Spatial Information Research

, Volume 25, Issue 3, pp 351–359

LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India

Article

Abstract

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.

Keywords

Geographic information system Land use land cover change Markov transition matrix Remote sensing data Satellite imagery 

References

  1. 1.
    Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2002). Multiagent system for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93, 314–337.CrossRefGoogle Scholar
  2. 2.
    Verburg, P. H., de Groot, W. T., & Veldkamp, A. J. (2003). Methodology for multi-scale land use change modeling: Concepts and challenges. In A. J. Dolman & A. Verhagen (Eds.), Global environmental change and land use (pp. 17–51). Dordrecht: Kluver Academic Publishers.CrossRefGoogle Scholar
  3. 3.
    Wu, J. (2004). Effects of changing scale on landscape pattern analysis: Scaling relations. Landscape Ecology, 19, 125–138.CrossRefGoogle Scholar
  4. 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. 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
  6. 6.
    Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., et al. (2001). The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change Human and Policy Dimensions, 11(4), 261–269.CrossRefGoogle Scholar
  7. 7.
    Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., et al. (2010). Impacts of land use/land cover change on climate and future research priorities. Bulletin of the American Meteorological Society, 91(1), 37–46.CrossRefGoogle Scholar
  8. 8.
    Yu, W., Zang, S., Wu, C., Liu, W., & Na, X. (2011). Analyzing and modeling land use land cover change (LUCC) in the Daqing city, China. Applied Geography, 31(2), 600–608.CrossRefGoogle Scholar
  9. 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
  10. 10.
    Yamamura, Y., Amano, T., Koizumi, T., Mitsuda, Y., Taki, H., & Okabe, K. (2009). Does land-use change affect biodiversity dynamics ata macro-ecological scale? A case study of birds over the past 20 years in Japan. Animal Conservation, 12(2), 110–119.CrossRefGoogle Scholar
  11. 11.
    Islam, K. R., & Weil, R. R. (2000). Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. Agriculture, Ecosystems & Environment, 79(1), 9–16.CrossRefGoogle Scholar
  12. 12.
    Wu, K., & Zhang, H. (2012). Land use dynamics, built-up land expansion patterns, and driving forces analysis of the fast-growing Hangzhou metropolitan area, eastern China (1978–2008). Applied Geography, 34, 137–145.CrossRefGoogle Scholar
  13. 13.
    Foley, J. A., De Fries, R., Asner, G. P., Barford, C., et al. (2005). Global consequences of land use. Science, 309(5734), 570–574.CrossRefGoogle Scholar
  14. 14.
    Xian, G., Crane, M., & Su, J. (2007). An analysis of urban development and its environmental impact on the Tampa Bay watershed. Journal of Environmental Management, 85(4), 965–976.CrossRefGoogle Scholar
  15. 15.
    Quetier, F., Lavorel, S., Thuiller, W., & Davies, I. (2007). Plant-trait-based modeling assessment of ecosystem-service sensitivity to land-use change. Ecological Applications, 17(8), 2377–2386.CrossRefGoogle Scholar
  16. 16.
    Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world’s largest megacity during the last 40 years. Remote Sensing of Environment, 127, 210–222.CrossRefGoogle Scholar
  17. 17.
    Patino, J. E., & Duque, J. C. (2013). A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, 37, 1–17.CrossRefGoogle Scholar
  18. 18.
    Taubenbock, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, A. (2012). Monitoring urbanization in mega cities from space. Remote Sensing of Environment, 117, 162–176.CrossRefGoogle Scholar
  19. 19.
    Van der Meer, F., Schmidt, K. S., Bakker, A., & Bijker, W. (2002). New environmental RS systems. In A. K. Skidmore (Ed.), Environmental modeling with GIS and RS (pp. 26–51). London: Taylor & Francis.CrossRefGoogle Scholar
  20. 20.
    Bhatta, B., Saraswati, S., & Bandyopadhyay, D. (2010). Urban sprawl measurement from remote sensing data. Applied Geography, 30, 731–740.CrossRefGoogle Scholar
  21. 21.
    Petit, C., Scudder, T., & Lambin, E. (2001). Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia. International Journal of Remote Sensing, 22(17), 3435–3456.CrossRefGoogle Scholar
  22. 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
  23. 23.
    Coppedge, B. R., Engle, D. M., & Fuhlendorf, S. D. (2007). Markov models of land cover dynamics in a southern Great Plains grass land region. Landscape Ecology, 22, 1383–1393.CrossRefGoogle Scholar
  24. 24.
    Tang, J., Wang, L., & Yao, Z. (2007). Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm. International Journal of Remote Sensing, 15(10), 3255–3271.CrossRefGoogle Scholar
  25. 25.
    Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4, 117–124.CrossRefGoogle Scholar
  26. 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.
  27. 27.
    Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization process in mega city of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149.CrossRefGoogle Scholar
  28. 28.
    Mallupattu, P. K., & Reddy, J. R. S. (2013). Analysis of landuse/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal. doi:10.1155/2013/268623.Google Scholar
  29. 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. 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
  31. 31.
    Behera, M. D., Borate, S. N., Panda, S. N., Behera, P. R., & Roy, P. S. (2012). Modeling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model–A geo-information based approach. Journal of Earth System Science, 121(4), 1011–1024.CrossRefGoogle Scholar
  32. 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
  33. 33.
    Kumar, A., Pandey, A. C., Hoda, N., & Jeyaseelan, A. T. (2011). Evaluation of urban sprawl pattern in the tribal-dominated cities of Jharkhand state, India. International Journal of Remote Sensing, 32, 7651–7675.CrossRefGoogle Scholar
  34. 34.
    Starck, J. L., Murtagh, F., & Bijaoui, A. (1998). Image processing and data analysis: The multi-scale approach. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. 35.
    Bolstad, P. V., & Lillesand, T. D. (1991). Rapid maximum likelihood classification. Photogrammetric Engineering & Remote Sensing, 57(1), 67–74.Google Scholar
  36. 36.
    Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment, 8, 127–150.CrossRefGoogle Scholar
  37. 37.
    Manandhar, R., Odeh, I. O. W., & Ancev, T. (2009). Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. Remote Sensing, 1, 330–344. doi:10.3390/rs1030330.CrossRefGoogle Scholar
  38. 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. 39.
    Lillesand, T. M., & Kiefer, R. W. (1999). Remote sensing and image interpretation. New York: Wiley.Google Scholar
  40. 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
  41. 41.
    Lu, D., Mausel, P., Brondiozio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2407.CrossRefGoogle Scholar
  42. 42.
    Lu, D., Moran, E., Hetrick, S., & Li, G. (2011). Land-use and land-cover change detection. In Q. Weng (Ed.), Advances in environmental remote sensing: Sensors, algorithms and applications (pp. 273–291). Boca Raton: CRC Press.CrossRefGoogle Scholar
  43. 43.
    Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. Upper Saddle: Prentice Hall.Google Scholar
  44. 44.
    Lopez, E., Boccoa, G., Mendozaa, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe. A case in Morelia city, Mexico. Landscape Urban Planning, 55, 271–285.CrossRefGoogle Scholar
  45. 45.
    Foody, G. M. (2001). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.CrossRefGoogle Scholar
  46. 46.
    Bhagat, R. B. (2011). Emerging pattern of urbanization. Economic & Political Weekly, 46(34), 10–12.Google Scholar
  47. 47.
    Guan, D., Li, H., Inohae, T., Su, W., et al. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modeling, 222, 3761–3772.CrossRefGoogle Scholar
  48. 48.
    Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modeling urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation, 10, 26–43.CrossRefGoogle Scholar

Copyright information

© Korean Spatial Information Society 2017

Authors and Affiliations

  1. 1.Vindhyan Ecology and Natural History FoundationMirzapurIndia
  2. 2.Department of Environment and ForestGovernment of Arunachal PradeshItanagarIndia

Personalised recommendations