Skip to main content

Advertisement

Log in

Land-use–land-cover change detection and application of Markov model: A case study of Eastern part of Kolkata

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Land-use change of a region acts as an indicator of human impact on the landscape. Increasing urban growth has induced adverse landscape alterations which need to be predicted and controlled, especially in the urban areas and ‘rurban’ fringes, to prevent the trends of urbanization from engulfing the ecology. The present discussion makes an attempt to address this issue by assessing the present and predicting the future spatio-temporal dynamisms in land use and land cover along the urban and rurban fringe area of eastern Kolkata, stretching from the Eastern Metropolitan Bypass to Bhangar areas in West Bengal, India. For the fulfilment of the work, Landsat imageries of 1991 and 2016 have been chosen to depict the present urban growth, following which the results have been predicted and validated to show how the various land-use categories might change, using the Markov model. The study has depicted that urban growth continues to shift eastwards, resulting in greater number of urban patches in eastwards. The validation of positive and negative growth of respective land-use patterns with Markov model is within 10%. So, if the current reclamation activities continue, the original land cover shall decrease by 70% of the study area and these shall be replaced by urban areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Source: Authors’ own elaboration

Fig. 2

Source: Landsat 7 and 8

Fig. 3

Source: Landsat 7 and 8

Fig. 4

Source: Landsat 7 and 8

Fig. 5

Source: Landsat 7 and 8

Fig. 6
Fig. 7
Fig. 8

Source: Landsat 7 ETM+ and 8 author’s own elaboration

Fig. 9

Source: Landsat 7 ETM+ and authors’ own elaboration

Fig. 10

Source: Landsat 7 ETM+ and authors’ own elaboration

Fig. 11

Source: Landsat 7ETM+ and author’s own elaboration

Similar content being viewed by others

References

  • Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2002). A review and assessment of land use change models: Dynamics of space time and human choice. CIPEC Collaborative Report Series No. 1, USDA Forest Service Indiana.

  • Ali, A. M. S. (2006). Rice to shrimp: Land use/land cover changes and soil degradation in Southwestern Bangladesh. Land Use Policy,23(4), 421–435.

    Article  Google Scholar 

  • Baker, W. L. (1989). A review of models of landscape change. Landscape Ecology,2, 111–133.

    Article  Google Scholar 

  • Banerji, S., Biswas, M., & Mitra, D. (2018). Semi quantitative analysis of land use homogeneity and spatial distribution of individual ecological footprint in selected areas of Eastern fringes of Kolkata, West Bengal. Geocarto International (just-accepted), pp. 1–21.

  • Bardhan, R. H., Kurisu, K., & Hanaki, K. (2011, October). Linking urban form and quality of life in Kolkata, India. In 47th ISOCARP Congress.

  • Bell, E. J. (1974). Markov analysis of land use change: An application of stochastic processes to remotely sensed data. Socio-Economic Planning Science,8, 311–316.

    Article  Google Scholar 

  • Chadchan, J., & Shankar, R. (2012). An analysis of urban growth trends in the post economic reforms period in India. International Journal of Sustainable Built Environment,1, 36–49.

    Article  Google Scholar 

  • Chakraborty, U. K., Deb, K., & Chakraborty, M. (1995). Analysis of selection algorithms: A Markov chain approach. Evolutionary Computation,4, 133–167.

    Article  Google Scholar 

  • Dewalt, B., Vergnc, P., & Hardin, M. (1996). Shrimp aquaculture development and the environment: People, mangroves and fisheries on the Gulf of Fonseca, Honduras. World Development,24(7), 1193–1208.

    Article  Google Scholar 

  • Ehlers, M., Greenlee, D. D., Smith, T., & Star, J. (1991). Integration of remote sensing and GIS: Data and data access. Photogrammetric Engineering and Remote Sensing,57(6), 669–675.

    Google Scholar 

  • Fazal, S. (2001). Land re-organisation in relation to roads in an Indian city. Land Use Policy,18(2), 191–199.

    Article  Google Scholar 

  • Flaherty, M., Vandergeest, P., & Miller, P. (1999). Rice paddy or shrimp pond: Tough decisions in rural Thailand. World Development,27(12), 2045–2060.

    Article  Google Scholar 

  • Fortin, M. J., Boots, B., Csillag, F., & Remmel, T. K. (2003). On the role of spatial stochastic models in understanding landscape indices in ecology. Oikos,102, 203–212.

    Article  Google Scholar 

  • Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography,63, 101–112.

    Article  Google Scholar 

  • Harris, P. M., & Ventura, S. J. (1995). The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogrammetric engineering and remote sensing,61(8), 993–998.

    Google Scholar 

  • Hoek J. W. (2008). The MXI (Mixed-use Index) as tool for urban planning and analysis. http://www.bk.tudelft.nl/fileadmin/Faculteit/BK/Actueel/Symposia_en_congressen/CRE_2008/Papers/doc/Paper03_vandenHoek.pdf. Accessed 10 December 2016.

  • Inam, S., Ertas, M., & Bozdag, A. (2012). The importance of the urban land policy for sustainable development, problems and solution recommendations. FIG Working Week 2012. Knowing to manage the territory, protect the environment, evaluate the cultural heritage. Rome, Italy, 6–10 May 2012.

  • Jabbar, M. T., Zhi-Hua, H., Tian-Wei, W., & Chong-Fa, C. (2006). Vegetation change prediction with geo-information techniques in the three gorges area of China. Pedosphere,16(4), 457–467.

    Article  Google Scholar 

  • Johnson, R. D., & Kasischke, E. S. (1998). Change vector analysis: A technique for the multispectral monitoring of land cover and condition. International Journal of Remote Sensing,19(3), 411–426.

    Article  Google Scholar 

  • Klosterman, R. E. (1999). The what if? Collaborative planning support system. Environment and Planning B: Planning and Design,26(3), 393–408.

    Article  Google Scholar 

  • Kumar, S., Radhakrishnan, N., & Mathew, S. (2014). Land use change modelling using a Markov model and remote sensing. Geomatics, Natural Hazards and Risk,5(2), 145–156.

    Article  Google Scholar 

  • Lambin, E. F., & Ehrlich, D. (1997). Land-cover changes in sub-Saharan Africa (1982–1991): Application of a change Index based on remotely sensed surface temperature and vegetation indices at a continental scale. Remote Sensing of Environment,61, 181–200.

    Article  Google Scholar 

  • Liu, J., Liu, M., Tian, H., Zhuang, D., Zhang, Z., Zhang, W., et al. (2005). Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM Data. Remote Sensing of Environment,98, 442–456.

    Article  Google Scholar 

  • Méaille, R., & Wald, L. (1990). Using geographical information system and satellite imagery within a numerical simulation of regional urban growth. International Journal of Geographical Information System,4(4), 445–456.

    Article  Google Scholar 

  • Mitra, S., Mitra, T., & Bardhan, S. (2013). Conflicts in land and housing markets in Kolkata: Emergence of a divided city. In 49th Isocarp Congress, 2013.

  • Treitz, P. M., Howarth, P. J., & Gong, P. (1992). Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe: A case study. Photogrammetric Engineering and Remote Sensing,58, 439–448.

    Google Scholar 

  • Wegener, M. (1994). Operational urban models: State of the art. Journal of the American Planning Association,60(1), 17–30.

    Article  Google Scholar 

  • Weng, Q. (2001). A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing,22, 1999–2014.

    Google Scholar 

  • Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management,64(3), 273–284.

    Article  Google Scholar 

  • Westmoreland, S., & Stow, D. A. (1992). Category identification of changed land-use polygons in an integrated image processing/geographic information system.

  • Wijanarto, A. B. (2006). Application of Markov change detection technique for detecting Landsat ETM derived land cover change over Banten Bay. Jurnal Ilmiah Geomatika, 12(1), 11–19.

    Google Scholar 

  • Ye, B., Bai, Z. (2007, August). Simulating land use/cover changes of Nenjiang County based on CA-Markov model. In International conference on computer and computing technologies in agriculture (pp. 321–329). Springer US.

  • Yeh, A. G. O., & Li, X. (1996). Urban growth management in the Pearl River delta – an integrated remote sensing and GIS approach. The ITC Journal,1, 77–85.

    Google Scholar 

  • Yeh, A. G. O., & Li, X. (1997). An integrated remote sensing-GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China. International Planning Studies,2, 193–210.

    Article  Google Scholar 

  • Yeh, A. G. O., & Li, X. (1999). Economic development and agricultural land loss in the Pearl River Delta, China. Habitat International,23, 373–390.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mery Biswas.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Biswas, M., Banerji, S. & Mitra, D. Land-use–land-cover change detection and application of Markov model: A case study of Eastern part of Kolkata. Environ Dev Sustain 22, 4341–4360 (2020). https://doi.org/10.1007/s10668-019-00387-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-019-00387-4

Keywords

Navigation