| Title | Spectral Unmixing for Monitoring Mine Tailings Site Rehabilitation, Copper Cliff Mine, Sudbury, Ontario |
| Download | Downloads (Preprint) |
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| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Lévesque, J; Szeredi, T; Staenz, K; Singhroy, V; Bolton, D |
| Source | Twelfth International Conference and Workshops on Applied Geologic Remote Sensing, Denver, Colorado, 17-19 November; 1997 p. 340-347, https://doi.org/10.4095/219063  Open Access |
| Year | 1997 |
| Alt Series | Earth Sciences Sector, Contribution Series 20042261 |
| Document | book |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Released | 1997 01 01 |
| Abstract | Hyperspectral imagery from the Compact Airborne Spectrographic Imager (casi) was used to characterize sulphide mine tailings at the Copper Cliff mine in Sudbury, Ontario. The objective of the
study was to evaluate the usefulness fo high spatial and spectral resolution data and their analysis techniques for characterizing mine tailings sitees and monitoring their restoration. Flight data were acquired in late August 1996 in 72 continguous
9 nm wide spectral bands from 400 nm to 950 nm along with a detailed ground survey. Image data were processed and analysed using the Imaging Spectrometer Data Anaysis System (ISDAS) developed at the Canada Centre for Remote Sensing. The image data
were classified using unconstrained linear spectral unmixing. Validation of the unmixing results was achieved by correlating image fractions to fractions measured on the ground. Five end-members were selected: oxidized tailings, lime, green
vegetation, fresh and contaminated water. Results show that image fractions of green vegetation and lime are well correlated with the ground fractions (r-square = 0.96 and 0.92). The lower r-squaare (0.65) of the oxidized tailings endmember could be
attributed to the variable concentrations of oxidizing agents (various degrees of oxidation) in the tailings which are difficult to assess visually. |
| GEOSCAN ID | 219063 |
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