The aim of this study was to detect spatio-temporal changes in sugarcane land use using satellite imagery for 1991–2006 in Umbumbulu, South Africa. This change detection study will enable quantification of change and the changes between different land use and land cover that has occurred over the study period 1991–2006. This work embarked on a change detection analysis using image-processing software namely ERDAS, IDRISI and ArcGIS to complete the study. Three Landsat TM images from 1991, 2001, and 2006 were used. The images were geometrically corrected to a common map projection, followed by image processing operations namely: radiometric correction, supervised image classification, accuracy assessment and post classification comparison change detection. Each image was separately classified into land cover categories of water, grassland, mix bush/shrub, forestry, sugarcane and built-up land using the supervised classification maximum likelihood algorithm in ERDAS. Final classification accuracy was determined to be ‘satisfactory’ or ‘good’ by means of employing standardized accuracy assessment measures, the error matrix. The post-classification comparison technique was applied to compare the classified images to assess for changes in sugarcane land use over time using IDRISI software. The classified images produced were exported into ArcMap GIS software for additional change analysis. The results are displayed as change maps. Change analysis has been executed based on digital interpretation of classification results. / Thesis (M.Env.Dev.) - University of KwaZulu-Natal, Pietermaritzburg, 2009.