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Spatial analysis of white-tailed deer wintering habitat in central New York

Posted on:1997-05-10Degree:Ph.DType:Dissertation
University:Cornell UniversityCandidate:Wairimu, SaphidaFull Text:PDF
GTID:1460390014982351Subject:Agriculture
Abstract/Summary:
The objective of this study was to evaluate a digital elevation model (DEM), and remotely sensed data in a geographical information system (GIS) as an alternative method to using traditional methods (i.e., aerial and field surveys) for locating and mapping white-tailed deer (Odocoileus virginianus) wintering areas. I studied 43 traditional deer wintering areas in central New York and quantitatively described their physical and vegetative characteristics using a DEM at a spatial resolution of 30 meters square, Landsat Thematic Mapper (TM) data, and field data. I used field data to examine the effect that DEM spatial resolution (30 m versus 90 m) had on terrain representation, and I developed a GIS-based decision tree model to predict potential locations of deer wintering areas at regional scale.; The 43 deer yards in this study ranged in size from 13 to 2210 ha. Deer preferred to winter on moderately steep (15-26%) and steep ({dollar}>{dollar}26%) slope gradients, at elevations {dollar}>{dollar}300 m, on south- and southeast-facing slopes, and in mixed northern hardwood forest type. There was good agreement between the DEM and field data; 72% and 85% for elevation for 90 m and 30 m resolution respectively, but only fair agreement between slope gradient and slope aspect. The 90 m DEM poorly represented heterogeneous landscapes, and the model failed to capture the spatial juxtaposition of important topographic characteristics.; Unsupervised land cover classification of multi-temporal Landsat TM data resulted in an overall accuracy of 75% when compared to reference data. Non-forest cover types were identified with high accuracy (96%) but classification accuracy for northern hardwoods forest types was low ranging from 50% to 89%. The GIS-based decision tree model had an overall accuracy of 62% but known deer wintering areas were detected with a 78% accuracy. The approach and methodology of this work may be used to predict locations for white-tailed deer wintering areas in other parts of New York State, and may also be used for assisting habitat evaluation and achieving wildlife management goals particularly for characterizing habitat for a variety of other wildlife species.
Keywords/Search Tags:Deer wintering, DEM, Habitat, Data, Spatial, New, Model
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