Multivariate Analysis of Physical Site Data for Wildland Classification
Abstract: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.
Document Type: Journal Article
Affiliations: Assistant Professor, College of Forestry and Natural Resources, Colorado State University, Fort Collins, Colorado.
Publication date: 1978-03-01
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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