Automatic Forest Species Classification using Combined LIDAR Data and Optical Imagery

Forest species classification is important for forest management and environment monitoring and protection. As the conventional methods are mainly based on the spectral signatures of forest canopies and the results are at stand level. With the high spatial resolution data, classification at individual tree level becomes achievable. The objective of this study is to develop a novel algorithm of tree crown segmentation for automatic classification at individual tree level. The approach uses the integrating laser scanning data with high spatial multispectral optical imagery. The automatic classification is composed of two stages: (1) tree crown segmentation, (2) species classification. The approach is applied to the test area consisting of both conifers and deciduous trees. The segmentation result shows good accuracy of crown delineation, and over 90% of the trees are segmented correctly.