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dc.contributor.authorMcDermid, Gregoryen 14:11:17 (GMT) 14:11:17 (GMT)
dc.description.abstractA framework designed to guide the effective use of remote sensing in large-area, multi-jurisdictional habitat mapping studies has been developed. Based on hierarchy theory and the remote sensing scene model, the approach advocates (i) identifying the key physical attributes operating on the landscape; (ii) selecting a series of suitable remote sensing data whose spatial, spectral, radiometric, and temporal characteristics correspond to the attributes of interest; and (iii) applying an intelligent succession of scale-sensitive data processing techniques that are capable of delivering the desired information. The approach differs substantially from the single-map, classification-based strategies that have largely dominated the wildlife literature, and is designed to deliver a sophisticated, multi-layer information base that is capable of supporting a variety of management objectives. The framework was implemented in the creation of a multi-layer database composed of land cover, crown closure, species composition, and leaf area index (LAI) phenology over more than 100,000 km<sup>2</sup> in west-central Alberta. Generated through a combination of object-oriented classification, conventional regression, and generalized linear models, the products represent a high-quality, flexible information base constructed over an exceptionally challenging multi-jurisdictional environment. A quantitative comparison with two alternative large-area information sources&mdash;the Alberta Vegetation Inventory and a conventional classification-based land-cover map&mdash;showed that the thesis database had the highest map quality and was best capable of explaining both individual&mdash;and population-level resource selection by grizzly bears.en
dc.format.extent7271200 bytes
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2005, McDermid, Gregory. All rights reserved.en
dc.subjectRemote sensingen
dc.subjecthabitat mappingen
dc.subjectgrizzly bear conservationen
dc.titleRemote Sensing for Large-Area, Multi-Jurisdictional Habitat Mappingen
dc.typeDoctoral Thesisen
uws-etd.degreeDoctor of Philosophyen

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