A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables Public Deposited

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  • One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m2/ha), top height (m), and cubic stem volume (m3/ha). The precision and bias of the estimation methods (synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay–Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.
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  • Goerndt, M. E., V. J. Monleon, and H. Temesgen. 2011. A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables. Canadian Journal of Forest Research. 41: 1189-1201, doi:10.1139/X11-033.
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  • description.provenance : Approved for entry into archive by Deborah Campbell(deborah.campbell@oregonstate.edu) on 2012-04-02T21:30:27Z (GMT) No. of bitstreams: 1 A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables.pdf: 459609 bytes, checksum: 3f9f1ca63dd66f34345da17464da2a02 (MD5)
  • description.provenance : Made available in DSpace on 2012-04-02T21:30:27Z (GMT). No. of bitstreams: 1 A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables.pdf: 459609 bytes, checksum: 3f9f1ca63dd66f34345da17464da2a02 (MD5) Previous issue date: 2011
  • description.provenance : Submitted by Logan Bernart (logan.bernart@gmail.com) on 2012-03-09T21:08:24Z No. of bitstreams: 1 A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables.pdf: 459609 bytes, checksum: 3f9f1ca63dd66f34345da17464da2a02 (MD5)

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