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TitreAn emerging paradigm for surficial geological mapping of Arctic Canada at the Geological Survey of Canada
AuteurRussell, H A J; Broscoe, D; Giroux, D; Grunsky, E; Harris, J; Kerr, D; Lesemann, J; Parkinson, W; Richardson, M; Sharpe, D R
Source39th Annual Yellowknife Geoscience Forum, abstracts of talks and posters; par Fischer, B J; Watson, D M; Northwest Territories Geoscience Office, Yellowknife Geoscience Forum Abstracts Volume vol. 2011, 2011 p. 117-118
Séries alt.Secteur des sciences de la Terre, Contribution externe 20110218
Réunion39th annual Yellowknife Geoscience Forum; Yellowknife; CA; Novembre 15 - 17, 2011
Documentpublication en série
ProvinceNunavut; Yukon; Territoires du Nord-Ouest
Sujetstechniques de cartographie; cartographie par ordinateur; télédétection; analyse de terrains; modélisation numérique de terrain; dépôts glaciaires; elements glaciaires; topographie glaciaire; géologie des dépôts meubles/géomorphologie; géophysique; Cénozoïque; Quaternaire
ProgrammeBases de données couvrant les trois territoires (la télécartographie prédictive), GEM : La géocartographie de l'énergie et des minéraux
LiensOnline - En ligne
Résumé(disponible en anglais seulement)
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 (GEMS), a methodology and data handling framework is being developed to improve mapping productivity.
SMART mapping 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))
Lake shape and landforms are being analyzed using form statistics and object-orientated, landscape-segmentation techniques. Spatial association of various landform metrics provides a challenge that is being undertaken using density functions and integration of specific expert interpreted data layers (drumlin, esker, etc.). Integration of this diverse suite of data layers is being completed using several techniques, including: statistical approaches, decision trees, fuzzy sets, and expert system approaches.
Two specific case studies are highlighted that represent new approaches to surficial geological mapping depending upon terrain complexity, study area scale, data availability and study resources. The Central Baffin case study illustrates mapping a large area at a scale of 1: 500,000 from LANDSAT and DEM derivatives: it serves as a proto-type for the SMART 'grey space' mapping initiative as well as the GEM Tri-Territorial Surficial Geology Compilation Project. A more detailed and modelling-intensive approach is being applied in the McKay Lake (75M) case study near Great Slave Lake using DEM, SPOT 4, LANDSAT data and a fuzzy set modelling approach. These examples illustrate the process from an initial predictive surficial materials map to a derivative predictive surficial geology map which can then be used as an aid to field-supported mapping. The transition from cognitive interpretation and recording of terrain elements to semi-automated approaches is a considerable challenge that requires careful consideration of the conceptual and semantic models employed by the geologists. The requirement to revisit the classification lexicon of glacial landscapes, landforms and geological legends will result in improved perspectives and understanding of the signatures and processes of the glaciated landscape of northern Canada derived from remote predictive mapping.