Publication

Individual tree heights are needed in many situations, including estimation of tree volume, dominant height, and simulation of tree growth. However, height measurements are tedious compared to tree diameter measurements, and therefore height-diameter (H-D) models are commonly used for prediction of tree height. Previous studies have fitted H-D models using approaches that include plot-specific predictors in the models and those that do not include them. In both these approaches, aggregation of the observations to sample plots has usually been taken into account through random effects, but this has not always been done. In this paper, we discuss four alternative model formulations and report an extensive comparison of 16 nonlinear functions in this context using a total of 28 datasets. The datasets represent a wide range of tree species, regions, and ecological zones, consisting of about 126 000 measured trees from 3717 sample plots. Specific R-functions for model fitting and prediction were developed to enable such an extensive model fitting and comparison. Suggestions on model selection, model fitting procedures, and prediction are given and interpretation of the predictions from different models are discussed. No uniformly best function, model formulation, or model fitting procedure was found. However, a 2-parameter Naslund and Curtis function provided satisfactory fit in most datasets for the plot-specific H-D relationship. Model fitting and height imputation procedures developed for this study are provided in an R-package for later use.