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Article: A review of regional and global gridded forest biomass datasets

TitleA review of regional and global gridded forest biomass datasets
Authors
KeywordsField biomass
Forest biomass maps
Large-scale mapping
Remotely sensed data
Uncertainty analysis
Issue Date2019
Citation
Remote Sensing, 2019, v. 11, n. 23, article no. 2744 How to Cite?
AbstractForest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.
Persistent Identifierhttp://hdl.handle.net/10722/321866
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Yuzhen-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorYang, Lu-
dc.date.accessioned2022-11-03T02:21:58Z-
dc.date.available2022-11-03T02:21:58Z-
dc.date.issued2019-
dc.identifier.citationRemote Sensing, 2019, v. 11, n. 23, article no. 2744-
dc.identifier.urihttp://hdl.handle.net/10722/321866-
dc.description.abstractForest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.-
dc.languageeng-
dc.relation.ispartofRemote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectField biomass-
dc.subjectForest biomass maps-
dc.subjectLarge-scale mapping-
dc.subjectRemotely sensed data-
dc.subjectUncertainty analysis-
dc.titleA review of regional and global gridded forest biomass datasets-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/rs11232744-
dc.identifier.scopuseid_2-s2.0-85076579624-
dc.identifier.volume11-
dc.identifier.issue23-
dc.identifier.spagearticle no. 2744-
dc.identifier.epagearticle no. 2744-
dc.identifier.eissn2072-4292-
dc.identifier.isiWOS:000508382100025-

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