Spatio-temporal analysis and forecasting of land use/land cover change in East Medinipur district, India using CA-Markov model

DOI: https://doi.org/10.3846/gac.2026.23669

Abstract

Land Use/Land Cover (LU/LC) serve as essential indicators of global environmental changes. This study examined the LU/LC change in the East Medinipur district of West Bengal, India, using remote sensing data and the CA-Markov modeling approach. Through GIS tools and satellite imagery, the study maps, monitors, and analyzes LU/LC changes over a 20-year period (2001–2021), utilizing data from Landsat 4–5 and Landsat 8. Analytical tools including ArcGIS 9, IDRISI Terset, and ERDAS 2014 are employed to project LU/LC trends up to 2041. The results highlight significant transformations, notable reductions in agriculture, vegetation, barren land, and water bodies, alongside substantial increases in aquaculture and built-up areas from 2001 to 2021. Projections indicates that these trends will continue through 2041, with further declines in agriculture, vegetation, barren land, and water bodies, accompanied by continued growth in aquaculture and built-up areas.

 

Keywords:

Land Use/Land Cover (LU/LC), remote sensing, East Medinipur, CA-Markov, aqua culture, prediction
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October 19, 2026
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Kashyap, P. J., & Bose, S. (2026). Spatio-temporal analysis and forecasting of land use/land cover change in East Medinipur district, India using CA-Markov model. Geodesy and Cartography, 52(3), 130–136. https://doi.org/10.3846/gac.2026.23669

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References

Acharyya, N., Bhattacharya, M., Das, P., Patra, B. C., & Bandyopadhyay, J. (2015). Changing phenomenon of aquaculture land through space and time using geoinformatics techniques: A case study of Moyna and Nandakumar Block, Purba Medinipur, West Bengal, India. American Research Thoughts, 1, 1337–1348.

Das, S., & Sarkar, R. (2019). Predicting the land use/land cover change using Markov model: A catchment level analysis of the Bhagirathi-Hugli River. Spatial Information Research, 27, 439–452. https://doi.org/10.1007/s41324-019-00251-7

Garg, R., Kumar, A., Prateek, M., Pandey, K., & Kumar, S. (2022). Land cover classification of spaceborne multifrequency SAR and optical multispectral data using machine learning. Advances in Space Research, 69(4), 1726–1742. https://doi.org/10.1016/j.asr.2021.06.028

Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), Article 3421. https://doi.org/10.3390/su10103421

Islami, F. A., Tarigan, S. D., Wahjunie, E. D., & Dasanto, B. D. (2022). Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conference Series: Earth and Environmental Science, 950, Article 012091. IOP Publishing. https://doi.org/10.1088/1755-1315/950/1/012091

Kashyap, P. J., Sarmah, K., Bose, S., & Sarma, P. K. (2025). Assessing land use and land cover changes in the buffer zone of Nambor Wildlife Sanctuary (WLS), Assam. Discover Geoscience, 3, Article 104. https://doi.org/10.1007/s44288-025-00217-3

Lambin, E. F. (1999). Monitoring forest degradation in tropical regions by remote sensing: Some methodological issues. Global Ecology and Biogeography, 8(3–4), 191–198. https://doi.org/10.1046/j.1365-2699.1999.00123.x

Mallupattu, P. K., & Sreenivasula Reddy, J. R. (2013). Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal, Article 268623. https://doi.org/10.1155/2013/268623

Mondal, A., Kundu, S., Chandniha, S. K., Shukla, R., & Mishra, P. K. (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing and GIS, 1(2), 116–123.

Nath, N., Sahariah, D., Meraj, G., Debnath, J., Kumar, P., Lahon, D., Chand, K., Farooq, M., Chandan, P., Singh, S. K., & Kanga, S. (2023). Land use/land cover change monitoring and prediction of a UNESCO world heritage site: Kaziranga eco-sensitive zone using cellular automata-Markov model. Land, 12(1), Article 151. https://doi.org/10.3390/land12010151

Ojha, A., & Chakrabarty, A. (2018). Brackish water aquaculture development and its impacts on agriculture land: A case study on coastal blocks of Purba Medinipur district, West Bengal, India using multi-temporal Satellite data and GIS Techniques. International Journal of Applied Engineering Research, 13(11), 10115–10123.

Omar, N. Q., Ahamad, M. S. S., Hussin, W. M. A. W., & Samat, N. (2014). Modelling land-use and land-cover changes using Markov-CA, and multiple decision making in Kirkuk city. International Journal of Scientific Research in Environmental Sciences, 2(1), 29–42.

Rawat, M. S., Zhimo, V. V., & Imchen, N. Deforestation in Nagaland, North-East India: Causes, effects and subsequent environmental degradation – a preliminary review. Global Journal of Current Research, 6(3), 61–70.

Saikia, A. (2014). Over-exploitation of forests: A case study from North East India. Springer. https://doi.org/10.1007/978-3-319-01408-1

Sarma, P. K., Sarma, K., Sarma, K., Nath, K. K., Talukdar, B. K., Huda, M. E., & Baruah, B. (2016). Analysis of land use/land cover changes and its future implications in Garo Hill region of Meghalaya: A geo-spatial approach. International Journal of Innovative Research in Engineering & Management (IJIREM), 3(1), 38–44.

Singh, A. M., & Devi, K. R. (2016). Landuse and landcover change detection of fringe areas Of Imphal City, Manipur, India. IOSR Journal of Humanities and Social Science, 21(2), 9–16.

Subedi, P., Subedi, K., & Thapa, B. (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. https://doi.org/10.12691/aees-1-6-5

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2026-10-19

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How to Cite

Kashyap, P. J., & Bose, S. (2026). Spatio-temporal analysis and forecasting of land use/land cover change in East Medinipur district, India using CA-Markov model. Geodesy and Cartography, 52(3), 130–136. https://doi.org/10.3846/gac.2026.23669

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