GEOSCAN Search Results: Fastlink

GEOSCAN Menu


TitlePredictive mapping of surficial materials, Schultz Lake area (NTS 66A), Nunavut, Canada
AuthorGrunsky, E; Harris, J; McMartin, I
SourceRemote Sensing and Spectral Geology ; by Bedell, R (ed.); Crosta, A P (ed.); Grunsky, E (ed.); Reviews in Economic Geology vol. 16, 2009 p. 177-198
Year2009
Alt SeriesEarth Sciences Sector, Contribution Series 20080009
Documentserial
Lang.English
Mediapaper
ProvinceNunavut
NTS66A
AreaSchultz Lake; Princess Mary Lake; Pitz Lake; Thelon River; Amarulik Lake; Akitit Hill; Ayaktuukvik Lake; White Hills Lake; Qikiqtaujak Island; Kingatnaaq Hill; Qikittalik Lake; Sigalausivik Lake; Thom Lake; Judge Sissons Lake; Aniguq River; Baker Lake
Lat/Long WENS-98.0000 -96.0000 65.0000 64.0000
Subjectssurficial geology/geomorphology; geophysics; remote sensing; satellite imagery; glacial deposits; glacial features; landforms; terrain types; mapping techniques; statistical analyses; boulders; organic deposits; sands; gravels; tills; soils; bedrock geology; Archean; Rae Domain; Western Churchill Province; RADARSAT-1; Landsat; Phanerozoic; Cenozoic; Quaternary; Proterozoic; Precambrian
Illustrationssatellite images; photographs; schematic diagrams; plots; tables
AbstractMultibeam RADARSAT-1 and multispectral Landsat TM-7 imagery have been used to map surficial materials in the Schultz Lake area of Nunavut (1:250,000 NTS map sheet 66A). Representative training areas of distinctive surficial materials (bedrock, boulder fields, organic deposits, sand-gravel, thick and thin till) have been identified through the analysis of aerial photographs, satellite images, and field mapping information. These training areas have been used to perform a maximum likelihood classification using combined multibeam radar and multispectral satellite imagery to produce a predictive map of surficial materials. The application of this methodology has resulted in a classified predictive map of the surficial materials in the area. Based on the training areas, the overall accuracy of the predictive map is greater than 80 percent. Classes with the least accuracy included sand-gravel and thin till due to their spectral and textural similarities with thick till. This remote predictive mapping approach can be used for ground follow-up in surficial mapping and mineral exploration programs.
GEOSCAN ID225043