This paper describes different change detection techniques, including image differencing, image rationing, image regression and change vector analysis (CVA) to assess their effectiveness for detecting land use/cover change in a Mediterranean environment. Three Landsat TM scenes recorded on 7 July 1985, 27 July 1993 and 21 July 2005 were used to minimize change detection error introduced by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. The four change detection techniques were applied and an object-based supervised classification was used as a crossclassification to determine 'from-to' change which enabled assessment of the four techniques. The change vector analysis resulted in the largest overall accuracy of 75.25 and 75.55% for the 1985-1993 and 1993-2005 image pairs, respectively. The ratio yielded the least accurate results with an overall accuracy of 59.10 and 61.05% for the 1985-1993 and 1993-2005 image pairs, respectively. Different change detection algorithms have their own merits and advantages. However, the change vector analysis change detection technique was the most accurate model for handling the variability present in Mediterranean land use/cover. (C) 2008 Elsevier B.V. All rights reserved.