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林业科学 ›› 2020, Vol. 56 ›› Issue (4): 46-54.doi: 10.11707/j.1001-7488.20200405

• 论文与研究报告 • 上一篇    下一篇

立木含碳量估算方法比较

董利虎,刘永帅,宋博,周翼飞,李凤日*   

  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

摘要:

目的: 研究立木各分项生物量、含碳量的分配及含碳率的变化规律,探索如何构建其生物量和含碳量可加性模型,并分析5种立木含碳量估算方法(立木含碳量模型法、各分项平均含碳率法、立木加权平均含碳率法、通用含碳率法Ⅰ和通用含碳率法Ⅱ)的预测精度,为全国性生物量和碳储量监测提供可靠的理论与技术知识。方法: 以小黑杨人工林为例,采用聚合型可加性模型建立其生物量、含碳量模型,模型参数估计采用非线性似乎不相关回归模型方法,利用"刀切法"对建立的立木生物量、含碳量模型进行评价。将样木和5种立木含碳量估算方法分别作为区组和处理,利用SAS POC GLM程序进行方差分析。结果: 建立的小黑杨人工林可加性生物量和含碳量模型拟合效果均较好,调整后其确定系数(Ra2)均大于0.80,平均相对误差(MRE)为-2%~2%,平均相对误差绝对值(MARE)均小于30%,所有模型的预测精度均在85%以上。5种立木含碳量估算方法评价结果表明,立木含碳量模型法和立木加权平均含碳率法具有一定优势,利用通用含碳率0.45和0.50估算立木含碳量可能会产生较大误差。结论: 为了使模型参数估计更有效,建立的生物量、含碳量模型应当考虑立木总生物量、含碳量及各分项生物量、含碳量的可加性。本研究建立的立木生物量和含碳量模型可对黑龙江省西部平原小黑杨人工林生物量、含碳量进行很好估算。

关键词: 生物量和含碳量分配, 可加性模型, 含碳量估算

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.

Key words: biomass and carbon partitioning, additive equations, quantifying carbon stock

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