Date of Award:

1997

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Natural Resources

Department name when degree awarded

Geography

Advisor/Chair:

Alan Falconer

Abstract

This study integrated remotely sensed data, geographic information system (GIS), and classification tree-based modeling to delineate ecological units for the Ashley National Forest. Data points , provided by the Ashley National Forest, with a known location and dominant vegetation type, were related to data layers that were determined to be helpful in a landtype classification. These layers included elevation, slope, aspect, potential solar irradiation, precipitation, geology, basins, Landsat thematic mapper (TM) bands 3, 4, 5, and 6, and basic land cover. These points, with their related information, were then used to train the tree-based model for landtype classification. This resulted in a set of rules, in the form of a binary decision tree, that could be applied to the entire study area. After the landtype classification was obtained, it was cross-classified with geology to produce a landtype association layer. This resulting data layer was compared to an existing landtype association map and it was determined, by cross-tabulation, that the two classifications identified many of the same patterns.

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