Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Ecology and Evolutionary Biology

Major Professor

H. Hank Shugart

Committee Members

Dewey Bunting, Louis Gross, Peter White


This research investigates the vegetation of Great Smoky Mountains National Park (GRSM) using three different techniques: 1) analysis of vegetation data collected circa 1930, 2) remote sensing of current (1984) vegetation using Landsat Thematic Mapper (TM) satellite imagery, and 3) the integration of gradient analysis with forest succession modeling.

Analysis of the 1930's data revealed that Castanea dentata was the dominant species in the GRSM at that time, even with the introduction of the chestnut blight to the park in 1926. Classification of the 1930's plot data identified 16 unique vegetation types ranging from the low elevation, xeric Pinus rigida type to the high elevation, mesic Abies fraseri type. Ordination of the data indicated that species are responding to a complex moisture - elevation gradient. Unsupervised classification of the satellite data was able to identify 14 vegetation types. The classification process used all seven TM bands and had an overall classification accuracy of 83%. The types identified were Spruce - Fir, Northern Hardwood, Cove Hardwood, Mesic Oak, Mixed Mesic Hardwood, Tulip Poplar, Xeric Oak, Pine - Oak, Heath Bald, Grassy Bald, Grape Thicket, Treeless, and Water. The Cove Hardwood type was the most prominent type and occupied 33% of the park's area. The integration of gradient analysis with a forest succession model yielded a model that was capable of replicating vegetation types throughout the entire GRSM landscape. The model was used to provide insights into the dynamics of Castanea dentata prior to the chestnut blight and patterns of replacement of Castanea dentata, and its effect on biomass after the blight, took place via multiple pathways depending on site conditions.

This research emphasizes the vegetation of GRSM as a whole rather than as a study of specific types or locations. It provides insights into vegetation dynamics in the context of both space and time.

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