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TitleRemote predictive mapping 3. Optical remote sensing - A review for remote predictive geological mapping in northern Canada
 
AuthorHarris, J R; Wickert, L; Lynds, T; Behnia, P; Rainbird, RORCID logo; Grunsky, E; McGregor, R; Schetselaar, E
SourceGeoscience Canada vol. 38, no. 2, 2011 p. 49-83 Open Access logo Open Access
LinksOnline - En ligne
Year2011
Alt SeriesEarth Sciences Sector, Contribution Series 20110103
Documentserial
Lang.English
Mediapaper
File formatpdf; html
Subjectsgeophysics; remote sensing; mapping techniques; optical properties; LANDSAT imagery; satellite imagery; LANDSAT
Illustrationssatellite images; Landsat images; diagrams; plots; tables
ProgramGEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource Assessment)
AbstractOptical remotely sensed data have broad application for geological mapping in Canada's North. Diverse remote sensors and digital image processing techniques have specific mapping functions, as demonstrated by numerous examples and associated interpretations. Moderate resolution optical sensors are useful for discriminating rock types, whereas sensors that offer increased spectral resolution (i.e. hyperspectral sensors) allow the geologist to identify certain rock types (mainly different types of carbonates, Fe-bearing rocks, sulphates and hydroxyl-(clay-) bearing rocks) as opposed to merely discriminating between them. Increased spatial resolution and the ability to visualize the earth's surface in stereo are now offered by a host of optical sensors. However, the usefulness of optical remote sensing for geological mapping is highly dependent on the geologic, surficial and biophysical environment, and bedrock predictive mapping is most successful in areas not obscured by thick drift and vegetation/lichen cover, which is typical of environments proximal to coasts. In general, predictive mapping of surficial materials has fewer restrictions. Optical imagery can be enhanced in a variety of ways and fused with other geo-science datasets to produce imagery that can be visually interpreted in a GIS environment. Computer processing techniques are useful for undertaking more quantitative analyses of imagery for mapping bedrock, surficial materials and geomorphic or glacial features.
GEOSCAN ID288809

 
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