Abstract

Multispectral classification approaches were applied to high-resolution ASTER (15 m) and ETM+ (30 m) imagery for the purpose of developing new techniques for mapping recently deglaciated LIA perennial ice cover in the Canadian Arctic. Four areas in the Queen Elizabeth Islands, with dissimilar surficial geology and diverse topographic complexity, were selected to test the efficacy of both sensors for mapping these subtle landscape features. Automated classification (band calculation) methods were found to be most effective on quartzitic sandstone and siliceous crystalline bedrock, whereas, semi-automated (supervised classification) techniques were most successful on substrates comprised primarily of carbonate lithologies. ASTER's superior spatial resolution yielded higher accuracies in topographically complex areas; however, ETM+ was more effective over a wider variety of substrate lithologies and topographic settings, with a mean overall accuracy of 91% (mean κ statistic = 0.71), compared to 87% (mean κ statistic = 0.60) for ASTER.

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