A Geometric Method for Wood-Leaf Separation Using Terrestrial and Simulated Lidar Data

Terrestrial light detection and ranging (lidar) can be used to record the three-dimensional structures of trees. Wood-leaf separation, which aims to classify lidar points into wood and leaf components, is an essential prerequisite for deriving individual tree characteristics. Previous research has tended to use intensity (including a multi-wavelength approach) and waveform information for wood-leaf separation, but use of the most fundamental information from a lidar point cloud, i.e., the x-, y-, and z- coordinates of each point, for this purpose has been poorly explored. In this study, we introduce a geometric method for wood-leaf separation using the x-, y-, and z- coordinates of each point. The separation results indicate that first-, second-, and third-order branches can be extracted from the raw point cloud by this new method, suggesting that it might provide a promising solution for wood-leaf separation.