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Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality

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  • Nyamekye, Clement
  • Kwofie, Samuel
  • Ghansah, Benjamin
  • Agyapong, Emmanuel
  • Boamah, Linda Appiah

Abstract

Population growth coupled with economic, housing and environmental factors have significantly contributed into accelerated land use change in the New Juaben Municipality of Ghana. These factors have caused destruction of natural habitat and increased natural hazards such as flooding in the Municipality. Monitoring land use/land cover change is essential in respect to the dynamics of both human and natural factors that affect the biophysical and biochemical properties of the land surface. This research investigates the transitions among the major land use/land cover categories in the Municipality as a highly populated urban region that is facing some environmental challenges such as deforestation and degradation of the environment. Random Forest was adopted for the classification of 1985, 1991, 2002 and 2015 land cover maps while the analysis of the dynamics was conducted using intensity analysis. The unique contribution of this article is the combine usage of machine learning algorithm and intensity analysis to assess the changes in land use/land cover. The results showed that 1985–1991 and 2002–2015 periods experience fast change and the land use transformation has been accelerating over the whole period. The major changes were caused by the Built-up and Agricultural activities constituting 21.24 % and 13.19 % respectively in the category level. It is recommended that, authorities should consider several structural transformation measures within Ghana, including inter-sectoral land use harmonization policies (e.g. the Land Use and Spatial Planning Act 2016), land use planning and legal reforms to help address the underlying drivers of urban led deforestation.

Suggested Citation

  • Nyamekye, Clement & Kwofie, Samuel & Ghansah, Benjamin & Agyapong, Emmanuel & Boamah, Linda Appiah, 2020. "Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality," Land Use Policy, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:lauspo:v:99:y:2020:i:c:s0264837720309583
    DOI: 10.1016/j.landusepol.2020.105057
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    References listed on IDEAS

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    1. Ali Kourosh Niya & Jinliang Huang & Hazhir Karimi & Hamidreza Keshtkar & Babak Naimi, 2019. "Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    2. Robert Gilmore Pontius & Yan Gao & Nicholas M. Giner & Takashi Kohyama & Mitsuru Osaki & Kazuyo Hirose, 2013. "Design and Interpretation of Intensity Analysis Illustrated by Land Change in Central Kalimantan, Indonesia," Land, MDPI, vol. 2(3), pages 1-19, July.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Yilmazer, Seckin & Kocaman, Sultan, 2020. "A mass appraisal assessment study using machine learning based on multiple regression and random forest," Land Use Policy, Elsevier, vol. 99(C).
    5. Mads Christensen & Jamal Jokar Arsanjani, 2020. "Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    6. Qu, Shijin & Hu, Shougeng & Li, Weidong & Wang, Hui & Zhang, Chuanrong & Li, Quanfeng, 2020. "Interaction between urban land expansion and land use policy: An analysis using the DPSIR framework," Land Use Policy, Elsevier, vol. 99(C).
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