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dc.contributor.authorYrttimaa, Tuomas
dc.contributor.authorSaarinen, Ninni
dc.contributor.authorLuoma, Ville
dc.contributor.authorTanhuanpää, Topi
dc.contributor.authorKankare, Ville
dc.contributor.authorLiang, Xinlian
dc.contributor.authorHyyppä, Juha
dc.contributor.authorHolopainen, Markus
dc.contributor.authorVastaranta, Mikko
dc.date.accessioned2019-12-13T12:28:35Z
dc.date.available2019-12-13T12:28:35Z
dc.date.issued2019
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/7870
dc.description.abstractDead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included (1) RANSAC-cylinder filtering, (2) point cloud rasterization, (3) raster image segmentation, and (4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m3/ha (59.3%), which was reduced to 6.4 m3/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions.
dc.language.isoenglanti
dc.publisherElsevier BV
dc.relation.ispartofseriesIsprs journal of photogrammetry and remote sensing
dc.relation.urihttp://dx.doi.org/10.1016/j.isprsjprs.2019.03.007
dc.rightsCC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTLS
dc.subjectbiodiversity
dc.subjectpoint cloud
dc.subjectcoarse woody debris
dc.subjectCWD
dc.subjectground-based
dc.subjectLiDAR
dc.titleDetecting and characterizing downed dead wood using terrestrial laser scanning
dc.description.versionfinal draft
dc.contributor.departmentSchool of Forest Sciences, activities
uef.solecris.id61156573en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
dc.relation.doi10.1016/j.isprsjprs.2019.03.007
dc.description.reviewstatuspeerReviewed
dc.format.pagerange76-90
dc.publisher.countryAlankomaat
dc.relation.issn0924-2716
dc.relation.volume151
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessEi


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