Evaluation of four methods of fitting Johnson’s SBBfor height and volume predictions

摘要

Johnson’s SBB is the most commonly used bivariate distribution model in forestry. There are different methods of fittingJohnson’s distribution, and their accuracies differ. In this article, the method of conditional maximum likelihood(CML), moments, mode and Knoebel and Burkart (KB) were used to fit Johnson’s SBB distribution. A total of 4,237diameter and height data obtained from 90 sample plots of Eucalyptus camaldulensis Dehnhardt were used. Evaluationwas based on tree height and volume predictions. The predicted and observed tree heights and volumes werecompared using the paired sample t-test. The average relative (%) bias and root mean square error of heights andvolumes were computed for the four methods. The results showed that CML- and moments-based methods were moresuitable than KB and mode methods for predicting tree height and volume. The level of significance and percentagebias were much lower in CML and moments. The mode-based method had the worst performance. The ranking orderwas: CML ≈ moments > KB > mode.