Landscape feature can be classified by creating categories based on aggregation of spatially explicit information. However, many landscape features appear continuous rather than discrete. The aggregation process likely involves loss of information and introduces a variety of uncertainties whose degree and extent may differ spatially. Since landscape classifications have found wide application in e. g natural resource policies or ecological research, assessments of spatial classification uncertainties are required.
We present a quantitative framework to identify the degree of landscape continuity (fuzziness) and structure (categorization) based on fuzzy classification and offer measures to quantify uncertainties originating from aggregating features into categories. Fuzzy classification is a non-hierarchical, quantitative method of assessing class definitions using degrees of association between features and class. This results in classes which are well defined and compositionally distinct, as well as classes which are less clearly defined but which, to various degrees, share characteristics with some or all classes. The spatial variation in the degree of class definition on the landscape is used to assess classification uncertainties. The two aspects of uncertainty investigated are the degree of association of a feature with the overall class definitions (membership diffusion), and the class-specific degree of association of each pixel on the landscape with each class (membership saturation).
Three classification scenarios, one fuzzy and one discrete, of the historical landscape of Wisconsin (USA) were compared for spatial classification uncertainties. Membership diffusion is highest in topographically heterogeneous environments, or areas characterized by many species occupying similar ecological niches. Classification uncertainties for individual classes show that differentiated species distributions can be identified, not only distribution centers.