1. POTENTIAL APPLICATION OF TERRESTRIAL LASER SCANNING FOR FUEL TYPE CHARACTERIZATION IN BROADLEAF MEDITERRANEAN FOREST Roberto Ferrara 2. LIDAR (Light Detection and Ranging o Laser Imaging Detection and Ranging) is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light ALS Lidar technology TLS Alghero, 26-29 May 2015 3. Forestry applications Alghero, 26-29 May 2015 Forest canopy, its major characteristics and the main biomass parameters - Seidel et al. 2011 4. Pros and cons Alghero, 26-29 May 2015 Non destructive measurements Data accuracy Highly detailed spatial distribution of biomass Permanent data collection Influence of wheather conditions Huge amounts of data Needs of algorithms and specific software High-cost instruments 5. Main aim of this study was : to set up a straightforward procedure for a precise estimation of main canopy forest characteristics for evergreen broadleaf. Specific tasks were: 1. to set up a procedure for automatic wood-leaf separation by using DBSCAN, clustering algorithm with TLS-based point-clouds; 2. to evaluate clustering algorithm potentials in direct sampling and quantification of canopy and wood structure. Main aims Alghero, 26-29 May 2015 6. Materials and methods Study Area Field measurements have been performed at an experimental farm located in Oristano (Sardinia Italy) owned by the University of Sassari. This area includes a collection of cork oak trees (Quercus suber L.). Sardinia Oristano Alghero, 26-29 May 2015 7. Leica HDS6100 Terrestrial laser scanner with phase-shift technology Materials and methods Alghero, 26-29 May 2015 The terrestrial laser scanner used 8. Materials and methods Alghero, 26-29 May 2015 Point clouds co-registration Cloud cleaning from spurious information CLOUD PROCESSING TLS scanning Trees scanning (point clouds acquisition) Target scanning FIELD SURVEY Voxels size Density threshold VOXELIZATION Parameters selection Adjacency threshold VOXELED TREE xc, yc, zc, avg.refl., n points, u, v, w ORIGINAL POINT CLOUDS WITH VOXEL INFO x, y, z, refl., u, v, w VOXELED TREE u, v, w LEGEND xc, yc, zc = voxel centroid georeferenced coordinates; avg. refl. = average of internal points reflectance; n points = voxel inside points; u, v, w = voxel relative coordinates; x, y, z = point georeferenced coordinates; Retrieval of forest structural parameters (canopy cover, ) DBSCAN Setting of two parameters Eps, MinPts POINTS CLOUDS CLUSTERING VOXELED TREE CLASSIFIED Trunk and branches >3cm Foliage and branches 3cm) Leaves and twigs ( 3cm) Leaves and twigs ( 3 cm Defolied tree Tree in natural condition, after elaboration 18. Results Alghero, 26-29 May 2015 Canopy density Tree N. of points Volume (m3) Voxels vs. volume 1 13.424.443 6,23 0,1298 2 18.346.030 7,78 0,1112 3 12.153.549 3,77 0,1406 4 13.545.040 8,87 0,0924 5 11.877.417 5,89 0,1279 6 18.478.426 5,49 0,1373 8 10.902.724 3,79 0,1429 The horizontal cross-section (2 cm thick) shows voxels occupied by point clouds and the virtual canopy perimeter (red line) 19. 0 0.2 0.4 0.6 0.8 1 1.42 1.82 2.22 2.62 3.02 3.42 3.82 4.22 -1000 -500 0 500 1000 Alghero, 26-29 May 2015 Results Vertical profile significantinterval Height(m) Canopy surface (m2) Biomass density (voxel/volume) Tree #1 Observation from crown base for whole crown height 20. Alghero, 26-29 May 2015 Results Tree # Total tree height (m) Crown base height (m) Crown length (m) Crown volume (m3 ) Canopy cover (m2 ) DBH (cm) Wood Volume branches > 3 cm (m3 ) 1 4.71 1.61 3.1 6.23 8.45 13.7 0.271 2 4.54 1.53 3.01 7.78 8.91 15.6 0.297 3 5.32 1.55 3.77 3.77 7.01 14.8 0.331 4 4.68 1.26 3.42 8.87 13.3 15.3 0.301 5 5.36 1.67 3.69 5.89 9.03 19.1 0.430 6 4.28 1.42 2.86 5.49 8.08 12.9 0.232 7 4.18 1.68 2.5 3.79 7.67 11.5 0.202 Quick results 21. Conclusion Alghero, 26-29 May 2015 This approach can be used to improve the accuracy and to calibrate actual methods of points cloud analysis, either from ground, drone or airborne. Except the first operation, the extraction of points belonging to the observed tree from the whole point cloud, all the other processes are fully automated. To obtain satisfactory results, this method needs only four parameters: Voxel dimension and density threshold; Epsilon and Min points for Dbscan clustering. The accuracy is related to the scanner resolution that influence the voxel dimension and the density threshold. 22. Acknowledgment Alghero, 26-29 May 2015 I would like to acknowledge the contribution of my colleagues of CNR IBIMET: Angelo Arca, Bachisio Arca, Pierpaolo Duce, Tiziano Ghisu, Pierpaolo Masia , Grazia Pellizzaro , Salvatore Virdis and Andrea Ventura. I would also like to acknowledge the support of Marcello Verdinelli of CNR ISE and Prof. Sandro Dettori of DipNet - University of Sassari. roberto.ferrara@cnr.it