Predicting individual tree growth of high-value timber species in mixed conifer-broadleaf forests in northern Japan using long-term forest measurement data

Abstract

Quantifying individual tree growth of economically high-value timber species is important for the simulation and development of forest management options. Long-term permanent plot data provides crucial information of forest stand dynamics that can be used to predict individual tree growth. In this study, we developed individual tree basal area growth models of three high-value timber species: monarch birch (Betula maximowicziana), castor aralia (Kalopanax septemlobus), and Japanese oak (Quercus crispula) in a cool temperate mixed forest in northern Japan using long-term measurement data collected in permanent plots. Data included species, diameter at breast height, and survival status, and stand management history. We applied linear mixed-effects modelling to predict the individual tree basal area growth as a function of individual tree size, competition, and forest management. Model prediction followed by leave-one-out cross validation revealed a correlation between predicted and observed basal area increments with r values of 0.62, 0.73, and 0.70 and root mean square errors values of 10.44, 7.91, and 11.62 cm 2 /year for monarch birch, castor aralia, and Japanese oak, respectively. The individual tree growth models developed in this study will provide valuable information for species-specific forest management of economically highvalue timber species.

Height estimation of pine (Pinus eldarica) single trees using slope corrected shadow length on unmanned aerial vehicle (UAV) imagery in a plantation forest

“…This information helps managers and planners in formulating policies and plans. One of the required pieces of information is accurate and effective measurements of quantitative parameters such as height and diameter at breast height (DBH), especially at single tree levels (Thu Moe & Owari 2020).…”