Evaluation of Tree Height Prediction Models for Stand Inventory
The study appraised nine models that predict total tree height from diameter by species within individual stands. Models were fitted with nonlinear least squares by species within individual stands using inventory data from western Washington. Stand-level models were examined with respect to species, geographic regions, dominance characteristics, and sample sizes. Models were evaluated for mean square error, bias by diameter class, overfitting, and consistency in relative ranking. No substantial differences in model performance were noted with respect to geographic regions, but small differences were evident by species, dominance characteristics, and sample sizes. Model bias occurred with some but not all models. Overfitting was detected and considered a problem in fitting three-parameter models with the often small height sample in some stands. Some models were consistently good across species and sample sizes, whereas others were consistently poor. Yet the performance of other models varied by species and sample sizes. For predicting heights by species within individual stands, a single model was recommended Height = 1.37 + b0eb1dbh-1.0 A method was examined for constraining height predictions for trees beyond the range of sample data. West. J. Appl. For. 13(4):109-119.
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Document Type: Journal Article
Affiliations: Biometrics Consultant, Kent, WA 98032
Publication date: 1998-10-01
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- Each regional journal of applied forestry focuses on research, practice, and techniques targeted to foresters and allied professionals in specific regions of the United States and Canada. The Western Journal of Applied Forestry covers the western United States, including Alaska, and western Canada; WJAF will also consider manuscripts reporting research in northern Mexico that has potential application in the southwestern United States.
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