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Article

A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees

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Département de Géomatique Appliquée, Centre d’Applications et de Recherches en Télédétection (CARTEL), Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
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Geophoton Inc., Montreal, QC H3X 2T3, Canada
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Ministry of Northern Development, Mines, Natural Resources and Forestry (Retired), North Bay, ON P1B 8G3, Canada
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Natural Resources Institute Finland (LUKE), P.O. Box 413, FI-90014 Oulu, Finland
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Département des Sciences Biologiques, Université du Québec à Montréal, Montreal, QC H2L 2C4, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Ali Khenchaf
Sensors 2022, 22(1), 35; https://doi.org/10.3390/s22010035
Received: 27 October 2021 / Revised: 7 December 2021 / Accepted: 20 December 2021 / Published: 22 December 2021
(This article belongs to the Section Radar Sensors)
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, 83–90%; 12 species, 46–54%; 4 species, 68–79%), 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. View Full-Text
Keywords: airborne lidar; tree species identification; multispectral lidar; single photon lidar; Random Forest; feature selection; individual tree crown delineation airborne lidar; tree species identification; multispectral lidar; single photon lidar; Random Forest; feature selection; individual tree crown delineation
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MDPI and ACS Style

Prieur, J.-F.; St-Onge, B.; Fournier, R.A.; Woods, M.E.; Rana, P.; Kneeshaw, D. A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees. Sensors 2022, 22, 35. https://doi.org/10.3390/s22010035

AMA Style

Prieur J-F, St-Onge B, Fournier RA, Woods ME, Rana P, Kneeshaw D. A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees. Sensors. 2022; 22(1):35. https://doi.org/10.3390/s22010035

Chicago/Turabian Style

Prieur, Jean-François, Benoît St-Onge, Richard A. Fournier, Murray E. Woods, Parvez Rana, and Daniel Kneeshaw. 2022. "A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees" Sensors 22, no. 1: 35. https://doi.org/10.3390/s22010035

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