LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India
- 175 Downloads
Monitoring of land use and land cover (LULC) change is one important drivers of global change, which plays a decisive role on the management and sustainable developmental planning for urban spaces. The study aims to develop series of LULC maps of urban areas of Ranchi, India and was studied during the years 1989 and 2015. It predicts LULC changes using geospatial tools such as remote sensing and GIS. Various satellite imagery datasets such as Landsat TM, ETM+ and Landsat 8 OLI of years 1989, 2002 and 2015 were used to analyze urban LULC, which was later used to predict for 2015 and 2028 using Markov transition matrix and was cross-validated with true LULC of 2015. The urban area growth was found 11% more than the predicted value. Slope map was also generated from digital elevation model and urban expansion in 2015 was 67% and with respect to roads it was 60% within 1 km road buffer in 2015 over 2002. Regression equation was developed over decadal population of 1961–2011 to estimate it for years 1989, 2002, 2015 and 2028. The population has increased 102% in 2015 over 1989. However, Markov predicted 43% more urban expansion for year 2028 over 2015. Coarse resolution temporal satellite data can be effectively harnessed to assess LULC change whereas prediction can be done with accuracy as high as 89.02% based on Markov transition matrix. An effective coordination between governments agencies are solicited to achieve sustainable development to be implemented systematically.
KeywordsGeographic information system Land use land cover change Markov transition matrix Remote sensing data Satellite imagery
The authors are grateful to the USGS for free download of Landsat and DEM (ASTER) data which was used in the analysis. Required GIS layers were downloaded from DIVA GIS.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no competing interests.
- 4.Hauser, P. N., Gardner, R. W., Laquian, A. A., & El-Shakhs, S. (1982). Population and the urban future. Albany: State University of New YorkPress.Google Scholar
- 5.United Nations. (2014). World population data sheet—population reference Bureau. http://www.un.org/en/development/desa/news/population/world-urbanization-prospects-2014.html. Accessed August 20, 2016
- 9.Srivastava, S., Singh, T. P., Singh, H., Kushwaha, S. P. S., & Roy, P. S. (2002). Assessment of large-scale deforestation in Sonitpur district of Assam. Current Science, 82(12), 1479–1484.Google Scholar
- 22.Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, and Canada. Landscape Ecology, 9(2), 151–157.Google Scholar
- 26.Corner, R. J., Dewan, A. M., & Chakma, S. (2013). Monitoring and prediction of land-use and land-cover (LULC) change megacity. In: Dhaka megacity, geospatial perspectives on urbanisation, environment and health. Part of the series (pp. 75–97). Springer Geography. doi: 10.1007/978-94-007-6735-5_5.
- 29.Ramesh, B. R., Menon, S., & Bawa, K. S. (1997). A vegetated based approach to biodiversity gap analysis in the Agastyamalai region, Western Ghats, India. Ambio, 26, 529–536.Google Scholar
- 30.Jha, C. S., Dutt, C. B. S., & Bawa, K. S. (2000). Deforestation and land use changes in Western Ghats, India. Current Science, 79, 231–238.Google Scholar
- 32.Ahmad, F., & Goparaju, L. (2016). Analysis of urban sprawl dynamics using geospatial technology in Ranchi City, Jharkhand, India. Journal of Environmental Geography, 9(1–2), 7–13.Google Scholar
- 35.Bolstad, P. V., & Lillesand, T. D. (1991). Rapid maximum likelihood classification. Photogrammetric Engineering & Remote Sensing, 57(1), 67–74.Google Scholar
- 38.Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. Washington, DC: United States Government Printing Office.Google Scholar
- 39.Lillesand, T. M., & Kiefer, R. W. (1999). Remote sensing and image interpretation. New York: Wiley.Google Scholar
- 40.Islam, M. A., Rai, R., & Quli, S. M. S. (2015). Forest resources usefor building livelihood resilience in ethnic communities of Jharkhand. Trends in Biosciences, 8(5), 1256–1264.Google Scholar
- 43.Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. Upper Saddle: Prentice Hall.Google Scholar
- 46.Bhagat, R. B. (2011). Emerging pattern of urbanization. Economic & Political Weekly, 46(34), 10–12.Google Scholar