刘建峰^1,,
倪健^1,2,,
1. 浙江师范大学化学与生命科学学院, 浙江 金华 321004
2. 浙江金华山亚热带森林生态系统野外科学观测研究站, 浙江 金华 321004

基金项目: 国家自然科学基金项目(批准号：31870462)、生态环境部生物多样性保护专项项目(批准号：9-1-1-1-5)和浙江省"****"科技创新领军人才项目(批准号：2019R52014)共同资助


详细信息

作者简介: 刘建峰, 男, 25岁, 硕士研究生, 全球变化生态学研究, E-mail: 1067842443@qq.com

通讯作者: 倪健, E-mail: nijian@zjnu.edu.cn

中图分类号: Q948

收稿日期:2021-02-22
修回日期:2021-05-02
刊出日期:2021-07-30

Comparison of general allometric equations of biomass estimation for major tree species types in China

LIU Jianfeng^1,,
NI Jian^1,2,,
1. College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua 321004, Zhejiang
2. Zhejiang Jinhua Mountain Observation and Research Station for Subtropical Forest Ecosystems, Jinhua 321004, Zhejiang


More Information

Corresponding author: NI Jian,E-mail:nijian@zjnu.edu.cn

MSC: Q948

--> Received Date: 22 February 2021
Revised Date: 02 May 2021
Publish Date: 30 July 2021


摘要
摘要:森林生态系统是全球植被及碳循环的主体，快速准确地估算与评估大尺度的森林生物量，对于揭示全球森林的碳储量以及应对全球气候变化具有重要意义。本文搜集了1982~2019年间发表的我国生物量研究文献，以其中287篇文献的生物量数据为基础，利用生物量基本模型、最小二乘支持向量机(Least Square Support Vector Machine，简称LSSVM)模型和哑变量模型3种方法，分别研建了落叶阔叶树种、常绿阔叶树种和针叶树种3种树种类型的通用生物量相对生长方程，并评估不同方法的优劣。结果表明，本文不同方法构建的方程大多能达到相对较高的预估精度，可应用于我国生物量评估当中。基本模型受限于自变量影响，模拟结果存在较大不确定性；LSSVM方法通过机器学习，得到了优化参数，提升了模型模拟效果；而哑变量模型引入了环境指标等哑变量，降低各环境指标的影响，提高了模型适用性。使用胸径和树高D²H变量模型构建地上生物量方程大多优于仅使用胸径D变量模型，地下生物量则相反，但仅使用D变量构建的生物量方程也能达到较好的预估效果。我们认为将不同的建模方法融入到生物量评估中，对提升大尺度生物量评估精度具有重要意义，是未来生物量评估的发展方向。
关键词: 生物量方程/
树种类型/
LSSVM/
哑变量模型/
模型评估

Abstract:Forest ecosystems play important roles in global vegetation and carbon cycles. Rapid and accurate estimation of large-scale forest biomass is important in estimating global forest carbon storage and mitigating anthropogenic global climate change.
In this study, we collected biomass data(including biomass and biomass equations for trunk, leaves, branches, roots and other parts of the tree) of the dominant tree species of 461 research sites in China(excluding Hong Kong, Macao, and Taiwan) from 287 articles published from 1982 to 2019. Three methods, namely, basic biomass model, least square support vector machine(LSSVM) model, and dummy variable model, were adopted to establish general biomass allometric equations for deciduous broadleaf species, evergreen broadleaf species, and coniferous species. The goodness evaluation and precision analysis of these different methods were conducted.
Biomass estimation using the general allometric equations established by the three methods achieved high prediction accuracy. Thus, these general allometric equations could be used to estimate forest biomass in China. The basic biomass model ignored the influence of non-tree measurement factors other than tree diameter at breast height (D) and height (H), whereas the simulated results presented the greatest uncertainty. The LSSVM model optimized the parameters, thereby improving the precision of model simulation. The dummy variable model affected various categorical factors, such as environmental indicators, reduced the influence of environmental factors, and improved the applicability of the model. Aboveground biomass allometric equations, which considered both the D and H variables(D² H), presented a higher prediction accuracy than the equations that considered the D variable only. Similarly, belowground biomass allometric equations, which considered the D variable only, achieved a high prediction accuracy. This study demonstrated that integrating different modeling methods into biomass estimation is necessary to improve the accuracy of large-scale biomass estimates. This endeavor is the future direction of biomass evaluation.
Key words:allometric equation/
tree species type/
LSSVM/
dummy variable model/
model evaluation



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