Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (−3.27%), bare surface (−33.47%), and water bodies (−0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.