摘要
应用2015年调查的黑龙江省江山娇实验林场48块杂种落叶松人工林样地的4129株样木数据,分析了冠幅(C_(W))与胸径(D_(BH))的相关关系,选出较好的冠幅—胸径线性关系模型作为基础模型;经过再参数化分析,选入对冠幅影响较大的单木因子和林分因子构建冠幅的通用线性模型;再采用混合效应模型的方法建立精确的冠幅预测模型。结果表明:冠幅与胸径呈较好的线性相关,冠长率(C_(R))、高径比(H_(DR))和林分断面积(B_(AS))对冠幅的影响最大,最终构建了样地水平的线性混合效应模型为杂种落叶松人工林单木冠幅预测模型。模型的修正决定系数R_(a)^(2)为0.6567,比基础模型(R_(a)^(2)为0.5701)增加了15.19%;均方根误差(R_(MSE))和赤池信息准则与基础模型相比,均减少了10%以上;模型采用留一交叉验证法进行检验,其平均绝对偏差(M_(AE))为0.3491 m、平均相对偏差绝对值(M_(APE))为18.36%,拟合效率(E_(F))为0.6164。考虑该模型的实用性,基于留一法,利用随机抽样,对比分析了不同样本量(2,3,4,…,30株树)对随机效应校正下的模型预测效果,结果表明每个样地随机调查5株树的冠幅,即可达到较好的预测效果。
With a data set consisting of 4129 Hybrid larch from 48 plots located in Jiangshanjiao forest farm in Heilongjiang Province in 2015,the correlation between crown width(C_(W))and diameter at breast height(D_(BH))was analyzed,and a better C_(W-D) BH relationship model was selected as the basic model.Then,through parametric analysis,the individual tree attribute and stand attribute factors which have great influence on crown width were selected to construct the generalized line of crown width.Finally,the mixed effect model was used to establish the accurate crown width prediction model.There was a good linear correlation between C_(W) and D BH,and the crown length ratio(C_(R)),the tree height-D BH ratio(H_(DR))and stand basal area(B_(AS))had the greatest influence on crown width.A crown width prediction model of hybrid larch plantation based on mixed effect was constructed,the modified determination coefficient(R_(a)^(2))is 0.6567,which was higher than that of the basic model(R_(a)^(2)=0.5701).Compared with the basic model,the root mean square error(R_(MSE))and the Akaike information criterion(A_(IC))were reduced by more than 10%compared with the basic model;the mean absolute deviation(M AE)was 0.3491 m,the absolute mean relative deviation(M_(APE))was 18.36%,and the fitting efficiency(E_(F))was 0.6164.Considering the practicability of the model,based on the leave-one-out method and using random sampling method,the prediction effect of different sample size(2,3,4,…,30 trees)under random effect correction was compared and analyzed.The results show that the better prediction effect can be achieved by randomly investigating the crown width of 5 trees in each sample plot.
作者
马爱云
李凤日
董利虎
Ma Aiyun;Dong Lihu;Li Fengri(Northeast Forestry University,Harbin 150040,P.R.China)
出处
《东北林业大学学报》
CAS
CSCD
北大核心
2021年第3期9-14,38,共7页
Journal of Northeast Forestry University
基金
国家重点研发计划项目(2017YFD0600402)
黑龙江省科学技术项目(GX18B041)
黑龙江头雁创新团队计划项目(森林资源高效培育技术研发团队)。