Urban sprawl modelling in Lira Municipality

Abstract

This research was carried out to model urban sprawl of Lira town (Lira municipality) one of the districts in northern Uganda that is growing tremendously in terms of population with an annual population growth rate of 3.6 percent. Remote sensing data was used to carry out this research, three land cover maps of 2001,2010 and 2017 were produced based on a supervised classification method. LCM in IDRISI selva was used to model urban sprawl which involved change analysis between two classified images, modeling Transition potential maps based on MLP neural network with an accuracy of 51% and predicting future scenario of 2030 growth of Lira town by use of Markov Chain. The results of the change analysis between Land cover of 2001 and 2010 showed an increase in percentage change of approximately 43.7% in Built up area, a 43.6% decrease in woodland, 2% decrease in wetlands and approximately 47.4% increase in impediment. The Future scenario map of 2030 shows an increase in Built up area by approximately 22% with a 77.4% decrease in woodland hence further loss in the other land cover classes. A landscape pattern analysis was performed which included normalized entropy evaluation for urban sprawl and change process to measure the nature of change taking place within each land cover class and for the case of lira town the change was categorized into Creation, dissection, aggregation and attrition. When adopted by most of the authorities that are in need for information and planning purposes, the land change modeler can be of much help in solving challenges affecting the planning department of Lira town and Uganda as a whole.