Allometric models for predicting the aboveground biomass of Canada yew (Taxus canadensis Marsh.) from visual and digital cover estimates

Abstract

The objectives of this study were to evaluate visual and digital estimates of percent cover as source data and to develop cover-based allometric models for the prediction of aboveground biomass of Canada yew ( Taxus canadensis Marsh.). Cover was determined from visual assessment and digital images captured over 25 plots (1 m2) at a model training site near Timmins, Ontario. Linear and power functions were fit to the cover–biomass data to develop models of foliage, stem, and total aboveground biomass. Both sources of cover data produced models that explained between 70% and 85% of the variance in the training data, with root mean square error estimates ranging from 27 g·m–2 (foliage) to 85 g·m–2 (total). Models based on visual cover data performed consistently better and were tested on independent data. Stem and total biomass were underestimated in the model testing data set; however, prediction statistics indicated that the linear and power forms of foliage biomass models were validated by the testing data. Final models of foliage biomass were developed from the entire data set, with mean absolute errors of 18.3 and 18.7 g·m–2 for the linear and power forms, respectively. Additional variables (e.g., plant height, age) may be required to provide general predictions of the woody biomass of Canada yew.