Extracting individual trees from lidar point clouds using treeseg
- Author
- Andrew Burt, Mathias Disney and Kim Calders (UGent)
- Organization
- Project
- Abstract
- Recent studies have demonstrated the potential of lidar-derived methods in plant ecology and forestry. One limitation to these methods is accessing the information content of point clouds, from which tree-scale metrics can be retrieved. This is currently undertaken through laborious and time-consuming manual segmentation of tree-level point clouds from larger-area point clouds, an effort that is impracticable across thousands of stems. Here, we present treeseg, an open-source software to automate this task. This method utilises generic point cloud processing techniques including Euclidean clustering, principal component analysis, region-based segmentation, shape fitting and connectivity testing. This data-driven approach uses few a priori assumptions of tree architecture, and transferability across lidar instruments is constrained only by data quality requirements. We demonstrate the treeseg algorithm here on data acquired from both a structurally simple open forest and a complex tropical forest. Across these data, we successfully automatically extract 96% and 70% of trees, respectively, with the remainder requiring some straightforward manual segmentation. treeseg allows ready and quick access to tree-scale information contained in lidar point clouds. treeseg should help contribute to more wide-scale uptake of lidar-derived methods to applications ranging from the estimation of carbon stocks through to descriptions of plant form and function.
- Keywords
- extraction, forests, laser scanning, lidar, point cloud, segmentation, trees, TERRESTRIAL, LASER, cavelab
Downloads
-
2019 Burt MEE.pdf
- full text
- |
- open access
- |
- |
- 1.84 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8587583
- MLA
- Burt, Andrew, et al. “Extracting Individual Trees from Lidar Point Clouds Using Treeseg.” METHODS IN ECOLOGY AND EVOLUTION, vol. 10, no. 3, 2019, pp. 438–45, doi:10.1111/2041-210x.13121.
- APA
- Burt, A., Disney, M., & Calders, K. (2019). Extracting individual trees from lidar point clouds using treeseg. METHODS IN ECOLOGY AND EVOLUTION, 10(3), 438–445. https://doi.org/10.1111/2041-210x.13121
- Chicago author-date
- Burt, Andrew, Mathias Disney, and Kim Calders. 2019. “Extracting Individual Trees from Lidar Point Clouds Using Treeseg.” METHODS IN ECOLOGY AND EVOLUTION 10 (3): 438–45. https://doi.org/10.1111/2041-210x.13121.
- Chicago author-date (all authors)
- Burt, Andrew, Mathias Disney, and Kim Calders. 2019. “Extracting Individual Trees from Lidar Point Clouds Using Treeseg.” METHODS IN ECOLOGY AND EVOLUTION 10 (3): 438–445. doi:10.1111/2041-210x.13121.
- Vancouver
- 1.Burt A, Disney M, Calders K. Extracting individual trees from lidar point clouds using treeseg. METHODS IN ECOLOGY AND EVOLUTION. 2019;10(3):438–45.
- IEEE
- [1]A. Burt, M. Disney, and K. Calders, “Extracting individual trees from lidar point clouds using treeseg,” METHODS IN ECOLOGY AND EVOLUTION, vol. 10, no. 3, pp. 438–445, 2019.
@article{8587583,
abstract = {{Recent studies have demonstrated the potential of lidar-derived methods in plant ecology and forestry. One limitation to these methods is accessing the information content of point clouds, from which tree-scale metrics can be retrieved. This is currently undertaken through laborious and time-consuming manual segmentation of tree-level point clouds from larger-area point clouds, an effort that is impracticable across thousands of stems. Here, we present treeseg, an open-source software to automate this task. This method utilises generic point cloud processing techniques including Euclidean clustering, principal component analysis, region-based segmentation, shape fitting and connectivity testing. This data-driven approach uses few a priori assumptions of tree architecture, and transferability across lidar instruments is constrained only by data quality requirements. We demonstrate the treeseg algorithm here on data acquired from both a structurally simple open forest and a complex tropical forest. Across these data, we successfully automatically extract 96% and 70% of trees, respectively, with the remainder requiring some straightforward manual segmentation. treeseg allows ready and quick access to tree-scale information contained in lidar point clouds. treeseg should help contribute to more wide-scale uptake of lidar-derived methods to applications ranging from the estimation of carbon stocks through to descriptions of plant form and function.}},
author = {{Burt, Andrew and Disney, Mathias and Calders, Kim}},
issn = {{2041-210X}},
journal = {{METHODS IN ECOLOGY AND EVOLUTION}},
keywords = {{extraction,forests,laser scanning,lidar,point cloud,segmentation,trees,TERRESTRIAL,LASER,cavelab}},
language = {{eng}},
number = {{3}},
pages = {{438--445}},
title = {{Extracting individual trees from lidar point clouds using treeseg}},
url = {{http://doi.org/10.1111/2041-210x.13121}},
volume = {{10}},
year = {{2019}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: