Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 353 - 353

Published: Feb. 16, 2025

This study focused on 16,101 Cunninghamia lanceolata trees across 133 plots in seven cities of Guangdong Province, China, to develop a comprehensive full growth cycle crown width (CW) model. We systematically analyzed the dynamic characteristics CW and its multi-scale influencing mechanisms. A binary basic model, with diameter at breast height (DBH) (H) as core predictor variables, effectively reflected tree patterns. The inclusion age groups dummy variables allowed model capture changes different stages. Furthermore, incorporation nested two-level nonlinear mixed-effects (NLME) accounting for random effects from forest block- sample plot-level effects, significantly improved precision applicability final (R2 = 0.731, RMSE 0.491). quantified both macro- micro-level region plot CW. Our findings showed that NLME incorporating groups, optimally accounted environmental heterogeneity cycles, resulting best-fitting statistics. proposed enhanced model’s efficiency predictive accuracy lanceolata, providing scientific support sustainable management monitoring plantation forests.

Language: Английский

A Framework for Analyzing Individual-Tree and Whole-Stand Growth by Fusing Multilevel Data: Stochastic Differential Equation and Copula Network

Forests, Journal Year: 2023, Volume and Issue: 14(10), P. 2037 - 2037

Published: Oct. 11, 2023

In forestry, growth functions form the basis of research and are widely used for mathematical modeling stand variables, e.g., tree or basal area, height, volume, site index, many more. this study, to estimate five-dimensional dependencies between diameter at breast potentially available crown area base we a normal copula approach whereby growths individual variables described using stochastic differential equation with mixed-effect parameters. The combines marginal distributions height into joint multivariate probability distribution. Copula models have advantage being able use collected longitudinal, multivariate, discrete data which number measurements does not match. This study introduced normalized interaction information measure based on entropy assess causality size variables. order accurately quantitatively processes variables’ provide scientific formalization models, an analysis method synergetic theory has been proposed. Theoretical findings illustrated uneven-aged, mixed-species empirical dataset permanent experimental plots in Lithuania.

Language: Английский