Spatio-Temporal Analysis of Urban Sprawl and Land Use/Land Cover Dynamics in Urban and Rural Settlements of Niger State, Nigeria

Urban sprawl has become one of the most significant manifestations of land use and land cover (LULC) change in developing countries, with far-reaching implications for environmental sustainability, ecosystem services, and urban planning. This study examined the spatio-temporal dynamics of urban sprawl and LULC changes in selected urban (Mokwa, Minna, and Kontagora) and rural (Muwo, Garatu, and Rafin Gora) settlements of Niger State, Nigeria, between 1994 and 2024, and projected future urban sprawl up to 2054. Landsat satellite imagery for 1994, 2004, 2014, and 2024 was processed and classified into three major LULC classes: built-up, vegetation, and bare ground using supervised classification techniques within a Geographic Information System (GIS) software (ArcGIS 10.1). Change detection analysis was employed to quantify the extent and pattern of urban sprawl, while the Cellular Automata–Markov (CA–Markov) model was used to simulate future urban expansion scenarios for 2034, 2044, and 2054. The results revealed substantial expansion of built-up areas across all settlements. Among the urban centres, Kontagora recorded the highest increase in built-up area (219.2%), followed by Mokwa (205.3%) and Minna (136.7%). In the rural settlements, Muwo exhibited the highest proportional growth (333.2%), followed by Garatu (283.4%) and Rafin Gora (95.5%). Conversely, vegetation cover declined considerably across the study area, with Muwo experiencing the highest vegetation loss (90.8%). The CA–Markov projections indicate that urban expansion will continue through 2054, with Minna remaining the dominant urban growth centre (increased by 14,320.8 km² of built-up area) and rural settlements increasingly undergoing peri-urban transformation. Model validation produced high overall accuracies ranging from 93.16% to 96.23%, with Kappa coefficients between 0.87 and 0.94, confirming the robustness and reliability of the projection model. The study concludes that urban sprawl is a major driver of landscape transformation in Niger State and recommends sustainable land use planning, vegetation conservation, and effective urban sprawl management strategies to mitigate future environmental challenges.

Urban-Sprawl; Land Use/Land Cover Change; CA–Markov

1. Aburas, M. M., Ho, Y. M., & Ramli, M. F. (2019). Urban growth modelling using CA–Markov and artificial neural network techniques. Sustainable Cities and Society, 44, 612–621. https://doi.org/10.1016/j.scs.2018.10.012 [Google Scholar] [Crossref]

2. Aburas, M. M., Ho, Y. M., Ramli, M. F., Ash'aari, Z. H., & Ahamad, M. S. S. (2021). Spatio-temporal simulation of future urban growth trends using CA-Markov model in rapidly urbanizing regions of Saudi Arabia. Sustainability, 13(3), 1302. https://doi.org/10.3390/su13031302 [Google Scholar] [Crossref]

3. Adelekan, I. O., Johnson, C., Manda, M., Matyas, D., Mberu, B., Parnell, S., Satterthwaite, D., & Vivekananda, J. (2020). Disaster risk and its reduction: An agenda for urban Africa. International Development Planning Review, 42(1), 1–23. [Google Scholar] [Crossref]

4. Akinyemi, F. O., Balogun, A. L., & Olatunji, S. O. (2017). Modelling urban growth in Lagos using CA–Markov. Journal of Geographic Information System, 9(3), 341–356. https://doi.org/10.4236/jgis.2017.93021 [Google Scholar] [Crossref]

5. Almeida, C. M., Gleriani, J. M., Castejon, E. F., & Soares-Filho, B. S. (2003). Using neural networks and cellular automata for modelling intra-urban land-use dynamics. International Journal of Geographical Information Science, 17(5), 481–513. [Google Scholar] [Crossref]

6. Araya, Y. H., & Cabral, P. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), 1549–1563. https://doi.org/10.3390/rs2061549 [Google Scholar] [Crossref]

7. Arsanjani, J. J., Helbich, M., Kainz, W., & Darvishi Boloorani, A. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275. [Google Scholar] [Crossref]

8. Chen, L., Zhang, Y., & Wang, H. (2021). Analysis of land use changes in urban areas using machine learning algorithms. International Journal of Remote Sensing, 39(7), 1986–2002. [Google Scholar] [Crossref]

9. Cobbinah, P. B., & Aboagye, H. N. (2017). A Ghanaian twist to urban sprawl. Land Use Policy, 61, 231–241. [Google Scholar] [Crossref]

10. Eastman, J. R. (2016). TerrSet Geospatial Monitoring and Modeling System Manual. Clark University. [Google Scholar] [Crossref]

11. Ewing, R., Hamidi, S., Grace, J. B., & Wei, Y. D. (2014). Does urban sprawl hold down upward mobility? Landscape and Urban Planning, 148, 80–88. [Google Scholar] [Crossref]

12. Ewing, R., & Hamidi, S. (2015). Compactness versus sprawl: A review of recent evidence from the United States. Journal of Planning Literature, 30(4), 413–432. https://doi.org/10.1177/0885412215595439 [Google Scholar] [Crossref]

13. Ewing, R., & Hamidi, S. (2021). Measuring urban sprawl and validating sprawl measures. Metropolitan Research Center Report, University of Utah. [Google Scholar] [Crossref]

14. Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., ... & Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772 [Google Scholar] [Crossref]

15. Gao, J., O'Neill, B. C., & Absar, S. M. (2020). Urban land expansion and sustainable urbanization under future scenarios. Nature Communications, 11, 537. https://doi.org/10.1038/s41467-020-14386-x [Google Scholar] [Crossref]

16. Gupta, S., Sharma, V., & Singh, R. (2022). Mapping land cover changes using Sentinel-2 data: A case study in a tropical region. Remote Sensing Letters, 13(1), 45–58. [Google Scholar] [Crossref]

17. Haaland, C., & van den Bosch, C. K. (2015). Challenges and strategies for urban green-space planning. Urban Forestry & Urban Greening, 14(4), 760–771. [Google Scholar] [Crossref]

18. Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., ... & Cook, W. M. (2015). Habitat fragmentation and its lasting impact on Earth's ecosystems. Science Advances, 1(2), e1500052. https://doi.org/10.1126/sciadv.1500052 [Google Scholar] [Crossref]

19. Lambin, E. F., & Geist, H. J. (2006). Land-Use and Land-Cover Change: Local Processes and Global Impacts. Springer, Berlin. [Google Scholar] [Crossref]

20. Kaur, R. (2024). Peri-urban Delineation and Urban Expansion Quantification. International Journal of Geoinformatics, 20(4). https://doi.org/10.52939/ijg.v20i4.3155 [Google Scholar] [Crossref]

21. Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323–343. [Google Scholar] [Crossref]

22. OECD. (2018). Rethinking Urban Sprawl: Moving Towards Sustainable Cities. OECD Publishing, Paris. https://doi.org/10.1787/9789264189881-en [Google Scholar] [Crossref]

23. Olokeogun, O. S., Omojola, A. S., & Fasona, M. J. (2014). CA–Markov modelling of Abuja urban expansion. Environmental Monitoring and Assessment, 186(1), 607–620. https://doi.org/10.1007/s10661-013-3398-4 [Google Scholar] [Crossref]

24. Pontius, R. G., & Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2), 243–265. [Google Scholar] [Crossref]

25. Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088. [Google Scholar] [Crossref]

26. Seto, K. C., Güneralp, B., & Hutyra, L. R. (2021). Urban land expansion and environmental impacts: A global review. Annual Review of Environment and Resources, 46, 1–28. https://doi.org/10.1146/annurev-environ-012320-083704 [Google Scholar] [Crossref]

27. Siedentop, S. (2022). Urban sprawl and sustainable spatial development. Raumforschung und Raumordnung, 80(1), 1–19. [Google Scholar] [Crossref]

28. Singh, S. K., Kumar, P., & Singh, R. P. (2023). Urban expansion and ecosystem service loss: Assessing carbon sequestration dynamics under land use change in rapidly urbanizing Indian cities. Environmental Monitoring and Assessment, 195(4), 1–18. [Google Scholar] [Crossref]

29. United Nations. (2019). World Urbanization Prospects 2018. United Nations Department of Economic and Social Affairs. [Google Scholar] [Crossref]

30. UN DESA. (2019). World Urbanization Prospects. United Nations. [Google Scholar] [Crossref]

31. UN-Habitat. (2022). World Cities Report 2022. [Google Scholar] [Crossref]

32. US EPA. (2022). Urban Sprawl and Smart Growth in the United States. Washington, DC. [Google Scholar] [Crossref]

33. Weng, Q. (2018). Remote Sensing and GIS Integration: Theories, Methods, and Applications. McGraw-Hill. [Google Scholar] [Crossref]

34. Weng, Q., Fu, P., & Gao, F. (2019). Generating daily land surface temperature at high spatial resolution. Remote Sensing of Environment, 233, 111349. [Google Scholar] [Crossref]

35. Zhou, D., Zhao, S., Liu, S., Zhang, L., & Zhu, C. (2021). Surface urban heat island and vegetation response to urban expansion. Ecological Indicators, 125, 107515. [Google Scholar] [Crossref]

• Methane Emissions from Municipal Solid Waste - Case Study in Cai Rang District, Can Tho City, Vietnam
• Youth Activism, Intentional Integration of Policies to Raise Awareness on Climate Change Action among the Youth
• Breathing Spaces: Environmental & User Experience in Dhanmondi and Zigatola Multistoried Apartments, Dhaka, Bangladesh
• Effects of Solid Waste Disposal on Soil Quality in Makurdi Metropolis, Benue State, Nigeria
• Environmental Impact of Artisanal and Small-Scale Gold Mining in Borgu Local Government Area