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Erratum published on 3 April 2018, see Remote Sens. 2018, 10(4), 548.

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Remote Sens. 2018, 10(2), 183; https://doi.org/10.3390/rs10020183

Exploring Multispectral ALS Data for Tree Species Classification

Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), 901 83 Umeå, Sweden
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Received: 15 November 2017 / Revised: 19 January 2018 / Accepted: 23 January 2018 / Published: 26 January 2018
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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Abstract

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°27′18.35″N, 13°39′8.03″E) 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. View Full-Text
Keywords: LiDAR; indvidual trees; ITC; spectral LiDAR; indvidual trees; ITC; spectral
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Axelsson, A.; Lindberg, E.; Olsson, H. Exploring Multispectral ALS Data for Tree Species Classification. Remote Sens. 2018, 10, 183.

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