Paper Titles

Assessment of the Influence of Powder Factor on Loading Performance in Selected Open-Pit Granite Aggregate Quarries
p.29

Paraffin Wax Deposition and Remediation Techniques in Crude Oil System: A Review
p.41

Comparative Studies of Coal Quality Obtained from Ankpa, and Chikila, Nigeria towards Metallurgical Applications
p.65

Waste Management Strategies in Mining Operations: A Sustainable Approach
p.75

Land Use/Land Cover Classification and Forecasting of South-Western Nigeria Using Backcasting and Machine Learning Techniques
p.89

Evaluation of Abrasive Properties of Selected Granite Rocks and Their Effects on Button Bit Wear
p.103

Drone Technology: A Pathway to Create Sustainable Agriculture in Nigeria
p.113

Response of Fluted Pumpkin (Telfairia Occidentalis) to Different Fertlizer Sources under the Humid Rainforest Climate of Akure, Nigeria
p.125

Design and Construction of a Drainage Lysimeter Prototype for Educational Objectives
p.139

HomeEngineering HeadwayEngineering Headway Vol. 33Land Use/Land Cover Classification and Forecasting...

Land Use/Land Cover Classification and Forecasting of South-Western Nigeria Using Backcasting and Machine Learning Techniques

Article Preview

Abstract:

This study assesses historical and projected land use and land cover (LULC) changes in South-Western Nigeria from 1993 to 2040 using MODIS satellite imagery and predictive modelling. Results reveal substantial urban expansion (+2,915.49 km²) and vegetation loss (−8,143.85 km²), with water bodies remaining relatively stable. Linear Regression outperformed Random Forest and Decision Tree models, accurately capturing gradual trends in land transformation. The classification achieved an accuracy of 87.3%–89.6%, providing a reliable basis for regional forecasting. The findings highlight critical environmental and planning challenges, including ecosystem degradation, flood vulnerability, and peri-urban sprawl. Recommendations emphasize integrating high-resolution imagery, nonlinear modelling, and stakeholder-engaged planning frameworks. Hence, this study contributes to sustainable urban management and LULC policy integration in rapidly urbanizing regions.

You might also be interested in these eBooks

7th FUTA Engineering Conference (7th FUTA-EC)

Info:

Periodical:

Engineering Headway (Volume 33)

Pages:

89-101

DOI:

https://doi.org/10.4028/p-in787B

Citation:

Cite this paper

Online since:

February 2026

Authors:

Ayoola Emmanuel Awode*, James Rotimi Adewumi, Obinna Obiora-Okeke, Akinola Adesuji Komolafe

Keywords:

Land Use Change, MODIS Classification, Predictive Modelling, Urban Expansion, Vegetation Loss

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2026 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Q. Wang, H. Deng, J. Jian, Hydrological processes under climate change and human activities: status and challenges, Water 15(23) (2023) 4164.

DOI: 10.3390/w15234164

[2] J.K. Mensah, E.A. Ofosu, S.M. Yidana, K. Akpoti, A.T. Kabo-bah, Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: A systematic review, Environ. Adv. 8 (2022) 100224.

DOI: 10.1016/j.envadv.2022.100224

[3] E.S. Assede, H. Orou, S.S. Biaou, C.J. Geldenhuys, F.C. Ahononga, P.W. Chirwa, Understanding drivers of land use and land cover change in Africa: A review, Curr. Landsc. Ecol. Rep. 8(2) (2023) 62–72.

DOI: 10.1007/s40823-023-00087-w

[4] L.S. Macarringue, É.L. Bolfe, P.R.M. Pereira, Developments in land use and land cover classification techniques in remote sensing: A review, J. Geogr. Inf. Syst. 14(1) (2022) 1–28.

DOI: 10.4236/jgis.2022.141001

[5] D. Phiri, J. Morgenroth, Developments in Landsat land cover classification methods: A review, Remote Sens. 9(9) (2017) 967.

DOI: 10.3390/rs9090967

[6] J. Wang, M. Bretz, M.A.A. Dewan, M.A. Delavar, Machine learning in modelling land-use and land cover-change (LULCC): Status, challenges and prospects, Sci. Total Environ. 822 (2022) 153559.

DOI: 10.1016/j.scitotenv.2022.153559

[7] D. Aradeon, T.A. Aina, J. Umo, South-West Nigeria, in: D. Satterthwaite (Ed.), Small and Intermediate Urban Centres, Routledge, London (2019) 228–278.

DOI: 10.4324/9780429306112-6

[8] S. Ghosh, D. Kumar, R. Kumari, Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with google earth engine (GEE), Environ. Chall. 9 (2022) 100605.

DOI: 10.1016/j.envc.2022.100605

[9] D. Liu, K. Song, J.R. Townshend, P. Gong, Using local transition probability models in Markov random fields for forest change detection, Remote Sens. Environ. 112(5) (2008) 2222–2231.

DOI: 10.1016/j.rse.2007.10.002

[10] K. Proswitz, M.C. Edward, M. Evers, F. Mombo, A. Mpwaga, K. Näschen, J. Sesabo, B. Höllermann, Complex socio-ecological systems: translating narratives into future land use and land cover scenarios in the Kilombero Catchment, Tanzania, Sustainability 13(12) (2021) 6552.

DOI: 10.3390/su13126552

[11] J. Titilayo, Enclave urbanism and infrastructure outcomes: The Eko Atlantic City and urban sustainability issues in Lagos, Nigeria, submitted (2023).

[12] R.O. Muchelo, T.F.A. Bishop, S. Ugbaje, S. Akpa, Patterns of urban sprawl and agricultural land loss in Sub-Saharan Africa: The cases of the Ugandan cities of Kampala and Mbarara, Land 13(7) (2024) 1056.

DOI: 10.3390/land13071056

[13] D.G. Olanipon, A.A. Adewoyin, A.O. Eludoyin, Climatic variability and associated changes in a Nigerian nature forest reserve, Oxf. Open Clim. Change 5(1) (2025) kgaf008.

DOI: 10.1093/oxfclm/kgaf008

[14] T.V. Borisade, A.I. Odiwe, A. Akinwumiju, N.O. Uwalaka, O.I. Orimoogunje, Assessing the impacts of land use on riparian vegetation dynamics in Osun State, Nigeria, For. People 5 (2021) 100099.

DOI: 10.1016/j.tfp.2021.100099

[15] N.G. Njar, C.J. Iheaturu, U.B. Inyang, C. Okolie, O. Daramola, M.J. Orji, Dynamics of land cover change in the Anambra River Basin of Nigeria and implications for sustainable land management, Quaest. Geogr. 43(1) (2024) 179–195.

DOI: 10.14746/quageo-2024-0011

[16] C.G. Ogochukwu, J.N.A. Chioma, O.F. Ogochukwu, E.C. Chukwudi, I.M. Ogorchukwu, Rapid spatial growth of cities and its planning implications for developing countries: a case study of Abuja, Nigeria, Indones. J. Geogr. 54(2) (2022) 313–320.

DOI: 10.22146/ijg.70316

[17] O.F. Kasim, B. Wahab, M.F. Oweniwe, Urban expansion and enhanced flood risk in Africa: The example of Lagos, Environ. Hazards 21(2) (2022) 137–158.

DOI: 10.1080/17477891.2021.1932404

[18] G. Oladehinde, A.F. Fatusin, V. Ojo, Urban expansion and loss of agricultural land in Osogbo, Osun State Nigeria, using multi-temporal imageries, J. Afr. Real Estate Res. 4(1) (2019) 139–156.

DOI: 10.15641/jarer.v4i1.735

[19] A.A. Fenta, A. Tsunekawa, N. Haregeweyn, M. Tsubo, H. Yasuda, K. Shimizu, T. Kawai, K. Ebabu, M.L. Berihun, D. Sultan, A.S. Belay, Cropland expansion outweighs the monetary effect of declining natural vegetation on ecosystem services in sub-Saharan Africa, Ecosyst. Serv. 45 (2020) 101154.

DOI: 10.1016/j.ecoser.2020.101154

[20] F.E. Onuegbu, A.U. Egbu, Employing post classification comparison to detect land use cover change patterns and quantify conversions in Abakaliki LGA Nigeria from 2000 to 2022, Sci. Rep. 14(1) (2024) 9384.

DOI: 10.1038/s41598-024-59056-w

[21] A. Akinbobola, Simulating land cover changes and their impacts on land surface temperature in Onitsha, South East Nigeria, Atmos. Clim. Sci. 9(2) (2019) 243.

DOI: 10.4236/acs.2019.92017

[22] J. Kiptala, Y. Mohamed, M. Mul, M. Cheema, P. Zaag, Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin, Eastern Africa, Phys. Chem. Earth 66 (2013) 112–122.

DOI: 10.1016/j.pce.2013.08.002

[23] A.M.E. Ngolo, T. Watanabe, Integrating geographical information systems, remote sensing, and machine learning techniques to monitor urban expansion: an application to Luanda, Angola, Geo-Spat. Inf. Sci. 26(3) (2022) 446–464.

DOI: 10.1080/10095020.2022.2066574

[24] L. Leroux, A. Jolivot, A. Bégué, D.L. Seen, B. Zoungrana, How reliable is the MODIS land cover product for crop mapping in Sub-Saharan agricultural landscapes? Remote Sens. 6(9) (2014) 8541–8564.

DOI: 10.3390/rs6098541

[25] M. Simwanda, Y. Murayama, Integrating geospatial techniques for urban land use classification in the developing Sub-Saharan African city of Lusaka, Zambia, ISPRS Int. J. Geo-Inf. 6(4) (2017) 102.

DOI: 10.3390/ijgi6040102

[26] A. Liepa, M. Thiel, H. Taubenböck, I. Steffan-Dewenter, I.O. Abu, M.S. Dhillon, I. Otte, M.H. Otim, M. Lutaakome, D. Meinhof, E.A. Martin, Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa, Remote Sens. Appl. Soc. Environ. 35 (2024) 101230.

DOI: 10.1016/j.rsase.2024.101230

[27] T. Govender, T. Dube, C. Shoko, Remote sensing of land use-land cover change and climate variability on hydrological processes in Sub-Saharan Africa: Key scientific strides and challenges, Geocarto Int. 37(44) (2022) 10925–10949.

DOI: 10.1080/10106049.2022.2043451

[28] Q. Li, C. Qiu, L. Ma, M. Schmitt, X. Zhu, Mapping the land cover of Africa at 10 m resolution from multi-source remote sensing data with Google Earth Engine, Remote Sens. 12(4) (2020) 602.

DOI: 10.3390/rs12040602

[29] A. Karmaoui, A. Moumane, S. El Jaafari, A. Menouni, J. Al Karkouri, M. Yacoubi, L. Hajji, Thirty years of change in the land use and land cover of the Ziz Oases (Pre-Sahara of Morocco) combining remote sensing, GIS, and field observations, Land 12(12) (2023) 2127.

DOI: 10.3390/land12122127

[30] K. Näschen, B. Diekkrüger, M. Evers, B. Höllermann, S. Steinbach, F. Thonfeld, The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios, Sustainability 11(24) (2019) 7083.

DOI: 10.3390/su11247083

[31] D.O. Yawson, M.O. Adu, P.A. Asare, F.A. Armah, Multifunctional landscape transformation of urban idle spaces for climate resilience in sub-Saharan Africa. African handbook of climate change adaptation. (2020) 1-27

DOI: 10.1007/978-3-030-42091-8_214-1

[32] O. Amponsah, D. Blija, R. Ayambire, S. Takyi, H. Mensah, I. Braimah, Global urban sprawl containment strategies and their implications for rapidly urbanising cities in Ghana, Land Use Policy 121 (2022) 105979.

DOI: 10.1016/j.landusepol.2022.105979

[33] K.B. Awuah, F. Hammond, J. Lamond, C. Booth, Estimating the benefits of Sub-Saharan Africa urban land use planning systems: A methodological discussion, Eur. Real Estate Soc. Conf. Proc. (2013) 1–12.

DOI: 10.15396/eres2013_30

[34] D. Moisa, A. Gemeda, Spatiotemporal urban expansion analysis and modeling in Addis Ababa, Ethiopia using remote sensing and CA–Markov model, J. Appl. Earth Obs. Geoinf. 102 (2021) 102444.

[35] T.J. Kolowa, M.N. Daams, M. Kuffer, Do informal settlements contribute to sprawl in Sub-Saharan African cities? Sustain. Cities Soc. 108 (2024) 105663.

DOI: 10.1016/j.scs.2024.105663

[36] S. Ale, K. Adjonou, K.N. Segla, K. Komi, J.B.B. Zoungrana, C. Aholou, K. Kokou, Urban forestry in Sub-Saharan Africa: Challenges, contributions, and future directions for combating climate change and restoring forest landscapes, Sustainability 16(1) (2024) 24.

DOI: 10.3390/su17010024

[37] D. Omeiza, Efficient machine learning for large-scale urban land-use forecasting in Sub-Saharan Africa, arXiv preprint (2019) arXiv:1908.00340. https://arxiv.org/abs/1908.00340

[38] A. Richmond, Water, land, and governance: Environmental security in dense urban areas in Sub-Saharan Africa, in: L.A. Swatuk, K. Wiebe (Eds.), The Environment–Conflict Nexus, Springer, Cham, (2018) 135–154.

DOI: 10.1007/978-3-319-90975-2_6

[39] M. Bertrand, B. Bon, Négocier la terre en attendant la ville: marchés fonciers et gouvernance périurbaine en Afrique subsaharienne, Can. J. Afr. Stud. 57(1) (2022) 1–26.

DOI: 10.1080/00083968.2021.2023359