• 研究报告 •

### 人工长白落叶松立木叶面积预估模型

1. 东北林业大学林学院, 哈尔滨 150040
• 收稿日期:2018-01-23 出版日期:2018-09-20 发布日期:2018-09-20
• 通讯作者: E-mail: fengrili@126.com
• 作者简介:谢龙飞, 男, 1990年生, 硕士研究生. 主要从事林分生长与收获模型研究. E-mail: xlflv0825@163.com
• 基金资助:

本文由“十三五”国家重点研发计划项目(2017YFD0600402)资助

### Predicting models of leaf area for trees in Larix olgensis plantation.

XIE Long-fei, DONG Li-hu, LI Feng-ri*

1. School of Forestry, Northeast Forestry University, Harbin 150040, China.
• Received:2018-01-23 Online:2018-09-20 Published:2018-09-20
• Supported by:

This work was supported by the “13th Five-Year” National Key R&D Program Project (2017YFD0600402).

Abstract: 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 olgensis trees 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+β4lnDBH+β5lnHT/DBH+(β6+b6)lnCR was selected as the optimal BLA mixed-effect model with the considera-tion of tree-level random effects, composed of three random-effect on lnBD, lnRDINC and lnCR (βi represented model fixed parameters, bi represented 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+γ1lnDBH+γ2CR (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 with R2 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. olgensis trees and provided a theoretical basis for the research on distribution of leaf area and photosynthesis.