Potential of terrestrial laser scanning in Mediterranean broadleaf trees

Information on forest canopy structure, is required at a wide range of spatial scales for several environmental applications (ecosystem productivity model, ecological and forest management, disease and stress detection, fuel properties). Traditional techniques for measuring vegetation canopy structure normally involves destructive sampling at the plant level, manual field measurements and extrapolation of the measurements to canopy scale. In the last years airborne Light Detection and Ranging (Lidar) technology has been applied in forestry applications especially at landscape level. Lidar instruments emit pulses of laser light and measure the time interval taken for each pulse to come back. This information is used to product a geo-referenced point cloud that can be used to generate three-dimensional representations of objects. More recently, several authors have reported the use of Terrestrial Laser Scanner (TLS) for detailed description of the canopy structure (tree height, canopy density, leaf area density, crown density, etc.). TLS technology can represent an alternative to overcome the limitations of the conventional ground based forest inventory techniques: expensive, time consuming, limited accuracy. The high-density three-dimensional point data can give information on vegetation structure more detailed than field-based measurement. In addition, post-processing of TLS point clouds enables extensive analysis of data if automatic or semi-automatic methods are specifically developed. Recent applications of TLS data analysis have been focused on description of plant structure in coniferous forests. However, the operational use of TLS techniques for canopy characterization of broadleaf forests needs further investigations. In particular, segmentation between points representing woody material, leaves and small branches is a key factor to improve the accuracy of tree and canopy description. The main objective of this work was to develop a semi-automatic segmentation method of broadleaf tree species for improving the estimate of both canopy density distribution and woody material volumes. A voxel-based approach was developed and tested using a field TLS data set collected by multiple scanning on four cork oak trees. After using noise reduction filters, voxels were used as input to generate clusters through a point density algorithm. Clustering process led to the identification of wood and leaf voxels. Points belonging to each voxel were then classified and quantified as wood, foliage and noise. Experimental results show that the semi-automatic segmentation algorithm can accurately discriminate wood and foliage clusters and consequently give the points of cloud associated to foliage, trunk and main branches.