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GIS-Based Multi-Temporal Analysis of Urban Expansion Dynamics in the Federal Capital Territory (FCT), Nigeria Using Sentinel-2 Imagery and Google Earth Engine

Authors

Ochimana Ebiojo

Department of Mission Planning and Satellite Data Management (MPSDM), National Space Research and Development Agency (NASRDA) (Nigeria)

Article Information

DOI: 10.51244/IJRSI.2026.1307000403

Subject Category: Education

Volume/Issue: 13/7 | Page No: 5483-5498

Publication Timeline

Submitted: 2026-08-06

Accepted: 2026-08-11

Published: 2026-08-21

Abstract

Rapid urbanisation has altered land use and land cover (LULC) in many emerging cities. This has posed issues for sustainable urban planning and environmental management. The study examined the dynamics of land use and land cover change and urban expansion of the Federal Capital Territory (FCT), Nigeria, using multi-temporal Sentinel-2 satellite images acquired for the years 2016, 2018, 2020, 2022 and 2025. Image processing and classification was done using Random Forest algorithm on Google Earth Engine (GEE) platform using spectral indices like Normalised Difference Vegetation Index (NDVI), Normalised Difference Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), Bare Soil Index (BSI) and Digital Elevation Model (DEM) as predictor variables. Five land cover classes were established (built-up, vegetation, agricultural land, bare land and water). The classification accuracy was tested using Overall Accuracy, Kappa Coefficient, Producer's Accuracy and User's Accuracy. The overall accuracy of the classification was 75.43%, and the Kappa coefficient was 0.693, showing a good agreement between the classified and reference data. The results revealed that vegetation was still the major land-cover class, but decreased from 4,543.49 km² in 2016 to 3,486.20 km² in 2025, whereas, agricultural land rose from 1,718.50 km² to 3,039.07 km² in the same period. The built-up land area increased from 780.67 km2 in 2016 to 1269.04 km2 in 2022 and then decreased to 572.78 km2 in 2025. However, the analysis of urban expansion found that there was about 228.48 km2 of new urban land, indicating that urban growth still existed despite classification uncertainty in the last year. The results also showed that the urban sprawl was mostly centred around the metropolitan core and the main transportation corridors and this led to changes in vegetation and agricultural land. The study concluded that the multi-temporal remote sensing approach with cloud-based geospatial analysis is a robust approach in monitoring urban expansion and landuse changes. It recommended institutionalisation of geospatial monitoring systems, better land use planning and protection of environmentally sensitive regions to assist sustainable urban development within the FCT.

Keywords

FCT, Google Earth Engine, Land use and land cover, Random Forest, Remote sensing, Sentinel-2, Urban expansion, Urban planning,

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References

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64. Fentaw, A. E., & Abegaz, A. (2024). Analyzing Land Use/Land Cover Changes Using Google Earth Engine and Random Forest Algorithm and Their Implications to the Management of Land Degradation in the Upper Tekeze Basin, Ethiopia. The Scientific World Journal, 2024(1), 3937558. https://doi.org/https://doi.org/10.1155/2024/3937558 [Google Scholar] [Crossref]

65. Gilbert, K. M., & Shi, Y. (2023). Land use/land cover changes detection in Lagos City of Nigeria using remote sensing and GIS. Adv Ismail NA, Aceska A, Adu-Ampong EA (2023) “We closed down mpape on the judgement day”: resistance and place-making in urban informal settlements in Abuja, Nigeria. Urban Forum. https://doi.org/10.1007/s12132-023-09492-0 [Google Scholar] [Crossref]

66. Gumel, I. A., Aplin, P., Marston, C. G., & Morley, J. (2020). Time-series satellite imagery demonstrates the progressive failure of a city master plan to control urbanization in Abuja, Nigeria. Remote Sensing, 12(7), 1112. https://doi.org/10.3390/rs12071112⁠ [Google Scholar] [Crossref]

67. Gupta, P., Kanga, S., Mishra, V. N., Kumar, S., & Singh, T. S. (2024). A Comparative Study and Machine Learning Enabled Efficient Classification for Multispectral Data in Agriculture. Baghdad Science Journal, 21(7), 2462–2462. [Google Scholar] [Crossref]

68. Issa, S., Dahy, B., Saleous, N., & Shamsi, M. A. (2021). A remote sensing-based time-series land use land cover change (LULCC) analysis: A case study from the United Arab Emirates (UAE). In Sixth International Conference on Engineering Geophysics, Virtual, 25?28 October 2021 (pp. 105–108). Society of Exploration Geophysicists. https://doi.org/10.1190/iceg2021-029.1 [Google Scholar] [Crossref]

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70. Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of high-resolution Google Earth satellite imagery in landuse map preparation for urban related applications. Procedia Technology, 24, 1835–1842. https://doi.org/https://doi.org/10.1016/j.protcy.2016.05.231 [Google Scholar] [Crossref]

71. Manohar, N., Pranav, M. A., Aksha, S., & Mytravarun, T. K. (2021). Classification of Satellite Images. In Smart Innovation, Systems and Technologies (pp. 703–713). https://doi.org/10.1007/978-981-15-7078-0_70 [Google Scholar] [Crossref]

72. Mehra, N., & Swain, J. B. (2024). Assessment of land use land cover change and its effects using artificial neural network-based cellular automation. Journal of Engineering and Applied Science, 71(1), 70. https://doi.org/10.1186/s44147-024-00402-0 [Google Scholar] [Crossref]

73. Nzabarinda, V., Bao, A., Tie, L., Uwamahoro, S., Kayiranga, A., Ochege, F. U., Muhirwa, F., & Bao, J. (2025). Expanding forest carbon sinks to mitigate climate change in Africa. Renewable and Sustainable Energy Reviews, 207, 114849. https://doi.org/10.1016/j.rser.2024.114849 [Google Scholar] [Crossref]

74. Ouma, Y., Nkwae, B., Moalafhi, D., Odirile, P., Parida, B., Anderson, G., & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 681–689. [Google Scholar] [Crossref]

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