Classification of tree species based on structural features derived from high density LiDAR data

Automated tree species classification using high density airborne light detection and ranging (LiDAR) data will support more precise forest inventory but further research is required to improve the associated methods. Most existing methods rely on geometric and vertical distribution features, which often do not accurately represent the internal foliage and branch patterns of an individual tree. Our study objective was to develop novel algorithms to characterize internal structures of an individual tree crown and to test their effectiveness for use in classifying tree species. We derived several LiDAR features to describe the three-dimensional texture, foliage clustering degree relative to tree envelop, foliage clustering scale, and gap distribution of an individual tree in both horizontal and vertical directions. Features were selected using a genetic algorithm and then tree species were classified using linear discriminant analysis based on the selected features. The four species, sugar maple (Acer saccharum Marsh.), trembling aspen (Populus tremuloides Michx.), jack pine (Pinus banksiana Lamb.) and eastern white pine (Pinus strobus L.), were classified with an overall accuracy of 77.5% and a Kappa coefficient of 0.7. The results demonstrate the significance of the derived structural features as aids to classify tree species. Our investigation also showed a positive linear correlation (R2 = 0.88) between LiDAR point density and species classification accuracy.

► Developed novel LiDAR features describing horizontal and vertical tree structures.
► A species classification framework combing segmentation and genetic algorithm.
► Relative degree and scale of foliage clustering features were proved to be significant for species classification.
► Individual-tree species classification were achieved with 77.5% overall accuracy.
► Effects of LiDAR point density on the classification accuracy – linear trends.