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dc.contributor.authorRees, WG
dc.contributor.authorTomaney, J
dc.contributor.authorTutubalina, O
dc.contributor.authorZharko, V
dc.contributor.authorBartalev, S
dc.date.accessioned2021-11-10T00:30:37Z
dc.date.available2021-11-10T00:30:37Z
dc.date.issued2021
dc.identifier.issn2072-4292
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330519
dc.description.abstract<jats:p>Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.</jats:p>
dc.description.sponsorshipBritish Council; Ministry of Science and Higher Education of the Russian Federation; EU Transnational Access Interact scheme; UK Foreign Commonwealth and Development Office
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleEstimation of boreal forest growing stock volume in russia from sentinel-2 msi and land cover classification
dc.typeArticle
prism.issueIdentifier21
prism.publicationDate2021
prism.publicationNameRemote Sensing
prism.volume13
dc.identifier.doi10.17863/CAM.77962
dcterms.dateAccepted2021-11-04
rioxxterms.versionofrecord10.3390/rs13214483
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-11-01
dc.contributor.orcidRees, Gareth [0000-0001-6020-1232]
dc.identifier.eissn2072-4292
rioxxterms.typeJournal Article/Review
pubs.funder-project-idBritish Council (352397111)
pubs.funder-project-idForeign and Commonwealth Office (INT-T5-02)
pubs.funder-project-idForeign and Commonwealth Office (Unknown)
cam.issuedOnline2021-11-08
cam.orpheus.successTue Feb 01 19:02:13 GMT 2022 - Embargo updated
cam.orpheus.counter2
rioxxterms.freetoread.startdate2021-11-08


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International