Date of Award:

1998

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Natural Resources

Department name when degree awarded

Fisheries and Wildlife

Advisor/Chair:

Thomas C. Edwards Jr.

Abstract

A significant question currently facing environmental managers is how to accurately and efficiently quantify forest diversity and resources. Numerous studies have demonstrated the use of modern spatial analytical tools , such as geographical information systems (GIS), remote sensing devices, and statistical models for predicting the distribution of dominant vegetation cover types. This study examines the ability of generalized additive models (GAMs) to delineate structural diversity in forested ecosystems (specifically the Uinta Mountain Range in Utah) using GIS tools and satellite spectral data, and analyzes the effect of including different forms of satellite data in model construction (i.e., Landsat thematic mapper (TM), advanced very high resolution radiometer (AVHRR), and the GAP Analysis TM-classified map). Based on the assumption that vegetation composition, as well as structural diversity, is a function of environmental gradients, temperature, precipitation, elevation, aspect, slope, and geology were included as independent environmental variables. Probability surface maps were generated for presence of forest , presence of lodgepole pine, basal area of forest trees, percent cover of shrubs, and density of snags.

The maps were validated using an independent set of field data collected from the Evanston Ranger District within the Uinta Mountain Range . In general, the models tended to underpredict at large numbers and overpredict at locations that were sampled as having no forest cover. The models predicting the presence of forest and lodgepole pine were 88% and 80% accurate, respectively, within the Evanston Ranger District and an average of 62% of the predictions of basal area, shrub cover , and snag density fell within an approximate 15% deviation from the field validation values . The addition of TM spectral data and the GAP Analysis TM-classified data were found to contribute significantly to the models' predictions, with some contribution from AVHRR data. The methods used in this study provide a systematic approach for delineating structural features within forest habitats, thus offering an efficient spatial tool for making management decisions.

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