Spatio-Temporal Urban Expansion Analysıs in a Growing City of Oyo Town ,Oyo State, Nigeria Using Remote Sensing And Geographic Information System (GIS) Tools

Year 2018, Volume: 5 Issue: 2, 104 - 113, 01.08.2018

Rafiu Jimoh , Yusuf Afonja Christopher Albert N Amoo

https://doi.org/10.30897/ijegeo.354627

Cited By: 4

Abstract

The assessment of the land use / land cover expansion that occurred in
the area over a period of thirty years is the utmost priority of this research
work. Multi temporal Landsat satellite imageries TM 1984, 1990 and ETM+ 2002,
2014 from the United States Geological Survey (USGS) website were used as the
primary dataset. Area of interest was clipped in ArcGIS 9.3 environment, image
enhancement and image classification were adequately done using ENVI 4.5 remote
sensing software. Using supervised classification algorithm, the images were
classified into bare soil, built-up area, vegetation, water body and wetland;
these were then used to carry out change detection analysis or time series
analysis. Results obtained from the analysis of built-up area dynamics for the
past three decades revealed that the town has been undergoing urban expansion
processes. The expansion was prolonged both from urban centre to adjoining
non-built-up areas in all directions. The total built up area in the town has
expanded from 28.04sq/km in 1984 to 49.51 sq/km in 2014 at an average expansion
rate of 0.7, 0.4 and 0.9 per annum during 1984 – 1990, 1990 – 2002 and 2002 –
2014 study periods respectively. The study period from 2002 – 2014 was the time
at which the town experienced the highest urban expansion. The analysis of
spatial trend revealed that the urban landscape has experienced a process of
sprawling and fragmented development pattern particularly in the fringe areas
while the town centre underwent infill and edge expansion development
processes. The fringe areas show scattered expansion pattern. Quantifying urban
expansion patterns and development processes of the past trends can help better
understand the dynamics of built up area and guide sustainable urban
development planning of the future urban growth.

Keywords

Multi temporal Landsat satellite imageries, supervised classification algorithm, Change detection analyses, Remote Sensing

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