Change-detection represents a powerful tool for monitoring the evolution of the Earth's surface by multitemporal remote-sensing imagery. Here, a multiscale approach is proposed, in which observations at coarser and finer scales are jointly exploited, and a multiscale contextual unsupervised change-detection method is developed for optical images. Discrete wavelet transforms are applied to extract multiscale features that discriminate changed and unchanged areas and Markovian data fusion is used to integrate both these features and the spatial contextual information in the change-detection process. Unsupervised statistical learning methods (expectation-maximization and Besag's algorithms) are used to estimate the model parameters. Experiments on burnt-forest area detection in multitemporal Landsat TM images are presented.