A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic

Habitat mapping is a core element in numerous tasks related to sustainability management, conservation planning and biodiversity monitoring. Land cover classifications, extracted in a timely and area-extensive manner through remote sensing data, can be employed to derive habitat maps, through the use of domain expert knowledge and ancillary information. However, complete information to fully discriminate habitat classes is rarely available, while expert knowledge may suffer from uncertainty and inaccuracies. In this study, a rule-based classification methodology for habitat mapping through the use of a pre-existing land cover map and remote sensing data is proposed to deal with uncertainty, missing information, noise afflicted data and inaccurate rule thresholds. The use of the Dempster–Shafer theory of evidence is introduced in land cover to habitat mapping, in combination with fuzzy logic. The framework is able to handle lack of information, by considering composite classes, when necessary data for the discrimination of the constituting single classes is missing, and deal with uncertainty expressed in domain expert knowledge. In addition, a number of fuzzification schemes are proposed to be incorporated in the methodology in order to increase its performance and robustness towards noise afflicted data or inaccurate rule thresholds. Comparison with reference data reveals the improved performance of the methodology and the efficient handling of uncertainty in expert rules. The further scope is to provide a robust methodology readily transferable and applicable to similar sites in different geographic regions and environments. Although developed for habitat mapping, the proposed rule-based methodology is flexible and generic and may be well extended and applied in various classification tasks, aiming at handling uncertainty, missing information and inaccuracies in data or expert rules. 2013 Elsevier B.V. All rights reserved.