Abstract
An understanding of the spatial and temporal variations of surface moisture content over a heap leach pad (HLP) is essential for leaching production and to achieve a high ore recovery. The current practice of leach pad monitoring and data collection remains highly manual and labor-intensive, and exposes technical staff to hazardous material (i.e., cyanide solution). To address these challenges, we propose using unmanned aerial vehicles (UAVs) equipped with thermal imaging sensors to remotely obtain high temporal and spatial resolution image data for monitoring the surface moisture distribution over HLPs. A field study was conducted over a sprinkler-irrigated HLP at El Gallo gold mine in Sinaloa State, Mexico, and the acquired data were used to derive an empirical relationship between the surface moisture content and the remotely sensed surface temperature using linear regression. Moreover, the data were used to generate moisture distribution maps of the entire HLP surface. In situ samples were taken manually during the field experiments to measure the ground-truth material moisture at selected sampling locations. The results show a good agreement between the remote sensing method and the measured ground-truth samples.
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