Multi-tree woody structure reconstruction from mobile terrestrial laser scanner point clouds based on a dual neighbourhood connectivity graph algorithm
A process is presented for the vector reconstruction of fruit plantations based on the model developed by Verroust and Lazarus. To solve occlusion problems, the use of a dual graph of local and extended connectivity is proposed. The process allows vegetation variables such as the length and volume o...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/58099 |
| Acceso en línea: | https://doi.org/10.1016/j.biosystemseng.2016.04.013 http://hdl.handle.net/10459.1/58099 |
| Access Level: | acceso abierto |
| Palabra clave: | Multi-tree reconstruction LiDAR Mobile terrestrial laser scanner Point cloud Tree training Ligneous structure |
| Sumario: | A process is presented for the vector reconstruction of fruit plantations based on the model developed by Verroust and Lazarus. To solve occlusion problems, the use of a dual graph of local and extended connectivity is proposed. The process allows vegetation variables such as the length and volume of the ligneous structure to be measured, enabling studies such as intensity of pruning operations. The process has been tested against simulated models and real trees with different training systems: open-vase system (peach trees) and central leader hedgerow system (pear trees). The cost of the algorithm will be given by the cost of the implementation of Dijkstra's algorithm, which in its standard version is of potential (O(n2)). Algorithm accuracy was checked against point clouds of virtual trees. The reconstruction was also applied before and after a pruning operation of real trees to enable a study of the evolution of the vegetation indices. Results showed the algorithm to be suitable for multi-tree reconstruction of both central leader and open-vase training systems. |
|---|