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Research article - Peer-reviewed, 2014

Delineation of Tree Crowns and Tree Species Classification From Full-Waveform Airborne Laser Scanning Data Using 3-D Ellipsoidal Clustering

Lindberg, Eva; Eysn, Lothar; Hollaus, Markus; Holmgren, Johan; Pfeifer, Norbert


Individual tree crowns can be delineated from dense airborne laser scanning (ALS) data and their species can be classified from the spatial distribution and other variables derived from the ALS data within each tree crown. This study reports a new clustering approach to delineate tree crowns in three dimensions (3-D) based on ellipsoidal tree crown models (i.e., ellipsoidal clustering). An im-portant feature of this approach is the aim to derive information also about the understory vegeta-tion. The tree crowns are delineated from echoes derived from full-waveform (fwf) ALS data as well as discrete return ALS data with first and last returns. The ellipsoidal clustering led to an im-provement in the identification of tree crowns. Fwf ALS data offer the possibility to derive also the echo width and the amplitude in addition to the 3-D coordinates of each echo. In this study, tree species are classified from variables describing the fwf (i.e., the mean and standard deviation of the echo amplitude, echo width, and total number of echoes per pulse) and the spatial distribution of the clusters for pine, spruce, birch, oak, alder, and other species. Supervised classification is done for 68 field plots with leave-one-out cross-validation for one field plot at a time. The total accuracy was 71% when using both fwf and spatial variables, 60% when using only spatial variables, and 53% when using discrete return data. The improvement was greatest for discriminating pine and spruce as well as pine and birch.


Canopy layers; individual trees; light detection and ranging (lidar); single tree detection; three-dimensional (3-D) model

Published in

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2014, volume: 7, number: 7, article number: 2331276

Authors' information

Swedish University of Agricultural Sciences, Department of Forest Resource Management
Eysn, Lothar
Vienna University of Technology (TU Wien)
Hollaus, Markus
Vienna University of Technology (TU Wien)
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Pfeifer, Norbert
Vienna University of Technology (TU Wien)

Associated SLU-program


Sustainable Development Goals

SDG15 Life on land

UKÄ Subject classification

Remote Sensing
Forest Science

Publication Identifiers


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