Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing

Adebayo Oluwasegun Hezekiah(1*), Otun. W. O(2), Daniel, I. Samuel(3)

(1) Department of Geography, Olabisi Onabanjo University, Ago-Iwoye, Ogun State
(2) Department of Geography, Olabisi Onabanjo University, Ago-Iwoye, Ogun State
(3) Department of Geography, Olabisi Onabanjo University, Ago-Iwoye, Ogun State
(*) Corresponding Author


This research paper examined the changes in land use/ land cover of Abeokuta, Nigeria between 1984 and 2015 using Multi-Temporal Landsat Remote Sensing paired with Geographic Information System (GIS) techniques. The evaluation of the trend, rate and magnitude changes was the objectives of this study.  Five Landsat satellite images of different dates,  i.e., Landsat Thematic Mapper (TM) of 1984, 2001, 2006, 2011 and 2015 with spatial resolution ranging from 15, 30 and 60metres were obtained from National Aeronautics Space Administration(NASA),United State Geological Survey Website and  GIS facility of Sioux Falls Website  and quantify the changes  over a period of thirty-one (31) years. Supervised classification methodology was applied to the acquired multi-band raster imageries using maximum livelihood technique in ERDAS Imagine 9.3. The images of the study area were classified into three (3) classes namely; vegetation, water body and built-up area and were overlay with vector maps of the study area generated in ArcGIS 10. The results show that for the period of 31years (1984-2015), vegetation which covered 76.20% of the total area has decreased to 39.29%, water body decreases from 6.63% to 1.89% while the built –up area which initially was 17.14% as at 1984 increased to 58.82%. The study, however, recommended that there is a need for a timely Land use/ Land cover mapping of the entire Abeokuta and its environs in order to reduce the effects of undiscrimate land utilization in the area. This will also facilitate necessary Land use planning and forestall the rising sprawl not only in Abeokuta but also in other urban centres.


Remote sensing, GIS, Landuse/cover, Change detection, Abeokuta.

Full Text:



Abuelgasim, A. A., Ross, W.D and Woodcock, C.E. (1999). Change detection using adaptive fuzzy neural networks. Remote Sensing of Environment 70(2): 208-223

Adu-Prah,S, Deidra,Y, and Rolanda, M. (2005). Integrating geospatial and temporal data for water quality monitoring in Southwest Mississippi. 31st International Symposium on Remote Sensing of Environment-Saint Petersburg.

Anderson, J. R., Hardy, E.E., Roach, J.T and Witmer, R.E. (1976). A land use and land cover classification for use with remote sensor data. USGS Professional Paper 964.Washington, DC: US Government Printing Office, 18 p.

Asiyanbola R.A, Adebayo O.H, Otun W.O, Raji B.A and Osibodu O.G. (2014),Remote Sensing and Geographic Information System for inferring Land cover and Land use Changes in Ibadan, Nigeria (1984-2013). International Journal of Ecology and Environmental Studies, 2(1): 63-80.

Baban, M.J. and Kamaruzaman W. Y. (2001). Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS.International Journal of Remote Sensing 22(10):1909-1908.

Baja, S, David, M.C and Deirdre D. (2007). Spatial based compromise programming for multiple criteria decision making in land use planning. Environmental Model Assessment 12: 171–184.

Baskent, E. Z., Kose, S and Keles, S (2005). The forest management planning system of Turkey:

Constructive criticism towards the sustainable management of forest ecosystems. International Forest Review 7 (3): 208–217.

Boyle, S. J., Tsanis, I.K and Member, E.D. (2001). Developing Geographic Information Systems for Land Use Impact Assessment in Flooding Condition. Journal of Water 6Resources Planning and Management 89-97.

Brandt, J. S. and Townsend, P.A. (2006). Land use/ land cover conversion, regeneration and degradation in the high elevation Bolivian Andes. Landscape Ecology 21 (4):607–623.

Brooks, K. N. (1991). Hydrology and the management of watersheds. Iowa: Iowa University Press.

Campbell, J. B. (2002). Introduction to remote sensing. New York: The Guilford Press

Carreiras, J, Jose, M.B., Pereira, M.C and Yosio, E.S. (2006). Land-cover Mapping in the BrazilianAmazon Using SPOT-4 Vegetation Data and Machine Learning Classification Methods. Photogrammetric Engineering & Remote Sensing897-910.

Eludoyin, O.S., Wokocha, C.C., and Ayolagbo, G., (2011). GIS Assessment of Land Use and Land Cover Change in OBIO/AKPOR L.G.A., Rivers State, Nigeria. Maxwell Scientific Organization. Research Journal of Environmental and Earth Sciences 3(4): 307-313.

Gbadebo, A.M., Oyedepo, J.A and Taiwo A.M..(2010).“Variability of Nitrate in Groundwater in Some Parts of Southwestern Nigeria”. The Pacific Journal of Science and Technology.11 (2): 572-584.

Jin, C, PengGong, C. H., Ruiliang, P and Peijun, S (2003). Land-use/ Land-cover Change Detection Using Improved Change-Vector Analysis. Photogrammetric Engineering & Remote Sensing, 69(4): 369-379.

Jwan, A, Shattri, B. M and Helmi, Z S. (2013). Change Detection Process and Techniques. Civil and Environmental Research, 3(10): 37-45.

Manish, K.T. and Aruna, S (2011), Change Detection of Land use/ Land cover Pattern in an Around Mandideep and Obedullaganj Area, Using Remote Sensing and GIS. International Journal of Technology and Engineering System (IJTES), 2(3):342-350.


Article Metrics

Abstract views : 3569 | views : 2922


  • There are currently no refbacks.

Copyright (c) 2019 Indonesian Journal of Geography

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)