Title page for ETD etd-6198-13595


Type of Document Dissertation
Author Doruska, Paul F.
Author's Email Address pdoruska@vt.edu
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
  • kernel
  • local linear
  • local polynomial
  • bandwidth
Date of Defense 1998-06-26
Availability unrestricted
Abstract
Physiological 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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