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Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

Author

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  • Auwalu Faisal Koko

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Wu Yue

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    International Center for Architecture and Urban Development Studies, Zhejiang University, Hangzhou 310058, China)

  • Ghali Abdullahi Abubakar

    (Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Roknisadeh Hamed

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Akram Ahmed Noman Alabsi

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

Abstract

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Suggested Citation

  • Auwalu Faisal Koko & Wu Yue & Ghali Abdullahi Abubakar & Roknisadeh Hamed & Akram Ahmed Noman Alabsi, 2020. "Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10452-:d:461791
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    References listed on IDEAS

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    Cited by:

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    7. Harik, G. & Alameddine, I. & Zurayk, R. & El-Fadel, M., 2023. "Uncertainty in forecasting land cover land use at a watershed scale: Towards enhanced sustainable land management," Ecological Modelling, Elsevier, vol. 486(C).
    8. Auwalu Faisal Koko & Yue Wu & Ghali Abdullahi Abubakar & Akram Ahmed Noman Alabsi & Roknisadeh Hamed & Muhammed Bello, 2021. "Thirty Years of Land Use/Land Cover Changes and Their Impact on Urban Climate: A Study of Kano Metropolis, Nigeria," Land, MDPI, vol. 10(11), pages 1-27, October.
    9. Markos Mathewos & Semaria Moga Lencha & Misgena Tsegaye, 2022. "Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation," Land, MDPI, vol. 11(10), pages 1-28, September.

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