Quantifying Land Use/Cover Dynamics of Nainital Town (India) Using Remote Sensing and GIS Techniques

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1 Quantifying Land Use/Cover Dynamics of Nainital Town (India) Using Remote Sensing and GIS Techniques Jiwan Rawat 1*, Vivekananda Biswas 1 and Manish Kumar 1 1 Centre of Excellence for NRDMS in Uttarakhand, Department of Geography, Kumaun University, SSJ Campus, Almora , India. Abstract The present study illustrates an integrated approach of remote sensing and GIS, i.e., Geospatial techniques for assessment of land use/cover dynamics of a tourist town located in the middle Himalayan range of the Uttarakhand State (India) viz., the Nainital. Landsat satellite imageries of two different time periods, i.e., Landsat TM of 1990 and Landsat TM 2010 were acquired by USGS Earth Explorer and quantified the land use/cover changes in the Nainital town from 1990 to 2010 over a period of 2 decades. Supervised Classification methodology has been employed using Maximum Likelihood Technique in ERDAS 9.3. The images of the study area were categorized into five different classes, viz., built-up area, vegetation, agricultural land, water bodies and sand bar. The results indicate that during the last two decades, built-up area and open space of the Nainital town area has been increased about 15.96% (i.e., 1.95 km 2 ) and 0.15% (i.e., 0.02 km 2 ), respectively, while area under other land categories such as vegetation, agricultural land have decreased about 15.36% (i.e., 1.86 km 2 ), 0.75% (i.e., 0.09 km 2 ) respectively. No traceable change is found in the water bodies of the town area. The study reveals that the Nainital town is expanding around the Nainital town with the nuclear settlement. Key words: Land use/cover, Remote Sensing, GIS, Digital Change Detection Techniques. 1. Introduction Land cover refers to variations in the state or type of physical materials on the Earth s surface, such as forests, grass, water, etc., which can be directly observed using remote-sensing techniques (Fisher et al., 2005). Land cover information has been identified as one of the crucial data components for many aspects of global change studies and environmental applications (Sellers et al., 1995). Land cover is a fundamental parameter describing the Earth s surface. This parameter is a considerable variable that impacts on and links many parts of the human and physical environments (Foody, 2002). Accurate monitoring of land cover is a matter of utmost importance in many different fields. On the other hand, land use refers to man's activities which are directly related to the land (Clawson and Stewart, 1965) AARS, All rights reserved. * Corresponding author Satellite or airborne-based monitoring of the Earth s surface provides information on the interactions between anthropogenic and environmental phenomena, providing the foundation to use natural resources better (Lu et al., 2004). The derivation of such information increasingly relies on remote sensing technology due to its ability to acquire measurement of land surfaces at various spatial and temporal scales. One of the major approaches to deriving land cover information from remotely sensed images is classification. Numerous classification algorithms have been developed since the first Landsat image was acquired in early 1970s (Townshend, 1992; Hall et al., 1995). Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. Essentially, it involves the ability to quantify

2 Quantifying Land Use/Cover Dynamics Using Remote Sensing and GIS Techniques of Nainital Town (India) temporal effects using multitemporal data sets. One of the major applications of remotely-sensed data obtained from Earth-orbiting satellites is change detection because of repetitive coverage at short intervals and consistent image quality (Anderson, 1977; Nelson, 1983). The digital nature of most of the satellite data make it easily amenable for computer-aided analysis. There is a definite need for a change detector which will automatically correlate and compare two sets of imagery taken of the same area at different times and display the changes and their locations to the interpreter (Shephard, 1964). Remote-sensing change detection, defined by Singh (1989) the process of identifying differences in the state of an object or phenomenon by observing it at different times, provides a means to study and understand the patterns and processes of ecosystems at a range of geographical and temporal scales. While the knowledge of land-cover conditions at a given point in time is important, the dynamics or trends related to specific change conditions offer unique and often important insights, ranging from natural disaster management to atmospheric pollution dispersion. Indeed, remotely sensed imagery is an important source of data available to characterize change systematically and consistently in terrestrial ecosystems over time. Land use/cover dynamics are widespread, accelerating and significant process driven by human action and also produce changes that impact humans (Agarwal et al., 2002). Many socio-economic and environmental factors are involved for the change in land use/cover. Land use/cover change has been reviewed from different perspectives in order to identify the drivers of land use/cover change, their process and consequences.the advent of high spatial resolution satellite imagery and more advanced image processing and GIS technologies has resulted in a switch to more routine and consistent monitoring and modeling of land use/cover patterns. Using remote sensing techniques to develop land use classification mapping is a useful and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region (Selçuk, 2003). Remotesensing has been widely used in updating land use/cover maps and land use/cover mapping has become one of the most important applications of remote sensing (Lo, 2004). 2. Study Area The study area, viz., Nainital town (Figure.1) in Uttarakhand, India extends between 29 24'19"N to 29 21'42"N latitudes and 79 25'46"E 79 28'25"E longitudes. As per the Master Plan of Nainital Town (MPNT) area developed by Kumaun Urban Planning, it encompasses an area of km 2. Out of the present total town area only about km 2 area falls under the Nainital Municipal area. The average height of the town stands at 2040 m above the mean sea level which varies between 1416 m to 2546 m. Nainital is a popular hill station in the Uttarakhand state of India and is head quarter of Nainital district in the lesser Himalayan zone. Nainital is set in a valley containing a pear-shaped lake with a mean depth of 18.55m (Rawat, 1987). Nainital enjoys cool temperate climatic conditions having average annual maximum and Figure 1. Location of Study area 8

3 Asian Journal of Geoinformatics, Vol.13,No.2 (2013) minimum temperature at 27 C and 7 C respectively and receives an average annual rainfall about 2324mm (Rawat et al., 2012). 3. Methodology 3.1 Land Use/Cover Detection and Analysis To work out the land use/cover classification, supervised classification method with maximum likelihood algorithm was applied in the ERDAS Imagine 9.3 Software. For better classification results some indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) were also applied to classify the Landsat images at a resolution of 30 m of 15 th November, 1990 and 15 th November With the help of GPS, ground verification was done for doubtful areas. Based on the ground verification the misclassified areas were corrected using recode option in ERDAS Imagine 9.3. Five land use/cover types are identified and used in this study, namely (i) built-up land (ii) vegetation cover (iii) agricultural land (iv) water body and (v) open space. 3.2 Land Use/Cover Change Detection and Analysis For performing land use/cover change detection, a postclassification detection method was employed. A change matrix (Weng, 2001) was produced with the help of ERDAS Imagine 9.3 software. Quantitative areal data of the overall land use/cover changes as well as gains and losses in each category between 1990 and 2010 were then compiled. 4. Results and Discussions The results obtained through the analysis of multi-temporal satellite imageries are diagrammatically illustrated in Figureure 2 to 4 and data are registered in Table 1 and 2. Figure 2 depicts land use/cover status, Figure 3 depicts land Table 1. Area and amount of change in different land use/cover categories in the Nainital town area during 1990 to Land use/cover categories Change km 2 % km 2 % km 2 % Built-up area Vegetation Agricultural land Water body not traceable not traceable Open Space Total Figure 2. Land use/cover status of the Nainital Town area: (A) - in 1990, (B) - in 2010 (based on Landsat Thematic Mapper Satellite Imagery). 9

4 Quantifying Land Use/Cover Dynamics Using Remote Sensing and GIS Techniques of Nainital Town (India) cover of the Nainital town area for the year 1990 while Figure 2(B) for the year These data reveal that in 1990, about 19.97% (2.43 km2) area of Nainital town was under built-up land, 65.07% (7.92 km2) under vegetation, 9.37% (1.14 km2) under agricultural land, 3.94% (0.48 km2) under 4.1 Land Use/Cover Status water body and 1.65% (0.20 km2) area was covered by open space. During 2010, the area under these land categories was Figure 2(A) depicts spatial distributional pattern of land water body, it constitute about 0.48 km2inuse/ 1990 and Due to degradation(4.38 in vegetation, found 35.93% km2) under built-up land, 49.71% 2 2 use/cover change in different land use categories, Figure 4 illustrates magnitude of change in different land categories in the study area during 1990 to A brief account of these results is discussed in the following paragraphs. open space has been increased from 0.20 km in 1990 to 0.22 km in 2010 which accounts 0.15% in area of total town area. Landin use/cover change in different categories during thetwo last two decades (1990( ); (A)-in built-up area, (B)-in Figure 3. Land use/coverfig.-3: change different categories during the last decades 2010); (A)-in built-up area, (B)-in vegetation cover, (C)-in agricultural land and (D)-in open vegetation cover, (C)-in agricultural land and (D)-in open space (based on Landsat Thematic Mapper Satellite Imagery). space (based on Landsat Thematic Mapper Satellite Imagery). Figure 4. Diagrammatic illustration of land use/cover change in percent during the last two decades ( ) in the Nainital town area.. 10

5 Asian Journal of Geoinformatics, Vol.13,No.2 (2013) Table 2. Land use/cover change matrix showing land encroachment in different categories of land (in %) of Nainital town area. Year 1990 Land use/cover categories Built-up area Vegetation Agricultural land Water body Open space Built-up area Vegetation Agricultural land Year 2010 Water body Open space Class total Class change (6.06km 2 ) under vegetation, 8.62% (1.05 km 2 ) under agricultural land, 3.94% (0.48 km 2 ) under water body and 1.80% (0.22 km 2 ) under open space (Table 1). 4.2 Land Use/Cover Change The data registered in Table 1 and Figure 3 and 4 depict that both positive and negative changes occurred in the land use/ cover pattern in the Nainital town area. During the last two decades the built-up area has increased from 2.43 km 2 in 1990 to 4.38 km 2 in 2010 which accounts for 15.96% of the total sprawl area. The vegetation cover has been decreased from 7.92 km 2 in 1990 to 6.06 km 2 in This decreased in vegetation accounts for 15.36% of the total town area. The agricultural land has slightly decreased from 1.14 km 2 in 1990 to 1.05 km 2 in 2010 which accounts for 0.75% of the total town area. There is no traceable change in water body, it constitute about 0.48 km 2 in 1990 and Due to degradation in vegetation, open space has been increased from 0.20 km 2 in 1990 to 0.22 km 2 in 2010 which accounts 0.15% in area of total town area. During the last two decades, how much encroachment in different land categories has been done for different purposes, to understand this, change detection matrix (Table 2) was prepared which reveals that during the last two decades: i. about 3.37% area of vegetation covered has been converted into agricultural land, 22.25% area under builtup area and 0.85% area under open space. ii. about 17.93% area of agricultural land has been converted in to vegetative area, 13.37% into built-up area and 0.39% in open space. iii. about 12.12% area of open space has been converted into vegetation cover, 14.28% into built-up and 2.16% into agricultural land, and iv. no traceable change is detected in water bodies. 5. Conclusion The study conducted in one of the towns of the Uttarakhand state located in the Kumaun Lesser Himalayan zone advocates that multi-temporal satellite data are very useful to detect the changes in land use quickly and accurately. The study reveals that the major land use in the Nainital town area is built-up area. During the last two decades the area under built-up land has been increased 15.96% (1.95 km 2 ) due to construction of new buildings on agricultural, vegetation and open space. The area under vegetation, agricultural land have decreased by 15.36% (1.86 km 2 ) and 0.75% (0.09 km 2 ) respectively. Area under open space has slightly increased 0.15% (0.02 km 2 ) due to deforestation. The approach adopted in this study clearly demonstrated the potential of GIS and remote sensing techniques in measuring change pattern of land use/cover in town area. Acknowledgement This paper constitutes a part of the DST project Sanction No. NRDMS/11/1438/08, dated 21/07/2009 on Setting up of Centre of Excellence for Natural Resource Data Management System in Uttarakhand. References Agarwal C., Green G. M., Grove J. M., Evans T. P. and Schweik C. M. (2002). A Review and Assessment of Land- Use Change Models: Dynamics of Space, Time, and Human Choice. Technical Report, Pennsylvania, U.S.A, p. 61. Anderson J. R. (1977). Land use and land cover changes. A 11

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