Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

Show simple item record Kuzmin, Anton Korhonen, Lauri Kivinen, Sonja Hurskainen, Pekka Korpelainen, Pasi Tanhuanpää, Topi Maltamo, Matti Vihervaara, Petteri Kumpula, Timo 2021-05-12T11:56:01Z 2021-05-12T11:56:01Z 2021-04-29
dc.identifier.citation Kuzmin , A , Korhonen , L , Kivinen , S , Hurskainen , P , Korpelainen , P , Tanhuanpää , T , Maltamo , M , Vihervaara , P & Kumpula , T 2021 , ' Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests ' , Remote Sensing , vol. 13 , no. 9 , 1723 .
dc.identifier.other PURE: 162793819
dc.identifier.other PURE UUID: bc5a1d71-1f9e-46da-ac78-c3625da42064
dc.identifier.other ORCID: /0000-0003-1039-3357/work/93741322
dc.identifier.other WOS: 000650731000001
dc.description.abstract European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests. fi
dc.format.extent 18
dc.language.iso eng
dc.relation.ispartof Remote Sensing
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 1171 Geosciences
dc.subject tree species classification
dc.subject European aspen
dc.subject UAV
dc.subject biodiversity
dc.subject deciduous trees
dc.subject machine learning
dc.subject multispectral data
dc.subject boreal forest
dc.subject OLD-GROWTH
dc.subject LIDAR DATA
dc.subject IMAGERY
dc.subject VEGETATION
dc.subject INVENTORY
dc.title Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests en
dc.type Article
dc.contributor.organization Earth Change Observation Laboratory (ECHOLAB)
dc.contributor.organization Department of Geosciences and Geography
dc.contributor.organization Department of Forest Sciences
dc.description.reviewstatus Peer reviewed
dc.relation.issn 2072-4292
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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