An Integrated Remote Sensing and GIS Approach in Monitoring Spatial Expansion of Federal University of Technology Akure Nigeria
In this study, a maximum likelihood supervised classification and post – classification change detection techniques were applied to Land Sat images acquired in 1986, 2002 and 2012 respectively to map Federal University of Technology (FUTA), Akure changes in Ondo state, Nigeria. The study employed surpervised digital image classification method using ILWIS 3.2, Arcview 3.1 GIS software and classified the Landuse into Built-up Area, Bareland, Dense forest, Exposed soils, Gallery Forest, Light Forest, Forest Reserve and Rock out crops. The results obtained shows that the Built-up area has been growing rapidly for the periods (1986-2012). The result also shows increase in the Bareland and Rock-outcrop between 2002 and 2012 while dense forest, forest reserves, Gallery forest and light forest decreased rapidly for the period (1986-2012). Adopting exponential growth formulae, the rate of change for projecting spatial expansion and landuse types and project the growth and landuse of Federal University of Technology (FUTA), Akure to year 2032. These results could help the city planners and policy makers to attain and sustain future urban and institutional development.
