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A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density

文献类型：期刊论文

作者Wang, Zhaosheng2; Gong, He2; Huang, Mei2; Gu, Fengxue4; Wei, Jie2; Guo, Qingchun1; Song, Wenchao3
刊名METHODS IN ECOLOGY AND EVOLUTION
出版日期2021-10-30
页码16
关键词multimodel multimodel ensemble mean (MMEM) method multimodel random forest ensemble (MMRFE) method national scale terrestrial vegetation carbon density
ISSN号2041-210X
DOI10.1111/2041-210X.13729
通讯作者Huang, Mei(huangm@igsnrr.ac.cn)
英文摘要Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national-scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method. Thus, using the state-of-the-art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982-2010. Compared with the nationally averaged TVCD field investigation data (27 +/- 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%-85.2%, and the remaining models were underestimated by 3.7%-77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD. In conclusion, the MMRFE method can effectively improve the accuracy of national-scale TVCD estimation, and effectively reduce the uncertainty of large-scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method that uses a machine learning approach to mine multimodel terrestrial carbon information to reduce the uncertainty in the estimation of terrestrial ecosystem carbon components.
WOS关键词MODEL ; ECOSYSTEM ; RAINFALL
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA20010202] ; National Key R&D Program of China[2017YFC0503905] ; Second Tibetan Plateau Scientific Expedition and Research[2019QZKK0305] ; National Natural Science Foundation of China[41801082] ; National Natural Science Foundation of China[41971135] ; Key Library of Eco-Environment and Meteorology Qinling Mountains and Loess Plateau[2020G-22]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000712880800001
出版者WILEY
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; National Key R&D Program of China ; Second Tibetan Plateau Scientific Expedition and Research ; National Natural Science Foundation of China ; Key Library of Eco-Environment and Meteorology Qinling Mountains and Loess Plateau
源URL[http://ir.igsnrr.ac.cn/handle/311030/167474]  
专题中国科学院地理科学与资源研究所
通讯作者Huang, Mei
作者单位1.Liaocheng Univ, Sch Environm & Planning, Liaocheng, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
3.Shangluo Meteorol Bur, Shangluo, Peoples R China
4.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Key Lab Dryland Agr, Minist Agr, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhaosheng,Gong, He,Huang, Mei,et al. A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density[J]. METHODS IN ECOLOGY AND EVOLUTION,2021:16.
APA Wang, Zhaosheng.,Gong, He.,Huang, Mei.,Gu, Fengxue.,Wei, Jie.,...&Song, Wenchao.(2021).A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density.METHODS IN ECOLOGY AND EVOLUTION,16.
MLA Wang, Zhaosheng,et al."A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density".METHODS IN ECOLOGY AND EVOLUTION (2021):16.

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来源：地理科学与资源研究所

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