MULTI-SEASONAL IRS-1C LISS III SATELLITE DATA FOR CHANGE
DETECTION ANALYSIS: A CASE STUDY
Praveen Kumar Rai, Sweta, Abhishek Mishra
ABSTRACT
Remote sensing techniques have been used to monitor land use changes; this has an important role in urban and rural
development and the determination of natural resources. Also remote sensing is very useful for the production of land use
and land cover statistics which can be useful to determine the distribution of land uses in the watershed. Using remote
sensing techniques to develop land use classification mapping is a useful and detailed way to improve the selection of areas
designed to agricultural, urban and/or industrial areas of a region. In Ranchi city and surrounding the land use has been
changing, every day new developments (urban, industrial, commercial and agricultural) are emerging. The purpose of this
work is to develop the land use of Ranchi and its surrounding area that is an important natural resource. Remote sensing
techniques can be used to assess several water quality parameters and also for land use classifications. For this work the
ERDAS Imagine V 9.1 computer software is used to develop a land use classification using IRS-1C LISS III images of year
1997, 2004 and 2009 respectively. The generated land use classification were compared with a land use generated using
ARC GIS V9.3, to decide which method provides better land use classification. Image processing supervised classification
technique was also used and important land use features i.e. forest area, waste land, settlement, fallow land agricultural land
etc. extracted on the basis of pixel variation and compare the results with the result came after on screen digitization. A
positive mean of data of year 2009 and data of year 1997 NDVI differencing is an indication of reduction in above ground
biomass within 12 years. This implies a decline in vegetation. In supervised classification map, after generating confusion
matrix, producer and user accuracy is collected for each class. Overall accuracy for classified image is collected as 92.70 %
and overall kappa accuracy is collected 0.8761.
Keywords: Change Detection, Multi Seasonal Remote Sensing Data, ERDAS, ARC GIS etc.
Mr. Praveen Kumar Rai, Assistant Professor (PGDRS & GIS), Department of Geography, B.H.U., Varanasi, U.P. Corresponding author:
rai.vns82@gmail.com +919451108531.
Mrs. Sweta, Centre for Environmental Science & Technology (CEST), RGSC, Barkachha.
Mr. Abhishek Mishra, Research Assistant, Department of Geography, B.H.U., Varanasi, U.P.
Introduction:
Change detection often involves comparing aerial photographs or satellite imagery of the area taken at different
times (Petit, 2001). The process is most frequently associated with environmental monitoring, natural resource
management, or measuring urban development.
Understanding landscape patterns, changes and interactions between human activities and natural phenomenon
are essential for proper land management and decision improvement (Prakasam et al., 2010).
Land use/cover Change detection is very essential for better understanding of landscape dynamic during a
known period of time having sustainable management. Land use and land cover change has been recognized as
an important driver of environmental change on all spatial and temporal scales (Tansey et al., 2006), as well as
emerging as a key environmental issue & on a regional scale is one of the major research endeavors in global
change studies.
Changes may involve the nature or intensity of change but may also include spatial (forest abatement at village
level, or for a large-scale agro industrial plant), and time aspects. Land use/ Land cover changes also involve the
modification, either direct or indirect, of natural habitats and their impact on the ecology of the area (Rogan,
2004). Remote Sensing technology for capturing the spatial data, Geographic Information System for
undertaking integrated analysis, presentation of spatial and associated attribute data are found to be much more
effective to known the change detection of Land Use/Land Cover (Lillesand et al., 2001).
Study area
Study area include part of the Ranchi City and its surrounding covering forested area of Ranchi district lies
between 23°21′ N to 23.35° N latitude and 85°20′E to 85.33°E longitude. Total area covered by the Ranch city
and surrounding is about 141 km2 and the average elevation of the city is 629 m above sea level. Ranchi the
capital of INDIAN state of Jharkhand. It is also known as Manchester of east due to large amount of mineral
production as well as city of “water-falls “the most popular waterfalls are Dasham, Hundru, Jonha Falls, Hirni
and Pinch-hit. Ranchi is located on the southern part of the Chota Nagpur plateau which forms the eastern edge
of the Deccan plateau. Ranchi has a humid subtropical climate. Temperature ranges from 20 to 37°C during
summer and 3 to 22°C during winter. The rainfall pattern is monsoonal covering the period from middle of June
to middle of October with an average annual rainfall of about 1530 mm (Fig.1).
Data used
Geocoded False Colour Composite scene of IRS-IC LISS III data on 1:50,000 scale (year 1997, 2004 and 2009
respectively) coinciding with Survey of India (SOI) Toposheet of year, 1962) is used in the present study.
Objective of the Study
To study the changing pattern of land use and land cover of Ranchi city by using Remote Sensing satellite data
& GIS, as a tool is the main objective of this paper, As well as to identify the main factors behind this changes.
Methodology
The work is done by visual image interpretation. The following steps are involved in the classification
procedure.
a) Data acquisition, loading, merging and georeferencing of Remote Sensing Data
b) Ground data collection
c) Training area definition, signature generation and classification
d) Annotation, demarcation of administrative boundaries and cultural features and extraction of different layers
through different multi temporal Remote Sensing data.
e) Generation of statistics from the classified outputs
Ranchi City
Fig. 1. Study area as viewed on IRS-1C LISS III data of year 1997 and 2009 respectively
The process of georeferencing of different multi temporal remote sensing data has been done using
georeferenced Survey of India (SOI) topographical map of year 1972 by identifying the ground control points
(GCP’s) from the map and the corresponding points on the satellite images and finally applying the map-image
transformation model. On screen digitization of different land use features from the SOI maps and satellite data
was done to transfer the same on the georeferenced image using ARC GIS software.
In this study two different image processing classification methods were used i.e., unsupervised and supervised
classification. Unsupervised classification is the identification of natural groups, or structures, within
multispectral data. Supervised classification is the process of using training samples, samples of known identity
to classify pixels of unknown identity. Broadly area is divided into six classes, mainly forest, water bodies,
agricultural land/vegetation, fallow land, waste land etc.
Result and Discussion:
There are a number of broad units that contain several inclusions. The inclusions couldn't be separated because
of their small extents. So approximations of inclusions are based on the tone reflected on the scene and
proportion of their extent. The land use/land cover types are classified as follows and details of Land use/Land
cover statistics of of study area for year 1997, 2004 and 2009 respectively is given in the table 1 and overall
units are shown in the figure 2 for year 2004 and 2009 respectively.
Area under major land use/land cover categories was calculated for the year 1997, 2004 & 2009. Land use/Land
cover has been categorised into 6 different classes that are forest, water bodies, settlement, vegetation,
wasteland & fallow land. Approx 36% of total area have changed from 1997 to 2009 where land cover part is
decreasing while land use part is increasing year by year.
Change in Forest Area:
Forest the most important part of land cover is decreasing year by year and shows negative change in search of
agricultural land just because of increasing human population need as well as to generate more income. In this
study, it was found that there is approx 5% decrease of forest cover since last 12 years i.e. from 1997 to 2009,
but this change is more from 1997 to 2004 but from year 2004 to 2009 there is slow rate of change showing Sin
of awareness of environment in people living nearby by the forest area. The result of area distribution of forest
using unsupervised classification methods shows the decreasing trend but here rate of change (10.6% from year
1997 to 2009) is more than value comes after on screen digitization and this is because of mixing pixel class in
forest area with agricultural and vegetated area.
Change in Water Bodies:
The streams/rivers, canals, ponds etc. is considered under this category. The prominent ponds/lakes are easily
detected on satellite imagery by their black and dark blue tones. The changing rate of water bodies of this area
is also showing decreasing trend. There is approximately 2.2 % decrease of water bodies since last 12 years
(from year 1997 to 2009). The result of classified map also indicates that decreasing rate and this is also
approximately equal to the result came after on screen digitization (Table 1 and Table 2).
Fig 2. Land use classification of Ranchi city and surrounding of year 2004 and 2009 respectively
Change in Settlement:
This study shows that there is about more than 10% increase of settlement area i.e. from 1997 to 2009 (result of
digitized map) as well in supervised classification a method also show the increasing trend of settlement
indicating. In 1997, the area under settlement was 15.78% of the total covered area, which was further increased
in 24.09 in year 2004 to 29.1 % in year 2009 of the total area. One noticeable change behind this change is only
increasing human population year by year. In Supervised classification method, the covered area of settlement
was increased from 16.5 % in year 1997 to 30.0% in year 2009.
Change in Agricultural Land/Vegetation:
The observation gained through the image interpretation reveals that the study area is predominantly comes
under poor to medium cultivation and paddy is the predominantly crop of this area. The crop growing
conditions are not very much favorable in this area due to Plateau and rock region. The growth rate of
vegetation is quite good here; there is overall 1.8% increase of agricultural and vegetated area since last 12
years of the total area.
Fig.3. Land Use Change Distribution from Year 1997, 2004 and 2009 respectively both using onscreen
digitization and supervised classification method.
Change in Fallow Land:
The result of this study is showing that there is overall 15% increase of fallow land (digitized map) as well as
approximate 25% due to classified map since last 12 years & there is only one noticeable factore behind this
change i.e directly or indirectly increasing human population and there need’s.
Change in Waste Land:
Waste land is described as degraded land, which can be brought under vegetation cover with reasonable effort.
Three patches are registered more in white color and a little of yellow or brown color in the satellite data
especially in north and N-E portion of the imagery. The study area comes under plateau region, so maximum
portion of the waste land is categorized under barren rocky waste.
In this study it was found that wasteland in part of Ranchi city is showing increasing trend. This may be because
of increasing human & animal population on land. The intensive cultivation has extended even to areas under
ecological stress leading to accelerated soil erosion and excessive land degradation, & there is overall 2%
increase of this part of land.
Vegetation Mapping Using Normalized Difference Vegetation Index Model (NDVI)
The Normalized Difference Vegetation Index (NDVI) is an index of plant “greenness” or photosynthetic
activity, and is one of the most commonly used vegetation indices. Vegetation indices are based on the
observation that different surfaces reflect different types of light differently. Photo synthetically active
vegetation, in particular, absorbs most of the red light that hits it while reflecting much of the near infrared light.
Vegetation that is dead or stressed reflects more red lights and less near infrared light. Likewise, non-vegetated
surfaces
have
a
much
more
even
reflectance
across
the
light
spectrum
(Congalton,
1999).
By taking the ratio of red and near infrared bands from a remotely-sensed image, an index of vegetation
“greenness” can be defined. The (NDVI) is probably the most common of these ratio indices for vegetation.
NDVI is calculated on a per-pixel basis as the normalized difference between the Red and near infrared bands
from an image.
NDVI= (Near Infra Red –Red)/ (Near Infra Red+ Red).
The Normalized Difference Vegetation Index (NDVI) is a simple numerical indicator that can be used to
analyze remote sensing measurements, typically but not necessarily from a space platform, and assess whether
the target being observed contains live green vegetation or not. NDVI = (Near IR - Red)/ (Near IR + Red) the
sub-scene bands 2 and 3 for each were used to create an NDVI image for each year and then differencing of the
images was carried out to detect change. Differencing involved the subtraction of the 2009 NDVI image from
the 1997 NDVI image using an image calculator in the software. Table 3 is showing histogram data for NDVI
difference (NDVI 1997-NDVI 2009). NDVI Value used for bare soil ranges from -0.21 to -0.05. NDVI value
for agriculture high crop is between 0.22 to 0.90. NDVI index value in Rabi season has been change slightly
for all class. Water bodies’ category NDVI index value in Rabi season has been changed as -0.82 to-0.19.
Agriculture high crop values in Rabi season is 0.30 to 0.91. In the Zaid season NDVI index value for
agriculture crop area has been quantified as 0.09-0.85 (Fig 4). Change in the composition, morphology and
density of green biomass can be assessed by comparing three season NDVI index value of image. Higher NDVI
values are associated with greater density, large leaf area and large green biomass of the canopy
A positive mean of data of year 2009 and data of year 1997 NDVI differencing is an indication of reduction in
above ground biomass within 12 years. This implies a decline in vegetation. It thus confirms the change
detected. From the post classification results there was tremendous reduction in Forest cover area by 766.75
hectares within a period of 12 years between 1997 and 2009. So it is equivalent to 10.76% decrease in the forest
area. With the help of remote sensing and GIS techniques it is clearly shows that the total forest cover is
continuously degrading and transforming into various land use/land cover category.
No classification is complete until its accuracy has been assessed (Lillesand and Kiefer 2001). In this context,
the “accuracy” means the level of agreement between labels assigned by the classifier and the class allocations
on the ground collected by the user as test data. For calculating accuracy for each classified class confusion
matrix is generated (Table 4). After generating confusion matrix, producer and user accuracy is collected for
each class. Overall accuracy for classified image is collected as 92.70 %and overall kappa accuracy is collected
0.8761 (Table 4 and 5).
The overall result of the study indicate that the result will follow the same trend either we will adopt any of the
method to analysis change detection of land use /land cover & the accuracy of the result not only depends on
software but also on individual accuracy level. As the present study also demonstrated the efficiency of Remote
Sensing and Geographic Information System as a tool in the study of land use /land cover changes. It gives a
fairly good understanding of land use/land cover changes for a period of two decades, which in turn will be very
helpful for local administrative bodies, decision makers, regional planners and Stakeholders. With the help of
this study it proves that this technology has the capability to provide the necessary input and intelligence for
preparation of base maps, formulation of planning proposals and as a monitoring tool during the implementation
phase, as the result of this study also show efficient effect of this tool in analyzing Land Use/Land Cover
change detection.
A positive mean of data of year 2009 and data of year 1997 NDVI differencing is an indication of reduction in
above ground biomass within 12 years. This implies a decline in vegetation. It thus confirms the change
detected. From the post classification results there was tremendous reduction in Forest cover area by 766.75
hectares within a period of 12 years between 1997 and 2009. So it is equivalent to 10.76% decrease in the forest
area. With the help of remote sensing and GIS techniques it is clearly shows that the total forest cover is
continuously degrading and transforming into various land use/land cover category. In supervised classification
map, after generating confusion matrix, producer and user accuracy is collected for each class. Overall accuracy
for classified image is collected as 92.70 % and overall kappa accuracy is collected 0.8761.
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CAPTION TO FIGURES
Fig. 1. Study area as viewed on IRS-1C LISS III data of year 1997 and 2009 respectively.
Fig 2. Land use classification of Ranchi city and surrounding of year 2004 and 2009 respectively
Fig.3. Land Use Change Distribution from Year 1997, 2004 and 2009 respectively both using onscreen
digitization and supervised classification method.
CAPTION TO TABLES
Table. 1 Spatio-Temoral Distribution of Different Land Use/Land Cover (in %) Using On Screen Digitization
Table. 2 Spatio-Temoral Distribution of Different Land Use/Land Cover (in %) Using Supervised
Classification Method
Table. 3 Status of Forest covered area during period 1997 and 2009 respectively on the basis of NDVI model
Table 4: Confusion Matrix
Table 4: Producer and User Accuracy