This contribution explores the potential of unmanned aerial systems (UAS) to monitor areas affected by acid mine drainage (AMD). AMD is an environmental phenomenon that usually develops in the vicinity of mining operations or in post-mining landscapes. The investigated area covers a re-cultivated tailing in the Sokolov lignite district of the Czech Republic. A high abundance of AMD minerals occurs in a confined space of the selected test site and illustrates potential environmental issues. The mine waste material contains pyrite and its consecutive weathering products, mainly iron hydroxides and oxides. These affect the natural pH values of the Earth’s surface. Prior research done in this area relies on satellite and airborne data, and our approach focuses on lightweight drone systems that enables rapid deployment for field campaigns and consequently-repeated surveys. High spatial image resolutions and precise target determination are additional advantages. Four field and flight campaigns were conducted from April to September 2016. For validation, the waste heap was probed in situ for pH, X-ray fluorescence (XRF), and reflectance spectrometry. Ground truth was achieved by collecting samples that were characterized for pH, X-ray diffraction, and XRF in laboratory conditions. Hyperspectral data were processed and corrected for atmospheric, topographic, and illumination effects using accurate digital elevation models (DEMs). High-resolution point clouds and DEMs were built from drone-borne RGB data using structure-from-motion multi-view-stereo photogrammetry. The supervised classification of hyperspectral image (HSI) data suggests the presence of jarosite and goethite minerals associated with the acidic environmental conditions (pH range 2.3–2.8 in situ). We identified specific iron absorption bands in the UAS-HSI data. These features were confirmed by ground-truth spectroscopy. The distribution of in situ pH data validates the UAS-based mineral classification results. Evaluation of the applied methods demonstrates that drone surveying is a fast, non-invasive, inexpensive technique for multi-temporal environmental monitoring of post-mining landscapes.

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.

The technical evolution of unmanned aerial systems (UAS) for mineral exploration advances rapidly. Recent sensor developments and improved UAS performance open new fields for research and applications in geological and geophysical exploration among others. In this study, we introduce an integrated acquisition and processing strategy for drone-borne multi-sensor surveys combining optical remote sensing and magnetic data. We deploy both fixed-wing and multicopter UAS to characterize an outcrop of the Otanmäki Fe-Ti-V deposit in central Finland. The lithology consists mainly of gabbro intrusions hosting ore bodies of magnetite-ilmenite. Large areas of the outcrop are covered by lichen and low vegetation. We use two drone-borne multi- and hyperspectral cameras operating in the visible to near-infrared parts of the electromagnetic spectrum to identify dominant geological features and the extents of ore bodies via iron-indicating proxy minerals. We apply band ratios and unsupervised and supervised image classifications on the spectral data, from which we can map surficial iron-bearing zones. We use two setups with three-axis fluxgate magnetometers deployed both by a fixed-wing and a multi-copter UAS to measure the magnetic field at various flight altitudes (15 m, 40 m, 65 m). The total magnetic intensity (TMI) computed from the individual components is used for further interpretation of ore distribution. We compare to traditional magnetic ground-based survey data to evaluate the UAS-based results. The measured anomalies and spectral data are validated and assigned to the outcropping geology and ore mineralization by performing surface spectroscopy, portable X-ray fluorescence (pXRF), magnetic susceptibility, and traditional geologic mapping. Locations of mineral zones and magnetic anomalies correlate with the established geologic map. The integrated survey strategy allowed a straightforward mapping of ore occurrences. We highlight the efficiency, spatial resolution, and reliability of UAS surveys. Acquisition time of magnetic UAS surveying surpassed ground surveying by a factor of 20 with a comparable resolution. The proposed workflow possibly facilitates surveying, particularly in areas with complicated terrain and of limited accessibility, but highlights the remaining challenges in UAS mapping.

Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.

The use of Unmanned Aerial Systems (UAS), also known as drones, is becoming increasingly important for geological applications. Thanks to lower operational costs and ease of use, UAS offer an alternative approach to aircraft-based and ground-based geoscientific measurements (Colomina & Molina 2014). Magnetic and hyperspectral UAS surveys hold particular promise for mineral exploration, and several groups have recently published studies of magnetic data collected by UAS for such applications (Malehmir et al. 2017; Cunningham et al. 2018), although equivalent studies using hyperspectral data are still rare (Kirsch et al. 2018). Combining both techniques is particularly useful. Magnetic measurements play an important role in mineral exploration, since magnetisation in rocks is mainly associated with magnetite and other iron minerals, which can be used in mapping and targeting of mineral deposits (Dentith & Mudge 2014). Hyperspectral imaging (HSI) is a powerful exploration and mapping technique in areas where the rock surface is well-exposed, and where geological units and mineral compositions can be estimated from spectral features of the electromagnetic spectrum in the visual and infrared range.

Mineral exploration in the West Greenland flood basalt province is attractive because of its resemblance to the magmatic sulfide-rich deposit in the Russian Norilsk region, but it is challenged by rugged topography and partly poor exposure for relevant geologic formations. On northern Disko Island, previous exploration efforts have identified rare native iron occurrences and a high potential for Ni–Cu–Co–PGE–Au mineralization. However, Quaternary landslide activity has obliterated rock exposure in many places at lower elevations. To augment prospecting field work under these challenging conditions, we acquire high-resolution magnetic and multispectral remote sensing data using drones in the Qullissat area. From the data, we generate a detailed 3D model of a mineralized basalt unit, belonging to the Asuk Member of the Palaeocene Vaigat Formation. Different types of legacy data and newly acquired geo- and petrophysical as well as geochemical-mineralogical measurements form the basis of an integrated geological interpretation of the unoccupied aerial system (UAS) surveys. In this context, magnetic data aim to define the location and the shape of the buried magmatic body, and to estimate if its magnetic properties are indicative for mineralization. UAS-based multispectral orthomosaics are used to identify surficial iron staining, which serves as a proxy for outcropping sulfide mineralization. In addition, UAS-based digital surface models are created for geomorphological characterization of the landscape to accurately reveal landslide features. UAS-based magnetic data suggest that the targeted magmatic unit is characterized by a pattern of distinct positive and negative magnetic anomalies. We apply a 3D magnetization vector inversion (MVI) model to the UAS-based magnetic data to estimate the magnetic properties and shape of the magmatic body. By means of introducing constraints in the inversion, (1) UAS-based multispectral data and legacy drill cores are used to assign significant magnetic properties to areas that are associated with the mineralized Asuk Member, and (2) the Earth's magnetic and the palaeomagnetic field directions are used to evaluate the general magnetization direction in the magmatic units. Our results suggest that the geometry of the mineralized target can be estimated as a horizontal sheet of constant thickness, and that the magnetization of the unit has a strong remanent component formed during a period of Earth's magnetic field reversal. The magnetization values obtained in the MVI are in a similar range to the measured ones from a drillcore intersecting the targeted unit. Both the magnetics and topography confirm that parts of the target unit were displaced by landslides. We identified several fully detached and presumably rotated blocks in the obtained model. The model highlights magnetic anomalies that correspond to zones of mineralization and is used to identify outcrops for sampling. Our study demonstrates the potential and efficiency of using high-resolution UAS-based multi-sensor data to constrain the geometry of partially exposed geological units and assist exploration targeting in difficult or poorly exposed terrain.