| Title | Predictive mapping of surficial materials, Schultz Lake area (NTS 66A), Nunavut, Canada |
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| Author | Grunsky, E; Harris, J; McMartin, I |
| Source | Remote 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 |
| Year | 2009 |
| Alt Series | Earth Sciences Sector, Contribution Series 20080009 |
| Document | serial |
| Lang. | English |
| Media | paper |
| Province | Nunavut |
| NTS | 66A |
| Area | Schultz 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 |
| Subjects | surficial 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 |
| Illustrations | satellite images; photographs; schematic diagrams; plots; tables |
| Abstract | Multibeam 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 ID | 225043 |
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