Remote predictive mapping of bedrock geology using image classification of Landsat and SPOT data, western Minto Inlier, Victoria Island, Northwest Territories, Canada

Abstract

Supervised classification (robust classification method) of Landsat-7 and SPOT-5 data was used to analyse the bedrock geology of a part of the western Minto Inlier on Victoria Island, Canada. The robust classification method was used as it provides a series of uncertainty measures for evaluating the classification results. Six bedrock classes including gabbro, basalt, carbonate of the Wynniatt Formation, quartz-arenite of the Kuujjua Formation, evaporite of the Minto Inlet and Killian Formations and Paleozoic carbonate together with six surficial classes including vegetation were defined as the training data set. The resulting classified images derived from the Landsat and SPOT data were very similar in terms of the regional distribution of lithological classes, as reflected by fairly high classification accuracies for both image types. Gabbro and basalt, despite having a similar mineralogical composition, are spectrally distinct throughout most of the study area. Complicating spectral signatures of overlying glacial sediments and/or other overburden materials and spectral similarities between some of the lithologies caused poorer classification in some areas. Generally, the Landsat imagery provided better spectral separability between most of the lithological units than the SPOT imagery. However, in certain areas where the spectral separation between different lithologies is not dependant on the shortwave infrared-2 (SWIR-2; band 7 on Landsat) and/or blue bands (band 1 on Landsat), the SPOT imagery provided a better classification because of higher spatial resolution.