A fuzzy-possibilistic scheme of study for objects with indeterminate boundaries: application to French Polynesian reefscapes

This communication describes the study of an ecological system using remote-sensing data and image-analysis tools derived from possibility theory. Possibility theory enables the construction of membership functions using a multisource fusion algorithm. The sources of information are the sampled training stations. The authors test to see if the possibilistic algorithm is able to provide results with an accuracy at least equal to that provided by traditional probabilistic-classification algorithms. Then, for each pixel, they analyze the hierarchy of membership degrees output by the fusion to study the spatial structure of an ecosystem composed of objects that lack precise boundaries. They characterize patches or gradients, boundary rates, and transition states. As an example, a scheme of analysis for underwater reefscapes at Moorea Island, French Polynesia, is proposed. The nonparametric multisource fusion method has an accuracy of 82% (overall normalized-percentage agreement), while a probabilistic maximum-likelihood classifier has an accuracy of 73%. The analysis of the hierarchy of membership degrees indicates that almost 25% of Moorea Island lagoon is heterogeneous, composed of real boundaries, transition states, and fragmented zones