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.

Delineating Areas of Past Environmental Degradation near Smelters using Rock Coatings: A Case Study at Rouyn-Noranda, Quebec

“…Notable associated absorption features were identified and were related to the mineralogical and elemental components of coatings. Comparisons between Rouyn-Noranda spectra and those previously collected in the Sudbury region were performed in order to determine if notable differences exist between the reflectance properties of coatings produced by emissions from the Horne smelter and those of coatings produced in the Sudbury region by the Falconbridge, Coniston, and Copper Cliff smelters 11 , 12 .…”

“…Prior research demonstrated the effectiveness of the maximum likelihood algorithm in the remote-sensing-based identification of coated rock surfaces in the Sudbury region of Ontario, Canada 11 , 12 . In that work, the maximum likelihood algorithm, with null class implemented, outperformed a feedforward backpropagation neural network classifier and the Spectral Angle Mapper classifier by consistently producing useful and representative coating maps characterized by low proportions of false positives.…”

“…This can improve classification outcomes for e.g. the rock coating class by reducing false positive detections in areas where materials with somewhat similar reflectance characteristics are present (e.g., mine tailings) 11 , 12 . Though rock coatings were of prime interest in this study, the utilized algorithm requires the specification of a representative and complete list of general surface cover classes.…”

“…The use of remote sensing techniques in the detection and mapping of rock coatings near smelters can help to delineate the geographic extents of areas subjected to past environmental degradation, and has the potential to be applied toward the monitoring of ongoing degradation at sites where emissions continue to promote the development of coatings 11 , 12 . Related techniques may also prove useful in the monitoring of environmental recovery where smelter emissions have been reduced.…”

“…Related techniques may also prove useful in the monitoring of environmental recovery where smelter emissions have been reduced. In prior work, the supervised classification of Landsat Enhanced Thematic Mapper Plus (ETM+) and Hyperion satellite images was successfully used to determine the spatial distribution of rock coatings near three smelting sites in the Sudbury region of Ontario, Canada, highlighting areas previously exposed to relatively high levels of acid rain and smelter-derived particulates 11 , 12 .…”