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dc.contributor.authorTuominen, Sakari
dc.contributor.authorBalazs, Andras
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorSaari, Heikki
dc.contributor.authorHakala, Teemu
dc.contributor.authorViljanen, Niko
dc.date.accessioned2017-10-04T10:49:19Z
dc.date.available2017-10-04T10:49:19Z
dc.date.issued2017
dc.identifier.citationTuominen, S., Balazs, A., Honkavaara, E., Pölönen, I., Saari, H., Hakala, T., & Viljanen, N. (2017). Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. <em>Silva Fennica</em>, 51 (5), 7721. <a href="https://doi.org/10.14214/sf.7721">doi:10.14214/sf.7721</a>
dc.identifier.otherTUTKAID_75189
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55555
dc.description.abstractRemote sensing using unmanned aerial vehicle (UAV) -borne sensors is currently a highly interesting approach for the estimation of forest characteristics. 3D remote sensing data from airborne laser scanning or digital stereo photogrammetry enable highly accurate estimation of forest variables related to the volume of growing stock and dimension of the trees, whereas recognition of tree species dominance and proportion of different tree species has been a major complication in remote sensing-based estimation of stand variables. In this study the use of UAV-borne hyperspectral imagery was examined in combination with a high-resolution photogrammetric canopy height model in estimating forest variables of 298 sample plots. Data were captured from eleven separate test sites under weather conditions varying from sunny to cloudy and partially cloudy. Both calibrated hyperspectral reflectance images and uncalibrated imagery were tested in combination with a canopy height model based on RGB camera imagery using the k-nearest neighbour estimation method. The results indicate that this data combination allows accurate estimation of stand volume, mean height and diameter: the best relative RMSE values for those variables were 22.7%, 7.4% and 14.7%, respectively. In estimating volume and dimension-related variables, the use of a calibrated image mosaic did not bring significant improvement in the results. In estimating the volumes of individual tree species, the use of calibrated hyperspectral imagery generally brought marked improvement in the estimation accuracy; the best relative RMSE values for the volumes for pine, spruce, larch and broadleaved trees were 34.5%, 57.2%, 45.7% and 42.0%, respectively.
dc.language.isofin
dc.publisherSuomen metsätieteellinen seura
dc.relation.ispartofseriesSilva Fennica
dc.subject.otherforests
dc.subject.othervariables
dc.subject.otherphotogrammetry
dc.subject.otheraerial imagery
dc.subject.otherhyperspectral imaging
dc.subject.otherforest inventory
dc.subject.otherradiometric calibration
dc.subject.otherUAVs
dc.subject.otherdigital photogrammetry
dc.subject.otherstereo-photogrammetric canopy modelling
dc.titleHyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201710023903
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-10-02T15:15:19Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.relation.issn0037-5330
dc.relation.volume51
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2017. This is an open access article distributed under the terms of a Creative Commons License.
dc.rights.accesslevelopenAccessfi
dc.rights.urlhttps://creativecommons.org/licenses/by-sa/4.0/
dc.relation.doi10.14214/sf.7721


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© the Authors, 2017. This is an open access article distributed under the terms of a Creative Commons License.
Except where otherwise noted, this item's license is described as © the Authors, 2017. This is an open access article distributed under the terms of a Creative Commons License.