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TitleClassification of remotely sensed imagery for surficial geological mapping in Canada's North
AuthorHarris, J; Grunsky, E; McMartin, I
SourceFirst Break vol. 25, 2007 p. 85-95
Year2007
Alt SeriesGeological Survey of Canada, Contribution Series 20070170
Documentserial
Lang.English
Mediapaper
File formatpdf (Adobe® Reader®)
ProvinceNunavut
NTS66A
AreaSchultz Lake; Thelon River
Lat/Long WENS-98.0000 -96.0000 65.0000 64.0000
Subjectssurficial geology/geomorphology; remote sensing; satellite imagery; mapping techniques; computer mapping; radar imagery; digital terrain modelling; airphoto interpretation; field work; statistical analyses; bedrock geology; Archean; postglacial deposits; glacial deposits; sands; gravels; organic deposits; tills; boulders; classification; RADARSAT-1 SAR; LANDSAT 7 ETM+; synthetic aperture radar (SAR); digital elevation models (DEM); remote predictive mapping; surface roughness; spectral properties; Phanerozoic; Cenozoic; Quaternary; Precambrian; Proterozoic
Illustrationssketch maps; digital elevation models; satellite images; tables; plots
ProgramNorthern Resources Development Program
LinksOnline - En ligne
Abstract(Summary)
Mapping in the North is an expensive proposition due to remoteness, lack of infrastructure, logistical problems and the generally short mapping season. Remotely sensed data offers a useful source of information to the mapping geologist for not only updating existing geological maps but as a first order source of geological information in areas that have not been well-mapped. Needless to say areas that have not been mapped in detail comprise many areas of Canada's North.
This paper focuses on the use of remotely sensed data (RADARSAT, LANDSAT) in conjunction with a digital elevation model (DEM) data for producing predictive (classifications) maps of surficial units in the Shultz Lake area (NTS 66A), Nunavut and is based on work by Grunsky (2002) and Grunsky et al. (2006). A computer-assisted approach is employed which involves identifying representative areas on air photos (supported by field work) of various surficial units, collecting signatures of these "training areas" from the remotely sensed imagery and then identifying similar areas on the imagery using a maximum likelihood classification algorithm. The methodology for producing predictive maps of surficial units presented in this paper can be used in other Northern environments.
GEOSCAN ID224138