• 论文与研究报告 •

### 立木含碳量估算方法比较

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
• 收稿日期:2018-01-17 出版日期:2020-04-25 发布日期:2020-05-29
• 通讯作者: 李凤日
• 基金资助:
国家自然科学基金项目(31971649);国家自然科学基金项目(31600510);黑龙江省科学技术项目(GX18B041);黑龙江头雁创新团队计划(森林资源高效培育技术研发团队)

### Comparison of Individual Tree Carbon Estimation Approaches

Lihu Dong,Yongshuai Liu,Bo Song,Yifei Zhou,Fengri Li*

1. Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education School of Forestry, Northeast Forestry University Harbin 150040
• Received:2018-01-17 Online:2020-04-25 Published:2020-05-29
• Contact: Fengri Li

Abstract:

Objective: Forest biomass and carbon, the foundation of researching many forestry and ecology problems, is a basic quantity character of the forest ecological system. Thus, accurate measurement of biomass and carbon is very important. Biomass and carbon model development is an efficacious way to biomass and carbon estimation. Based on the data of biomass and carbon for Populus×xiaohei, we compared the partition and variation of biomass and carbon concentration for four tree components(i.e. stem, root, branch, and foliage), and studied how to establish the additive system of individual tree biomass and carbon equation. Furthermore, five approaches(i.e. carbon allometric equation, the respective mean carbon concentration, the weighted mean carbon concentration, the generic carbon concentration proportion Ⅰ and the generic carbon concentration proportion Ⅱ)for calculating carbon stock of individual trees were evaluated and compared. These were expected to provide technical and theoretical support for accounting and monitoring the Chinese forest biomass and carbon stock. Methods: The aggregation system was used to establish the individual tree biomass and carbon additive models, while nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass and carbon equations. The individual tree biomass and carbon model validation was accomplished by Jackknifing technique in this study. ANOVA based on the SAS POC GLM was applied to test the differences between the five approaches to estimate carbon stock(treatment), using the sampling trees as blocks, followed by the contrasts between the five approaches. Results: The model fitting results showed that all biomass and carbon equations fitted the data well, of which the adjusted coefficient of determination(Ra2)of biomass and carbon additive systems for were above 0.80, the mean relative error(MRE)was between -2%-2%, the mean absolute relative error(MARE)was less than 30%, and all models had a good prediction precision(85% or more). Furthermore, the results of five approaches for calculating carbon stock of individual trees showed that the carbon allometric equations and the estimated biomass multiplied by weighted mean carbon concentration were more advantageous, whereas the approach using the generic carbon concentration constants(i.e. 0.45 or 0.50)might produce significant biases in estimating the carbon stock of individual trees. Conclusion: In order to estimate model parameters more effectively, the additive property of estimating tree total, sub-totals, and component biomass or carbon should be taken into account. Overall, the biomass and carbon models would be suitable for predicting individual tree biomass and carbon of Populus×xiaohei in west plain of Heilongjiang Province.