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New forest aboveground biomass maps of China integrating multiple datasets

hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
hal.structure.identifierUniversity of Chinese Academy of Sciences [Beijing] [UCAS]
dc.contributor.authorCHANG, Zhongbing
hal.structure.identifierUniversity of South Wales [USW]
dc.contributor.authorHOBEICHI, Sanaa
hal.structure.identifierUniversity of South Wales [USW]
dc.contributor.authorWANG, Ying-Ping
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorTANG, Xuli
hal.structure.identifierUniversity of South Wales [USW]
dc.contributor.authorABRAMOWITZ, Gab
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
hal.structure.identifierUniversity of Chinese Academy of Sciences [Beijing] [UCAS]
dc.contributor.authorCHEN, Yang
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
hal.structure.identifierUniversity of Chinese Academy of Sciences [Beijing] [UCAS]
dc.contributor.authorCAO, Nannan
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorYU, Mengxiao
hal.structure.identifierSun Yat-sen University [Guangzhou] [SYSU]
dc.contributor.authorHUANG, Huabing
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
hal.structure.identifierNanjing University of Information Science and Technology [NUIST]
dc.contributor.authorZHOU, Guoyi
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorWANG, Genxu
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorMA, Keping
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorDU, Sheng
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorLI, Shenggong
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorHAN, Shijie
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorMA, Youxin
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorJ.-P., Wigneron
hal.structure.identifierSouthwest University [Chongqing]
dc.contributor.authorFAN, Lei
hal.structure.identifierJet Propulsion Laboratory [JPL]
dc.contributor.authorSAATCHI, Sassan
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorYAN, Junhua
dc.date.accessioned2024-04-08T11:50:15Z
dc.date.available2024-04-08T11:50:15Z
dc.date.issued2021-07-23
dc.identifier.issn2072-4292
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195377
dc.description.abstractEnMapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (-4.6 and -3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.
dc.language.isoen
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enfield measurements
dc.subject.enremote sensing
dc.subject.enChina
dc.subject.enforest aboveground biomass
dc.subject.encarbon stock
dc.title.enNew forest aboveground biomass maps of China integrating multiple datasets
dc.typeArticle de revue
dc.identifier.doi10.3390/rs13152892
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing
bordeaux.page1-20
bordeaux.volume13
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.issue15
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03358038
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03358038v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing&rft.date=2021-07-23&rft.volume=13&rft.issue=15&rft.spage=1-20&rft.epage=1-20&rft.eissn=2072-4292&rft.issn=2072-4292&rft.au=CHANG,%20Zhongbing&HOBEICHI,%20Sanaa&WANG,%20Ying-Ping&TANG,%20Xuli&ABRAMOWITZ,%20Gab&rft.genre=article


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