Spatio-Temporal Change Detection of Built-up Areas in Ilorin Metropolis and Implications for Green Space Conservation

Abstract

Urbanization is a leading driver of green space depletion, and accurate land cover mapping is an essential, cost-effective technique for monitoring this phenomenon. In this study, we analyzed the spatio-temporal changes (1986 to 2020) of built-up areas in the city of Ilorin- Nigeria, and discussed the findings’ implications for green spaces. A supervised pixel-based SVM algorithm was adopted for image classification to produce the land cover maps with multi-temporal Landsat images. The analyzed post-classification result yielded an overall accuracy (OA) of 99.87 percent and a Kappa coefficient (KA) of 0.998. The change metrics showed that built-up areas witnessed a decadal increase in spatial extent from 1986 to 2020. Whereas, vegetation land cover witnessed a decadal decline during these periods. The net-growth rate (%) for built-up areas and vegetation land cover between 1986 to 1996, 1996 to 2006 and 2016 to 2020 were +11.67, +5.04, +6.647, +0.06, and −14.1, −36.52, +25.81, −0.10 respectively. Meanwhile, the trend forecast predicted a decline in vegetation of 9510 hectares by the year 2030 if the current land use pattern continues unabated. The land consumption ratio (LCR) and land absorption coefficient (LAC) results were both positive, indicating a rise in the urban population correlating with larger consumption of new land areas over time.

Keywords:

• Green space
• land consumption rate and land absorption coefficient
• land use and land cover
• supervised pixel-based classification
• support vector machine

Disclosure statement

The authors report there is no competing interest to declare.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.