A fuzzy sets approach to the representation of vegetation continua from remotely sensed data : an example from lowland health

Conventional image classification routines are often inappropriate for the mapping of continuous phenomena suchy as heathland vegetation. To allow for the natural fuzziness of such an environment, a fuzzy sets algorithm may be used to model the heathland vegetation more appropriately than a classification. The results of this study show that the fuzzy c-means algorithm can be used to discriminate accurately between the end points of a set of continua, and that class membership functions derived from the analysis are sensitive to the botanical composition of the vegetation canopy. Mapping the fuzzy membership functions will therefore enable a more realistic portrayal of the healthland vegetation than a conventional classification.