{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T02:25:15Z","timestamp":1780107915667,"version":"3.54.0"},"reference-count":77,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T00:00:00Z","timestamp":1487808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Finnish Funding Agency for Technology and Innovation Tekes","award":["2208\/31\/2013"],"award-info":[{"award-number":["2208\/31\/2013"]}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["273806"],"award-info":[{"award-number":["273806"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Small 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.<\/jats:p>","DOI":"10.3390\/rs9030185","type":"journal-article","created":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T11:45:01Z","timestamp":1487850301000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":361,"title":["Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4826-2929","authenticated-orcid":false,"given":"Olli","family":"Nevalainen","sequence":"first","affiliation":[{"name":"Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-2145","authenticated-orcid":false,"given":"Eija","family":"Honkavaara","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sakari","family":"Tuominen","sequence":"additional","affiliation":[{"name":"Natural Resources Institute Finland, PL 2 00791 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6307-1637","authenticated-orcid":false,"given":"Niko","family":"Viljanen","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-4582","authenticated-orcid":false,"given":"Teemu","family":"Hakala","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Yu","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juha","family":"Hyypp\u00e4","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heikki","family":"Saari","sequence":"additional","affiliation":[{"name":"VTT Microelectronics, P.O. Box 1000, FI-02044 VTT, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5129-7364","authenticated-orcid":false,"given":"Ilkka","family":"P\u00f6l\u00f6nen","sequence":"additional","affiliation":[{"name":"Department of Mathematical Information Tech., University of Jyv\u00e4skyl\u00e4, P.O. Box 35, FI-40014 Jyv\u00e4skyl\u00e4, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-0567","authenticated-orcid":false,"given":"Nilton","family":"Imai","sequence":"additional","affiliation":[{"name":"Department of Cartography, Univ. Estadual Paulista (UNESP), Presidente Prudente, SP 19060-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0483-1103","authenticated-orcid":false,"given":"Antonio","family":"Tommaselli","sequence":"additional","affiliation":[{"name":"Department of Cartography, Univ. Estadual Paulista (UNESP), Presidente Prudente, SP 19060-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0034-4257(88)90028-4","article-title":"Estimating forest biomass and volume using airborne laser data","volume":"24","author":"Nelson","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"1","article-title":"Taper Curve and Volume Functions for Pine, Spruce and Birch","volume":"108","author":"Laasasenaho","year":"1982","journal-title":"Commun. Inst. 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