AN AGE-INDEPENDENT INDIVIDUAL TREE HEIGHT PREDICTION MODEL FOR BOREAL SPRUCE ASPEN STANDS IN ALBERTA

This study presents an individual tree height prediction model for white spruce (Picea glauca (Moench) Voss) and trembling aspen (Populus tremuloides Michx.) grown in boreal mixed-species stands in Alberta. The model is based on a three-parameter Chapman-Richards function fitted to data from 164 permanent sample plots using the parameter prediction method. It is age independent and expresses tree height as a function of tree diameter, tree basal area, stand density, species composition, site productivity, and stand average diameter. This height-prediction model was fitted by weighted nonlinear regression for spruce and unweighted nonlinear regression for aspen. Almost all estimates of parameters were significant at alpha = 0.05 and model R2-values were high (0.9192 for white spruce and 0.9087 for aspen). No consistent underestimate or overestimate of tree heights was evident in plots of studentized residuals against predicted heights. The model was also tested on an independent data set representing the population on which the model was to be used. Results showed that the average prediction biases were not significant at alpha = 0.05 for either species, indicating that the model appropriately described the data and performed well when predictions were made.