Modeling and Predicting Of Future Urban Growth Using Geospatial Technologies: The Case of Hossana Town, Snnpr, Ethiopia

Abstract

This study examines the use of Geo-spatial techniques to model and predict urban growth of Hossana town between 2003 and 2018 so as to detect and analyze the change that has taken in the town between these periods. In order to achieve these the remotely sensed Google Earth Image of 2003, Ortho-photo of 2013 and 2018 have been obtained and pre-processed using EARDAS IMAGINE. Supervised image classification has been used to generate land use/land cover maps. Land use/land cover classification, accuracy assessment, and change and prediction map in ERDAS IMAGINE and IDRSI software. For the accuracy of the classified LULCC maps the confusion matrix was used to generate it in standard form. The overall accuracy and kappa coefficient results were 89.50% for 2003 and 92.46% for 2018 which had been above the minimum and acceptable threshold level. The result revealed that annual aggregate rate of changes of urban growth of Hosanna town has been occurred within 15 years from 2003-2013 and 2013-2018 were 135.35% and 121.36% respectively. Though the period of 2003 from 2018 there were dramatic change in built-up areas, whereas agricultural, open, and forest land has decreased in area. Accordingly, more land was occupied under built up. The results also shows that increase in built-up coverage of the town was due to population pressure, road infrastructure network development, and other physical and proximity factors on land. So the rate of change has been dynamic and it needs also infrastructure and other facilities development in the town. Generally, spatial analysis and modelling enables for sustainable managements of urban growth through proper planning, wise decision making, monitoring of urban expansion and development