Date of Award
12-2008
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Legacy Department
Forest Resources
Committee Chair/Advisor
Post, Christopher J
Committee Member
Waldrop , Thomas A
Committee Member
Gerard , Patrick D
Committee Member
Mikhailova , Elena A
Abstract
This dissertation describes three GIS models developed to better model topographic features and the occurrence of mountain laurel (Kalmia latifolia) in the southern Appalachian Mountains. The first study presented 'A LiDAR based GIS model to calculate Terrain Shape Index on a landscape scale', attempts to develop a GIS based model to calculate the Terrain Shape Index (TSI). TSI is typically collected in the field using a series of elevation measurements to determine the average elevation change within the study plot. In this study, a GIS model is developed and TSI values compared to those collected using conventional methods. The second study, 'A GIS model for determining landform type and slope position', uses a progressive scanning method developed within a GIS to identify ridges and subsequently landform type. The results from this study are compared to landform classifications made visually by a group of volunteers. The third study presented, 'A predictive GIS model for determining the probability of mountain laurel occurrence in the southern Appalachian Mountains', attempts to develop a statistical model to better predict the presence or absence of mountain laurel on the landscape. Mountain laurel, often associated with decreased hardwood regeneration and its role as a vertical fuel, is important in both stand and fire management. In this study, results from a comprehensive, long term field study are used predict the occurrence of mountain laurel across the landscape. The GIS models described herein were designed to be efficient, user friendly and accurate in their results as well as easily transferable between parties and locations.
Recommended Citation
Hall, Steven, "Topographic Analysis and Predictive Modeling using Geographic Information Systems" (2008). All Dissertations. 322.
https://open.clemson.edu/all_dissertations/322