State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.
PLoS One. 2013 Apr 30;8(4):e62605. doi: 10.1371/journal.pone.0062605. Print 2013.
The objective of this study was to introduce application of the Richards equation on modelling and prediction of stand diameter distribution. The long-term repeated measurement data sets, consisted of 309 diameter frequency distributions from Chinese fir (Cunninghamia lanceolata) plantations in the southern China, were used. Also, 150 stands were used as fitting data, the other 159 stands were used for testing. Nonlinear regression method (NRM) or maximum likelihood estimates method (MLEM) were applied to estimate the parameters of models, and the parameter prediction method (PPM) and parameter recovery method (PRM) were used to predict the diameter distributions of unknown stands. Four main conclusions were obtained: (1) R distribution presented a more accurate simulation than three-parametric Weibull function; (2) the parameters p, q and r of R distribution proved to be its scale, location and shape parameters, and have a deep relationship with stand characteristics, which means the parameters of R distribution have good theoretical interpretation; (3) the ordinate of inflection point of R distribution has significant relativity with its skewness and kurtosis, and the fitted main distribution range for the cumulative diameter distribution of Chinese fir plantations was 0.4∼0.6; (4) the goodness-of-fit test showed diameter distributions of unknown stands can be well estimated by applying R distribution based on PRM or the combination of PPM and PRM under the condition that only quadratic mean DBH or plus stand age are known, and the non-rejection rates were near 80%, which are higher than the 72.33% non-rejection rate of three-parametric Weibull function based on the combination of PPM and PRM.
本研究旨在介绍 Richards 方程在林分直径分布建模和预测中的应用。使用了来自中国南方杉木(Cunninghamia lanceolata)人工林的 309 个直径频率分布的长期重复测量数据集。此外,使用了 150 个林分作为拟合数据,其余 159 个林分用于测试。应用非线性回归方法(NRM)或最大似然估计方法(MLEM)来估计模型的参数,并使用参数预测方法(PPM)和参数恢复方法(PRM)来预测未知林分的直径分布。得出了四个主要结论:(1)R 分布比三参数 Weibull 函数具有更准确的模拟;(2)R 分布的参数 p、q 和 r 被证明是其规模、位置和形状参数,与林分特征有很深的关系,这意味着 R 分布的参数具有良好的理论解释;(3)R 分布拐点的纵坐标与偏度和峰度有显著的相关性,杉木人工林累积直径分布的拟合主分布范围为 0.4∼0.6;(4)拟合优度检验表明,在仅知道二次平均胸径或加上林龄的情况下,通过 PRM 或 PPM 和 PRM 相结合应用 R 分布可以很好地估计未知林分的直径分布,且不拒绝率接近 80%,高于基于 PPM 和 PRM 相结合的三参数 Weibull 函数的 72.33%不拒绝率。