.02,22.96 respectively. The area occupied by
the barren lands ,cashew plantation, settlements, water bodies , mines and vegetataion in the base map 2006 are
172.39, 126.89, 204.68, 15.46, 38.60, 181.86 Km2 respective of the percentage of 23.31, 17.16, 27.68, 2.09, 5.22,
24.59 respectively. From the above table it is seen that the mines and vegetation tremendously increased during the
year, 1977 to 2006, these lands are increased to 38.60 km2 and 181.86 km2 respectively of the percentage increase of
5.22 and 24.59. Barren lands have been reduced to 172.39 km2of the percentage of 23.31. However, of all the major
attributes, built-up areas occurred at the fate of other land use and land cover. This may result in disastrous affect on
the physical, ecological and biological environment. The land use / land cover map and areas of the different
attributes for the year 1977, 1991 and 2006 are compared and shown in the Figure 5. The pixel values of the NDVI
data layer range from -1 to +1. The higher NDVI values indicate increase in biomass per unit area and the layer is
presented in Fig. 1. In this figure, the NDVI values for the year 1991 vary from -0.43 to +0.61 (Figure 6). The
NDVI values for the year 2006 vary from -0.42 to +0.55 (Figure 7). The positive values represent different types of
vegetation classes, whereas near zero and negative values indicate non-vegetation classes, such as water, and barren
land. It has been noted that the values which are greater than 1 represent the low as well as the dense vegetation. In
this study it is identified that is a reduction of vegetation cover during the year 2006 may be due the increase of
settlements within the study area.
5. CONCLUSION
The landuse/land cover pattern in Neyveli town was studied by using remote sensing and GIS techniques.
Survey of India toposheet (1977) and LANDSAT TM satellite data sets such as 1991 and 2006 were used. The
Landuse/landcover patterns such Barren land; Vegetation, Water bodies, Settlements and Mining areas were visually
interpreted and classified as various themes in the satellite image. The study has determined the changes that have
occurred over a time period of three decades and those changes impact the surrounding environment, affect the
availability of natural resources such as water, and alter the landscape and how it’s used. From the study it is found
that the barren lands is decreased by 36.38% (1991), cashew plantation has decreased by 2.63% (1991), water bodies
decreased by 14.57% (1991), mines increased by 3.73% (1991). The area of the mines increased by 1.22% in
(2006), barren lands, cashew plantation, water bodies, decreased by 3.71%, 2.24%, 14.0% in 2006 respectively. This
information, in turn, can help people anticipate and plan for future changes. Remote sensing data are pretty good for
classification of urban areas. An attempted has been made to classify the reflectance characteristics of remote
sensing data by using open source Quantum GIS software. It has been noted that the values which are >1 represent
the low as well as the dense vegetation. These data sets can provide base line details of the study area. Therefore, the
use of ancillary datasets in addition to remote sensing data has been recommended.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 3 Issue 3 No 4 – June 2012
DATA COLLECTION
LANDSAT
SATELLITE IMAGE
BASEMAP/TOPOGRAPHIC
SHEETS
GEOREFERENCING
VISUAL
INTERPRETATIO
N
BASE MAPS
THEMATIC MAPS
FIELD VALIDATION
DETECTING LANDUSE/LANDCOVER CHANGE
FIG 1 Flow Chart of methodology
Table 2 Areas of different attributes for the years 1977, 1991 and 2006
Land Use Land
Toposheet (1977)
1991
Cover
Sq.Km
%
Sq.Km
%
Barren land
469.46
63.49
200.39
27.10
Cashew plantation
128.21
17.34
147.65
19.97
Settlement
106.38
14.39
173.21
23.43
Water bodies
33.24
4.50
18.67
2.52
Mines
2.12
0.29
29.75
4.02
Vegetation
0.00
0.00
169.74
22.96
Total
739.41
100
739.41
100
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2006
Sq.Km
172.39
126.89
204.68
15.46
38.60
181.86
739.41
%
23.31
17.16
27.68
2.09
5.22
24.59
100
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International Journal of Engineering Trends and Technology (IJETT) – Volume 3 Issue 3 No 4 – June 2012
Figure 6. NDVI index during 1991
Figure
during 2006
7. NDVI index
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