Classification of vegetation data from an open beach environment in southwestern Ontario: cluster analysis followed by generalized distance assignment

Abstract

Vegetation data from a tract of open beach in southwestern Ontario were classified in two stages to aid in the description and mapping of the plant communities of the area. Firstly, the similarity matrix generated for half the sample was analyzed by a method of similarity clustering designed to produce homogeneous and distinct groups. The four groups that emerged conformed with the main topographic features in the study area, these being the shoreline zone, the middle beach zone, the wet slack zone, and the upper beach zone. This four-type classification was then imposed upon the rest of the sample, using generalized distance (D2) as the assignment function. The problem faced in the inversion of the singular group covariance matrix (5k) for each group was overcome by orthogonal transformation. Although considerable computation was involved, the results indicated that D2, when used in a deterministic sense, has much potential in helping to allocate individuals to groups.