{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T18:11:07Z","timestamp":1774375867870,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["CRDPJ 462973 \u2013 14"],"award-info":[{"award-number":["CRDPJ 462973 \u2013 14"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood\/Softwood, 83\u201390%; 12 species, 46\u201354%; 4 species, 68\u201379%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood\/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.<\/jats:p>","DOI":"10.3390\/s22010035","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T02:02:57Z","timestamp":1640224977000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees"],"prefix":"10.3390","volume":"22","author":[{"given":"Jean-Fran\u00e7ois","family":"Prieur","sequence":"first","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beno\u00eet","family":"St-Onge","sequence":"additional","affiliation":[{"name":"Geophoton Inc., Montreal, QC H3X 2T3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard A.","family":"Fournier","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murray E.","family":"Woods","sequence":"additional","affiliation":[{"name":"Ministry of Northern Development, Mines, Natural Resources and Forestry (Retired), North Bay, ON P1B 8G3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2578-9680","authenticated-orcid":false,"given":"Parvez","family":"Rana","sequence":"additional","affiliation":[{"name":"Natural Resources Institute Finland (LUKE), P.O. Box 413, FI-90014 Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Kneeshaw","sequence":"additional","affiliation":[{"name":"D\u00e9partement des Sciences Biologiques, Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al, Montreal, QC H2L 2C4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s40725-015-0019-3","article-title":"Integrating Data from Discrete Return Airborne LiDAR and Optical Sensors to Enhance the Accuracy of Forest Description: A Review","volume":"1","author":"Xu","year":"2015","journal-title":"Curr. For. 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