Other Scholarly Content

 

Prediction of Wood Fiber Attributes from LiDAR-Derived Forest Canopy Indicators Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/defaults/f7623d013

To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. This is the publisher’s final pdf. The published article is copyrighted by Society of American Foresters and can be found at:  http://www.safnet.org/.

Descriptions

Attribute NameValues
Creator
Abstract
  • We investigated the potential use of airborne light detection and ranging (LiDAR) data to predict key wood fiber properties from extrinsic indicators in lodgepole pine leading forest stands located in the foothills of central Alberta, Canada. Six wood fiber attributes (wood density, cell perimeter, cell coarseness, mature fiber length, microfibril angle, and modulus of elasticity) were measured at 21 plots, and with use of data reduction techniques, two components of wood properties were derived: wood strength, stiffness, and fiber yield and fiber strength and smoothness. These wood fiber components were then compared with extrinsic indicators of wood characteristic-derived LiDAR-estimated topographic morphology, tree height, and canopy light metrics. The first principal component indicating wood strength and stiffness was significantly correlated to the depth of different canopy zones (or light regimes; r² = 0.55, P < 0.05). The second component, related to fiber strength and smoothness, was significantly correlated to the height of the canopy and canopy thickness (r² = 0.65, P < 0.05). The results indicate that airborne LiDAR attributes can explain about half of the observed variance in intrinsic wood fiber attributes, which is approximately 5-10% less than that explained by growth-related field-measured variables such as diameter increment and height. This reduction in explained variance can be balanced by the opportunities for much broader spatial characterizations of wood quantity and quality at the stand and landscape levels. FOR. SCI. 59(2):231-242.
Resource Type
DOI
Date Available
Date Issued
Citation
  • Hilker, T., Frazer, G., Coops, N., Wulder, M., Newnham, G., Stewart, J., . . . Culvenor, D. (2013). Prediction of wood fiber attributes from LiDAR-derived forest canopy indicators. Forest Science, 59(2), 231-242. DOI: 10.5849/forsci.11-074.
Academic Affiliation
Series
Keyword
Rights Statement
Funding Statement (additional comments about funding)
  • The Canadian Wood Fiber Centre of the Canadian Forest Service of Natural Resources Canada provided funding to support this research with additional support from an Natural Sciences and Engineering Research Council Discovery grant to NC Coops.
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Deborah Campbell (deborah.campbell@oregonstate.edu) on 2013-05-21T17:29:58Z No. of bitstreams: 1 HilkerThomasForestryPredictionWoodFiber.pdf: 1348938 bytes, checksum: 194f45c8090696113ad924dd3d4ad565 (MD5)
  • description.provenance : Made available in DSpace on 2013-05-21T17:30:49Z (GMT). No. of bitstreams: 1 HilkerThomasForestryPredictionWoodFiber.pdf: 1348938 bytes, checksum: 194f45c8090696113ad924dd3d4ad565 (MD5) Previous issue date: 201-04-19
  • description.provenance : Approved for entry into archive by Deborah Campbell(deborah.campbell@oregonstate.edu) on 2013-05-21T17:30:49Z (GMT) No. of bitstreams: 1 HilkerThomasForestryPredictionWoodFiber.pdf: 1348938 bytes, checksum: 194f45c8090696113ad924dd3d4ad565 (MD5)

Relationships

Parents:

This work has no parents.

Items