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Scholars Research Library Archives of Applied Science Research, 2011, 3 (1):277-286

(http://scholarsresearchlibrary.com/archive.html) ISSN 0975-508X CODEN (USA) AASRC9

Dynamics of urban sprawl, changing direction and mapping: A case study of Salem city, Tamilnadu ,India. S.Tamilenthi1*, J. Punithavathi1, R. Baskaran1 and K.ChandraMohan2 1

2.

Dept of Earth Science, Tamil university, Thanavur, India. School of Earth and Atmospheric Sciences, Madurai Kamaraj University, Madurai, TN, India.

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ABSTRACT Urban sprawl refers to the extent of urbanisation, which is a global phenomenon mainly driven by population growth and large scale migration. In developing countries like India, where the population is over one billion, one-sixth of the world’s population, urban sprawl is taking its toll on the natural resources at an alarming place.Urban planners require information related to the rate of growth, pattern and extent of sprawl to provide basic amenities such as water, sanitation, electricity, etc. In the absence of such information, most of the sprawl areas lack basic infrastructure facilities. Pattern and extent of sprawl could be dectected with the help of statelite images and temporal data. This is used to analysing the growth, pattern and extent of sprawl. This paper brings out the extent of sprawls directions taking place for the period of nearly three decades using GIS and Remote Sensing. Keywords: Urban sprawl; Urbanisation; GIS; Remote sensing; trend; urban Change, Changing direction.

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INTRODUCTION Urban sprawl may be defined as the scattering of new development on isolated tracts, separated from other areas by vacant land (Lata, et al. 2001). It has also been described as leapfrog development (Jothimani, 1977; Torrens and Albert, 2000). The need for monitoring urban development has become imperative to help curb the problems of this type of growth. Monitoring urban development is mainly to find the type, amount and the location of land conversion for future planning (Shekhar, 2001). Urban sprawl varies in degrees between the developed and the developing world and subsequently they have differing consequences. The trends towards sub urbanisation and urban sprawl (lowdensity, spatially dispersed, and segregated land use) emerging in urban spaces have a direct and indirect repercussion on mobility (European Environmental Agency, 2006). Major changes in mobility threaten the environmental and economic sustainability of urban spaces (Banister, 2008). Urban planners need to understand the role played by the urban structure on the journey to work, in order to mitigate the negative aspects of mobility (Giuliano and Small, 1993). 277 Scholar Research Library

S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________ Urban sprawl, a consequence of socioeconomic development under certain circumstances, has increasingly become a major issue facing many metropolitan areas. Although a general consensus regarding the definition and impact of urban sprawl has not been achieved (Johnson, 2001), urban sprawl is often referred to as uncontrolled, scattered suburban development that increases traffic problems, depletes local resources, and destroys open space (Peiser, 2001). It is critically important to properly characterize urban sprawl in order to develop a comprehensive understanding of the causes and effects of urbanization processes. However, due to its association with poorly planned urban land use and economic activity (Pendall, 1999), urban sprawl is often evaluated and characterized exclusively based on major socioeconomic indicators such as population growth, commuting costs, employment shifts, city revenue change, and number of commercial establishments (Brueckner, 2000; Lucy & Phillips, 2001). This approach cannot effectively identify the impacts of urban sprawl in a spatial context. The process of urbanisation is a universal phenomenon taking place the world over, where humans dwell. All countries are prone to this bewildering phenomenon chiefly responsible due to the increase in population growth, economy and infrastructure initiatives. The extent of urbanisation or the sprawl is one such phenomenon that drives the change in land use patterns. The sprawl normally takes place in radial direction around the city centre or in linear direction along the highways. Usually sprawl takes place on the urban fringe, at the edge of an urban area or along the highways. The study on urban sprawl (The Regionalist, 1997; Sierra Club, 1998) is attempted in the developed countries (Batty et al., 1999; Torrens and Alberti, 2000; Barnes et al., 2001, Hurd et al., 2001; Epstein et al., 2002) and recently in developing countries such as China (Yeh and Li, 2001; Cheng and Masser, 2003) and India (Jothimani, 1997; Lata et al., 2001; Sudhira et al., 2003). In India alone currently 25.73% of the population (Census of India, 2001) live in the urban centres, while it is projected that in the next fifteen years about 33% would be living in the urban centres. This indicates the alarming rate of urbanisation and the extent of sprawl that could take place. In order to understand this increasing rate of urban sprawl, an attempt is made to understand the sprawl dynamics and evolve appropriate management strategies that could aid in the region’s sustainable development. Understanding such a phenomenon and its pattern helps in planning for effective natural resource utilisation and provision of infrastructure facilities. The built-up is generally considered as the parameter for quantifying urban sprawl (Torrens and Alberti, 2000; Barnes et al., 2001; Epstein et al.,2002). It is quantified by considering the impervious or the built-up as the key feature of sprawl, which is delineated using toposheets or through the data acquired remotely. The convergence of GIS, remote sensing and database management systems has helped in quantifying, monitoring, modelling and subsequently predicting this phenomenon. At the landscape level, GIS aids in calculating the fragmentation, patchiness, porosity, patch density, interspersion and juxtaposition, relative richness, diversity, and dominance in order to characterise landscape properties in terms of structure, function, and change (ICIMOD, 1999; Civco et al., 2002). Nowadays, metropolitan expansion is represented through the urban sprawl model. These lowdensity patterns are encouraged by cultural changes and the rise in income levels. High densities are associated with greater accessibility, understood as greater ease in carrying out activities. In the traditional compact city, journeys are shorter and car use is lower (Friedman et al., 1994). Higher densities and land use mix patterns enable residents to perform more activities in a single journey, sometimes reducing the total number of trips by 20% (Stover and Koepke, 1991), but their greatest effect is to facilitate non-mechanised trips (on foot or by bicycle) and the provision 278 Scholar Research Library

S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________ of public transport (UITP, 2005; Cervero, 2002). In fact, car ownership numbers in families resident in high density spaces are much lower than in residents in the new dispersed suburbs (Stead, 2001). Urban growth and low-density are accompanied by a growing discontinuity and spatial fragmentation of suburban areas. Isolated housing estates, often closed off, sprawl over the outskirts beyond the supposed city limits. The growing social polarisation leads to greater spatial fragmentation, and this affects both residential spaces and activity and employment areas. There are marked differences between building typologies, income groups and activities. With such a clear differentiation between residential spaces and activities, mobility needs increase (Litman, 2008). The separation and differentiation of land use intensifies mobility (Banister, 1997). Remote sensing has been used to monitor this urban development, similar research have been carried out elsewhere by Howarth (1986), Fung and LeDew (1987), Li and Yeh (1998) and various techniques have been developed for land growth detection efficiency, including image differencing (Toll et al, 1980), Image rationing (Nelson, 1983), Masking Method (Pilon et al, 1988) and principal component analysis (Fung and LeDew, 1987; Li and Yeh, 1998). After careful review of different study thepresent study attempts 1)To find out the urban sprawl for different points of the year.2)Analyse the urban sprawl direction. 3)Mapping-overlay of most spread urban sprawl for 1973-2010. 2. Study area The study area Salem city situated in Salem District of Tamilnadu, India. (Fig.1). The town is surrounded by hills on all sides: the Nagaramalai to the north, the Jarugumalai to the south, the Kanjamalai to the west, and the Godumalai to the east. It is divided by the river Thirumanimuthar in the main division. The fort area is the oldest part of the town. The study area covers the part of toposheets of Survey of India No.58 I/2 (1:50,000,1972), 11° 39′ 0″ to 11.65N and 78.16 to 78° 9′ 36″ E. Salem Corporation consists of 60 wards categorized under 4 Zonal namely Suramangalam Zonal, Hasthampatty Zonal, Ammapet Zonal, Kondalampatty Zonal with 91.34 (sq.km).It is 278m above Mean Sea Level. The soil types of the study area are red noncalcareous and red calcareous soils.

Fig.1. Study area

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S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________ The average annual rainfall is 363.5 mm. The temperature is generally very high during summer and it ranges from 20.0 to 37.9° degree celsius. According to 2001 census, the total population of the Salem town is around 30,16,346 of which 12,79,846 are workers and the rest are a nonworker. The area has a good transport system of road network and well connected by the adjacent towns namely Banglore,Chennai,Trichy and coimbatore. It also has the good communication facilities. MATERIALS AND METHODS 3. Data Sets and Methodology The data collection was done from both primary and secondary data sources. The primary data collected were the Survey of India toposheets of 1:50,000 scale for the corresponding region,Land sat corresponding path -143, row-52 for year 1973 from GLCF web site down loaded and the multispectral satellite imagery of the Indian Remote Sensing (IRS) satellite, 1980 MSS, LISS-III 1991,2000 and 2010 from the National Remote Sensing Agency, Hyderabad, India. The secondary data collected included the demographic details from the census abstracts of the study area for 1971, 1981, 1991 and 2001, from the Directorate of Census Operations, Census of India. The corporation map of this region was obtained from Town and Country Planning, Salem,Tamilnadu,India.

Landuse/land cover classification obtained by image analysis and ground truthing for 19732010. From Land use Land cover classification the most spread area were extracted and isolated with two zones of the town were selected, which serves as growth poles. It was however a bit difficult to separate various landuses i.e. the Residential from the commercial, the six growth points where characterized by mixed landuses. The area selected includes north west and north east part of Salem corporation limits. The image analyses included bands extraction, restoration, classification, and enhancement. The original classification of land-use of 12 categories was aggregated to vegetation, built-up (residential and commercial), crop lands and water bodies was computed. Most accumulated areas were found based on the new formation between the decadal years from 1973 to 2010. RESULTS AND DISCUSSION 4.1. Image analyses The standard image processing techniques such as, image extraction, rectification, restoration, and classification were applied in the current study. Training polygons were chosen from the composite image and corresponding attribute data was obtained in the field using GPS. Based on these signatures, corresponding to various land features, image classification was done and the classified image the between period of sprawl zones extracted is given in Fig. (4a,b,c&d).

4.2. Land use and Land cover change Characterising pattern involves detecting and quantifying it with appropriate scales and summarising it statistically. There are scores of metrics now available to describe landscape pattern. The only major components that were considered for this study are composition and structure. The landscape pattern metrics are used in studying forest patches (Trani and Giles, 1999; Civco et al., 2002). Most of the indices are correlated among themselves, because there are only a few primary measurements that can be made from patches (patch type, area, edge, and neighbour type), and all metrics are then derived from these primary measures.The below

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S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________ fig.(2a,b,c,d&e). shows urban occupancy area and land use land cover change over the years 1973-2010.

Figures.(2a,b,c and below d&e) Land use land cover

Fig. 2. Land use land cover classified 1973 – 2010

4.3. Principal Component Analysis (PCA) In the figure below (Fig.2) the pink areas represent urban/bare areas in 1973-2010. This is because in the PCA analysis, urban areas have a high reflectance values than any other landcover/land-use category. The other areas are represented by dark blue colour.

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S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________

Fig.3.Principal Component Analysis (PCA)

From the above PCA analysis only urban covered area is extracted and analysed the sprawl trend and mapped as urban sprawl direction. 4.4. Dynamics of urban sprawl and Urban sprawlchange direction: Defining this dynamic phenomenon and predicting the future sprawl is a greater challenge than the quantification of sprawls. Although different sprawl types were identified and defined there

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S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________

4(a).Urban sprawl direction from 1973–1980.

4(c).Urban sprawl direction from 1990–2000.

4.(b)Urban sprawl direction from 1980–1990.

4(d).Urban sprawl direction from 2000–2010

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S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________ has been an inadequacy with respect to developing mathematical relationships to define them. This necessitates characterisation and modelling of urban sprawl, which will aid in regional planning and sustainable development.Urban sprawl dynamics was analysed considering some of the causal factors. The below figures 4(a,b,c,d&e) shows most spread urban areas during the different decadal points with red colour and the green colour is for least spread.The yellow colour is given for starting or existing occupancy of uban.

Fig. 4(e) Over lay of urban sprawl direction from 1973–2010.

CONCLUSION With the population of India increasing as ever, the pressures on land and resources are also increasing. The urban sprawl is seen as one of the potential threats to sustainable development where urban planning with effective resource utilisation and allocation of infrastructure initiatives are key concerns. The study attempts to identify such sprawls change for 1973-2010. The study was carried out Salem corporation, Tamil nadu, India using the techniques of GIS and remote sensing to identify and detect the urban sprawl. The spatial data along with the attribute data of the region aided to analyse statistically and fore casting. 284 Scholar Research Library

S.Tamilenthi et al Arch. Appl. Sci. Res., 2011, 3 (1):277-286 _____________________________________________________________________________ It is found that the Old suramangalam cluster or north west cluster extends from andipatti to Burns colony.The Kalarampatti to NGGO colony /Chinna kollapatti extensions is second cluster which occupies in the north eastern part of the corporation.This implies that by 2020 and 2050, the built-up area in the region would rise beyond the corporation limits and hence the corporation limit will be extended.

Acknowledgement Authors are highly thankful to Director, National Remote Sensing Agency, Hyderabad and the Director, Town and Country Planning Office, Salem,Tamilnadu,India for their kind help in providing reference data and valuable suggestions. REFERENCES [1.]H.S. Sudhira , T.V. Ramachandra , K.S.Jagadish. International Journal of Applied Earth Observation and Geoinformation 5, (2004) 29–39, [2]Batty M., Xie Y., Sun Z. The dynamics of urban sprawl. Working Paper Series, Paper 15, Centre for Advanced Spatial Analysis, University College, London(1999) [3].Cheng, J., Masser, I. Urban growth pattern modelling: a case study of Wuhan City, PR China. Landscape Urban Plan. (2003). 62, 199–217 [4]Civco, D.L., Hurd, J.D., Wilson, E.H., Arnold, C.L., Prisloe, M.,. Eng. Remote Sens. ,(2002).68 (10), 1083–1090 [5]Claramunt, C., Jiang, B. J. Geography. Syst. 3, 411–428,(2001). [6]Eastman, J.R., Idrisi32: Guide to GIS and Image Processing, vols. 1 and 2, Clark Labs, Clark University, USA(1999). [7]Fischer, M.M,. J. Geography. Syst ( 2002)4, 287– 299, [8]Fung, T. and LeDrew, E. Application of principal components Analysis change Detection,photogram metric Engineering and Remote sensing of Environment(1987). [9]Hurd, J.D., Wilson, E.H., Lammey, S.G., Civco, D.L., Characterisation of forest fragmentation and urban sprawl using time sequential Landsat Imagery. In: Proceedings of the ASPRS Annual Convention, St. Louis, MO, 2001,April 23–27, [10]Jothimani, P. Operational urban sprawl monitoring using satellite remote sensing: excerpts from the studies of Ahmedabad, Vadodara and Surat, India. Paper presented at 18th Asian Conference on Remote Sensing held during October 20–24, 1997, Malaysia. [11].Lata, K.M., Sankar Rao, C.H., Krishna Prasad, V., Badrinath, K.V.S., Raghavaswamy, Measuring urban sprawl: a case study of Hyderabad. GIS Dev5 ,2001(12). [12]Leonard I. Nwosu, Cyril N. Nwankwo and Anthony S. Ekine An SP survey for groundwater and correlation with Resistivity survey results in parts of Mbano area of Imo State, Nigeria. Archives of Applied Science Research, 2010, 2, 5, 45-55. [13]Lo, C.P., Yang, X.,. Photogrammetr. Eng. Remote Sens. 2002,68 (10), 1062–1073. [14]Murphy, D.L., Eng. Remote Sens. 1985, 51 (6), 667–674 . [15]Pilon, P. G., Howarth, A. and Bullock, R. A. Photogrammetric engineering and Remote sensing,1988.

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