Individual tree species classification using structure features from high density airborne lidar data

The paper investigated the advantage of high density airborne LiDAR data for improving species classification of individual tree. The investigation is comprised of two stages, feature extraction and classification. Several feature metrics were derived from LiDAR data, most of which were to characterize the vertical structural properties of difference species. Some other metrics were calculated statistically from intensity and return number information. A supervised decision tree algorithm was applied on the extracted features to perform both feature selection and classification. Two classification themes were carried out: classification of coniferous and deciduous trees, and classification of five species. Experiment was conducted in Canadian boreal forests dominated by mature trees. The results demonstrated LiDAR derived vertical profile metrics are capable for species classification either to separate coniferous and deciduous or to separate multiple species. The best overall classification accuracy is 81.7% validated by using the test data from the same ecosystem as the training data.