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Application of multiple regression and principal component analysis to growth prediction and phytosociological studies of black spruce stands

Summary

Forestry research paper No. 7

This report demonstrates the suitability of using multiple regression and principal component analyses for growth prediction and phytosociological studies in black spruce forests of eastern Canada.

The data come from 125 black spruce stands located in the Boreal forest of Canada from Newfoundland to western Ontario. The observed factors in this study are: dominance of species and species groups, stand density, soil moisture regime and site index.

The prediction of site index is satisfactory using the dominance of species and species groups in multiple regression. Principal component analysis shows some possibilities of application for the identification of broad site quality classes. Further study is needed to perfect the vectorial ordinations before this method can be used for prediction purposes.

The classification of forest stands as to types and associations seems to be relatively simple with the component analysis. For application to field conditions each region should be studied separately in order to increase the precision of the vectorial ordinations.

Sector(s): 

Forests

Categorie(s): 

Forestry Research Paper

Theme(s): 

Forestry Research, Forests

Author(s)

VALLÉE, Gilles et Gerald L. LOWRY

Year of publication :

1972

Format :

PDF

Keywords :

régression multiple, fertilité, indice de fertilité, classe de fertilité, conifère, Picea mariana, épinette noire, forêt boréale, statistique, estimation, mémoire de recherche forestière

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