Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

Abstract

Base metal smelting activities can produce acidic rain that promotes vegetation loss and the development of black coatings on bedrock. Such coatings can form over large areas and are among the most prominent long-term vestiges of past smelting activities. In this study, multispectral images derived from Hyperion reflectance data were evaluated with regard to their utility in the discrimination and mapping of black rock coatings near Sudbury. Spectral angle mapper (SAM) classifications generated on the basis of image-derived endmember spectra could not be used to properly identify major exposures of coated bedrock without also producing substantial confusion with uncoated classes. Neural network and maximum likelihood classifications produced improved representations of the spatial distribution of coated bedrock, though confusion between coated and uncoated classes is problematic in most outputs. Maximum likelihood results generated using a null class are noteworthy for their effectiveness in highlighting exposures of coated bedrock without substantial confusion with uncoated classes. Although challenges remain, classification results confirm the potential of remote sensing techniques for use in the worldwide detection, mapping, and monitoring of coating-related environmental degradation in the vicinities of base metal smelters.