Individual tree detection and classification with UAV-Based photogrammetric point clouds and hyperspectral imaging

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dc.contributor.authorNevalainen, Olli
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorTuominen, Sakari
dc.contributor.authorViljanen, Niko
dc.contributor.authorHakala, Teemu
dc.contributor.authorYu, Xiaowei
dc.contributor.authorHyyppä, Juha
dc.contributor.authorSaari, Heikki
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorImai, Nilton N. [UNESP]
dc.contributor.authorTommaselli, Antonio M.G. [UNESP]
dc.contributor.institutionNational Land Survey of Finland
dc.contributor.institutionNatural Resources Institute Finland
dc.contributor.institutionVTT Microelectronics
dc.contributor.institutionUniversity of Jyväskylä
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:11:58Z
dc.date.available2018-12-11T17:11:58Z
dc.date.issued2017-03-01
dc.description.abstractSmall unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.en
dc.description.affiliationFinnish Geospatial Research Insititute National Land Survey of Finland, Geodeetinrinne 2
dc.description.affiliationNatural Resources Institute Finland
dc.description.affiliationVTT Microelectronics, P.O. Box 1000
dc.description.affiliationDepartment of Mathematical Information Tech University of Jyväskylä, P.O. Box 35
dc.description.affiliationDepartment of Cartography Univ. Estadual Paulista (UNESP)
dc.description.affiliationUnespDepartment of Cartography Univ. Estadual Paulista (UNESP)
dc.description.sponsorshipSuomen Akatemia
dc.description.sponsorshipIdSuomen Akatemia: 273806
dc.identifierhttp://dx.doi.org/10.3390/rs9030185
dc.identifier.citationRemote Sensing, v. 9, n. 3, 2017.
dc.identifier.doi10.3390/rs9030185
dc.identifier.file2-s2.0-85019363955.pdf
dc.identifier.issn2072-4292
dc.identifier.scopus2-s2.0-85019363955
dc.identifier.urihttp://hdl.handle.net/11449/174586
dc.language.isoeng
dc.relation.ispartofRemote Sensing
dc.relation.ispartofsjr1,386
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectClassification
dc.subjectForest
dc.subjectHyperspectral
dc.subjectPhotogrammetry
dc.subjectPoint cloud
dc.subjectRadiometry
dc.subjectUAV
dc.titleIndividual tree detection and classification with UAV-Based photogrammetric point clouds and hyperspectral imagingen
dc.typeArtigo
unesp.author.lattes5493428631948910[11]
unesp.author.orcid0000-0003-0483-1103[11]

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