College of Forestry, Northeast Forestry University/Key Laboratory of Sustainable Management of Forest Ecosystem, Ministry of Education, Harbin 150040, China.
Ying Yong Sheng Tai Xue Bao. 2023 Nov;34(11):2907-2918. doi: 10.13287/j.1001-9332.202311.001.
We constructed base model, dummy variable model, and mixture model with three variables including knot diameter, loose knot length, and sound knot length with three typical coniferous species, , , and var. , from the Linkou Forestry Bureau and Mengjiagang forest farm in Heilongjiang Province in 2020. We analyzed the differences in knot properties among different tree species and simplified the modeling work. Firstly, we collected relevant knot property data through the sectioning method based on relevant literature, transformation of the model form and substitution of related variables to conduct a base model. We transformed the species into dummy variables as qualitative factors, and introduced the dummy variable model of the relevant attributes into the base model. We introduced the random effects of sample trees and sample plots when constructing the mixture model. By comparing evaluation indicators, such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the mixture model with the best fitting effect was selected. We selected the optimal universal equation by comparing the fitting accuracy of the base model, dummy variable model and mixture model. The fitting accuracy of the dummy variable model and mixture model was higher than that of the basic model. The evaluation indicators (AIC and BIC) showed that the mixture model had a better fitting effect on knot properties than the dummy variable model. In the model comparison results, of mixture models for sound knot length, the loose knot length, and knot diameter increased by 13.2%, 84.8% and 40.3%, respectively. The predictive accuracy of the three base models for different tree species' knot attributes was above 90%, and both the prediction accuracy of the dummy variable model and mixture model were above 94%, indicating that the constructed models could well predict knot-related properties. From the perspective of tree species, the sound knot length, knot diameter, and loose knot length was in order of var. > > . Fitted results of the dummy variable model and the mixture model were superior to the basic model, with higher accuracy.
我们构建了基础模型、哑变量模型和混合模型，纳入了 2020 年来自黑龙江省林口林业局和孟家岗林场的 3 个典型针叶树种（ 、 、 var. ）的 3 个变量（节子直径、松节长度和声音节长度），分析了不同树种之间节子性质的差异，并简化了建模工作。首先，我们通过基于相关文献的切片法收集了相关节子性质数据，通过模型形式的转换和相关变量的替换来构建基础模型。我们将树种转化为哑变量作为定性因素，并将相关属性的哑变量模型引入基础模型中。在构建混合模型时，我们引入了样本树和样本林分的随机效应。通过比较 Akaike 信息准则（AIC）和贝叶斯信息准则（BIC）等评价指标，选择拟合效果最佳的混合模型。通过比较基础模型、哑变量模型和混合模型的拟合精度，选择了最佳的通用方程。哑变量模型和混合模型的拟合精度均高于基础模型。评价指标（AIC 和 BIC）表明，混合模型对节子性质的拟合效果优于哑变量模型。在模型比较结果中，声音节长度、松节长度和节子直径的混合模型分别增加了 13.2%、84.8%和 40.3%。3 个基础模型对不同树种节子属性的预测精度均在 90%以上，哑变量模型和混合模型的预测精度均在 94%以上，表明所构建的模型可以很好地预测与节子相关的性质。从树种角度来看，声音节长度、节子直径和松节长度的顺序为 var. > >. 哑变量模型和混合模型的拟合结果优于基础模型，精度更高。
