Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. Geographical information system and image processing techniques used for the analysis of land use/land cover and change detection of Sukhana Basin of Aurangabad District, Maharashtra state. The tools used ArcGIS10.1 and ERDAS IMAGINE9.1, landsat images of 1996, 2003and 2014. From land use / land cover change detection it is found that during 1996-2014, water bodies cover have loss of 4 Sq. Km. Barren land have 146 Sq.Km. loss and forest area with 96 Sq.Km. loss. It is found that urbanization area has gain of 51 Sq.Km. and agricultural land cover also have gain of 195 Sq.Km.

1. http://www.iaeme.com/IJCIET/index.asp 329 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 7, Issue 3, May–June 2016, pp. 329–336, Article ID: IJCIET_07_03_032
Available online at
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Journal Impact Factor (2016): 9.7820 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
LAND USE /LAND COVER
CLASSIFICATION AND CHANGE
DETECTION USING GEOGRAPHICAL
INFORMATION SYSTEM: A CASE STUDY
S.D. Vikhe
Research Scholar, Govt. Engineering College,
Aurangabad, Maharashtra, India
Dr. K.A. Patil
Associate Professor of Civil Engineering,
Govt. Engineering College, Aurangabad, Maharashtra, India
ABSTRACT
Land use and land cover change has become a central component in
current strategies for managing natural resources and monitoring
environmental changes. Geographical information system and image
processing techniques used for the analysis of land use/land cover and change
detection of Sukhana Basin of Aurangabad District, Maharashtra state. The
tools used ArcGIS10.1 and ERDAS IMAGINE9.1, landsat images of 1996,
2003and 2014. From land use / land cover change detection it is found that
during 1996-2014, water bodies cover have loss of 4 Sq. Km. Barren land
have 146 Sq.Km. loss and forest area with 96 Sq.Km. loss. It is found that
urbanization area has gain of 51 Sq.Km. and agricultural land cover also
have gain of 195 Sq.Km.
Key words: GIS, Land Use, Land Cover, Supervised Classification, Change
Detection.
Cite this Article: S.D. Vikhe and. K.A. Patil, Land Use /Land Cover
Classification and Change Detection Using Geographical Information System:
A Case Study. International Journal of Civil Engineering and Technology,
7(3), 2016, pp.329–336.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=3

2. S.D. Vikhe and K.A. Patil
http://www.iaeme.com/IJCIET/index.asp 330 editor@iaeme.com
1. INTRODUCTION
Land cover refers to the physical characteristics of Earth's surface, captured in the
distribution of vegetation, water, soil and other physical features. Land use refers to
the way in which land has been used by humans and their habitats. Although land use
is generally inferred based on the cover, yet both the terms land use and land cover
being closely related are interchangeable.
Land use/cover and its dynamics are important factors that affect ecosystem
conditions and functions. In the past 40 years, land use cover change dynamics has
been considerably changing the biogeochemical cycling leading to changes in surface
atmospheric energy exchanges, carbon and water cycling, soil quality, biodiversity,
ability of biological systems to support human needs and, ultimately the climate at all
scales (Amare Sewnet 2016).
Jayakumar S. and D.I. Arockiasamy (2003), attempted to map land use / land cover
and change detection analysis in Kolli hill, part of Eastern Ghats of Tamil Nadu, using
remote sensing and GIS. About 467 ha increase has been observed in single crop
category and about 434 ha decrease has been observed in land with or without scrub
category. Majority of the area (13639 ha) is under scrubland. Lesser changes could be
noticed in double crop, plantation and barren/rocky categories.
Sathees kumar P and Nisha Radhakrishnan (2010), have studied Remote Sensing
And Gis In Land Use Planning. The different land use categories and their spatial and
temporal variability in Tiruchirappalli city has been studied over a period of eight
years (1998-2006), from the analysis of topographical map, IRS 1D and IRS P6 for
the year 1973, 1998, 2002 and 2006 using Arc GIS and ERDAS Imagine 9.1. Based
on the results of classified images, the agricultural land coverage area was reduced
7.8% from the year 1998 to 2006, while the area under settlement increased 14.7%
from the year 1998 to 2006.
He-Bing Hu, et al. (2012), have analysed Land Use Change Characteristics Based On
Remote Sensing and Gis In The Jiuxiang River Watershed. Based on remote sensing
and GIS technology, the remote sensing images from 2003 to 2009 were used to the
basic data sources, to analyze the characteristics of land-use change in Jiuxiang River
watershed. Results showed that watershed land use structure were changed greatly
from 2003 to 2009; the proportion of arable land decreased from 34.86% to 19.52%,
whereas other types of land use increased. The area of construction land increased
most rapidly, from 17.80% to 25.80%.
Jeffry Swingly Frans Sumarauw and Koichiro Ohgushi (2012) have analysed On
Curve Number, Land Use and Land Cover Changes in the Jobaru River Basin, Japan.
Arc GIS tool to delineate river basin and sub-basin, and HEC-GeoHMS tool for
estimating the CN. The result shows that from 1948 to 2005 the CN of the Jobaru
River basin decreased from 53.29 to 52.03, which indicates that the land use changes
in Jobaru River basin makes the land capability for reducing flood becomes better
during this period.
Singh R B and Dilip Kumar (2012) have used Remote sensing and GIS for land
use/cover mapping and integrated land management: case from the middle Ganga
plain. Alternative land use systems and the integration of livestock enterprises with
the agricultural system have been suggested for land resources management.
Kotoky, P. M. et al. (2012), have studied, Changes in Land use and Land cover along
the Dhansiri River Channel, Assam by A Remote Sensing and GIS Approach.
Information on land use/ land cover change is a critical input for natural resource

3. Land Use /Land Cover Classification and Change Detection Using Geographical Information
System: A Case Study
http://www.iaeme.com/IJCIET/index.asp 331 editor@iaeme.com
management policy decisions. Remote sensing data under GIS domain were utilized
to evaluate the changes in land-use/land-cover (LU/LC) spanning a period of thirty
three years during 1975 to 2008 along the Dhansiri River channel, Assam, India.
Seven different types of LU/LC were categorized and out of them cropland was
evident as the most important land use /land cover practices followed by dense mixed
jungle in 1975 and the settlement in 2008.
Mani.N. and Rama Krishnan. (2013), have estimated the LU/LC changes in Tamil
Nadu State using remote sensed data and Geographic Information System (GIS) with
field verification for the objective of the study. Landsat 1973,1990 and IRS-P6 images
(2008) were used. The LU/LC of the study area were classified into 7 types as
followed respectively built-up, agricultural, forest, grazing land, aste land, wetland
and water bodies which adopting NRSC LU/LC classification. The research
concludes that the area under built-up and agriculture has increased while the area
extent of forest, grazing land, wasteland, wetland and water bodies has decreased in
the study area.
Wagner, P. D. S. Kumar, and K. Schneider (2013) have studied an assessment of
land use change impacts on the water resources of the Mula and Mutha Rivers
catchment upstream of Pune, India. Analyzed past land use changes between 1989
and 2009 and their impacts on the water balance in the Mula and Mutha Rivers
catchment upstream of Pune. Land use changes were identified from three Rivers
catchment multitemporal land use classifications for the cropping years 1989/1990,
2000/2001, and 2009/2010. The hydrologic model SWAT (Soil and Water
Assessment Tool) was used to assess impacts on runoff and evapotranspiration.
Praveen Kumar et al. (2013), have analyzed Land Use/Land Cover Changes Using
Remote Sensing Data and GIS at an Urban Area, Tirupati, India Land use/land cover
(LU/LC) changes were determined from 1976 to 2003 by using Geographical
Information Systems and remote sensing technology. These studies were employed by
using the Survey of India topographic map 57O/6 and the remote sensing data of LISS
III and PAN of IRS ID of 2003.The study area was classified into eight categories on
the basis of field study, geographical conditions, and remote sensing data. The
comparison of LU/LC in 1976 and 2003 derived from toposheet and satellite imagery
interpretation indicates that there is a significant increase in built-up area, open forest,
plantation, and other lands. It is also noted that substantial amount of agriculture land,
water spread area, and dense forest area vanished during the period of study which
may be due to rapid urbanization of the study area.
As maximum villages in the study area are selected for water conservation works
under Jalyukt Shiwar Yojana by state government of Maharashtra .To preserve the
natural resources and to understand the causes and its effect of over exploitation of
soil and water resources the land use and land cover analysis study was carried out.
2. METHODOLOGY
2.1. Study Area
Geographical information system and image processing techniques used for the
analysis of land use/land cover and change detection of Sukhana Basin of Aurangabad
District, Maharashtra state which is divided into 35 sub watersheds of Aurangabad
District, Maharashtra state are evaluated. Study area is located between 75.33°, 75.76°
E longitudes, and 19.66°, 19.98° N latitudes. Details of study area is given in table 1

4. S.D. Vikhe and K.A. Patil
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Table 1 Summary of study area
Name of
watershed
No of watershed
Area of watershed
In Sq Km
No .of villages
covered
AU/GP-10 09 351.75 52
AU/GP-17’ 06 172.25 32
AU/GP-17 02 66.00 09
Total---03 17 590.00 93
2.2. Data used
Details of Image information are shown in following table.
Table 2 Image information
Sr. No. Type Year Bands Path Row Resolution (m)
1. LT5 March-1996 5 146 46 30 x 30
2. LT 7 March-2003 7 146 46 30 x 30
3 LC 8 March-2014 8 146 46 30 x 30
Survey of India (SOI) Toposheets at 1:50,000 scales were used for the preparation
of the base maps and for remote sensing data interpretation.
2.3. Land Use and Land Cover
Different land use/land cover classes like urbanization Barren land, Settlement, Forest
land, Water body, Agricultural land. etc. which is shown in table 3. were then
identified using visual interpretation keys such as colour, tone, texture, pattern, size
and shape. Description of these land cover classes are shown in Table 3. In the
supervised classification technique, three images with different years are
independently classified. Av supervised classification method was carried out using 8
to 10 training sites were made by demarcating a polygon or an area of interest for all
the five land use and land cover types. Then for each class average signature was
developed. Data is also for accuracy assessment. Maximum Likelihood Algorithm
was employed to detect the land cover types in ERDAS Imagine9.1.
After ground truthing , Accuracy assessment was carried out. To determine the
accuracy of classification, a sample of pixels is selected on the classified image and

5. Land Use /Land Cover Classification and Change Detection Using Geographical Information
System: A Case Study
http://www.iaeme.com/IJCIET/index.asp 333 editor@iaeme.com
their class identity is compared with the ground reference data. The detailed
methodology is illustrated in flowchart in Figure 1
Table 3 Description of land use/land covers units.
S .No Land cover class Description
1 Urbanization Urban/Rural Settlement
2 Agricultural Land Crop Land, Fallow/Harvested Land, Plantation
3 Forest Dense Forest, Degraded / Open forest, Forest plantation
4 Water bodies River / Stream, Lake / Reservoir /Tank / canal
Tank with scrub / Plantation
5 Barren land Salt affected Land, Gullied / Ravinous, Land with / without
scrub, Sandy Area, Barren Rocky / Stony waste / Sheet Rock
Flowchart adopted for analysis of land use/ Land cover in Basin.
Figure 1 Methodology Flowchart
Define classes on Land
use/Land cover
Data acquisition
GPS field survey and
toposheet information
Image processing registration
and study area clipping
Creation of signature for
each land class
Supervised
Classification
Post-classification
Processing
Accuracy assessment
Land use & land cover
change detection
Analyzing Land use & land
Cover changes using multitemporal
images

6. S.D. Vikhe and K.A. Patil
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3. RESULTS AND DISCUSSION
Output after processing in shown figure.2, the water bodies extent and its proportion
for the year 1996, 2003 & 2004 in basin was 6 Sq.Km. which is 0.9% of the total area
in 1996. Its cover was decreased to 0.40% in year 2003 and 0.30% for the year 2014.
Land under forest cover in the year 1996, 2003 and 2014 in basin was 206 Sq.Km.
amounting 30.80% of the total area in 1996. It was found decreased to 29.00% and
16.40% in the year 2003 and 2014 respectively.
Urbanization extent and its proportion for the year 1996 was 13 Sq.Km.
accounting 1.9% of total area. Its cover was increased by 4.2% and 9.6% in the year
2003 and 2014 respectively. Urbanization found in increasing trend in the basin.
Agricultural land in basin was 68.00 Sq.Km. which is 10.20% of the total area in
1996. Agriculture land cover extent and its proportion decreased to 8.1% in 2003,
whereas it is increased 39.30% in 2014, it shows five times increased than which is in
1996.
Barren land in the basin was 376 Sq.Km. which is approximately half the total
area of the basin. In 2003 it decreased to 58.30% and 34.40% in 2014. This shows that
area Barren land area decreases from 1996 to 2014 decrease in barren land have
compensated by increase in urbanization and Agricultural land. Details are presented
in table 4 and also in graph.
Table 4 Land use/cover of Sukhana Basin from 1996-2014.
Land class 1996 2003 2014 1996 - 2014
Area
(sq.km)
%
Area
(sqkm)
%
Area
(sqkm)
%
Total loss or gain
area (Sq. Km)
Water body 6 0.9 3 0.4 2 0.3 -4
Urbanization 13 1.9 28 4.2 64 9.6 51
Barren 376 56.2 390 58.3 230 34.4 -146
Agricultural 68 10.2 54 8.1 263 39.3 195
Forest 206 30.8 194 29 110 16.4 -96
Total 669 100 669 100 669 100

7. Land Use /Land Cover Classification and Change Detection Using Geographical Information
System: A Case Study
http://www.iaeme.com/IJCIET/index.asp 335 editor@iaeme.com
Figure 2 Land use/land cover maps of Sukhana Basin in 1996, 2003 and 2014.
The assessment of the accuracy of classification found by ratio of the mapped area
that has been correctly classified in comparison to reference data such as toposheet ,
reconnaissance survey ground truth to the total area mapped. The accuracy found to
be 85 %.
4. CONCLUSION
From study of land use / land cover change detection ,it is found that during 1996-
2014, water bodies cover have loss of 4 Sq. Km. Barren land have 146 Sq.Km. loss
and forest area with 96 Sq.Km. loss. It is found that urbanization area has gain of 51
Sq.Km. and agricultural land cover also have gain of 195 Sq.Km. Land use / land
cover must be improved with reference to resource management and application of
conservation treatment in the basin.
REFERENCES
[1] Sudhir Mahajan, Pankaj Panwar And Deepak Kaundal (2001). GIS Application to
Determine the Effect of Topography on Landuse in Ashwani Khad Watershed.
Journal of the Indian Society of Remote Sensing, 29,(4), pp.243–248 .
[2] Bisht B S and B P Kothyari (2002). Land-Cover Change Analysis of Garur
Ganga Watershed Using GIS/Remote Sensing Technique, Journal of the Indian
Society of Remote Sensing, 29(3). Pp. 138–141.
[3] Jayakumar S. and D.I. Arockiasamy (2003), Land use/Land Cover Mapping and
Change Detection in part of Eastern Ghats of Tamil Nadu using Remote Sensing
and GIS. Journal of the Indian Society of Remote Sensing, 31(4), pp.252–260
[4] Genxu Wang, Yibo Wang and Jumpei Kubota.(2006), Land-Cover Changes and
Its Impacts On Ecological Variables In The Headwaters Area of the Yangtze
River, China.” Journal of Environmental Monitoring and Assessment Springer
120, pp. 361–385.
[5] Herold, M., Latham, J.S., Di Gregorio, A., and Schmullius, C.C. (2006) Evolving
Standards in Land Cover Characterization. Journal of Land Use Science, 1(2–4),
pp. 157–168.
[6] Yu, H., Joshi, P.K., Das, K.K., Chauniyal, D.D., Melick D.R., Yang, X., and Xu,
J. (2007) Land Use/ Cover Change and Environmental Vulnerability Analysis in
Birahi Ganga Sub-Watershed of Garhwal Himalaya. Tropical Ecology, 48(2), pp.
241-250.

8. S.D. Vikhe and K.A. Patil
http://www.iaeme.com/IJCIET/index.asp 336 editor@iaeme.com
[7] Boakye, E., Odai, S.N., Adjei, K.A., and Annor, F.O. (2008) Landsat Images for
Assessment of the Impact of Land Use and Land Cover changes on the Barekese
.Catchment in Ghana. European Journal of Scientific Research, 22(2), pp. 269-
278.
[8] Chaudhary, B.S., Saroha, G.P., and Yadav, M. (2008) Human Induced Land Use
Land Cover Changes in Northern Part of Gurgaon District, Haryana, India:
Natural Resources Census oncept. Journal of Human Ecology, 23(3), pp. 243-
252.
[9] Bazgeera S., P.K. Sharmab, R.K. Maheya, S.S. Hundala, A. Soodb (2008).
“Assessment of land use changes using remote sensing and GIS and their
implications on climatic variability for Balachaur watershed in Punjab, India.
DESERT Online at http://jdesert.ut.ac.ir 12, 139–147.
[10] Sathees kumar P, Nisha Radhakrishnan (2010), Remote Sensing and Gis in Land
Use Planning, 11th ESRI India User Conference 2010. Pp. 1–7.
[11] He-Bing Hu, Hong-Yu Liu, Jing-Feng Hao Jing An. (2012), Analysis of Land
Use Change Characteristics Based On Remote Sensing And Gis In The Jiuxiang
River Watershed, International Journal On Smart Sensing and Interlligent
Systems, 5(4).
[12] Kotoky, P. M. K. Dutta and G. C. Borah (2012), Changes in Landuse and
Landcover along the Dhansiri River Channel, Assam – A Remote Sensing and
GIS Approach, Journal Geological Society of India Volume 79, pp.61–68.
[13] Jeffry Swingly Frans Sumarauw, and Koichiro Ohgushi (2012), Analysis On
Curve Number, Land Use And Land Cover Changes In The Jobaru River Basin,
Japan, ARPN Journal of Engineering and Applied Sciences 7(7), pp. 1819–6608.
[14] Singh R B and Dilip Kumar (2012), Remote sensing and GIS for land use/cover
mapping and integrated land management: case from the middle Ganga plain
Journal of Front. Earth Sci. (Springer), 6(2): pp.167–176.
[15] Wagner, P. D. S. Kumar, and K. Schneider (2013), An assessment of land use
change impacts on the water resources of the Mula and Mutha Rivers catchment
upstream of Pune, India, Journal of Hydrology and Earth System. Science., Vol.
17, pp.2233–2246,
[16] Praveen KumarMallupattu and Jayarama Reddy Sreenivasula Reddy. (2013).
“Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and
GIS at an Urban Area, Tirupati, India, Scientific world Journal Hindawi
Publishing Corporation The Vol 02013, pp.107.
[17] Mani.N. and Rama Krishnan. (2013). Assessment of Changes In Land Use/ Land
Cover In Tamil Nadu State In India Using GIS, African Journal of Science and
Research 2(6): pp : 01-06.
[18] W.A. Siahaya, P. Danoedoro, N. Khakhim, M. Baiquni.(2015) The comparison
analysis of land cover change based on vegetation index and multispectral
classification (a case study at Leihitu Peninsula of Ambon City District). Journal
of Degraded Andmining Landsmanagement, Volume 29 (4), pp. 415 – 422.
[19] Amare Sewnet (2016) Land use/cover change at Infraz watershed by using GIS
and remote sensing techniques, Northwestern Ethiopia, Intl. J. River Basin
Management 14(2), pp. 133–142.
[20] Prof. Dr. Mohammad A. Alanbari Asst. Prof. Saif S. Alquzweeni and Rusul A.
Aldaher, Spatial Distribution Mapping For Various Pollutants of Al-Kufa River
Using Geographical Information System (GIS). International Journal of Civil
Engineering and Technology, 6(10), 2015, pp.1–14.