Opeyemi O. TOPE-AJAYI*1, Oludare H. ADEDEJI1, Clement O. ADEOFUN1, S. O. AWOKOLA2
* Corresponding author
1 Federal University of Agriculture, Departmenet of Environmental Management and Toxicology, Abeokuta, Ogun State, NIGERIA
2 Federal University of Agriculture, Department of Civil Engineering, Abeokuta, Ogun State, NIGERIA
E-mail: topeajayiopeyemi@gmail.com, adedejioh@funaab.edu.ng, clemluv2000@yahoo.com, osawokola@yahoo.co.uk
Pages: 51-63. DOI: 10.19188/06JSSP012016
Cite: Tope-Ajayi O. O., Adedeji O. H., Adeofun C. O., Awokola S. O. (2016), Land Use Change Assessment, Prediction Using Remote Sensing and GIS Aided Markov Chain Modelling at Eleyele Wetland Area, Nigeria. Journal of Settlements and Spatial Planning, 7(1), 51-63. DOI: 10.19188/06JSSP012016
Abstract. Land use change involve changes in the area extent of a given type of land use or cover and such changes have significant impact on natural and human environment at all geographic scales. We performed a land use classification and analysis by using GIS and Remote sensing technique, and GIS aided ‘Markov Cellular Automata’ technique was used to model the land use change and determine the magnitude, rate and dynamics of change in the spatial extent of the Eleyele wetland area, Nigeria. We also identified the factor responsible for the observed changes with the objectives of predicting future change in the next 30 years. Four multi-temporal datasets comprising Landsat TM 1984, 2000 and Landsat OLI/TIRS 2014 imageries were classified using ArcGIS 10.0 version with support of ground truth data. Post-classification comparison with GIS overlay to map the spatial dynamics of land use/cover change was conducted. Land use Change Modeller (LCM) and Markovian processes were employed to analyze the pattern and trend of change. Based on the past trend of land use changes (from 1984 to 2014), the future land use map of Eleyele wetland area for the year of 2044 was generated using the neural network built-in module in the Idrisi Selva. The study revealed that the lake area decreased from 1.28 km2 in 1984 to 0.99 km2 in 2000 and further to 0.60 km2 in 2014. The study concluded that the increase in built area around the lake has resulted in the loss of vegetal cover, which has a negative implication for biodiversity conservation in the study area. This type of research will help shaping the urban form of the city in a planned manner.
K e y w o r d s: landsat images, urban planning, Geographic Information Systems (GIS), land use, land cover change (LUCC), remote sensing, urban sprawl, IDRISI