Evaluation of the mixed-effects model and quantile regression approaches for predicting tree height in larch (Larix olgensis) plantations in northeastern China

Abstract

Tree height (H) is one of the most important tree variables and is widely used in growth and yield models, and its measurement is often time-consuming and costly. Hence, height–diameter (H–D) models have become a great alternative, providing easy-to-use and accurate tools for H prediction. In this study, H–D models were developed for Larix olgensis A. Henry in northeastern China. The Chapman–Richards function with three predictors (diameter at breast height, dominant tree height, and relative size of individual trees) performed best. Nonlinear mixed-effects (NLME) models and nonlinear quantile regressions (NQR9, nine quantiles; NQR5, five quantiles; and NQR3, three quantiles) were further used and improved the generalized H–D model, successfully providing accurate H predictions. In addition, the H predictions were calibrated using several measurements from subsamples, which were obtained from different sampling designs and sizes. The results indicated that the predictive accuracy was higher when calibrated by using any number of height measurements for the NLME model and more than three height measurements for the NQR3, NQR5, and NQR9 models. The best sampling strategy for the NLME and NQR models involved sampling medium-sized trees. Overall, the newly developed H–D models can provide highly accurate height predictions for L. olgensis.