Potential application of terrestrial laser scanning for fuel type characterization in broadleaf Mediterranean forest

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). Aerial laser scanner (ALS) demonstrated to be a promising techniques and an important source of accurate data and information in forestry studies. More recently several studies reported different potential applications of the terrestrial laser scanner (TLS) for forest stand and canopy variables estimation. The two systems can be seen as mutual: the plotwise forest data, collected through TLS, can be used as a reference for the calibration of large-area inventory data measured by aerial and space-borne remote sensing techniques, by ALS or multispectral scanners. TLS technology can represent an alternative to overcome limitations of the conventional ground based techniques, that are time consuming and characterized by a limited accuracy. TLS allows the collection of high resolution point clouds, which can potentially and productively be used to derive tree attributes. Post-processing of TLS point clouds enables extensive analysis of data by automatic or semi-automatic methods specifically developed. However, the operational use of TLS techniques for vegetation characterization of broadleaf forests needs further investigations. In particular, discriminating between laser pulses that were intercepted by 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 segmentation method for broadleaf tree species in order to automatically (or semi-automatically) extract branches and stems from foliage. A voxel-based approach was developed and tested using a TLS data set collected in field 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 segmentation algorithm can accurately discriminate wood and foliage clusters and consequently give the points of cloud associated to foliage, trunk and main branches. In conclusion, the method proposed seems to be a promising approach for improving the estimate of both canopy density distribution and woody material volumes In addition, our findings suggest that the terrestrial laser scanner (TLS) can be conveniently applied to characterize forest canopy fuel characteristics at plot level.