Communications Project

<ETD> Submission Form - Cataloger

Document Type:Master's Thesis
Name:John Wayland McCombs II
Title:Geogaphic Information System Topographic Factor Maps for Wildlife Management
Degree:Master of SCience
Department:Fisheries and Wildlife
Committee Chair: Dr. Robert H. Giles, Jr.
Committee\ Members:Dr. Richard Oderwald
Dr. Gerald Cross
Keywords:elevation, slope, aspect, landform, phenology, slope position, logistic regression, habitat modeling, topography, geographic information system
Date of defense:July 24, 1997


Geographic Information System Topographic Factor Maps for Wildlife Management by John Wayland McCombs II Robert H. Giles, Jr., Chair Fisheries and Wildlife Sciences (ABSTRACT) A geographic information system (GIS) was used to create landform measurements and maps for elevation, slope, aspect, landform index, relative phenologic change, and slope position for 3 topographic quadrangles in Virginia. A set of known observation points of the Northern dusky flying squirrel (Glaucomys sabrinus) was used to build 3 models to delineate sites with landform characteristics equivalent to those known points. All models were built using squirrel observation points from 2 topographic quadrangles. The first model, called “exclusionary”, excluded those pixels with landform characteristics different from the known squirrel pixels based on histogram analyses. Logistic regression was used to create the other 2 models. Each model resulted in an image of pixels considered equivalent to the known squirrel pixels. Each model excluded approximately 65% of the Highland study area, but the exclusionary model excluded the fewest known squirrel pixels (12.62%). Both logistic regression models excluded approximately 10% more known squirrel pixels than the exclusionary approach. The models were tested in the area of a third quadrangle with points known to be occupied by squirrels. After the model was applied to the third topographic quadrangle, the exclusionary model excluded the least amount of full-area pixels (79.30%) and only 14.81% of the known squirrel pixels. The second logistic regression excluded 81.16 % of the full area and no known squirrel pixels. All models proved useful in quickly delineating pixels equivalent to areas where wildlife were known to occur.

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