A 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 km² 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—the Alberta Vegetation Inventory and a conventional classification-based land-cover map—showed that the thesis database had the highest map quality and was best capable of explaining both individual—and population-level resource selection by grizzly bears.

A 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 km² 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—the Alberta Vegetation Inventory and a conventional classification-based land-cover map—showed that the thesis database had the highest map quality and was best capable of explaining both individual—and population-level resource selection by grizzly bears.

In 1975, the US Fish and Wildlife Service listed the grizzly bear as a threatened species under the Endangered Species Act. Following the listing, a recovery plan was drafted in which the Bitterroot Ecosystem of central Idaho and extreme western Montana was one of six proposed grizzly bear recovery areas. It was the only one of the six, however, which did not contain a resident population of grizzlies. The Fish and Wildlife Service eventually accepted a proposal submitted by a coalition of environmental and timber industry groups. The coalition proposed to reestablish a population of grizzlies in the Bitterroot by translocating 25 bears over five years from existing populations in the US and Canada. The proposal, however, included significant concessions, including reduced protection for the reintroduced grizzlies and management of the grizzly population by a Citizen Management Committee. A large contingent of regional and national environmental groups quickly rose up in vociferous objection to the proposal exposing a significant rift within the environmental movement. These environmentalists objected to the very idea of Citizen Management and also claimed that the proposed recovery area was too small to ensure recovery. Drawing on interviews and document analyses, this dissertation employs an environmental pragmatist approach to examine the intra-environmentalist disputes that flared up throughout the Bitterroot grizzly recovery debates. The dissertation focuses on the relationship between environmental ideologies, science, and conservation advocacy, with an eye toward examining how environmentalists crafted and defended rival proposals for grizzly recovery. Through this interpretive lens, the dissertation aims to explain the existence and persistence of this intra-environmentalism rift as well as explore its ramifications for environmentalism in the region. While no wholly unified environmental movement can ever be possible or is even necessarily desirable unwavering commitments to unreachable ideals on the part of many environmentalists are hindering the growth, flexibility and efficacy of conservation in the region. The main contribution of this dissertation will be to provide an empirical case study that defends the environmental pragmatist assertion that hostile and unnecessary divisiveness within the environmental movement ultimately obstructs the development of a more successful environmentalism.