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TitleLeaf area Index mapping for Arctic Canada
AuthorAbuelgasim, A; Leblanc, SORCID logo; White, H PORCID logo; Maloley, M
Source33rd Canadian Symposium on Remote Sensing, abstracts; by Canadian Symposium on Remote Sensing; 2012 p. 35 Open Access logo Open Access
LinksOnline - En ligne
LinksAbstracts (PDF, 1.22 MB)
Alt SeriesEarth Sciences Sector, Contribution Series 20140082
Meeting33rd Canadian Symposium on Remote Sensing; Ottawa; CA; June 11-14, 2012
Mediaon-line; digital
File formatpdf
Subjectsgeophysics; Nature and Environment; remote sensing; satellite imagery; analytical methods
ProgramClimate Change Geoscience
Released2012 01 01
AbstractVolcanogenic massive sulphide (VMS) deposits are a globally important resource of base metals (Cu, Pb, Zn). Formation of VMS deposits is accompanied by hydrothermal alteration of wall rocks; this alteration is typically zoned into distinct mineral assemblages. We investigate airborne and field-collected hyperspectral imagery as a means to identify and delineate the hydrothermal alteration zone at the Izok Lake Zn-Cu-Pb-Ag VMS deposit in Nunavut, Canada. The deposit is hosted within a sequence of predominantly felsic pyroclastic rocks of Archean (2.68 Ga) age. The hydrothermal alteration is characterized by widespread muscovite-enrichment and zones of biotite-dominated assemblages. Most rock outcrops are covered by lichens and shrubs that partially obscure the spectral signature of the rock substrate. In the early stage of this study we use spectral analysis of a hyperspectral regional survey followed by fuzzy clustering and trend analysis to uncover the spatial distribution of key alteration minerals. Preliminary results indicate that variability exists in the location of the 2200 nm absorption feature of muscovite, and the band depths near 2245 nm, associated to chlorite abundance. Both band depths and band locations will be examined by trend analysis with an objective of uncovering the changes in mineral species and hydrothermal alteration. The applicability of fuzzy clustering techniques in noise removal will be tested with the prevailing hypothesis that outliers caused by H2O absorption and sensor noise can be removed from the dataset by means of unsupervised classification. The results will be validated through mineral identification by optical microscopy.

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