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TitreSurficial materials mapping using remote sensing and classification methods, Repulse Bay area, Nunavut - geological knowledge vs. statistical approach
AuteurWityk, U L; Ross, M; McMartin, I; Campbell, J; Harris, J; Grunsky, E
SourceCANQUA-CGRG Binannual Meeting, abstracts; 2013 p. 258
Année2013
Séries alt.Secteur des sciences de la Terre, Contribution externe 20130052
ÉditeurCANQUA
RéunionCANQUA-CGRG Binannual Meeting; Edmonton; CA; août 18-21, 2013
Documentlivre
Lang.anglais
Mediapapier
ProvinceNunavut
SNRC46M; 46L; 46N; 46O
Lat/Long OENS -88.0000 -87.0000 4.0000 2.0000
ProgrammeGisements polymétalliques - Presqu'île Melville (Nunavut), GEM : La géocartographie de l'énergie et des minéraux
Résumé(disponible en anglais seulement)
Geological mapping of Northern regions in Canada represents an essential step in providing key knowledge for resource development and economic prosperity of northern communities. However, mapping this large remote region presents a major challenge both in terms of financial resources and the time required to cover such a large area. New techniques are emerging that support the surficial geological mapping of vast northern regions at scales appropriate for mineral exploration and related land-use management.
An approach using LANDSAT 7 TM imagery, field-based data and a maximum likelihood classification algorithm is employed to produce remote predictive maps of the surficial materials in the Repulse Bay area, Nunavut (NTS 46M-SW, 46L-W and S and 46K-SW). Two approaches in the remote predictive mapping (RPM) process are used to determine the optimal class combination and resultant maps: 1) The geological knowledge-based approach, which employs general and field knowledge from the Quaternary geologists to the map evaluation. This knowledge determines how the training areas are grouped and merged based on qualitative Quaternary geology principles and 2) a statistical-based approach which produces classified maps based on training areas along with measures of classification accuracy. Both methods are used and compared to discover optimal class combinations - the first method qualitatively, and the second quantitatively. Four classification maps that offer the highest overall classification accuracies determined through analysis of a confusion matrix and associated variability maps were produced (two for each approach). Exposed marine sediments, carbonate-rich tills, organics and boulder terrains are the most accurately (>75%) classified of the surficial materials classes; confusion occurs between remaining till, sand and gravel, and bedrock units. Variability maps were produced using these optimal class combinations and corresponding classifications, through which it is found that the geological knowledge- based approach is more suitable for remotely mapping surficial materials in this study area.
Résumé(Résumé en langage clair et simple, non publié)
Les matériaux superficiels laissés par le retrait des derniers glaciers ont été cartographiés près du village de Repulse Bay, Nunavut central continental. La cartographie des formations superficielles est basée sur l'interprétation et la classification des données de télédétection, et des données acquises sur le terrain. Ce travail a été entrepris afin d'offrir de nouvelles connaissances géologiques sur la distribution et la nature des matériaux de surface, à l'appui du processus de prise de décisions éclairées pour la mise en valeur des ressources et l'utilisation des terres. Ce travail fait partie d'un projet de recherche d'études supérieures à l'Université de Waterloo, effectué sous le projet de la péninsule de Melville à la Commission géologique du Canada, dans le cadre du Programme en géocartographie de l'énergie et des minéraux (GEM) de Ressources Naturelles Canada.
GEOSCAN ID292583