Non-classificatory analysis and representation of heathland vegetation from remotely sensed imagery

Remotely sensed imagery are an attractive source of data for vegetation mapping. Conventional image classification routines used to produce thematic maps from remotely sensed imagery rely on a one-pixel-one-class approach and generate discrete thematic units. Where the environmental phenomena to be mapped exhibit gradients such a representation is inappropriate. This paper discusses the representation of semi-natural vegetation, drawing on examples of heathland vegetation that lie along continua. Two alternative approaches to the representation of heathland vegetation are examined, both of which aim to model the continuous character of the vegetation with measures of the strength of membership to discrete classes, namely probabilities of class membership from a maximum likelihood classification and fuzzy membership functions from the fuzzy c-means algorithm. Since both approaches imply partial class membership they can be considered as non-classificatory, even though the measures of the strength of class membership may be derived from classification routines. The measures of class membership generated from both approaches were found to be significantly correlated to the variations in heathland composition along a transect which graded from dry heath to wet heath/bog. Furthermore for cases drawn from the class end-points both approaches were able to discriminate class membership accurately.