Remote sensing support for the gain-loss approach for greenhouse gas inventories

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMcRoberts, Ronald E.en_US
dc.contributor.authorNæsset, Eriken_US
dc.contributor.authorSannier, Christopheen_US
dc.contributor.authorStehman, Stephen V.en_US
dc.contributor.authorTomppo, Erkki O.en_US
dc.contributor.departmentDepartment of Electronics and Nanoengineeringen
dc.contributor.groupauthorJaan Praks Groupen
dc.contributor.organizationUniversity of Minnesota Twin Citiesen_US
dc.contributor.organizationNorwegian University of Life Sciencesen_US
dc.contributor.organizationSystèmes d’Information à Référence Spatialeen_US
dc.contributor.organizationState University of New Yorken_US
dc.date.accessioned2020-06-25T08:39:47Z
dc.date.available2020-06-25T08:39:47Z
dc.date.issued2020-06-01en_US
dc.description.abstractFor tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.en
dc.description.versionPeer revieweden
dc.format.extent18
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMcRoberts, R E, Næsset, E, Sannier, C, Stehman, S V & Tomppo, E O 2020, 'Remote sensing support for the gain-loss approach for greenhouse gas inventories', Remote Sensing, vol. 12, no. 11, 1891. https://doi.org/10.3390/rs12111891en
dc.identifier.doi10.3390/rs12111891en_US
dc.identifier.issn2072-4292
dc.identifier.otherPURE UUID: 79956025-4f51-4c7b-a0c3-cba672fd6a08en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/79956025-4f51-4c7b-a0c3-cba672fd6a08en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/43713726/remotesensing_12_01891_v2.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/45160
dc.identifier.urnURN:NBN:fi:aalto-202006254117
dc.language.isoenen
dc.publisherMDPI AG
dc.relation.ispartofseriesRemote Sensingen
dc.relation.ispartofseriesVolume 12, issue 11en
dc.rightsopenAccessen
dc.subject.keywordActivity dataen_US
dc.subject.keywordEmissions factoren_US
dc.subject.keywordIPCC good practice guidelinesen_US
dc.subject.keywordRemovals factoren_US
dc.subject.keywordStatistical estimatoren_US
dc.titleRemote sensing support for the gain-loss approach for greenhouse gas inventoriesen
dc.typeA2 Katsausartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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