| Title | An emerging paradigm for surficial geological mapping of Arctic Canada at the Geological Survey of Canada |
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| Author | Russell, H A J ;
Broscoe, D; Giroux, D; Grunsky, E; Harris, J; Kerr, D; Lesemann, J; Parkinson, W; Richardson, M; Sharpe, D R |
| Source | 33rd Canadian Symposium on Remote Sensing, abstracts; by Canadian Symposium on Remote Sensing; 2012 p. 8  Open Access |
| Links | Online - En ligne
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| Links | Abstracts (PDF, 1.22 MB)
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| Year | 2012 |
| Alt Series | Earth Sciences Sector, Contribution Series 20140071 |
| Meeting | 33rd Canadian Symposium on Remote Sensing; Ottawa; CA; June 11-14, 2012 |
| Document | book |
| Lang. | English |
| Media | on-line; digital |
| File format | pdf |
| Subjects | surficial geology/geomorphology; remote sensing; terrain analysis; landforms |
| Program | GEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & Databases (Remote Predictive Mapping / Mineral Resource
Assessment) |
| Abstract | Terrain 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 ID | 294572 |
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