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.

中文翻译：

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使用无人机和热成像绘制 El Gallo 矿金堆浸出垫的表面水分图

了解堆浸垫 (HLP) 上表面水分含量的时空变化对于浸出生产和实现高矿石回收率至关重要。浸出垫监测和数据收集的当前做法仍然是高度人工和劳动密集型的，并使技术人员暴露于危险材料（即氰化物溶液）。为了应对这些挑战，我们建议使用配备热成像传感器的无人机 (UAV) 远程获取高时空分辨率图像数据，以监测 HLP 上的表面水分分布。对墨西哥锡那罗亚州 El Gallo 金矿的喷水灌溉 HLP 进行了实地研究，所获得的数据用于通过线性回归推导出地表水分含量与遥感地表温度之间的经验关系。此外，这些数据用于生成整个 HLP 表面的水分分布图。在现场实验期间手动采集原位样品，以测量选定采样位置的地面真实材料水分。结果表明，遥感方法与测量的地面实况样本之间具有良好的一致性。