Effects of Sample Plot Size and Prediction Models on Diameter Distribution Recovery

Abstract

Abstract Distribution of tree diameters in a stand is characterized using models that predict diameter moments and/or percentiles in conjunction with a mathematical system to recover the parameters of an assumed statistical distribution. Studies have compared Weibull diameter distribution recovery systems but arrived at different conclusions regarding the best approach for recovering a stand’s diameter distribution from predicted stand-level statistics. We assessed the effects of sample plot size and diameter moments/percentiles prediction models on the accuracy of three approaches used in recovering Weibull distribution parameters—method of moments, percentile method, and moments-percentile hybrid method. Data from five plot sizes, four of which were virtually created from existing larger plots, from unthinned loblolly pine (Pinus taeda) plantations, were used to fit moments/percentile prediction models and to evaluate the accuracy of the diameter distribution recovered using three approaches. Both plot size and prediction model form affected the accuracy of the recovery approaches as indicated by the changes in their ranking from one plot size to another for the same model form. The method of moments approach ranked best when the evaluation error index did not account for tree stumpage value, but the moments-percentile hybrid approach ranked best when stumpage value was considered.