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Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods : Journal canadien de la recherche forestieĢ€re

Permanent URL:
http://handle.nal.usda.gov/10113/35182
File:
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Abstract:
Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods. The models were developed and fit to data collected by the Forest Inventory and Analysis program of the US Forest Service in Washington, Oregon, and California. For predicting cavity tree and snag abundance per stand, all three NB regression models performed better in terms of mean square prediction error than the NN imputation methods. The most similar neighbor imputation, however, outperformed the NB regression models in predicting overall cavity tree and snag abundance.
Author(s):
Eskelson, Bianca N.I. , Temesgen, Hailemariam , Barrett, Tara M.
Note:
Includes references
Source:
Canadian journal of forest research = 2009 Sept., v. 39, no. 9
Language:
English
Year:
2009
Collection:
Journal Articles, USDA Authors, Peer-Reviewed
Rights:
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.