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dc.contributor.authorYrttimaa, Tuomas
dc.contributor.authorSaarinen, Ninni
dc.contributor.authorKankare, Ville
dc.contributor.authorViljanen, Niko
dc.contributor.authorHynynen, Jari
dc.contributor.authorHuuskonen, Saija
dc.contributor.authorHolopainen, Markus
dc.contributor.authorHyyppä, Juha
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorVastaranta, Mikko
dc.date.accessioned2020-06-22T06:41:55Z
dc.date.available2020-06-22T06:41:55Z
dc.date.issued2020
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/8198
dc.description.abstractTerrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.
dc.language.isoenglanti
dc.publisherMDPI AG
dc.relation.ispartofseriesIsprs international journal of geo-information
dc.relation.urihttp://dx.doi.org/10.3390/ijgi9050309
dc.rightsCC BY http://creativecommons.org/licenses/by/4.0/
dc.subjectterrestrial laser scanning
dc.subjectunmanned aerial vehicle
dc.subjectimage matching
dc.subjectremote sensing
dc.subjectforest inventory
dc.titleMultisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands
dc.description.versionpublished version
dc.contributor.departmentSchool of Forest Sciences, activities
uef.solecris.id71142355en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© 2020 by the authors
dc.relation.doi10.3390/ijgi9050309
dc.description.reviewstatuspeerReviewed
dc.format.pagerange309
dc.publisher.countrySveitsi
dc.relation.issue5
dc.relation.volume9
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessOpen access -julkaisukanavassa ilmestynyt julkaisu


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