• 研究报告 •

### 大兴安岭东部主要林分类型乔木层生物量估算模型

1. 东北林业大学林学院, 哈尔滨 150040
• 收稿日期:2018-01-15 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:董利虎,男,1986年生,副教授,博士.主要从事林分生长与收获、森林生物量和碳储量研究. E-mail: donglihu2006@163.com
• 基金资助:

本文由国家自然科学基金项目(31600510)和黑龙江省留学归国人员科学基金项目(LC2016007)资助

### Stand-level biomass estimation models for the tree layer of main forest types in East Daxing’an Mountains, China.

DONG Li-hu, LI Feng-ri*

1. College of Forestry, Northeast Forestry University, Harbin 150040, China.
• Received:2018-01-15 Online:2018-09-20 Published:2018-09-20
• Supported by:

This work was supported by the Natural Science Foundation of China (31600510) and the Scientific Research Foundation for the Return Overseas Scholars, Heilongjiang Province, China (LC2016007).

Abstract: Forest biomass estimation methods of regional scale attract most attention of the resear-chers, with developing stand-level biomass model being a research trend. Based on the biomass data from fix forest types, two additive systems of biomass equations based one- and two-variable were developed. The model error structure (additive vs. multiplicative) of the allometric equation was evaluated using the likelihood analysis. The nonlinear seemingly unrelated regression (NSUR) was used to estimate the parameters in the additive system of stand-level biomass equations. The results showed that the assumption of multiplicative error structure was strongly supported for the stand-level biomass equations of total and components for those forest types. Thus, the additive system of log-transformed biomass equations was developed. The adjusted coefficient of determination of the additive system of biomass equations was 0.78-0.99, the mean relative error was between -2.3%-6.9%, and the mean absolute relative error was between 6.3%-43.3%. Adding mean tree height in the additive systems of biomass equations could significantly improve the model fitting performance and predicting precision for most of the models. The biomass equations of total, aboveground and stem were better than biomass equations of root, branch, foliage and crown. In order to estimate model parameters more effectively, the additivity property of estimating tree total, sub-totals, and component biomass should be taken into account. Overall, the stand-level biomass models established in this study would be suitable for predicting stand-level biomass of six forest types in Daxing’an mountains.