三种针叶树种节子属性通用方程的构建  

Construction of universal equations for knot attributes of three coniferous species

作　　者：李泽霖 贾炜玮[1] 郭昊天 敖子琦 赵阳[1] LI Zelin;JIA Weiwei;GUO Haotian;AO Ziqi;ZHAO Yang(College of Forestry,Northeast Forestry University/Key Laboratory of Sustainable Management of Forest Ecosystem,Ministry of Education,Harbin 150040,China)

机构地区：[1]东北林业大学林学院/森林生态系统可持续经营教育部重点实验室,哈尔滨150040

出　　处：《应用生态学报》2023年第11期2907-2918,共12页Chinese Journal of Applied Ecology

基　　金：国家自然科学基金区域创新发展联合基金重点项目(U21A20244);中央高校基本科研业务费专项资金项目(2572019CP08)资助。

摘　　要：以2020年在黑龙江省林口林业局与孟家岗林场选取的3种典型针叶树种红松、长白落叶松、樟子松为研究对象,对节子直径、疏松节长度、健全节长度3种属性构建基础模型、哑变量模型和混合模型,分析不同树种节子属性的差异,简化模型的建模工作。首先通过剖析法收集相关节子属性数据,结合相关文献,转换模型形式以及替换相关变量,构建基础模型;将树种作为定性因子,转化为哑变量,引入基础模型中,构建相关属性的哑变量模型;在构建混合模型时,引入样木与样地水平的随机效应,通过比较赤池信息准则(AIC)、贝叶斯信息准则(BIC)等评价指标,选出拟合效果最佳的混合模型。之后对基础模型、哑变量模型、混合模型的拟合精度进行对比,选出最优的通用方程。结果表明:3种模型中,哑变量模型与混合模型的拟合精度均大于基础模型。AIC、BIC等评价指标显示,混合模型对节子属性的拟合效果优于哑变量模型。模型对比结果中,健全节长度、疏松节长度、节子直径混合模型的R~2相较于基础模型分别提升了13.2%、84.8%、40.3%。不同树种3种节子属性基础模型的预测精度均大于90%,哑变量模型与混合模型的预测精度均在94%以上,说明构建的模型能够对节子相关属性进行较好的预测。3个树种健全节长度、节子直径、疏松节长度大小均为樟子松>红松>长白落叶松。哑变量模型与混合模型的拟合结果较基础模型更佳,精度更高。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,Pinus koraiensis,Larix olgensis,and Pinus sylvestris var.mongolica,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,R~2 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 P.sylvestr

关 键 词：针叶树 节子直径 健全节长度 疏松节长度 哑变量模型 混合模型 

分 类 号：S781[农业科学—木材科学与技术]