Leaf Area Prediction Using Three Alternative Sampling Methods for Seven Sierra Nevada Conifer Species

Prediction of projected tree leaf area using allometric relationships with sapwood cross-sectional area is common in tree- and stand-level production studies. Measuring sapwood is difficult and often requires destructive sampling. This study tested multiple leaf area prediction models across seven diverse conifer species in the Sierra Nevada of California. The best-fit whole tree leaf area prediction model for overall simplicity, accuracy, and utility for all seven species was a nonlinear model with basal area as the primary covariate. A new non-destructive procedure was introduced to extend the branch summation approach to leaf area data collection on trees that cannot be destructively sampled. There were no significant differences between fixed effects assigned to sampling procedures, indicating that data from the tested sampling procedures can be combined for whole tree leaf area modeling purposes. These results indicate that, for the species sampled, accurate leaf area estimates can be obtained through partially-destructive sampling and using common forest inventory data.