Abstract: Several environmental and forest applications require detailed information on forest canopy structure. In particular, a correct characterization and classification of fuel needs accurate estimates of tree and crown attributes. Many fuel characteristics are often hard to measure operationally requiring manual field measurements and cutting. Terrestrial Laser Scanner (TLS), based on Lidar technology can be an effective alternative to overcome the limitations of the conventional ground based forest inventory techniques: time consuming, limited accuracy, destructive measurements. Lidar instruments emit laser beam and record the three-dimensional (3D) position of every laser pulse intercepted by plant material. This information is used to product a geo-referenced point cloud that can be used to generate three-dimensional (3D) representations of plants. The high-density 3D point data, can give information on vegetation structure and tree attributes more detailed than field-based measurement. Recent applications of TLS have been directed to detailed description of the canopy structure. However, the accuracy and applicability of TLS techniques for canopy characterization of broadleaf evergreen forests needs further investigations. In particular, estimation of tree attributes such as, canopy density, crown bulk density, branch size distribution etc. in non deciduous plants presupposes a correct separation between points representing woody material, leaves and small branches. The main objective of this research was to improve the estimate of both canopy density distribution and woody material volumes in evergreen broadleaf tree species by developing a semi-automatic segmentation method for separating wood points from leaf points. In the present work we tested the effectiveness of this method using cork oak trees. TLS data sets were collected in field by multiple scanning on six cork oak trees. After using noise reduction filters, the 3D point clouds were processed to obtain voxel based models of each tree. 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.
Application of Terrestrial Laser Scanning to estimate tree and crown attributes in evergreen broadleaf trees
AVentura;
2016
Abstract
Abstract: Several environmental and forest applications require detailed information on forest canopy structure. In particular, a correct characterization and classification of fuel needs accurate estimates of tree and crown attributes. Many fuel characteristics are often hard to measure operationally requiring manual field measurements and cutting. Terrestrial Laser Scanner (TLS), based on Lidar technology can be an effective alternative to overcome the limitations of the conventional ground based forest inventory techniques: time consuming, limited accuracy, destructive measurements. Lidar instruments emit laser beam and record the three-dimensional (3D) position of every laser pulse intercepted by plant material. This information is used to product a geo-referenced point cloud that can be used to generate three-dimensional (3D) representations of plants. The high-density 3D point data, can give information on vegetation structure and tree attributes more detailed than field-based measurement. Recent applications of TLS have been directed to detailed description of the canopy structure. However, the accuracy and applicability of TLS techniques for canopy characterization of broadleaf evergreen forests needs further investigations. In particular, estimation of tree attributes such as, canopy density, crown bulk density, branch size distribution etc. in non deciduous plants presupposes a correct separation between points representing woody material, leaves and small branches. The main objective of this research was to improve the estimate of both canopy density distribution and woody material volumes in evergreen broadleaf tree species by developing a semi-automatic segmentation method for separating wood points from leaf points. In the present work we tested the effectiveness of this method using cork oak trees. TLS data sets were collected in field by multiple scanning on six cork oak trees. After using noise reduction filters, the 3D point clouds were processed to obtain voxel based models of each tree. 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
