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Airborne remote sensing has been used at Highland Valley Copper since 2001 as part of the reclamation program to provide annual high spatial resolution maps of vegetation cover. These maps cover the entire mine site and complement the vegetative assessments. This unique and growing dataset allows us to examine vegetation changes over time with spatial resolution of about 3 m. We can now classify the entire mine site according to its vegetation history and whether or not it has reached and maintained biomass above 1,500 kg/ha, which is the permit threshold for self-sustaining status. The resulting ‘reclamation status maps’ provide a detailed synopsis of the vegetation trends between 2001 and 2010. They are designed to be helpful to reclamation managers and provide a means to focus further remediation effort on specific locations most needing it, rather than having to make wholesale changes to entire sites. An analysis of the ten year time series has revealed statistically significant correlations between the temporal patterns of remotely sensed vegetation cover and precipitation across much of the Highland Valley site. The strength and time scale of these correlations are related to the water retention capability of each location, which in turn is related to slope and material. These factors vary spatially across the mine site. Maps of these precipitations vs. vegetation correlations offer great potential value for reclamation management, because they identify the particular locations most likely to benefit from more rigorous soil remediation measures such as the addition of wood chips, biosolids, or trenching and mounding. In this presentation we demonstrate how a comparison of time series reclamation coverage maps with precipitation response maps can aid in the identification of areas where growth is limited by excessively dry conditions, which can then be targeted for specific interventions thus improving the likelihood of successful reclamation.

Assessment of reclamation status and identification of water stress using airborne remote sensing