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Exploring Multispectral ALS Data for Tree Species Classification

Axelsson, Arvid and Lindberg, Eva and Olsson, Håkan (2018). Exploring Multispectral ALS Data for Tree Species Classification. Remote Sensing. 10 , 183
[Research article]

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Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58 degrees 2718.35N, 13 degrees 398.03E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user's and producer's accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification.

Authors/Creators:Axelsson, Arvid and Lindberg, Eva and Olsson, Håkan
Title:Exploring Multispectral ALS Data for Tree Species Classification
Series Name/Journal:Remote Sensing
Year of publishing :2018
Article number:183
Number of Pages:15
Additional Information:Erratum published on 3 April 2018, see Remote Sens. 2018, 10(4), 548; https://doi.org/10.3390/rs10040548
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:LiDAR, indvidual trees, ITC, spectral
Permanent URL:
Additional ID:
Type of IDID
Web of Science (WoS)000427542100030
ID Code:23356
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:27 Apr 2021 11:04
Metadata Last Modified:23 Feb 2022 21:08

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