Advanced search
Add to list

Rapid characterisation of forest structure from TLS and 3D modelling

Author
Organization
Abstract
Raumonen et al.[1] have developed a new method for reconstructing topologically consistent tree architecture from TLS point clouds. This method generates a cylinder model of tree structure using a stepwise approach. Disney et al.[2] validated this method with a detailed 3D tree model where structure is known a priori, establishing a reconstruction relative error of less than 2%. Here we apply the same method to data acquired from Eucalyptus racemosa woodland, Banksia ameula low open woodland and Eucalyptus spp. open forest using a RIEGL VZ-400 instrument. Individual 3D tree models reconstructed from TLS point clouds are used to drive Monte Carlo ray tracing simulations of TLS with the same characteristics as those collected in the field. 3D reconstruction was carried out on the simulated point clouds so that errors and uncertainty arising from instrument sampling and reconstruction could be assessed directly. We find that total volume could be recreated to within a 10.8% underestimate. The greatest constraint to this approach is the accuracy to which individual scans can be globally registered. Inducing a 1cm registration error lead to a 8.8% total volumetric overestimation across the data set.
Keywords
CANOPY STRUCTURE, TREE MODELS, LIDAR

Citation

Please use this url to cite or link to this publication:

MLA
Burt, A et al. “Rapid Characterisation of Forest Structure from TLS and 3D Modelling.” IEEE International Symposium on Geoscience and Remote Sensing IGARSS. New York, NY, USA: IEEE, 2013. 3387–3390. Print.
APA
Burt, A, Disney, M., Raumonen, P., Armston, J., Calders, K., & Lewis, P. (2013). Rapid characterisation of forest structure from TLS and 3D modelling. IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 3387–3390). Presented at the 2013 IEEE International Geoscience and Remote Sensing symposium (IGARSS) , New York, NY, USA: IEEE.
Chicago author-date
Burt, A, MI Disney, P Raumonen, J Armston, Kim Calders, and P Lewis. 2013. “Rapid Characterisation of Forest Structure from TLS and 3D Modelling.” In IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 3387–3390. New York, NY, USA: IEEE.
Chicago author-date (all authors)
Burt, A, MI Disney, P Raumonen, J Armston, Kim Calders, and P Lewis. 2013. “Rapid Characterisation of Forest Structure from TLS and 3D Modelling.” In IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 3387–3390. New York, NY, USA: IEEE.
Vancouver
1.
Burt A, Disney M, Raumonen P, Armston J, Calders K, Lewis P. Rapid characterisation of forest structure from TLS and 3D modelling. IEEE International Symposium on Geoscience and Remote Sensing IGARSS. New York, NY, USA: IEEE; 2013. p. 3387–90.
IEEE
[1]
A. Burt, M. Disney, P. Raumonen, J. Armston, K. Calders, and P. Lewis, “Rapid characterisation of forest structure from TLS and 3D modelling,” in IEEE International Symposium on Geoscience and Remote Sensing IGARSS, Melbourne, VIC, Australia, 2013, pp. 3387–3390.
@inproceedings{8544410,
  abstract     = {Raumonen et al.[1] have developed a new method for reconstructing topologically consistent tree architecture from TLS point clouds. This method generates a cylinder model of tree structure using a stepwise approach. Disney et al.[2] validated this method with a detailed 3D tree model where structure is known a priori, establishing a reconstruction relative error of less than 2%. Here we apply the same method to data acquired from Eucalyptus racemosa woodland, Banksia ameula low open woodland and Eucalyptus spp. open forest using a RIEGL VZ-400 instrument. Individual 3D tree models reconstructed from TLS point clouds are used to drive Monte Carlo ray tracing simulations of TLS with the same characteristics as those collected in the field. 3D reconstruction was carried out on the simulated point clouds so that errors and uncertainty arising from instrument sampling and reconstruction could be assessed directly. We find that total volume could be recreated to within a 10.8% underestimate. The greatest constraint to this approach is the accuracy to which individual scans can be globally registered. Inducing a 1cm registration error lead to a 8.8% total volumetric overestimation across the data set.},
  author       = {Burt, A and Disney, MI and Raumonen, P and Armston, J and Calders, Kim and Lewis, P},
  booktitle    = {IEEE International Symposium on Geoscience and Remote Sensing IGARSS},
  isbn         = {9781479911141},
  issn         = {2153-6996},
  keywords     = {CANOPY STRUCTURE,TREE MODELS,LIDAR},
  language     = {eng},
  location     = {Melbourne, VIC, Australia},
  pages        = {3387--3390},
  publisher    = {IEEE},
  title        = {Rapid characterisation of forest structure from TLS and 3D modelling},
  url          = {http://dx.doi.org/10.1109/igarss.2013.6723555},
  year         = {2013},
}

Altmetric
View in Altmetric
Web of Science
Times cited: