TEMPORAL ANALYSIS OF MULTISENSOR DATA FOR FOREST CHANGE DETECTION 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 illegal logging and forest degradation. We propose a hidden Markov model based framework for analyzing multi-source time series of remote sensing images of tropical forests with the aim of detecting changes in the spatial coverage of the forest. Multi-source is supported by the hidden Markov model by applying specific data distributions for each source. The proposed methodology is demonstrated on a time series of Landsat TM and Radarsat-2 quad-pol images covering tropical forest in Tanzania. The results are evaluated by visual inspection of Landsat 5 TM images.