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Analysing Land Use and Cover Transformations in Berhampore, West Bengal, India: A CA-Markov and ANN Simulation Approach for Future Predictions

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Abstract

Land use and land cover (LULC) change is a multifaceted and dynamic process influenced by factors like population growth, economic development, and climate shifts. This study delves into the LULC changes spanning 1991–2021 in Berhampore, a district headquarters in Murshidabad, West Bengal, India. Notable findings include expanding built-up land from 5.25 to 9.30%, reducing agricultural land from 81.98 to 72.36%, and increasing plantation or forest land from 8.45 to 13.23%. The change transition matrix highlights significant shifts, notably the transformation of agricultural land into built-up areas (15.92 km2) and conversion to plantation or forest land (25.96 km2) and water bodies (5.54 km2). A chord diagram visually represents the transition matrix’s outcomes. Utilising the future land use simulation (FLUS) model, the study forecasts Berhampore’s LULC for 2031. Forecasts indicate an ongoing increase in built-up land and a decrease in agricultural land. Concurrently, the area of plantation or forest land is projected to expand. Employing remote sensing and GIS techniques, the research tracks LULC changes and engages in a focus group discussion with local stakeholders. Findings underscore the intricate interplay between LULC, urbanisation, and environmental dynamics. The study underscores the urgency of sustainable city planning, resource management, and community involvement to manage these transformations while preserving community well-being and ecological equilibrium. As cities expand and populations grow, this research highlights the imperative to balance development with environmental preservation for the more significant benefit of society and nature.

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References

  1. Abbas Z, Yang G, Zhong Y, Zhao Y (2021) Spatiotemporal change analysis and future scenario of LULC using the CA-ANN approach: a case study of the greater bay area, china. Land 10(6):584

    Article  Google Scholar 

  2. Abebe G, Getachew D, Ewunetu A (2022) Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district. Northeast Ethiop SN Appl Sci 4(1):30

    Article  Google Scholar 

  3. Addae B, Oppelt N (2019) Land-use/land-cover change analysis and urban growth modelling in the greater accra metropolitan area (GAMA). Ghana Urban Sci 3(1):26

    Article  Google Scholar 

  4. Adedeji OH, Tope-Ajayi OO, Abegunde OL (2015) Assessing and predicting changes in the status of Gambari forest reserve, Nigeria using remote sensing and GIS techniques. J Geogr Inf Syst 7(03):301

    Google Scholar 

  5. Al-Darwish Y, Ayad H, Taha D, Saadallah D (2018) Predicting the future urban growth and it’s impacts on the surrounding environment using urban simulation models: case study of Ibb city–Yemen. Alex Eng J 57(4):2887–2895

    Article  Google Scholar 

  6. Alam A, Bhat MS, Maheen M (2020) Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal 85:1529–1543

    Article  Google Scholar 

  7. Alam N, Saha S, Gupta S, Chakraborty S (2021) Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach. Ann GIS 27(3):299–314

    Article  Google Scholar 

  8. Amin A, Fazal S (2012) Land transformation analysis using remote sensing and GIS techniques (a case study)

  9. Andualem TG, Belay G, Guadie A (2018) Land use change detection using remote sensing technology. J Earth Sci Clim Chang 9(10):6

    Article  Google Scholar 

  10. Aneesha Satya B, Shashi M, Deva P (2020) Future land use land cover scenario simulation using open source GIS for the city of Warangal, Telangana, India. Appl Geomat 12:281–290

    Article  Google Scholar 

  11. Bhalli M, Abdul G (2015) Use of geospatial techniques in monitoring urban expansion and land use change analysis: a case of Lahore, Pakistan. J Basic Appl Sci 11:265–273

    Article  Google Scholar 

  12. Bose A, Chowdhury IR (2020) Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using markov chain model: a case study in Siliguri Metropolitan area, West Bengal, India. Model Earth Syst Environ 6:2235–2249

    Article  Google Scholar 

  13. Bufebo B, Elias E (2021) Land use/land cover change and its driving forces in Shenkolla watershed, south Central Ethiopia. Sci World J 2021(1–13):9470918. https://doi.org/10.1155/2021/9470918

    Article  Google Scholar 

  14. Chavez PS (1996) Image-based atmospheric corrections-revisited and improved. Photogramm Eng Remote Sens 62(9):1025–1035

    Google Scholar 

  15. Cheruto MC, Kauti MK, Kisangau DP, Kariuki PC (2016) Assessment of land use and land cover change using GIS and remote sensing techniques: a case study of Makueni County, Kenya

  16. Choudhury D, Das K, Das A (2019) Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. Egypt J Remote Sens Sp Sci 22(2):203–218

    Google Scholar 

  17. Chowdhury M, Hasan ME, Abdullah-Al-Mamun M (2020) Land use/land cover change assessment of Halda watershed using remote sensing and GIS. Egypt Remote Sens Sp Sci 23(1):63–75

    Google Scholar 

  18. Das S, Angadi DP (2022) Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study. GeoJournal 87(3):2101–2123

    Article  Google Scholar 

  19. Das S, Sarkar R (2019) Predicting the land use and land cover change using Markov model: a catchment level analysis of the Bhagirathi-Hugli River. Spat Inf Res 27:439–452

    Article  Google Scholar 

  20. Dey NN, Al Rakib A, Kafy A-A, Raikwar V (2021) Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City. Bangladesh Environ Chall 4:100148

    Article  Google Scholar 

  21. Duraisamy V, Bendapudi R, Jadhav A (2018) Identifying hotspots in land use land cover change and the drivers in a semi-arid region of India. Environ Monit Assess 190(9):535

    Article  PubMed  PubMed Central  Google Scholar 

  22. Fasona MJ, Akintuyi AO, Adeonipekun PA, Akoso TM, Udofia SK, Agboola OO, Ogunsanwo GE, Ariori AN, Omojola AS, Soneye AS (2022) Recent trends in land-use and cover change and deforestation in south–west Nigeria. GeoJournal 87(3):1411–1437

    Article  Google Scholar 

  23. Garai D, Narayana A (2018) Land use/land cover changes in the mining area of Godavari coal fields of southern India. Egypt J Remote Sens Sp Sci 21(3):375–381

    Google Scholar 

  24. Gharaibeh A, Shaamala A, Obeidat R, Al-Kofahi S (2020) Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon 6(9):e05092. https://doi.org/10.1016/j.heliyon.2020.e05092

    Article  Google Scholar 

  25. Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr 63:101–112

    Article  Google Scholar 

  26. 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):3421

    Article  Google Scholar 

  27. Hua A (2017) Application of Ca-Markov model and land use/land cover changes in Malacca River Watershed. Malays Appl Ecol Environ Res 15(4):605–622

    Article  Google Scholar 

  28. John J, Chithra N, Thampi SG (2019) Prediction of land use/cover change in the Bharathapuzha river basin, India using geospatial techniques. Environ Monit Assess 191:1–15

    Article  Google Scholar 

  29. Kaliraj S, Chandrasekar N, Ramachandran K, Srinivas Y, Saravanan S (2017) Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS. Egypt J Remote Sens Sp Sci 20(2):169–185

    Google Scholar 

  30. Kamaraj M, Rangarajan S (2022) Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environ Sci Pollut Res 29(57):86337–86348

    Article  Google Scholar 

  31. Kangabam RD, Selvaraj M, Govindaraju M (2019) Assessment of land use land cover changes in Loktak Lake in Indo-Burma Biodiversity Hotspot using geospatial techniques. Egypt J Remote Sens Sp Sci 22(2):137–143

    Google Scholar 

  32. Li X, Zhang Y, Liu X, Chen Y (2012) Assimilating process context information of cellular automata into change detection for monitoring land use changes. Int J Geogr Inf Sci 26(9):1667–1687

    Article  Google Scholar 

  33. Liang X, Liu X, Chen G, Leng J, Wen Y, Chen G (2020) Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones. Int J Geogr Inf Sci 34(10):1930–1952

    Article  Google Scholar 

  34. Liang X, Liu X, Li D, Zhao H, Chen G (2018) Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. Int J Geogr Inf Sci 32(11):2294–2316

    Article  Google Scholar 

  35. Li** C, Yujun S, Saeed S (2018) Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—a case study of a hilly area, Jiangle China. PLoS ONE 13(7):e0200493

    Article  PubMed  PubMed Central  Google Scholar 

  36. Liu X, Li X, Shi X, Zhang X, Chen Y (2010) Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. Int J Geogr Inf Sci 24(5):783–802

    Article  Google Scholar 

  37. Liu X, Liang X, Li X, Xu X, Ou J, Chen Y, Li S, Wang S, Pei F (2017) A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landsc Urban Plan 168:94–116

    Article  Google Scholar 

  38. Lukas P, Melesse AM, Kenea TT (2023) Prediction of future land use/land cover changes using a coupled CA-ANN model in the upper omo–gibe river basin. Ethiop Remote Sens 15(4):1148

    Article  Google Scholar 

  39. Maity B, Mallick SK, Rudra S (2020) Spatiotemporal dynamics of urban landscape in Asansol municipal corporation, West Bengal, India: A geospatial analysis. GeoJournal 87:1619–1637. https://doi.org/10.1007/s10708-020-10315-z

    Article  Google Scholar 

  40. Mhawish YM, Saba M (2016) Impact of population growth on land use changes in Wadi Ziqlab of Jordan between 1952 and 2008. Int J Appl Sociol 6(1):7–14

    Google Scholar 

  41. Mishra PK, Rai A, Rai SC (2020) Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egypt J Remote Sens Sp Sci 23(2):133–143

    Google Scholar 

  42. Mitra S, Roy S, Hore S (2023) Assessment and forecasting of the urban dynamics through lulc based mixed model: evidence from Agartala. India GeoJournal 88(2):2399–2422

    Article  Google Scholar 

  43. Muthusamy S, Arunkumar RX, Raj NT, Lakshumanan C, Jayaprakash M (2010) Land use and land cover changes detection using multitemporal satellite data, Cuddalore coastal zone, se-coast of India. Int J Geomat Geosci 1(3):610

    Google Scholar 

  44. Naikoo MW, Rihan M, Ishtiaque M (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: Spatio-temporal analysis of Delhi NCR using landsat datasets. J Urban Manag 9(3):347–359

    Article  Google Scholar 

  45. Novin MS, Ebrahimipour A (2019) Spatio-temporal modelling of land use changes by means of ca–Markov model. Model Earth Syst Environ 5:1253–1263

    Article  Google Scholar 

  46. Patel LK, Tripathi S (2016) A geospatial approach to analyze the impact of population growth on Bundelkhand landscape. Cent India Int J 5(6):1755–1767

    Google Scholar 

  47. Prakasam C (2010) Land use and land cover change detection through remote sensing approach: a case study of Kodaikanal taluk, Tamil nadu. Int J Geomat Geosci 1(2):150

    Google Scholar 

  48. Rahman A, Kumar S, Fazal S, Siddiqui MA (2012) Assessment of land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques. J Indian Soc Remote Sens 40:689–697

    Article  Google Scholar 

  49. Rawat JS, Biswas V, Kumar M (2013) Changes in land use/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt J Remote Sens Sp Sci 16(1):111–117

    Google Scholar 

  50. Roy B, Kasemi N (2021) Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India. Egypt J Remote Sens Sp Sci 24(2):221–230

    Google Scholar 

  51. Saber A, El-Sayed I, Rabah M, Selim M (2021) Evaluating change detection techniques using remote sensing data: case study new administrative capital Egypt. Egypt J Remote Sens Sp Sci 24(3):635–648

    Google Scholar 

  52. Showqi I, Rashid I, Romshoo SA (2014) Land use land cover dynamics as a function of changing demography and hydrology. GeoJournal 79:297–307

    Article  Google Scholar 

  53. Wang SW, Gebru BM, Lamchin M, Kayastha RB, Lee W-K (2020) Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and GIS. Sustainability 12(9):3925

    Article  Google Scholar 

  54. Wang SW, Munkhnasan L, Lee W-K (2021) Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environ Chall 2:100017

    Article  CAS  Google Scholar 

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Acknowledgements

The second author of this study is thankful to the University Grant Commission (UGC), New Delhi, India, for providing the Senior Research Fellowship (SRF) during this research work. The authors are highly indebted to Aliah University, Kolkata, for providing research environment. The authors also grateful to the USGS for making the Landsat data freely accessible. The authors are highly thankful to the editor(s) and anonymous reviewer(s) for their scholarly comments and suggestions which help to improvement the manuscript.

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  1. Department of Geography, Aliah University, Kolkata, 700014, India

    Md. Mustaquim & Woheeul Islam

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All authors contributed to the study’s conception and design equally. Material preparation, data collection, map preparation, and analysis were performed by [Woheeul Islam] and [Md. Mustaquim]. The first draft of the manuscript was written by [Woheeul Islam] and edited by [Md. Mustaquim], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Mustaquim, M., Islam, W. Analysing Land Use and Cover Transformations in Berhampore, West Bengal, India: A CA-Markov and ANN Simulation Approach for Future Predictions. Agric Res (2024). https://doi.org/10.1007/s40003-024-00745-3

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