Geospatial Analysis of Urbanisation Trend and its Effects on the Vegetal Cover of Jos South Local Government Area of Plateau State, Nigeria

Abstract

The high rate of urbanization coupled with population growth has caused changes in land use. Therefore, understanding and quantifying the spatio- temporal dynamics of urbanization and its driving factors is essential for monitoring mechanisms and decision making. The study aimed at determining how urbanization trend affect land cover and vegetation changes in Jos South Area. Landsat TM for 1991, ETM+ and operational land imager (OLI) for 2003 and 2015 was obtained and preprocessed using Erdas Imagine 2014, Idrisi software and ArcGIS 10.2. The Maximum Likelihood Algorithm of Supervised Classification was used to generate land use and land cover maps. Confusion matrix was used to derive overall accuracy and results were above the minimum and acceptable threshold level. Land Change Modeler was run to model land use and land cover changes in Jos South Area to predict future urban land use changes. To achieve this, six land cover transitions were incorporated in the modeling process. Makovian transition estimator was used to model the transition potential matrix. This result was used to make prediction using CA_ Markov chain analysis for year 2039. The results revealed that there was an increased in built up areas in the last 24years from 535.68 ha (1.18%) in 1991 to 4608.99 ha (10.17%) in 2003 and 15600.96 ha (34.43%) in 2015 at the expense of vegetated areas. The prediction results showed that built up will increase from 15600.96ha (34.43%) to 20972.88ha (46.29%), bare ground will decrease from 6691.23ha (14.77%) to 5719.23ha (12.62%), farmland will decrease from 3874.32ha (8.55%) to 3418.74ha (7.55%), while vegetation will also decrease from 4675.86ha (10.32%) to 3125.34ha (6.90%). The study concluded that geospatial techniques are a viable tool for assessing urbanization trend. It is recommended that high resolution imageries such as IKONOS be made readily available, because urban areas have complex and heterogonous features, high resolution imagery will provide better information by mapping these areas