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Monitoring and prediction of land use land cover change of Chittagong Metropolitan City by CA-ANN model

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A Correction to this article was published on 08 April 2024

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Abstract

Anthropogenic activities have significantly changed global land use land cover (LULC), especially in areas with extreme population expansion and climate change. Remote sensing and the geographic information system are popular methods for keeping track of LULC changes. This research analyzed historical changes and predicted future patterns of LULC in Chittagong Metropolitan City, Bangladesh. LULC maps were created using the semi-automated classification plug-in in QGIS, which was used to classify the satellite images from 1990 to 2020. Eight distinct LULC groups, including (i) agricultural land, (ii) fallow land, (iii) trees outside of forest, (iv) hill vegetation, (v) mangrove, (vi) built-up, (vii) pond/lake, and (viii) river, were used to classify the images. The generated LULC maps showed the changes in the area from 1990 to 2020 in several classifications, showing increases in built-up, pond/lake, and river waterbodies of 278.5, 407.69, and 10.07%, respectively. However, a significant decline was observed in agricultural (61.64%), hill vegetation (43.26%), mangrove forest (30.7%), fallow (22.02%), and Tree Outside of Forest (TOF) land (8.81%). The LULC changes between 2020 and 2035 were predicted using the cellular automata-artificial neural network (CA-ANN) model. The prediction maps from 2020–2035 illustrated increasing trends of built-up land (+ 15.65%) and decreasing trends of all other LULC types, predominantly, agricultural (−3.53%), fallow (−3.13%), trees outside of forest (−6.53%), and pond/lake (−1.44%). The findings of this research may be helpful to design future strategies for sustainable landscape management and help decision-makers in government make better choices for the environment and the ecosystem.

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Acknowledgements

Authors would like to sincerely acknowledge the United States Geological Society (USGS) for making Landsat data freely accessible online. Additionally, sincere gratitude goes out to the editor as well as the reviewers who remained anonymous for their insightful comments and helpful recommendations.

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Authors and Affiliations

  1. United Nations Development Programme (UNDP), Rajbari Road, Rangamati, Bangladesh

    I. Islam

  2. Coastal and Ocean Management Institute, Xiamen University, Xiamen, 361005, China

    K. F. Tonny

  3. Institute of Climate Change and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh

    M. Z. Hoque, H. M. Abdullah & F. A. Prodhan

  4. Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh

    M. Z. Hoque & F. A. Prodhan

  5. Department of Remote Sensing and GIS, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh

    H. M. Abdullah

  6. Department of Geography and Spatial Science, University of Delaware, Newark, NJ, USA

    B. M. Khan & N. T. Mohana

  7. Department of Geography, Binghamton University, Binghamton, NY, USA

    K. H. S. Islam

  8. Department of Agroforestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh

    M. Ahmed

  9. Agricultural Center, Louisiana State University, Baton Rouge, LA, 71112, USA

    J. Ferdush

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  1. I. Islam
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  2. K. F. Tonny
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  3. M. Z. Hoque
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  4. H. M. Abdullah
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  6. K. H. S. Islam
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  9. N. T. Mohana
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Contributions

II, KFT, MZH, and HMA contributed to conceptualization. II, KFT, and MZH contributed to data analysis, validation, and visualization. II and KFT contributed to writing (original draft). II, KFT, MZH, HMA, BMK, KHSI, FAP, MA, NTM, and JF contributed to writing (review and editing).

Corresponding author

Correspondence to M. Z. Hoque.

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Authors declare no competing interests and funding.

Additional information

Editorial responsibility: S. Mirkia.

The original online version of this article was revised: to correct accepted year

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Islam, I., Tonny, K.F., Hoque, M.Z. et al. Monitoring and prediction of land use land cover change of Chittagong Metropolitan City by CA-ANN model. Int. J. Environ. Sci. Technol. 21, 6275–6286 (2024). https://doi.org/10.1007/s13762-023-05436-0

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