Year of Award

2011

Document Type

Thesis

Degree Type

Master of Science (MS)

Other Degree Name/Area of Focus

Forest Biometrics

Department or School/College

College of Forestry and Conservation

Committee Chair

David Affleck

Commitee Members

David Patterson, John Goodburn

Keywords

unbiased estimator, biomass, Randomized branch sampling

Abstract

Randomized branch sampling (RBS) is a sampling scheme which can be implemented to estimate many different attributes of an object displaying a branched or forked form. The aboveground structure of trees (stem and branches) lends itself naturally to this type of sampling design. RBS utilizes the branching form of the crown itself to draw probability samples and generate unbiased estimates. When implemented correctly, RBS can also greatly reduce the costs in time and labor of sampling when the purpose is estimating attributes borne within crown portions of trees. However, RBS was created for and has been implemented primarily in applications on trees with a decurrent crown structure. Considerations when applying RBS to excurrent crown structures, which are a common trait of conifer species, are examined in this thesis. The applications of several RBS schemes are examined within the context of sampling to estimate green crown biomass. The way branches are aggregated into groups for sampling along the main stem is the distinction between the proposed RBS schemes in this thesis. For estimating green crown mass, RBS was found to produce estimates with accuracy between that of simple random and list sampling methods. A sample size of five or six branches was sufficient to obtain standard errors within ten percent of the actual crown weight.

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© Copyright 2011 Ryan Michael Schlecht