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Title: Estimating divergent forest carbon stocks and sinks via a knife set approach
Authors: Wang, Shitephen
Kobayashi, Keito
Takanashi, Satoru
Liu, Chiung-Pin
Li, Dian-Rong
Chen, San-Wen
Cheng, Yu-Ting
Moriguchi, Kai
Dannoura, Masako
Author's alias: ワン, シティフン
檀浦, 正子
Keywords: Aboveground net primary production
Belowground net primary production
Chaotic estimation
Hybrid machine learning
Phyllostachys edulis
Wandering through random forests
Issue Date: 15-Mar-2023
Publisher: Elsevier BV
Journal title: Journal of Environmental Management
Volume: 330
Thesis number: 117114
Abstract: Forest carbon stocks and sinks (CSSs) have been widely estimated using climate classification tables and linear regression (LR) models with common independent variables (IVs) such as the average diameter at breast height (DBH) of stems and root shoot ratio. However, this approach is relatively ineffective when the explanatory power of IVs is lower than that of unobservable variables. Various environmental and anthropogenic factors affect target variables that cause the correlation between them to be chaotic. Here, we designed a knife set (KS) approach combining LR models and the wandering through random forests (WTF) algorithm and applied it in a specific case of Phyllostachys edulis (Carrière) J. Houz. (P. edulis) forests, which have an irregular relationship between their belowground carbon (BGC) stocks and average DBH. We then validated the KS approach performed by cluster computing to estimate the aboveground carbon (AGC) and BGC stocks and the total net primary production (TNPP). The estimated CSSs were compared to the benchmark of the methodology that applied Tier 1 in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories via 10-fold cross validation, and the KS approach significantly increased precision and accuracy of estimations. Our approach provides general insights to accurately estimate forest CSSs relying on evidence-based field data, even if some target variables are divergent in specific forest types. We also pointed out the reason why current fancy models containing machine learning (ML) or deep learning algorithms are not effective in predicting the target variables of certain chaotic systems is perhaps that the total explanatory power of observable variables is less than that of the total unobservable variables. Quantifying unobservable variables into observable variables is a linchpin of future works related to chaotic system estimation.
Rights: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
The full-text file will be made open to the public on 15 March 2025 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/278828
DOI(Published Version): 10.1016/j.jenvman.2022.117114
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