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Multivariate Analysis of Physical Site Data for Wildland Classification

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Multivariate statistical techniques were utilized to classify and analyze sites within the Manitou Experimental Forest, Pike National Forest, Colorado. A hierarchic, agglomerative clustering procedure was applied to a random sample of 147 sites. A set of twelve site classes were developed for the area. This classification scheme was then utilized with discriminant analysis and canonical ordination techniques to identify functions for determining class membership and to examine the class separability in coordinate space. Cluster analysis was found to be a useful tool for aggregating multivariate site data and identifying a classification scheme. Discriminant analysis provided an objective means of determining the class membership of sites and canonical ordination was determined to be an appropriate means of further examining the separability of class groups. Forest Sci. 24:2-10.

Keywords: Cluster analysis; canonical ordination; discriminant analysis; land classification

Document Type: Journal Article

Affiliations: Assistant Professor, College of Forestry and Natural Resources, Colorado State University, Fort Collins, Colorado.

Publication date: March 1, 1978

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