Abstract
The exposure of metal sulfides to air or water, either produced naturally or due to mining activities, can result in environmentally damaging acid mine drainage (AMD). This needs to be accurately monitored and remediated. In this study, we apply high-resolution unmanned aerial system (UAS)-based hyperspectral mapping tools to provide a useful, fast, and non-invasive method for the monitoring aspect. Specifically, we propose a machine learning framework to integrate visible to near-infrared (VNIR) hyperspectral data with physicochemical field data from water and sediments, together with laboratory analyses to precisely map the extent of acid mine drainage in the Tintillo River (Spain). This river collects the drainage from the western part of the Rio Tinto massive sulfide deposit and discharges large quantities of acidic water with significant amounts of dissolved metals (Fe, Al, Cu, Zn, amongst others) into the Odiel River. At the confluence of these rivers, different geochemical and mineralogical processes occur due to the interaction of very acidic water (pH 2.5–3.0) with neutral water (pH 7.0–8.0). This complexity makes the area an ideal test site for the application of hyperspectral mapping to characterize both rivers and better evaluate contaminated water bodies with remote sensing imagery. Our approach makes use of a supervised random forest (RF) regression for the extended mapping of water properties, using the samples collected in the field as ground-truth and training data. The resulting maps successfully estimate the concentration of dissolved metals and related physicochemical properties in water, and trace associated iron species (e.g., jarosite, goethite) within sediments. These results highlight the capabilities of UAS-based hyperspectral data to monitor water bodies in mining environments, by mapping their hydrogeochemical properties, using few field samples. Hence, we have demonstrated that our workflow allows the rapid discrimination and mapping of AMD contamination in water, providing an essential basis for monitoring and subsequent remediation.
Highlights
Acid mine drainage (AMD) is an environmental phenomenon that can occur either by the natural exposure of sulfide minerals to weathering conditions or as a consequence of certain mining activities
In relation to AMD detection and monitoring, multi and hyperspectral sensors has been widely used, due to the distinctive spectral absorption features of iron-minerals present in the visible to shortwave infrared region of the electromagnetic spectrum [4]. These studies have covered a wide range of spatial dimensions depending on the platform used for data acquisition: including satellite studies [4] for water reservoirs protection [5] and indirect pH estimations by mapping iron-bearing minerals precipitated on the stream bed [6] to airborne surveys over mine tailings [7,8,9,10]
The strong absorption features found in the water spectra between 400 and 700 nm might be mostly influenced by the presence of Fe3+ ions which turns the acidic waters a deep red color
Summary
Acid mine drainage (AMD) is an environmental phenomenon that can occur either by the natural exposure of sulfide minerals to weathering conditions or as a consequence of certain mining activities. In relation to AMD detection and monitoring, multi and hyperspectral sensors has been widely used, due to the distinctive spectral absorption features of iron-minerals present in the visible to shortwave infrared region of the electromagnetic spectrum [4]. These studies have covered a wide range of spatial dimensions (scales) depending on the platform used for data acquisition: including satellite studies [4] for water reservoirs protection [5] and indirect pH estimations by mapping iron-bearing minerals precipitated on the stream bed [6] to airborne surveys over mine tailings [7,8,9,10]. The obtained hydrogeochemical maps can be used to monitor water bodies surrounding mining ecosystems, by targeting sources and/or acidic contamination in water, promoting its continuous supervision or assisting in the selection of the most adequate remediation treatment
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