<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDermid, Gregory John</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Remote sensing for large-area, multi-jurisdictional habitat mapping</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">foothills model forest</style></keyword><keyword><style  face="normal" font="default" size="100%">Grizzly bears</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://search.proquest.com/docview/304930006</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">University of Waterloo</style></pub-location><volume><style face="normal" font="default" size="100%">Geography</style></volume><pages><style face="normal" font="default" size="100%">271</style></pages><language><style face="normal" font="default" size="100%">en</style></language><abstract><style face="normal" font="default" size="100%">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 km 2 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.</style></abstract><issue><style face="normal" font="default" size="100%">Ph. D.</style></issue><custom1><style face="normal" font="default" size="100%">Geography</style></custom1><custom2><style face="normal" font="default" size="100%">Hinton</style></custom2><custom3><style face="normal" font="default" size="100%">http://www.worldcat.org/oclc/213399556</style></custom3><custom4><style face="normal" font="default" size="100%">Science</style></custom4></record></records></xml>