A Three-Level Model System of Biomass and Carbon Storage for All Forest Types in China

Forests Pub Date : 2024-07-25 DOI:10.3390/f15081305
Weisheng Zeng, Wentao Zou, Xinyun Chen, Xueyun Yang
{"title":"A Three-Level Model System of Biomass and Carbon Storage for All Forest Types in China","authors":"Weisheng Zeng, Wentao Zou, Xinyun Chen, Xueyun Yang","doi":"10.3390/f15081305","DOIUrl":null,"url":null,"abstract":"Forest biomass and carbon storage models are crucial for inventorying, monitoring, and assessing forest resources. This study develops models specific to China’s diverse forests, offering a methodological foundation for national carbon storage estimation and a quantitative basis for national, regional, and global carbon sequestration projections. Utilizing data from 52,700 permanent plots obtained during China’s 9th national forest inventory, we calculated biomass and carbon storage per hectare for 35 tree species groups using respective individual tree biomass models and carbon factors. We then constructed a three-level volume-based model system for forest biomass and carbon storage, applying weighted regression, dummy variable modeling, and simultaneous equations with error-in-variables. This system encompasses one population of forests, three forest categories (level I), 20 forest types (level II), and 74 forest sub-types (level III). Finally, the assessment of these models was carried out with six evaluation indices, and comparative analyses with previously established biomass models of three major forest types were conducted. Determination coefficients (R2) for the population average model, and three dummy models on levels I, II, and III, exceed 0.78, 0.85, 0.92, and 0.95, respectively, with corresponding mean prediction errors (MPEs) of 0.42%, 0.34%, 0.24%, and 0.19%, and mean percent standard errors (MPSEs) of approximately 22%, 21%, 15%, and 12%. Models for 20 forest types and 74 sub-types yield R2 values above 0.87 and 0.85, with MPE values below 3% and 5%, respectively. Notably, the estimates of previous biomass models of three major forest types demonstrated considerable uncertainty, with TRE ranging from −20% to 74%. However, accuracy has improved with larger sample sizes. In total biomass and carbon storage estimations, the R2 values of dummy models for levels I, II, and III progressively increase and MPSE and MPE values decrease, whereas TRE approximates zero. The tiered model system of simultaneous equations developed herein offers a quantitative framework for precise evaluations of biomass and carbon storage on different scales. For enhanced accuracy in such estimations, applying level III models is recommended whenever feasible, especially for national estimation.","PeriodicalId":505742,"journal":{"name":"Forests","volume":"105 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forests","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/f15081305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Forest biomass and carbon storage models are crucial for inventorying, monitoring, and assessing forest resources. This study develops models specific to China’s diverse forests, offering a methodological foundation for national carbon storage estimation and a quantitative basis for national, regional, and global carbon sequestration projections. Utilizing data from 52,700 permanent plots obtained during China’s 9th national forest inventory, we calculated biomass and carbon storage per hectare for 35 tree species groups using respective individual tree biomass models and carbon factors. We then constructed a three-level volume-based model system for forest biomass and carbon storage, applying weighted regression, dummy variable modeling, and simultaneous equations with error-in-variables. This system encompasses one population of forests, three forest categories (level I), 20 forest types (level II), and 74 forest sub-types (level III). Finally, the assessment of these models was carried out with six evaluation indices, and comparative analyses with previously established biomass models of three major forest types were conducted. Determination coefficients (R2) for the population average model, and three dummy models on levels I, II, and III, exceed 0.78, 0.85, 0.92, and 0.95, respectively, with corresponding mean prediction errors (MPEs) of 0.42%, 0.34%, 0.24%, and 0.19%, and mean percent standard errors (MPSEs) of approximately 22%, 21%, 15%, and 12%. Models for 20 forest types and 74 sub-types yield R2 values above 0.87 and 0.85, with MPE values below 3% and 5%, respectively. Notably, the estimates of previous biomass models of three major forest types demonstrated considerable uncertainty, with TRE ranging from −20% to 74%. However, accuracy has improved with larger sample sizes. In total biomass and carbon storage estimations, the R2 values of dummy models for levels I, II, and III progressively increase and MPSE and MPE values decrease, whereas TRE approximates zero. The tiered model system of simultaneous equations developed herein offers a quantitative framework for precise evaluations of biomass and carbon storage on different scales. For enhanced accuracy in such estimations, applying level III models is recommended whenever feasible, especially for national estimation.
中国所有森林类型生物量和碳储量的三级模型系统
森林生物量和碳储量模型对于森林资源的清查、监测和评估至关重要。本研究建立了针对中国不同森林的模型,为全国碳储量估算提供了方法论基础,并为全国、区域和全球碳固存预测提供了定量依据。利用中国第九次全国森林资源清查中获得的 5.27 万个永久性地块的数据,我们使用各自的树木生物量模型和碳因子计算了 35 个树种组每公顷的生物量和碳储量。然后,我们运用加权回归、虚拟变量建模和带误差变量的同期方程,构建了基于体积的三级森林生物量和碳储量模型系统。该系统包括 1 个森林种群、3 个森林类别(I 级)、20 个森林类型(II 级)和 74 个森林子类型(III 级)。最后,用六项评价指标对这些模型进行了评估,并与以前建立的三大森林类型生物量模型进行了比较分析。种群平均模型和 I、II、III 级三个虚拟模型的判定系数(R2)分别超过 0.78、0.85、0.92 和 0.95,相应的平均预测误差(MPE)分别为 0.42%、0.34%、0.24% 和 0.19%,平均百分比标准误差(MPSE)分别约为 22%、21%、15% 和 12%。20 种森林类型和 74 种亚类型的模型 R2 值分别高于 0.87 和 0.85,MPE 值分别低于 3% 和 5%。值得注意的是,以前对三种主要森林类型的生物量模型的估算显示出相当大的不确定性,TRE 从 -20% 到 74% 不等。不过,随着样本量的增加,准确性也有所提高。在总生物量和碳储量估算中,Ⅰ、Ⅱ、Ⅲ级虚拟模型的 R2 值逐渐增加,MPSE 和 MPE 值逐渐减少,而 TRE 接近零。本文开发的分层同步方程模型系统为精确评估不同尺度的生物量和碳储量提供了一个定量框架。为了提高估算的准确性,建议在可行的情况下,尤其是在全国范围内,采用三级模型进行估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信