Model-Assisted Forest Yield Estimation with Light Detection and Ranging Public Deposited

http://ir.library.oregonstate.edu/concern/defaults/hq37vn969

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 or Summary
  • Previous studies have demonstrated that light detection and ranging (LiDAR)-derived variables can be used to model forest yield variables, such as biomass, volume, and number of stems. However, the next step is underrepresented in the literature: estimation of forest yield with appropriate confidence intervals. It is of great importance that the procedures required for conducting forest inventory with LiDAR and the estimation precision of such procedures are sufficiently documented to enable their evaluation and implementation by land managers. In this study, we demonstrated the regression estimator, a model-assisted estimator (approximately design-unbiased), using LiDAR-derived variables for estimation of total forest yield. The LiDAR-derived variables are statistics associated with vegetation height and cover. The estimation procedure requires complete coverage of the forest with LiDAR and a random sample of precisely georeferenced field measurement plots. Regression estimation relies on sample-based ordinary least squares (OLS) regression models relating forest yield and LiDAR-derived variables. Estimation was performed using the OLS models and LiDAR-derived variables for the entire population. Regression estimates of basal area, volume, stand density, and biomass were much more precise than simple random sampling estimates (design effects were 0.25, 0.24, 0.44, and 0.27, respectively).
Resource Type
DOI
Date Available
Date Issued
Citation
  • Strunk, J., Reutebuch, S., Andersen, H., Gould, P., & McGaughey, R. (2012). Model-assisted forest yield estimation with light detection and ranging. Western Journal of Applied Forestry, 27(2), 53-59. doi: 10.5849/wjaf.10-043
Academic Affiliation
Series
Keyword
Rights Statement
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Deborah Campbell (deborah.campbell@oregonstate.edu) on 2013-01-16T19:01:00Z No. of bitstreams: 1 StrunkJacobLForestryModelAssistedForest.pdf: 199653 bytes, checksum: f6de847e40a43d411798795746b77e31 (MD5)
  • description.provenance : Approved for entry into archive by Deborah Campbell(deborah.campbell@oregonstate.edu) on 2013-01-16T19:03:52Z (GMT) No. of bitstreams: 1 StrunkJacobLForestryModelAssistedForest.pdf: 199653 bytes, checksum: f6de847e40a43d411798795746b77e31 (MD5)
  • description.provenance : Made available in DSpace on 2013-01-16T19:03:52Z (GMT). No. of bitstreams: 1 StrunkJacobLForestryModelAssistedForest.pdf: 199653 bytes, checksum: f6de847e40a43d411798795746b77e31 (MD5) Previous issue date: 2012-04

Relationships

Parents:

This work has no parents.

Last modified

Downloadable Content

Download PDF

Items