摘要
叶面积影响着树木干物质的生产,进而影响树木乃至整个林分的生长,而叶面积准确估计对分析树木和林分生长具有重要作用.本研究基于黑龙江省长白落叶松人工林中76株解析木数据,分别建立枝条层面和单木层面的叶面积预估模型.结果表明:考虑样木层次随机效应的最优枝条叶面积混合效应模型包含lnBD(BD为枝条基径)、lnRDINC(RDINC为相对着枝深度)和lnCR(CR为冠长率)3个随机效应参数,具体形式为:lnBLA=β+1β+(β2+b2)lnBD(β+3+b3)lnRDINC4lnDBH+β(DBH 5lnHT/DBH+(β6+b6)lnCR,其中:βi和bi分别是模型的固定效应参数和随机效应参数;为树木胸高处直径;HT/DBH为树高与胸径的比值.模型的修正决定系数R2 a)为(0.90,均方根误差(RMSE)为0.5477,平均偏差(ME)为-0.03,平均绝对偏差MAE)为0.24,预测精度(P)为91%,枝条叶面积预估模型的预估效果较好.以枝条叶面积预估模型为基础,计算树冠叶面积并建立树冠叶面积预估模型,最终形式为:lnCLA=γ0+γln1DBH+γ2CR,其中,γi为模型参数.似然比检验结果(P>0.05)说明该模型不用考虑样地层次的随机效应.本研究所建立的立木树冠叶面积预估模型的决定系数(R2)为,0.87,RMSE为0.3847,拟合效果好可以很好地预测人工长白落叶松立木树冠叶面积,为以后叶面积分布和光合作用的研究提供了理论基础.
Leaf area influences dry matter production of trees, as well as the growth of trees and forest stands. The accurate estimation of leaf area plays an important role in analyzing the growth of trees and forest stands. Based on data of 76 Larix olgensistrees in a plantation of Heilongjiang Province,predicting models of branch leaf area（BLA） and crown leaf area（CLA） were constructed, respectively. The results showed that a form of lnBLA=β1＋（β2＋b2）lnBD＋（β3＋b3）lnRDINC＋β4 lnDBH＋β5 lnHT/DBH＋（β6＋b6）lnCRwas selected as the optimal BLA mixed-effect model with the consideration of tree-level random effects, composed of three random-effect on lnBD, lnRDINCand lnCR（βirepresented model fixed parameters,birepresented model random-effect parameters, BD was branch diameter, RDINC was the relative depth into crown from tree apex, DBH was tree diameter at breast height, HT/DBH represented the ratio of tree height to DBH, and CR represented the ratio of crown length to tree height）. The adjusted coefficient of determination（Ra2）, residual mean squares error（RMSE）, mean error（ME）, mean absolute error（MAE） and precision estimation（P） of the optimal BLA mixed model were 0.90, 0.5477,-0.03, 0.24 and 91%, respectively,indicating the model had a good performance in predicting. The CLA was calculated by predicted values of all branches based on developed BLA model and the final form of CLA model was as follows： lnCLA=γ0＋γ1 lnDBH＋γ2 CR（γi, model parameters）. Results of likelihood ratio test（P〉0.05） showed that plot-level random effect had no influence on the model performance, which can be ignored. The CLA model got a good-fitting effect withR2 and RMSE being 0.87 and 0.3847,respectively. The CLA predicting model developed in this study could provide a good prediction of CLA for L. olgensistrees and provided a theoretical basis for the research on distribution of leaf area and photosynthesis.
作者
谢龙飞
董利虎
李凤日
XIE Long-fei;DONG Li-hu;LI Feng-ri(School of Forestry,Northeast Forestry University,Harbin 150040,China）.)
出处
《应用生态学报》
CAS
CSCD
北大核心
2018年第9期2843-2851,共9页
Chinese Journal of Applied Ecology
基金
"十三五"国家重点研发计划项目(2017YFD0600402)资助~~
