Urban growth assessment and its impact on deforestation in Bauchi metropolis, Nigeria using remote sensing and GIS techniques

Abstract

Urban areas are rapidly expanding due to population growth and development, leading to deforestation and land degradation. This study employed remote sensing and GIS techniques to assess urban growth and its impact on deforestation in Bauchi metropolis, Nigeria within the last three decades (1986-2016). The study made use of Land sat images of four epochs; Thematic Mapper (TM) of 1986 and 1996, Enhanced Thematic Mapper of 2006, and Operational Land Imager (OLI) of 2016. Color compositions were made after which the images were geometrically and radio metrically restituted. The images were classified using maximum likelihood algorithm and the accuracy of the classification was assessed by cross-validation using confusion matrices and ground truthing by the use of a hand-held Global Positioning System (GPS). The classified images with their kappa indexes were TM of 1986 (0.83%) and 1996 (0.87%), ETM+ of 2006 (0.90%) and OLI of 2016 (0.92%), respectively. Post-classification comparisons and analyses were performed and the results revealed that changes have taken place in bare surface (+32.43%), built-up area (+565.24%), farm land (+66.42%), forest (-91.80%) and rock outcrop (-49.21%) in the metropolis between 1986 and 2016. The land cover features of the metropolis were reclassified into forest and non-forest for cross-tabulation analysis and the result of the analysis indicates a change-over of 14965.97Ha (39.68%) form forest to non-forest (deforestation) and that of 467.69Ha (1.24%) form non-forest to forest (afforestation) between 1986 and 2016. This shows a rapid increase in built-up area (urban growth) and rapid decrease in forest (deforestation), which may be attributed to lack of improper environmental protection strategy in place in the metropolis. The study demonstrates the potentialities of remote sensing and GIS in assessing urban growth and its impacts on deforestation. The outcome of the study can serve as input into a relationship model for predicting the impact of urban growth on deforestation.