Sustainable forest management requires accurate and up-to-date information, which can nowadays be obtained using digital earth observation technology. This paper introduces a modified change vector analysis (mCVA) approach and conceptually contrasts it against traditional CVA. The results of a comparative study between this change detection algorithm and three other widely used change detection algorithms: standardized differencing, ratioing and selective principal component analysis are summarized. Landsat Thematic Mappper (TM) imagery and detailed change maps of a forested area in Northern Minnesota were used. Change indicators (vegetation indices) were grouped into three conceptually independent categories corresponding to soil, vegetation and moisture characteristics. Change periods of two, four and six years were considered. All change detection outputs were multidimensional and of a continuous nature, and could therefore be subjected to a supervised maximum likelihood algorithm using identical data training sets. Change extraction accuracies were determined by computing overall accuracy and Kappa coefficients of agreement against independent reference datasets. The mCVA outperformed the three other change detection methods in all cases, and we have shown that there is a clear advantage in running mCVA with three change indicator inputs where each input comes from a different change indicator category. Further validations with more detailed reference data are needed to improve this method and test its performance for other types of change events.