INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 5, No 6, 2015
© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0
Research article ISSN 0976 – 4402
Received on March 2015 Published on May 2015 1061
Earth observation and assessment of land use and land cover dynamics -A
case study of Guwahati city, Assam, India Das S and Choudhury M.R
Department of Civil Engineering, SRPEC (Gujarat Technological University), Unjha,
Gujarat, India
doi: 10.6088/ijes.2014050100100
ABSTRACT
Remote Sensing and GIS is a fundamental and essential tool, widely applicable for
investigating the LULC at the village as well as the regional levels. This paper shows a
Geographical Information Systems & Science (GISc) approach for modeling land use and
land cover change (LUCC) in a rapid urban growing region of Guwahati city, Assam. In this
project, we used multi-temporal satellite images(IRS LISS-III) for the years of 2006 and 2010
and topographical map as raw data source for monitoring and assessment of land use and land
cover changes. The supervised classification of both the satellite images and analytical works
are carried out in ERDAS IMAGINE 9.2 and ARC GIS 9.3 softwares. LU/LC classification
of temporal satellite images represent the overall change scenario of the several years and the
approach of change matrix analysis is determined the overall reduction and increment of
LU/LC areas. The result demonstrated that, the overall boundary area of Guwahati city has
been decreased from 2006 to 2010. In that, Scrub land and Population increased rapidly,
whereas, Dense vegetation class is decreased due to rapid urbanization which leads to
environmental degradation.
Keywords: IRS LISS-III, Remote sesing and GIS, LU/LC classification, change analysis,
land management.
1. Introduction
Land use/land cover (LULC) changes play a major role in the study of global change. Land
use/land cover and human/natural modifications have largely resulted in deforestation,
biodiversity loss, global warming and increase of natural disaster-flooding (Dwivedi, R.S.
et.al., 2005; Mas, J.F. et.al., 2004; Zhao, G.X., 2004). These environmental problems are
often related to LULC changes. Therefore, available data on LULC changes can provide
critical input to decision-making of environmental management and planning the future (Fan,
F. et.al., 2007; Prenzel, B., 2004). The growing population and increasing socio-economic
necessities creates a pressure on land use/land cover. This pressure results in unplanned and
uncontrolled changes in LULC (Seto, K.C. et.al., 2002).
The LULC alterations are generally caused by mismanagement of agricultural, urban, range
and forest lands which lead to severe environmental problems such as landslides, floods etc.
Remote sensing and Geographical Information Systems (GIS) are powerful tools to derive
accurate and timely information on the spatial distribution of land use/land cover changes
over large areas (Carlson,T.N. and Azofeifa, S.G.A, 1999; Guerschman J.P. et.al., 2003;
Rogana J. and Chen, D., 2004; Zsuzsanna, D. et.al., 2005) Past and present studies conducted
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1062
by organizations and institutions around the world, mostly, has concentrated on the
application of LULC changes. GIS provides a flexible environment for collecting, storing,
displaying and analyzing digital data necessary for change detection (Demers, M. N., 2005;
Wu, Q. et.al., 2006). Remote sensing imagery is the most important data resources of GIS.
Satellite imagery is used for recognition of synoptic data of earth’s surface (Ulbricht, K.A.;
Heckendorf, W.D., 1998).
Temporal IRS LISS-III(Linear Imaging and self-scanning Sensor) data with spatial resolution
of 23.5 mts. have been broadly employed in this study towards the determination of land use
and land cover from 2006 to 2010. The aim of change detection process is to recognize
LULC on digital images that change features of interest between two or more dates
(Muttitanon W.; Tiıpathi, N.K., 2005). There are many techniques developed in literature
using post classification comparison, conventional image differentiation, using image ratio,
image regression, and manual on-screen digitization of change principal components analysis
and multi date image classification (Lu, D. et.al., 2005). A variety of studies have addressed
that post-classification comparison was found to be the most accurate procedure and
presented the advantage of indicating the nature of the changes (Mas, J.F., 1999; Yuan, F.
et.al., 2005). In this study, change detection comparison (pixel by pixel) technique was
applied to the Land use\land cover maps derived from satellite imagery.
The land use change has a direct bearing on the hydrologic cycle. Various hydrologic
processes such as interception, infiltration, evapotranspiration, soil moisture, runoff and
ground water recharge are influenced by landuse / landcover characteristics of the
catchment(John Rogan et al., 2003). Geographic Information Systems (GIS) and Remote
Sensing (RS) techniques provide effective tools for analyzing the landuse dynamics of the
region as well as for monitoring, mapping and management of natural resources. Some recent
studies (Jaiswal RK, Saxena R and Mukherjee S, 1999; Minakshi,R Chaursia and P K
Sharma, 1999; Samant HP and V Subramanyan, 1998) have shown the use of remote sensing
and GIS in landuse change detection. Micro watershed study helps in identifying the areas
causing problems and ultimately becomes a step towards
planning to mitigate the problems.
Daniel et al (Daniel, et al, 2002) in their comparison of land use land cover change detection
methods, made use of 5 methods viz; traditional post – classification cross tabulation, cross
correlation analysis, neural networks, knowledge – based expert systems, and image
segmentation and object – oriented classification. A combination of direct T1 and T2 change
detection as well as post classification analysis is employed. Nine land use land cover classes
are selected for analysis. They observed that there are merits to each of the five methods
examined, and that, at the point of their research, no single approach can solve the land use
change detection problem. Arvind C. Pandy and M. S. Nathawat (Arvind C. Pandy and M. S.
Nathawat, 2006) carried out a study on land use land cover mapping of Panchkula, Ambala
and Yamunanger districts, Hangana State in India. They observed that the heterogeneous
climate and physiographic conditions in these districts has resulted in the development of
different land use land cover in these districts, an evaluation by digital analysis of satellite
data indicates that majority of areas in these districts are used for agricultural purpose. The
hilly regions exhibit fair development of reserved forests. It is inferred that land use land
cover pattern in the area are generally controlled by agro – climatic conditions, ground water
potential and a host of other factors.
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1063
The objective of the present study is to analyze LULC changes using LISS-III imageries and
GIS in Guwahati city, Assam. In order to achieve this objective, supervised classification
technique by using Maximum likelihood classifier algorithm and change detection study was
employed to identify the dynamism of LULC.
2. Study area
The study area located in a metropolitan part of the Guwahati city, Assam, India from
26.18°N to 91.76°E geographical coordinates. The area encompasses of 140 square
kilometers and elevated 55 meters from the sea level. The city is located 440 km east of
Siliguri, West Bengal while shillong lies merely 100 km away. Figure 1 represents the
boundary area of the location.
Figure 1: Boundary map of the study area
Guwahati is a fast growing and most important city in the state of Assam. Today it is known
as the largest commercial, educational and industrial center of the entire northeastern region
in India. It is rapidly increasing in population as well. People from all over the country have
settled here due to its booming economic prospects. The population since 1971 has grown
manifold and it is estimated that more than 1.6 million people currently live here.
The city lies idly on the banks of the mighty Brahmaputra River at the foothills of the
Shillong Plateau. It is also a major cultural hub and a center for sports in the north-eastern
region. Guwahati is also an important transportation junction in the entire region.The city is
surrounded by Narengi town to the east and the LGB international airport to the west. The
city straddles the valley of the river Bharalu, which is the tributary of Brahmaputra River.
Numerous hills surround the city which makes the view irresistibly scenic. Nilachal hills lie
to the west of the city and revered as the place of Goddess Kamakhya.
The climate is subtropical and humid but the weather is not extreme. The minimum average
temperature normally hangs around the 19°C mark while the maximum stays around 29°C.
The high humidity is inherent and often rises past 80% except during the winter season when
it is dry. Summer begins in March and ends by June. The hottest month of the year is June.
The monsoon arrives in June and stays till September. The annual rainfall received by the city
is a healthy 1613 mm. Guwahati also experiences an autumn season after the monsoons that
begins in September and ends by November. Winter begins in November and stays till
February. During winters the temperature can get as low as 10°C. The best time to visit
Guwahati is from October to April when the climate is pleasant and enjoyable. Figure 2
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1064
shows the location of the study area.
Figure 2: location map of the study area
3. Materials and Methods
3.1 Remote sensing data
IRS LISS-III was obtained in the year of 2006 and 2010 from NRSC(National Remote
Sensing Centre), Hyderabad (Figure 3(a) and (b) & Table 1).
(a) (b)
Figure 3: LISS-III satellite image of 2006(a) and 2010(b) year.
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1065
Table 1: Satellite data characteristics
satellite sensor Year of
acquisition
No.of
spectral
bands
Range of spectral
Wavelength (µm)
Spatial
resolution
(mts)
Source
IRS Liss-III 2006 4
0.52-0.59
23.5
NRSC
(ISRO)
0.62-0.68
0.77-0.86
1.55-1.70
IRS Liss-III 2010 4
0.52-0.59
23.5 0.62-0.68
0.77-0.86
1.55-1.70
The satellite image was obtained at a pre processing level (Level IA) at which radiometric
and geometric corrections were required. The images underwent atmospheric correction by
computing the reflectance at the Top of the Atmosphere (TOA) for each image, in order to
account for the variation in the relative positions between the sun, the earth and the satellite
(Updike, T. and Comp, C. 2010). Converting the Digital Numbers (DN) to Top of
Atmosphere reflectance (ρ) will be done using Equation (1) and (2) (Clark, B., Suomalainen,
J. And Pellika, P. 2010). Radiance (Lλ) values (expressed as W m−2sr−1µm−1) is computed
using Equation (1), with gain (G) and offset (B) values that were supplied in the image
metadata. Then reflectance (ρ) values were computed for the two bands using Equation (2).
Radiance ( ) = (1)
ρ = (2)
Where ρ is the reflectance, L λ is the spectral radiance at the sensor's aperture (W
m−2sr−1µm−1), d is the date corrected earth–sun distance (astronomical miles) Esunλ is the
LISS-III sensor and band specific equivalent solar irradiance and θ s is the solar zenith angle.
To confirm the pixel grids and remove any geometric distortions, the images were registered
to a UTM map projection using a nearest neighbour resampling routine (Lillesand, T.M.,
Kiefer, R.W. and Chipman, J.W. 2008). Based upon thirty-six ground control points collected
from topographical map (1:50 000) and field work using a hand-held global positioning
system with an accuracy of 4 m, a sub-pixel root mean square error was achieved for each
image. Classification of remote sensing data was done through the use of a maximum
likelihood classification method.
The advantage of the maximum likelihood algorithm is that it takes the variability of the
classes into account by using the covariance matrix (Lillesand, T.M., Kiefer, R.W. and
Chipman, J.W. 2008). The land cover types identified in the image scene were Scrub land,
Clear water, Dense population, Less Dense population, Dense vegetation, Marshy vegetation,
Barren land, Rocky terrain and Turbid water. A 3*3 spatial convolution filter was used to
clean the classified images to the generalization of the study area. To assess the accuracy of
the classification process, high resolution field data, Google Earth image and 1:50,000
topographic maps of 2010 were used for validation.
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1066
3.2 Other ancillary data
Block map, Ground truth and some field photographs(2010) also have been collected as
ancillary and associated information of the study area which were very useful for further
analyses and mapping.
3.3 Supervised classification and Accuracy Assessment
In this study, totally, nine LULC classes were established as Scrub land, Clear water, dense
population, less dense population, dense vegetation, Marshy vegetation, Barren land, Rocky
terrain and turbid water(Figure 6 and 8). Two dated LISS-III images were compared
supervised classification technique. In the supervised classification technique, two images
with different dates are independently classified. Accurate classifications are imperative to
insure precise change-detection results (Jensen, J.R., 1996). A Supervised classification
method was carried out using training areas and test data for accuracy assessment. Maximum
Likelihood Algorithm was employed to detect the land cover types in ERDAS Imagine 9.2.
Image segmentation using eCognition 3.0 was employed to select training samples. About 60
training samples were selected for each year. These training samples were as pure as possible
and their location was maintained, when possible, over the two images. All bands, equally
weighted, were used in image segmentation. Accuracy assessment was critical for a map
generated from any remote sensing data. Error matrix is in the most common way to present
the accuracy of the classification results (Fan, F. et al, 2007). Overall accuracy, user’s and
producer’s accuracies, and the Kappa statistic were then derived from the error matrices. The
Kappa statistic incorporates the off diagonal elements of the error matrices and represents
agreement obtained after removing the proportion of agreement that could be expected to
occur by chance (Yuan, F. et al, 2005). Accuracy of classified maps was evaluated using 80
sample points systematically distributed. These points were converted into cells with the
same resolution of the satellite images (23.5 m) and classified as different classes. The
selected pixels had to be pure instead of mixed pixels to ensure that the correct class was
identified for each pixel (Gong & Howarth, 1990). Whenever it was not a pure pixel, the
closest pure pixel was selected. Confusion matrices were used to compare classification
results and ground truth information. A cross tabulation technique was used to quantify
changes in the land use/cover classes between 2006 and 2010. The statistical dependence was
tested as in any contingency table (Murteira, 1990) displaying the estimated values against
the measured ones. The random variable , with the chi-square distribution was defined by
Equation (3).
(3)
Where, N will be the contingency matrix of measured land use change, and M a contingency
matrix with the estimated values of change (Murteira, 1990). measures the difference
between the observed values of land use change and the estimated ones. This variable follows
the chi-square distribution for 4 degrees of freedom (m-1)*(n-1), therefore, our critical value
is 0.741356 for a confidence level of 0.93. Spatial metrics are algorithms used for quantifying
spatial characteristics of patches, classes of patches, or entire landscape mosaics (McGarigal
et al., 2004). They were developed in the late 1980s and include measures from information
and fractal theory (Herold et al., 2003). Selected spatial metrics of classified scenes used in
this study have already been used in previous researches (Parker et al., 2001; Herold et al.,
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1067
2003; Cabral et al., 2004) and were calculated using FRAGSTATS public domain software
(McGarigal et al., 2004). The term patch defines scale-independent homogeneous regions in
a landscape (e.g., grassland, forest, urban, etc.).
The study of quantitative approximations to the solutions of mathematical problems including
consideration of and bounds to the errors involved and then to calculate the numerical
analysis for extraction of each of individual classes of both images. Retrieve the data of both
the years stored into GIS and then prepare graphical representation. Graphical means giving a
clear and effective picture which helps to analyze change matrix of the land use and land
cover classes. Figure 5 clearly demonstrated the work flow for this study.
3.4 Monitoring Change in Land use and land cover
The strategies used for detecting change depend upon the change information required. The
common types of change analysis using remotely sensed data include: Identification of Areas
with Rapid Change: This type of change information is the most direct and uses differences
in multispectral measurements in different dates of remotely sensed images to target areas of
potential change. Rapid change identification has the advantage of simplicity and can be
efficiently applied from local to global scales. The disadvantage is that spectral anomalies
may be "false positives" and represent differences in vegetation seasonality, differences in
image sources and calibration, or may actually correspond to true land cover changes.
Changes in biophysical conditions (e.g., canopy density, height, leaf area index, phenology)
that correspond to the land use intensification, ecological succession, biogeochemical
variations, or other ecological or social processes can be determined by comparing either
calibrated spectral values or transformations (e.g., vegetation indices). In order to understand
the magnitude of condition change, it is necessary to establish the relationships between
image data and the land cover variable of interest.
Because condition monitoring uses direct comparisons of image-derived variables, image
dates should be from similar calendar dates and be precisely calibrated. Otherwise, the
changes may represent variations related to sensor differences or vegetation seasonality. Land
Use and Land Cover Conversions: Changes from one use or cover type to another, such as
changes from forest to developed cover, is the goal of this category of change analysis.
Studies of specific categorical changes require very accurate maps of land use or land cover
at two or more points in time in order to determine the types of conversions taking place.
Summaries of the various methods used to detect landscape change can be found in (Singh,
1989; Sohl, 1999).
The effective use of remote sensing for generating land cover information is highly
dependent on the measurable quality of the required information (Congalton and Green,
1999). All too often, change research using remote sensing has not been driven by the
practical information needs of users, nor with consideration of the information content of
remotely sensed images (Ryerson, 1989). To better understand techniques used to analyze
land cover change from remotely sensed imagery, it is necessary to understand the following
characteristics of change (summarized from Sohl et al, 2004). Change in use or cover is a
(relatively) rare event: The landscape is in a constant state of flux due to seasonal changes,
vegetation growth, and ecological succession. However, when considering changes from one
land use or cover type to another, in terms of area, change typically covers a very small
proportion of the total land surface. The percentage of land that thematically changes during
each interval is generally quite small, compared to the total area of the Earth, continent, or
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1068
country, depending on the eco region and the time interval. Longer time intervals can, in
effect, increase the percentage of area changed per interval. Use of different classification
schemes may also result in higher percentages of overall change per interval. In general, the
amount of change reported increases as thematic detail increases. Change is a local event.
Land use and land cover conversions are generally localized, with relatively small patches of
contiguous changed land. Patch size is somewhat a function of time. Longer intervals
between image dates allow more opportunity for change to occur, along with associated
clumping of individual changed patches into larger patches. Typically, however, individual,
changes occur locally and over relatively small areas.
While certain land cover transitions exhibit larger average patch sizes (such as forest to clear-
cut and forest to mining), patch sizes of most land cover change are much smaller. The small
patch size of many land cover changes has important remote sensing implications. Relatively
coarse scale imagery such as the AVHRR (1 km2 pixels) and MODIS (250 m2 pixels) is best
suited to the detection of changes in landscape biophysical properties or targeting of locations
with large transformations of the landscape associated with events such as the conversion of
large tracts for mechanized agriculture. Higher resolution images such as the IRS LISS-III,
LISS-IV, IKONOS, QUICKBIRD with 23.5m., 5.4m., 0.82 m.(multispectral) and
0.61(multispectral) respectively are suited to the detection of thematic change more typical of
urban, suburban, or agricultural expansion.
Change is spatially variable. Although changes in condition are ubiquitous, there is
considerable variability in the geographic distribution of land use and land cover change. The
rates, types, and patterns of change can vary substantially from place to place, depending on
the driving forces of change, settlement history, and natural resource base. For example,
urban transformations are generally clustered around existing cities and towns, while changes
in forests or agriculture may vary either uniformly or unevenly in space, depending on such
factors as access to markets and land suitability. Change is temporally variable. Different
forms of landscape transitions occur at different temporal scales. The period of time in which
change is measured can have a strong effect on results.
A key difficulty with the detection of land surface change is the proper detection and
reporting of cyclic change. Unidirectional land cover changes, such as the conversion of an
agricultural field or forested area to a developed (urban) use, are less problematic, as the
change can occur at any point between target date ends. However, the magnitude of cyclic
changes such as the timber harvest, replanting, forest regeneration cycle may be under-
tabulated if the temporal window is too wide. The establishment of the temporal window
must be based on both geographic and sectoral considerations. Basically, the determination of
change rates must consider the local dynamics of change in order to accurately determine the
rates of change. Change can be spectrally ambiguous. Automated detection of land cover
change assumes that differences in spectral properties between dates imply a change in land
cover or use. Automated change detection results are greatly improved when the remotely
sensed data being compared are calibrated to common reference and physical units (i.e.,
radiance, percent reflectance) and corrections for atmospheric effects are applied. Changes in
calibrated spectral values can indicate changing land cover conditions, such as increases or
decreases in forest canopy density caused by selective thinning or succession. However,
changes in spectral values do not necessarily indicate thematic change. For example, clear-cut
forest patches and fallow or recently harvested agricultural fields may have very similar
spectral properties. It is important to note that discretely partitioning land cover is often
problematic because the cover is better defined in terms of a continuum.
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1069
Figure 5: Work flow diagram
4. Results and discussions
4.1 Land use and land cover classification
Two land use/land cover maps were produced, respectively, for years 2006 and 2010 using
the maximum-likelihood algorithm (Figure 6 and 8).
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1070
Figure 6: Land use and land cover classification map of 2006
Figure 7: Statistics of the area occupancy of Land use and land cover in 2006
Figure 7 describes the class wise area of LU/LC in 2006. The total area of 2006 is 139.12
km2. Maximum area occupied by Barren is 31% or 42.63 km2 area. Minimum area covered
by clear water i.e, 1% or 1.39 km2 and Marshy vegetation i.e, 2% or 2.32 km2. Turbid water
and Scrub land are 3% with the area coverage of 4.33 km2 and 3.91km2 respectively. Dense
population is occupied 10% of the total area and Rocky terrain fall 8% of the total area. Less
dense population and dense vegetation are 21% of the total area with 28.76 km2 and 29.69
km2 respectively.
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1071
Figure 8: Land use and land cover classification map of 2010
Figure 9: Statistics of the area occupancy of Land use and land cover in 2010
Figure 9 represents the class wise area occupancy of LU/LC in 2010. The total area coverage
is determined 113.18 km2 , which is the reduction of 25. 94 km2 area from 2006. The
maximum area are occupied by Less dense population and dense population i.e, 23% each
and minimum area is covered by marshy vegetation and clear water i.e, 1% each. Dense
vegetation is 19% of the total area or 20.90 km2. Besides, Scrub land, Barren land, Rocky
terrain and turbid water are covered 6%, 17%, 7% and 3% of the total area respectively.
4.2 Change detection
Land cover change has been attributed by various reasons and those reasons are site specific.
Shifting cultivation and immensely increasing of population over two decades have been
identified as a major cause of natural forest destruction and barren land formation, which is
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1072
prominent in case of Guwahati city, Assam. Conversion of forest to agricultural land,
agriculture to fallow land etc. were noticed in many countries including the present study.
Land cover conversion pattern varies from place to place. The following diagram (Figure 10)
shows a typical conversion pattern of LULC.
Figure 10: Major Change pattern showing strong linkages between LULC classes
The following observation and evaluation has been made of various land use and land cover
areas in this study through change detection technique (Table 2).
Table 2: LULC change matrix from 2006 to 2010
Class 2006 2010 2006 to
2010
2006 to
2010 Remark
Area in sq
km.
Area in sq
km.
Change in
sq km.
Change
in %
Scrub land 3.9155 7.0478 3.1324 12% Area increased
Clear water 1.3922 1.0957 -0.2966 1.14% Area decreased
Dense
population 14.3839 26.0408 11.6569 44.93% Area increased
Less dense
population 28.7684 26.4346 -2.3338 9.00% Area decreased
Dense
vegetation 29.5956 20.9027 -8.6930 33.51% Area decreased
Marshy
vegetation 2.3211 1.3491 -0.9720 3.75% Area decreased
Barren land 42.6381 18.8604 -23.7777 91.66% Area decreased
Rocky terrain 11.7712 8.2567 -3.5145 13.55% Area decreased
Turbid water 4.3357 3.1915 -1.1443 4.41% Area decreased
Total 139.1217 113.1792 -25.9425 24% Total Area
Decreased
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1073
Analyzing the changes of the LULC area from 2006 to 2010 on the classified images we
come to know the scrub land area is increased 3.91 km2 to 7.04 km2 from 2006 to 2010
respectively. Clear water class is decreased from 2006 to 2010 i.e. 1.39 km2 to 1.09 km2
respectively, the negative change in the area of 0.29 km2 or 1.14%. Dense population
increased from 2006 to 2010 is 14.38 km2 to 26.04 km2 respectively, the positive change in
the area of 11.65 km2 or 44.93%. Less dense population and Dense vegetative areas are
decreased from 2006 to 2010 i.e. 28.7684 to 26.43 km2 and 29.59 km2 to 20.90 km2
respectively, whereas, the negative changes in area is 2.3338 km2 or 9.00% and 8.69 km2 or
33.51%. Similarly, Marshy vegetation, Barren lands, Turbid water and Rocky terrain areas
are decreased from 2006 to 2010. It is clear that, the total area of Guwahati city is reduced
24% during those 4 years from 2006 to 2010. Following diagram (Figure 11) shows the
statistical overview of the area changes of LULC classes.
Figure 11: Year wise changing scenario of LULC classes from 2006 to 2010
5. Conclusion and recommendations
Geographical Information System (GIS) and Remote Sensing have been used to derive
accurate information on the spatial distribution of land use/land cover changes over large
areas from Past to present studies conducted by organizations and institutions around the
world. The capability of GIS has been used in analyzing a large amount of data/within no
time. Arc GIS has been used in the project of land use / land cover (LU/LC) changes. It is
approximated from the data of year 2006 to 2010 of Guwahati vast changes over the land use
and land cover can be evaluated with the amalgamation of Remote Sensing and GIS
Techniques. Using the Land Use Land cover, map was divided into various classes such as
scrub land, Clear water, dense population, less dense population, dense vegetation, Marshy
vegetation, Barren land, Rocky terrain and turbid water. The result shows the overall
boundary area of Guwahati city has been decreased from 2006 to 2010. In that, Scrub land
and Population increased rapidly, whereas, Dense vegetation class is decreased due to rapid
urbanization which leads to environmental degradation. So, it is recommended to the local
authorities, govt. agencies and land department for a proper land management strategy and
intensive care of the study area to protect it’s biodiversity and sustainable development.
Earth observation and assessment of land use and land cover dynamics (a case study of Guwahati city, Assam,
India)
Das S and Choudhury M. R
International Journal of Environmental Sciences Volume 5 No.6 2015 1074
Acknowledgements
Authors places on record their deep sense of gratitude to the Director, RRSC, Assam and all
concerned authorities of RRSC, Assam. The authors are also grateful to Smt. S. R. Patel Engg.
College(Affiliated with the Gujarat Technological University) for supporting and providing
laboratory facilities for this research. Besides, the author wishes to express gratitude to the
anonymous reviewers, who helped to improve this paper through their thorough review.
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