earth observation and assessment of land use and …...earth observation and assessment of land use...

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

[email protected]

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.


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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


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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.


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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.


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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.,


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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


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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.


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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).


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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.


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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


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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


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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.


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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.

6. References

1. Arvind C. Pandy and M. S. Nathawat, (2006), Land Use Land Cover Mapping

Through Digital Image Processing of Satellite Data – A case study from Panchkula,

Ambala and Yamunanagar Districts, Haryana State, India.

2. Bhagawatrimal, (2011), Application of remote sensing and GIS on land use/land

cover change in Kathmandu metropolitan city, Nepal, Journal of Theoretical and

applied information Technology, 23(2), p. 80.

3. Cabral, P., M. Painho, et al., (2004), Spatio-temporal analysis of urban growth pattern

in Lisbon Metropolitan Area. 24th Urban Data Management Symposium, Chioggia,

Italy.

4. Carlson,T.N., Azofeifa, S.G.A., 1999. Satellite Remote Sensing of land Use changes

in and around San Jose´, Costa Rica. Remote Sensing of Environment, 70, pp 247–

256.

5. Clark, B., Suomalainen, J. &Pellika, P. (2010), A comparison of methods for the

retrieval of surface reflectance factor from multitemporal SPOT HRV, HRVIR, and

HRG multispectral satellite imagery. Canadian Journal of Remote Sensing, 36, pp

397–411.

6. Congalton, R. G., and K. Green, (1999), Assessing the Accuracy of Remotely Sensed

Data: Principles and Practices, CRC Press, Boca Raton, FL, 137.

7. Ryerson, R., Image interpretation concerns for the (1990), and lessons from the past,

Photogrammetric Engineering and Remote Sensing, 55, pp 1427-1430.

8. Daniel, et al, (2002), A comparison of Landuse and Landcover Change Detection

Methods.ASPRS-ACSM Annual Conference and FIG XXII Congress pg.2.

9. Demers, M. N., (2005), Fundamentals of Geographic Information Systems, John

Wiley&Sons, Inc., Newyork, USA.

10. Dwivedi, R.S., Sreenivas K., Ramana, K.V., (2005), Land-use/land-cover change

analysis in part of Ethiopia using Landsat Thematic Mapper data. International

Journal of Remote Sensing, 26(7), pp 1285-1287.

11. Fan, F., Weng, Q., Wang, Y., (2007), Land use land cover change in Guangzhou,

China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7, pp

1323-1342.


India)



12. Gong, P. and J. Howarth, (1990), the use of structural information for improving land-

cover classification accuracies at the rural-urban fringe. Photogrammetric Engineering

& Remote Sensing 56(1), pp 67-73.

13. Guerschman J.P., Paruelo, J.M., Bela, C.D., Giallorenzi, M.C., Pacin, F., (2003), Land

cover classification in the Argentine Pampas using multi-temporal Landsat TM data.

International Journal of Remote Sensing, 24, pp 3381–3402.

14. Herold, M., N. Goldstein, et al., (2003), The spatio -temporal form of urban growth:

measurement, analysis and modeling. Remote Sensing of Environment 85, pp 95-105.

15. Jaiswal RK, Saxena R and Mukherjee S., (1999), Application of Remote Sensing

Technology for Landuse / Landcover change analysis, J. Indian Soc. Remote Sensing

27(2), pp 123-128.

16. John Rogan, Jennifer Miller, Doug Stow, Janet Franklin, Lisa Levin and Chris

Fischer, (2003), Land-Cover Change Monitoring with Classification Trees Using

Land sat TM and Ancillary Data, photogrammetric engineering & remote sensing,

69(7), pp 793-804.

17. Lillesand, T.M., Kiefer, R.W., and Chipman, J.W., (2008), Remote Sensing and

Image Interpretation, John Wiley and Sons, Inc., 111 River Street, Hoboken: NJ.

18. Lu, D., Mausel, P., Batistella M., Moran, E., (2005), Land-cover binary change

detection methods for use in the moist tropical region of the Amazon: a comparative

study, International Journal of Remote Sensing, 26 (1), pp 101–114.

19. Mas, J.F., (1999), Monitoring land-cover changes: a comparison of change detection

techniques, International Journal of Remote Sensing, 20 (1), pp 139-152.

20. Mas, J.F., Velazquez, A., Gallegos, J.R.D., Saucedo, R.M., Alcantare, C., Bocco, G.,

Castro, R.; Fernandez, T., Vega, A.P., (2004), Assessing land use/cover changes: a

nationwide multi-date spatial database for Mexico. International Journal of Applied

Earth Observation and Geo information, 5, pp 249-261.

21. McGarigal, K., B. Marks, et al., (2004), FRAGSTATS.

22. Minakshi,R Chaursia and P K Sharma, (1999), Landuse / Landcover Maping and

Change Detection Using Satellite Data – A Case Study of Dehlon Block, District

Ludhiana, Punjab, Journal of Indian Society and Remote Sensing 27(2), pp 115-121.

23. Murteira, B., (1990), Probabilidades e estatística, McGraw-Hill de Portugal.

24. Muttitanon W., Tiıpathi, N.K., (2005), Land use/land cover changes in the coastal

zone of Ban Don Bay, Thailand using Landsat 5 TM data, International Journal of

Remote Sensing, 26 (11), pp 2311-2323.


India)



25. Parker, D., T. Evans, et al., (2001), Measuring emergent properties of agent-based

land use/land cover models using spatial metrics. Seventh annual conference of the

International Society for Computational Economics.

26. R.H. Fraser, I. Olthof, D. Pouliot, (2009), Monitoring land cover change and

ecological integrity in Canada's national parks, Remote Sensing of Environment,

113(7), p. 1397-1409.

27. Rogana J., Chen, D., (2004), Remote sensing technology for mapping and monitoring

land-cover and landuse change, Progress in Planning, 61, pp 301–325.

28. Samant HP and V Subramanyan, (1998), Landuse / Landcover Change in Mumbai –

Navi Mumbai Cities and its Effects on the Drainage Basins and Channels – A Study

Using GIS, Journal of Indian Society and Remote Sensing, 26(1&2), pp 1-6.

29. Seto, K.C., Woodcock, C.E., Song, C., Huang, X., Lu, J., Kaufmann, R.K., (2002),

Monitoring land use change in the Pearl River Delta using Landsat TM. International.

Journal of Remote Sensing, 23 (10), pp 1985-2004.

30. Singh, A., (1989), Digital change detection techniques using remotely sensed data,

International Journal of Remote Sensing, 10, 989-1003.

31. S. Kilica, F. Evrendilekb, S. Berberogluc, A. C. Demirkesend, (2006), Environmental

monitoring of land use and land cove changes in amik plain, turkey,114(3), pp 157-

168.

32. Sohl, T. L., (1999), Change analysis in the United Arab Emirates: an investigation of

techniques, Photogrammetric Engineering and Remote Sensing, 65, pp 475-184,

33. Sohl, T. L., A. L. Gallant, and T. R. Loveland, (2004), The characteristics and

interpretability of land surface change and implications for project design,

Photogrammetric Engineering and Remote Sensing, 70, pp 439-450.

34. Ulbricht, K.A., Heckendorf, W.D., (1998), Satellite images for recognition of

landscape and land use changes. ISPRS Journal of Photogrammetry & Remote

Sensing, 53, pp 235-243.

35. Updike, T. & Comp, C., (2010), Radiometric Use of Worldview-2 Imagery,

http://www.gsdi.org/gsdiconf/gsdi13/papers/189.pdf., Accessed 16-05-2013.

36. Vemusreenivasulu and Pinnamaneniudayabhaskar, (2010), Change Detection in Land

use and land cover using Remote Sensing and GIS Techniques, International Journal

of Engineering Science and Technology, 2(12), pp 7758-7762.

37. Wu, Q., Li, H. Q., Wang, R.S., Paulussen, J., He, H., Wang, M., Wang, B.H., Wang,

Z., (2006), Monitoring and predicting land use change in Beijing using remote

sensing and GIS, Landscape and Urban Planning, 78, pp 322–333.

38. Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E., (2005), Land cover

classification and change analysis of the Twin Cities (Minnesota) metropolitan areas


India)



by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98, 317-

328.

39. Zhao, G.X., Lin, G., Warner, T., (2004), Using Thematic Mapper data for change

detection and sustainable use of cultivated land: a case study in the Yellow River

delta, China, International, Journal of Remote Sensing, 25 (13), pp 2509-2522.

40. Zsuzsanna, D., Bartholy, J., Pongracz, R., Barcza, Z., (2005), Analysis of land-

use/land-cover change in the Carpathian region based on remote sensing techniques,

Physics and Chemistry of Earth, 30, pp 109-115.

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