Type of Document Dissertation Author Doruska, Paul F. Author's Email Address email@example.com URN etd-6198-13595 Title Methods for Quantitatively Describing Tree Crown Profiles of Loblolly pine (Pinus taeda L.) Degree PhD Department Forestry Advisory Committee
Advisor Name Title Burkhart, Harold E. Committee Chair Burger, James A. Committee Member Gregoire, Timothy G. Committee Member Oderwald, Richard G. Committee Member Reynolds, Marion R. Jr. Committee Member Keywords
- nonparametric regression
- local linear
- local polynomial
Date of Defense 1998-06-26 Availability unrestricted AbstractPhysiological process models, productivity studies, and
wildlife abundance studies all require accurate
representations of tree crowns. In the past, geometric
shapes or flexible mathematical equations approximating
geometric shapes were used to represent crown profiles.
Crown profile of loblolly pine (Pinus taeda L.) was
described using single-regressor, nonparametric regression
analysis in an effort to improve crown representations.
The resulting profiles were compared to more traditional
representations. Nonparametric regression may be applicable
when an underlying parametric model cannot be identified.
The modeler does not specify a functional form. Rather, a
data-driven technique is used to determine the shape a
curve. The modeler determines the amount of local curvature
to be depicted in the curve. A class of local-polynomial
estimators which contains the popular kernel estimator as a
special case was investigated. Kernel regression appears
to fit closely to the interior data points but often
possesses bias problems at the boundaries of the data, a
feature less exhibited by local linear or local quadratic
regression. When using nonparametric regression, decisions
must be made regarding polynomial order and bandwidth.
Such decisions depend on the presence of local curvature,
desired degree of smoothing, and, for bandwidth in
particular, the minimization of some global error criterion.
In the present study, a penalized PRESS criterion (PRESS*)
was selected as the global error criterion. When individual-
tree, crown profile data are available, the technique of
nonparametric regression appears capable of capturing more
of the tree to tree variation in crown shape than multiple
linear regression and other published functional forms.
Thus, modelers should consider the use of nonparametric
regression when describing crown profiles as well as in any
regression situation where traditional techniques perform
unsatisfactorily or fail.
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