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TitleAn emerging paradigm for surficial geological mapping of Arctic Canada at the Geological Survey of Canada
 
AuthorRussell, H A JORCID logo; Broscoe, D; Giroux, D; Grunsky, E; Harris, J; Kerr, D; Lesemann, J; Parkinson, W; Richardson, M; Sharpe, D R
Source33rd Canadian Symposium on Remote Sensing, abstracts; by Canadian Symposium on Remote Sensing; 2012 p. 8 Open Access logo Open Access
LinksOnline - En ligne
LinksAbstracts (PDF, 1.22 MB)
Year2012
Alt SeriesEarth Sciences Sector, Contribution Series 20140071
Meeting33rd Canadian Symposium on Remote Sensing; Ottawa; CA; June 11-14, 2012
Documentbook
Lang.English
Mediaon-line; digital
File formatpdf
Subjectssurficial geology/geomorphology; remote sensing; terrain analysis; landforms
ProgramGEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource Assessment)
AbstractTerrain analysis of glaciated terrains is approaching a "tipping point" as remotely sensed digital data and digital elevation models become more available and cost-effective alternatives to aerial photographs. The challenge in remote predictive mapping (RPM) of glaciated landscapes is recognition of the series of complex steps in the traditional cogitative terrain analysis process and encapsulating them within computational workflows based on image analysis and statistical modelling. Within the SMART (Systematic Mapping of Arctic Canada by Remote Techniques) project of Geo-Mapping for Energy and Minerals Program (GEM), a methodology and data handling framework is being developed to improve mapping productivity.
Remote Predictive Mapping (RPM) is a complex challenge that involves: i)development of a science language for glaciated terrain, ii) integration of expert knowledge and legacy datasets, iii) parsing knowledge into machine operable components (morphology, texture, shape etc.), iv) classification of attributes, v) evaluation of various geoscience data types (i.e. remotely sensed , topographic and various calculated derivative images) for surficial mapping, and vi) statistical analysis, modelling and expert systems integration of the diverse landscape attributes within a geoscience data stack. Morphology, for example, is being extracted through analysis of Digital Elevation Model data and derivatives. This work forms the basis for specific landform analysis (e.g. eskers) and as a component of the data stack. Material (texture, lithology) types are primarily being captured using remotely sensed data (LANDSAT, Radar) in concert with pixel-to-pixel-based classification algorithms (e.g. Robust Classification Method (RCM)).
GEOSCAN ID294572

 
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