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TitleDeveloping and testing surficial sediments classification methods using remote predictive mapping, Repulse Bay area, Nunavut
AuthorWityk, U; Ross, MORCID logo; McMartin, IORCID logo; Campbell, J; Grunsky, E; Harris, J
Source33rd Canadian Symposium on Remote Sensing, abstracts; by Canadian Symposium on Remote Sensing; 2012 p. 57 Open Access logo Open Access
LinksOnline - En ligne
LinksAbstracts (PDF, 1.22 MB)
Alt SeriesEarth Sciences Sector, Contribution Series 20120002
Meeting33rd Canadian Symposium on Remote Sensing; Ottawa; CA; June 11-14, 2012
Mediaon-line; digital
File formatpdf
AreaRepulse Bay
Subjectsgeophysics; surficial geology/geomorphology; glacial deposits; glacial features; mapping techniques; remote sensing; satellite imagery; LANDSAT 7 TM; SPOT 4/5; Cenozoic; Quaternary
ProgramGEM: Geo-mapping for Energy and Minerals Multiple Metals - Melville Peninsula (Nunavut)
AbstractNew techniques are emerging to support the surficial geological mapping of vast northern regions at scales appropriate for mineral exploration and related land use management. Remote Predictive Mapping (RPM) techniques help streamline the process in the initial mapping stages by generating first-order maps from analysis and classification of satellite imagery and limited field observations. These techniques are applied to map the nature and distribution of surficial sediments west of Repulse Bay, Nunavut, located within an active mineral exploration area of the Western Churchill Geological Province. .
A multi-data approach using LANDSAT 7 TM and SPOT 4/5 imagery and field-based data is employed to determine the optimal combination of data to refine classification of surficial materials and maps with quantitative measures of accuracy. The imagery and data interpretation explore advantages of using different spatial and spectral resolutions.
Two methods are used to determine the optimal class combination and produce the most accurately classified maps. The first combines remote sensing with traditional surficial mapping to determine where confusion occurs. The second is based on statistics which calculate separability of classes prior to classification/predictive mapping as well as confusion between and within mapped surficial material types after the classification. Both methods are used and compared to discover optimal class combinations - the first method qualitatively, and the second quantitatively. Preliminary statistical results illustrate that exposed marine sediments, carbonate-rich tills, organics and boulder terrains are most accurately classified; while confusion occurs between remaining tills, sand and gravel and bedrock. Grouping of non-carbonate till sub-classes into a single till class has generated maps that are more geologically consistent.

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