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

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 in Northeast 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, 9 quantiles; NQR5, 5 quantiles; and NQR3, 3 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 3 height measurements for the NQR3, NQR5 and NQR9 models. The best sampling strategy for the NLME and NQR models involved sampling the medium-sized trees. Overall, the newly developed H-D models can provide highly accurate height predictions for L. olgensis.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author