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Visualization of Urban Growth Pattern in Chennai Using Geoinformatics and Spatial Metrics

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Abstract

Urban growth is the spatial pattern of land development to accommodate anthropogenic demand that influences other land uses (e.g.: open spaces, water bodies, etc.). Driven by population increase, urban growth alters the community’s social, political and economic institutions with changing land use and also affects the local ecology and environment. India’s urban population has increased by 91 million between 2001 and 2011, with migration, the inclusion of new/adjoining areas within urban limits, etc. Evidently, the percentage of urban population in India has increased tremendously: from 1901 (10.8 %) to 2011 (31.16 %). Chennai has an intensely developed urban core, which is surrounded by rural or peri-urban areas that lack basic amenities. Studying the growth pattern in the urban areas and its impact on the core and periphery are important for effective management of natural resources and provision of basic amenities to the population. Spatial metrics and the gradient approach were used to study the growth patterns and status of urban sprawl in Chennai city’s administrative boundary and areas within a 10 km buffer, for the past forty years. It is found that though Chennai experiences high sprawl at peri-urban regions, it also has the tendency to form a single patch, clumped and simple shaped growth at the core. During this transition, substantial agricultural and forest areas have vanished. Visualization of urban growth of Chennai for 2026 using cellular automata indicates about 36 % of the total area being converted to urban with rapid fragmented urban growth in the periphery and outskirts of the city. Such periodic land-use change analysis monitoring, visualization of growth pattern would help the urban planner to plan future developmental activities more sustainably and judiciously.

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References

  • Aguilera, F., Valenzuela, L., & Leitao, A. (2011). Landscape metrics in the analysis of urban land use patterns: a case study in a Spanish metropolitan area. Landscape and Urban Planning, 99, 226–238.

    Article  Google Scholar 

  • Alsharif, A. A. A., & Pradhan, B. (2013). Urban sprawl analysis of Tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149–163.

    Article  Google Scholar 

  • Anindita, D., Kumar, U., & Ramachandra, T. V. (2010). Urban Landscape analysis through spatial metrics, In the proceedings of the Conference on Infrastructure, Sustainable Transportation and Urban Planning, CISTUP@CISTUP2010, JN Tata Auditorium, Indian Institute of Science, Bangalore. 18–20 October, 2010.

  • Arsanjani, J., Helbich, M. W., Kainz, & Darvishi, A. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion - the case of Tehran. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275.

    Article  Google Scholar 

  • Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. International Journal of Remote Sensing, 30, 4733–4746.

    Article  Google Scholar 

  • Bhatta, B. (2010). Analysis of urban growth and sprawl from remote sensing data. Berlin: Springer.

    Book  Google Scholar 

  • Brockerhoff, M. P. (2000). An urbanizing world. Population Bulletin, 55, 3–44.

    Google Scholar 

  • Chang, C. Y. (2003). Landscape structure and bird’s diversity in the rural areas of Taiwan. Journal of Environmental Sciences, 15, 241–248.

    Google Scholar 

  • Civco, D. L., Hurd, J. D., Wilson, E. H., Arnold, C. L., & Prisloe, S. (2002). Quantifying and describing urbanizing landscapes in the northeast United States. Photogrammetric Engineering and Remote Sensing, 68, 1083–1090.

    Google Scholar 

  • Clarke, K. C., & Gaydos, L. (1998). Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington Baltimore. International Journal of Geographical Information Science, 12, 699–714.

    Article  Google Scholar 

  • Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton: CRC Press Taylor & Francis Group.

    Google Scholar 

  • Congalton, R. G., Oderwald, R. G., & Mead, R. A. (1983). Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing, 49, 1671–1678.

    Google Scholar 

  • Dessì, F. G., & Niang, A. J. (2008) Thematic mapping using quickbird multispectral imagery in Oung El-Jemel area, Tozeur (SW Tunisia). In Desertification and risk analysis using high and medium resolution satellite data (207–212) by Springer.

  • Dowall, D. E., & Monkkonen, P. (2008). Chennai urban land market assessment. International Real Estate Review, 11(2), 142–165.

    Google Scholar 

  • Duda, R. O., Hart P. E., & Stork, D. G. (2000). Pattern classification, A Wiley-Interscience Publication, Second Edition, ISBN 9814-12-602-0.

  • Eastman, J. R. (2009). IDRISI Taiga, guide to GIS and remote processing. Guide to GIS and remote processing. Worcester: Clark University.

    Google Scholar 

  • Epstein, J., Payne, K., & Kramer, E. (2002). Techniques for mapping suburban sprawl. Photogrammetric Engineering and Remote Sensing, 68, 913–918.

    Google Scholar 

  • Guan, D. J., Gao, W. J., Watari, K., & Fukahori, H. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences, 18, 455–468.

    Article  Google Scholar 

  • He, C. Y., Okada, N., Zhang, Q. F., Shi, P. J., & Li, J. G. (2008). Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning, 86, 79–91.

    Article  Google Scholar 

  • He, J., Liu, Y., Yu, Y., Tang, W., Xiang, W., & Liu, D. (2013). A counterfactual scenario simulation approach for assessing the impact of farmland preservation policies on urban sprawl and food security in a major grain-producing area of China. Applied Geography, 37, 127–138.

    Article  Google Scholar 

  • Herold, M., Scepan, J., & Clarke, K. C. (2002). The use of remote sensing and landscape-metrics to describe structures and changes in urban land uses. Environment and Planning, 34, 1443–1458.

    Article  Google Scholar 

  • Herold, M., Goldstein, N., & Clarke, K. (2003a). The spatio-temporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 85, 95–105.

    Google Scholar 

  • Herold, M., Goldstein, N. C., & Clarke, K. C. (2003b). The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86, 286–302.

    Article  Google Scholar 

  • Herzog, F., & Lausch, A. (2001). Supplementing land-use statistics with landscape metrics: some methodological considerations. Environmental Monitoring and Assessment, 72, 37–50.

    Article  Google Scholar 

  • Jensen, J. R. (1986). Introductory digital image processing, a remote sensing perspective. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Ji, W., Ma, J., Twibell, R. W., & Underhill, K. (2006). Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics. Computers, Environment and Urban Systems, 30, 861–879.

    Article  Google Scholar 

  • Johnson, M. P. (2001). Environmental impacts of urban sprawl: a survey of the literature and proposed research agenda. Environment & Planning A, 33, 717–735.

    Article  Google Scholar 

  • Kumar, A., Pandey, A. C., Hoda, N., & Jeyaseelan, A. T. (2011a). Evaluating the long-term urban expansion of Ranchi Township. India using Geospatial Technology. Journal of Indian Society of Remote Sensing, 39(2), 213–124.

    Article  Google Scholar 

  • Kumar, A., Pandey, A. C., Hoda, N., & Jeyaseelan, A. T. (2011b). Evaluation of urban sprawl pattern in tribal dominated cities of Jharkhand State, India. International Journal of Remote Sensing, 32, 7651–7675.

    Article  Google Scholar 

  • Leitao, A. B., & Ahern, J. (2002). Applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning, 59, 65–93.

    Article  Google Scholar 

  • Lillesand, T. M., & Kiefer, R. W. (2002). Remote sensing and image interpretation, Fourth Edition (215–216). Wiley ISBN 9971-51-427-3.

  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, 823–870.

    Article  Google Scholar 

  • Matsuoka, M., Hayasaka, T., Fukushima, Y., & Honda, Y. (2004). Analysis of the land cover and its change over yellow river basin using satellite data. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 35, 836–839.

    Google Scholar 

  • McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-351.

  • Mundia, C. N., & Murayama, Y. (2010). Modeling spatial processes of urban growth in African cities: a case study of Nairobi city. Urban Geography, 31, 259–272.

    Article  Google Scholar 

  • Nassauer, J. I., Corry, R. C., & Cruse, R. M. (1999). The landscape in 2025 alternative, future landscape scenarios: a means to consider agricultural policy. Journal of Soil and Water Conservation, 57, 44–53.

    Google Scholar 

  • O’Neill, R. V., Ritters, K. H., Wichham, J. D., & Jones, K. B. (1999). Landscape pattern metrics and regional assessment. Ecosystem Health, 5, 225–233.

    Article  Google Scholar 

  • Opeyemi, Z. (2008). Monitoring the growth of settlements in Ilorin, Nigeria (a GIS and remote sensing approach). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 225–232.

    Google Scholar 

  • Pathan, S. K., Shukla, V. K., Patel, R. G., & Mehta, K. S. (1991). Urban land use mapping - A case study of Ahmedabad city and its environs. Journal of the Indian Society of Remote Sensing, 19(2), 95. I 12.

    Article  Google Scholar 

  • Praveen, S., Kabiraj, S., & Bina, T. (2013). Application of a hybrid cellular automaton – Markov (CA-Markov) Model in land-use change prediction: a case study of saddle creek drainage Basin, Florida. Applied Ecology and Environmental Sciences, 1(6), 126–132.

    Article  Google Scholar 

  • Rahman, Y., Kumar, S., Fazal, S., & Bhaskaran. (2011). Urbanization and quality of urban environment using remote sensing and GIS techniques in East Delhi-India. Journal of Geographic Information System, 3, 61–83.

    Article  Google Scholar 

  • Ramachandra, T. V., & Kumar, U. (2008). Wetlands of Greater Bangalore, India: automatic delineation through pattern classifiers. Electronic Green Journal, 26, https://escholarship.org/uc/item/3dp0q8f2#page-3

  • Ramachandra, T. V., Bharath, A. H., & Durgappa, D. S. (2012a). Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 18, 329–343.

    Article  Google Scholar 

  • Ramachandra, T. V., Bharath, A. H., & Sreekantha, S. (2012b). Spatial metrics based landscape structure and dynamics assessment for an emerging Indian Megalopolis. International Journal of Advanced Research in Artificial Intelligence, 1(1), 48–57.

    Google Scholar 

  • Ramachandra, T. V., Bharath, H. A., Vinay, S., Kumar, U., & Joshi, N. V. (2013a).Modelling urban revolution in Greater Bangalore, India, In the proceedings of 30th Annual In-house Symposium on Space Science and Technology, 7–8 November 2013, ISRO-IISc Space Technology cell, Indian Institute of Science, Bangalore

  • Ramachandra, T. V., Bharath, A. H., & Sowmyashree, M. V. (2013b). Analysis of spatial patterns of urbanisation using geoinformatics and spatial metrics. Theoretical and Empirical Researches in Urban Management, 8(4), 5–24.

    Google Scholar 

  • Ramachandra, T. V., Bharath, H. A., & Sowmyashree, M. V. (2014). Urban structure in Kolkata: metrics and modeling through geo-informatics. Applied Geomatics, 6(4), 229–244.

    Article  Google Scholar 

  • Ramachandra, T. V., Bharath, H. A., & Shreejith, K. (2015a). GHG footprint of major cities in India. Renewable and Sustainable Energy Reviews, 44, 473–495.

    Article  Google Scholar 

  • Ramachandra, T. V., Bharath, H. A., & Sowmyashree, M. V. (2015b). Monitoring urbanization and its implications in a mega city from space: spatiotemporal patterns and its indicators. Journal of Environmental Management, 148, 67–91.

    Article  Google Scholar 

  • Setturu, B., Bharath, H. A., Durgappa, D. S., & Ramachandra, T. V. (2012). Landscape dynamics through spatial metrics, Proceedings of India GeoSpatial Conference, Epicentre, Gurgaon, India, 7–9 February, 2012.

  • Sharma, R., & Joshi, P. K. (2013). Monitoring urban landscape dynamics over Delhi (India) using remote sensing (1998–2011) inputs. Journal of the Indian Society of Remote Sensing, 41(3), 641–650.

    Article  Google Scholar 

  • Singh, A. (1989). Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing, 10, 989–1003.

    Article  Google Scholar 

  • Sudhira, H. S., Ramachandra, T. V., & Jagdish, K. S. (2004). Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29–39.

    Article  Google Scholar 

  • Torrens, P. M. (2000). How cellular models of urban systems work, CASA Centre for Advanced Spatial Analysis, University College London, Working paper 28, URL http://www.casa.ucl.ac.uk/working_papers/paper28.pdf (accessed 15th May 2015)

  • Weber, C. (2001). Remote sensing data used for urban agglomeration delimitation. In J. P. Donnay, M. J. Barnsley, & P. A. Longley (Eds.), Remote sensing and urban analysis (pp. 155–167). London: Taylor and Francis.

    Google Scholar 

  • Wentz, E. A., Anderson, S., Fragkias, M., Netzband, M., Mesev, V., Myint, S. W., Quattrochi, D., Rahman, A., & Seto, K. C. (2014). Supporting global environmental change research: a review of trends and knowledge gaps in urban remote sensing. Remote Sensing, 6(5), 3879–3905.

  • Yang, X., Ren, L., Yong, B., & Jiao, D. (2008). Remote sensing-based land use and land cover change in Shalamulun catchment. IEEE Computer Society, 2, 504–507.

    Google Scholar 

  • Yorke, C., & Margai, F. (2007). Monitoring land use change in the Densu river basin, Ghana using GIS and remote sensing. African Geographical Review, 26, 87–110.

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to NRDMS Division, The Ministry of Science and Technology, Government of India; ISRO-IISc Space Technology Cell, Indian Institute of Science; Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP), Indian Institute of Science for the financial and infrastructure support. Remote sensing data were downloaded from public domain (http://glcf.umiacs.umd.edu/data). We are also thankful to National Remote Sensing Centre, Hyderabad (http://nrsc.gov.in) for providing the latest data of IRS 1D.

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Authors and Affiliations

  1. Energy & Wetlands Research Group, CES TE15, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560012, India

    Bharath H. Aithal & T. V. Ramachandra

  2. Centre for Sustainable Technolgies (astra), Indian Institute of Science, Bangalore, 560012, India

    T. V. Ramachandra

  3. Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP], Indian Institute of Science, Bangalore, Karnataka, 560 012, India

    T. V. Ramachandra

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  1. Bharath H. Aithal
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Aithal, B.H., Ramachandra, T.V. Visualization of Urban Growth Pattern in Chennai Using Geoinformatics and Spatial Metrics. J Indian Soc Remote Sens 44, 617–633 (2016). https://doi.org/10.1007/s12524-015-0482-0

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