Preliminary Analysis on the Use of a Process-Based Forest Growth Model of Cryptomeria japonica Planted Forest to Represent the Effects of Canopy Structure Change

Abstract

The objective of this study is to modify the process-based stand growth model for Cryptomeria japonica in the photosynthetic production process so as to more reasonably represent the stand growth pattern after thinning. We introduce five leaf strata to the canopy structure, and then parameterize the light-curve for each leaf stratum by Bayesian calibration via the Markov Chain Monte Carlo (MCMC) method. We examine the behavior of the model using the estimated light-curve parameters through a simulation approach. We used the forest stand data derived from repeated measurements of three permanent plots. The posterior distribution of light-curve parameters derived from MCMC were well converged. We virtually applied three thinning scenarios, upper thinning, random thinning and lower thinning, and then simulated stand growth. The modified model could well reflect the changes in canopy structure derived from thinning on the growth pattern after thinning. The results suggest that the multiple light-curve model adopted in this study would be represent more realistic stand dynamics after thinning than single light-curve model adopted in the former model.