Use of remote sensing in reclamation assessment at Teck Cominco’s Bullmoose mine site Straker, Justin; Blazecka, M.; Sharman, Kevin; Woelk, S.; Boorman, S.; Kuschminder, J.
The Bullmoose mine site is located in northeastern British Columbia, 40 km west of the town of Tumbler Ridge. The mine was an open-pit operation that produced metallurgical coal from October 1983 until its closure in April, 2003. The mine development footprint covers 789 hectares, and includes the Engelmann Spruce – Subalpine Fir moist very cold Bullmoose variant (ESSFmv2), the subalpine ESSFmvp2 parkland, and Alpine Tundra biogeoclimatic classes (ranging in elevation from the plant site, at 1100 m a.s.l. in the Bullmoose Creek valley, to waste dumps located at 1800 m). As a component of assessing reclamation to date and to prepare for closure, Bullmoose Operating Corporation and C.E. Jones & Associates Ltd. undertook in 2002 and 2003 a site-wide assessment on the status and success of currently reclaimed areas on the mine site. To accomplish this, a reclamation assessment program was developed using supervised computer classification of satellite imagery to identify discrete vegetation units on the reclaimed mine site. Ground-truthing using established reclamation success-and-sustainability assessment methods was conducted in August 2002 to validate and refine the classification system. The remote sensing-based classification resulted in the definition of 10 vegetation classes on reclaimed coal waste on the Bullmoose mine site. The defined classes are identifiable by unique spectral signatures, and are statistically separable based on selected ground-truth vegetation productivity and sustainability parameters. This information will be used to document achievement of post-closure equivalent productivity objectives on older reclaimed sites, and to predict the success of more recent reclamation, while reducing the need for more costly and labour-intensive ground-based assessment work. This paper summarizes the methods employed and results derived from this remote sensing-based assessment program.
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