Publication:
Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo

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dc.contributor.authorXu, L.
dc.contributor.authorSaatchi, S.S.
dc.contributor.authorShapiro, A.
dc.contributor.authorMeyer, V.
dc.contributor.authorFerraz, A.
dc.contributor.authorYang, Y.
dc.contributor.authorBastin, J.F.
dc.contributor.authorBanks, N.
dc.contributor.authorBoeckx, P.
dc.contributor.authorVerbeeck, H.
dc.contributor.authorLewis, S.L.
dc.contributor.authorMuanza, E.T.
dc.contributor.authorBongwele, E.
dc.contributor.authorKayembe, F.
dc.contributor.authorMbenza, D.
dc.contributor.authorKalau, L.
dc.contributor.authorMukendi, F.
dc.contributor.authorIlunga, F.
dc.contributor.authorEbuta, D.
dc.date.accessioned2022-01-23T18:58:36Z
dc.date.available2022-01-23T18:58:36Z
dc.identifier.urihttps://open.fsc.org/handle/resource/1091
dc.titleSpatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congoen
dcterms.abstractNational forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the frst time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed across key forest types in DRC. LiDAR samples provided estimates of mean and uncertainty of aboveground carbon density at provincial scales and were combined with optical and radar satellite imagery in a machine learning algorithm to map forest height and carbon density over the entire country. By using the forest defnition of DRC, we found a total of 23.3±1.6 GtC carbon with a mean carbon density of 140±9 MgC ha?1 in the aboveground and belowground live trees. The probability based LiDAR samples capture variations of structure and carbon across edaphic and climate conditions, and provide an alternative approach to national ground inventory for efcient and precise assessment of forest carbon resources for emission reduction (ER) programs.en
dcterms.accessRightsPublic
dcterms.accessRightsOpen access
dcterms.bibliographicCitationXu, L., Saatchi, S.S., Shapiro, A., Meyer, V., Ferraz, A., Yang, Y., Bastin, J.F., Banks, N., Boeckx, P., Verbeeck, H. and Lewis, S.L., 2017. Spatial distribution of carbon stored in forests of the Democratic Republic of Congo. Scientific reports, 7(1), pp.1-12.en
dcterms.issued2017
dcterms.languageen
dcterms.licenseCC-BY-4.0en
dcterms.typeJournal Article
dspace.entity.typePublication
is.availability.fullTextFull text available
is.contributor.funderTypePrivate funds (NGOs, companies, VSS self-funded etc)
is.coverage.countryDemocratic Republic of the Congo
is.coverage.geographicLevelRegion
is.coverage.latitude-4.038333
is.coverage.longitude21.758664
is.coverage.regionAfrica
is.evaluation.collectionMapping e.g. remote sensing
is.evaluation.dataSourceGeospatial data layers
is.evidenceSubTypeModeling study - patterns at a large scale using context variables
is.evidenceTypeModeling study
is.focus.productsOther forestry and logging
is.focus.sdgSDG 13 - Climate Action
is.focus.sectorsAgriculture
is.focus.sectorsForestry
is.focus.sustainDimensionEnvironmental
is.focus.sustainDimensionEconomic
is.focus.sustainIssueClimate change
is.focus.sustainLensInnovation / innovative solutions
is.focus.sustainOutcomeCarbon sequestration
is.focus.systemElementMandE outcomes and impacts
is.focus.systemElementMandE performance monitoring
is.identifier.codeImpacts
is.identifier.doihttps://doi.org/10.1038/s41598-017-15050-z
is.identifier.fscdoihttp://dx.doi.org/10.34800/fsc-international715
is.identifier.schemeTypeVoluntary Sustainability Standards
is.item.reviewStatusPeer reviewed
is.journalNameScientific Reports
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