Date of Graduation

2015

Document Type

Thesis

Degree Type

MS

College

Davis College of Agriculture, Natural Resources and Design

Department

Division of Forestry and Natural Resources

Committee Chair

John R. Brooks

Committee Member

Shawn Grushecky

Committee Member

Gary Miller

Committee Member

Jamie Schuler

Committee Member

Jingxin Wang

Abstract

A generalized stand table projection algorithm was used to disaggregate species group level data for the Fernow Experimental Forest in the Allegheny Front physiographic region of the Central Appalachian Hardwoods region. The generalized stand table projection method has proved effective in projecting multi and uni-modal distributions in single species stands. Exploration into the potential for use in mixed species Central Appalachian Hardwoods thinned and unthinned stands proved promising. When compared to basal area projections using SILVAH, generalized stand table projection outperformed SILVAH at the plot level. Generalized stand table projection produced smaller errors on non-overlapping growth projections for both thinned and unthinned stands. For unthinned non-overlapping growth projections, species group root mean squared errors ranged from 0.791 to 3.643 ft2/ac. For unthinned overlapping projections, species group root mean squared errors ranged from 2.003 to 16.365 ft2/ac. For thinned non-overlapping projections, species group root mean squared errors ranged from 0.652 to 2.661 ft 2/ac. For thinned overlapping projections, species group root mean squared errors ranged from 1.818 to 14.994 ft2/ac. Kolmogorov-Smirnov tests indicated that 48.6 percent of the predicted diameter distributions for individual species groups were not significantly different that observed distributions while 51.3 percent of the SILVAH distributions were not significantly different. The generalized stand table projection system provided future basal area estimates as good as SILVAH for the northern red oak, maples, white oaks, hickory and ash, yellow-poplar, striped maple and pin cherry, black cherry, other, scarlet and black oaks, and birch species groups in at least one dataset and projection type combination, and better than SILVAH for the hickory and ash, yellow-poplar, black cherry, other, and birch species groups in at least one dataset and projection type combination.

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