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TitleHyperspectral TRWIS III data to delineate the Kam Kotia Mine tailings areas (Ontario, Canada)
AuthorRichter, N; Kaufmann, H; Staenz, K
SourceProceedings of SPIE, the International Society of Optical Engineering vol. 5574, 32, 2004 p. 214-223,
Alt SeriesNatural Resources Canada, Contribution Series 20181377
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
ProgramCanada Centre for Remote Sensing Divsion
AbstractThe Kam Kotia mine tailings generated acidic mine drainage waters which killed large areas of adjacent forest and badly damaged surrounding ecosystems over a 30-year period. With the start of the site's rehabilitation in 2001, a remote sensing-based monitoring program was initiated. In this first phase, the baseline study was carried out to come up with a method to delineate the Kam Kotia mine tailings areas into distinct zones, which enable the monitoring of the rehabilitation status. This study was based on airborne hyperspectral TRWIS III imagery. Data pre-processing included the retrieval of surface reflectance and corrections of radiometric and spectral errors. After application of a destriping procedure, surface reflectances were retrieved indicating a varying across-track wavelength shift, known as the spectral smile. The detection and correction of this phenomenon used an algorithm based on the comparison of measured and modeled at-sensor radiance data within certain wavelength regions. Finally, sensor calibration problems required a scene-based radiometric calibration performed on the destriped and spectrally corrected reflectance data. The subsequent spectral unmixing analysis included an iterative error analysis (IEA) technique to automatically extract endmembers from the data. The resulting fraction images were first grouped into three major surface materials (vegetation, vegetation residues and oxidized tailings). Three boundaries were determined, delineating the three surface materials and a less vegetated transition by subdividing the entire tailings area into four distinct zones. The area change of each zone and the course of the boundaries in future data sets will provide information of the site's rehabilitation status.

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