Temporal analysis of forest cover using hidden Markov models

Remote sensing plays a key role in monitoring the quality and coverage of the tropical forests, and for early warning of il legal logging and forest degradation. We propose a hidden Markov model based methodology for analyzing time series of remote sensing images of tropical forests with the aim of detecting changes in the spatial coverage of the forest. Two different methods are investigated; the most likely state sequence and the minimum probability of state error. The pro posed methodology is demonstrated on a time series of Land sat TM images covering tropical forest in Brazil. The results are evaluated by visual inspection, and show that for change detection the most likely state sequence method is recommended.