Assessment of the classification accuracy of the derived land cover maps from satellite data was carried out. Error matrices were used to assess the classification accuracy and are summarized for all 5 years (excluding 1960 land cover) in Table 3. The overall accuracies for 1975, 1988, 1999, 2003, and 2005 were 85.6%, 86.4%, 90.4%, 90%, and 88.2% respectively, with Kappa statistics of 82.7%, 83.7%, 88.5%, 87.9%, and 85.6%. Producer’s and user’s accuracy was also consistently high, ranging from 71% to 100%. The MSS resulted in the lowest overall accuracy (85.6%) among the dataset. It can be noted that the MSS imagery is too coarse to study land cover of urban environment and the accuracy gets reduced due to mixed pixels (Haack 1987). Moreover,decreases of image spatial resolution lead to spectral mixing of different categories produce spectral confusion between covers (Yang and Lo 2002). These could be the reasons to have the least accuracy for the land cover map derived from the MSS data in addition to registration error (Townshend et al.1992). Misclassifications were between built-up areas and bare soil/landfill category. In addition, some water bodies were interpreted as wetland. Built-up areas are generally expected not to change to other cover types such as agriculture or wetland. The changes may have been resulted from classification errors. The examination of the accuracies of land cover data however, revealed that all the datasets met the minimum USGS total accuracy set out by Anderson et al. (1976), hence the application of rule-based post-classification refinement found to be effective that improved the map accuracy by 10–12%.It is necessary to mention here that all the images used in this study represented only the winter time,therefore other seasonal data, i.e. spring image can be considered to determine the seasonal spectral properties as well as land cover change characteristics of a highly dynamic urban environment.