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The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 77184

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Dr. Richard Gloaguen

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Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany
Interests: hyperspectral imaging; mineral exploration; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Moritz Kirsch

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Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division of Exploration, Chemnitzer Straße 40, 09599 Freiberg, Germany
Interests: 3D geomodelling; structural geology; tectono-magmatic processes

Special Issue Information

Dear Colleagues,

The high demand for raw materials in our post-industrial societies contrasts the increasing difficulties to find new mineral deposits. Accessible and high-grade deposits are mostly exhausted or currently mined. Hence, future exploration must focus on the remaining, more remote locations and penetrate much deeper into the Earth’s crust or make use of the huge amount of left-overs (tailings).

The monitoring of mining of processing sites can also benefit from the use of UAVs. Mine surveying covers all aspects concerning the mining of rock mass and related safety issues (e.g., pit slope stability) and UAVs recently became a new option for geo- referenced, quantitative assessments of the mined tonnage. Recently, the near real time assessment of quality and quantity of ores has become a challenge for an efficient processing. UAVs might support a rapid survey of the mining operations and of the material flow to and out of the processing facilities.

The renaturation of mining areas is a permanent task, which requires high-resolution monitoring. Spectral sensors are useful for the detection of vegetation health and Acidic Mine Drainage (AMD) aspects. Unmanned Aerial Systems (UAS) equipped with spectral sensors suite in particular vegetation monitoring to assess the success in renaturation, for example, in the case of shallow surface mining covering a large area with a high turnover of top soils. The acquired data allows monitoring the response of vegetation to stress factors at a high temporal and spatial resolution and hence, is the basis for an improved management of renaturation. In addition, slope failures are of concern for sustainable renaturation programs and may be monitored using UAS structure-from-motion photogrammetry.

These aspects, coupled with the challenging market conditions, outline the need for more creative and integrative approaches for the exploration of new mineral deposits and the monitoring of (post-) mining activities. However, there is a gap between the scales of airborne and ground-based data in terms of spatial resolution. UAS can overcome the scale gap providing high- resolution multi-temporal data. They are easy to use, flexible and deliver data within cm-scale resolution. The UAVs currently mainly relies on structure-from-motion photogrammetry, aerial photography and agricultural monitoring. However, only few sensors are available for UAS (e.g., VNIR hyperspectral, but no SWIR). The field of drone-borne hyperspectral and geophysical applications is rather unexplored but provides great potential. So, an integration of aircraft based- and drone-borne data is mostly remains necessary for applications along the value chain of raw materials. The combination of multi scale applications might allow a more efficient exploration, surveillance of mining operations and geotechnical problems and the rehabilitation of mining landscapes.

For this Special Issue, we seek submissions about the design and the evaluation of new and adapted concepts for the use of UAV based multi-sensor observation and monitoring methods. Satellite remote sensing can provide baselines for the required high temporal and spatial resolutions. The methods include geophysical tools, LiDAR as well as hyperspectral sensors covering the visible to thermal-infrared part of the electromagnetic spectrum. The new combined sensor approach should address various purposes, such as, but not limited to:

- Exploration and mineral mapping to secure sustainability in mining

- Monitoring of mining operations to improve efficiency and safety

- Ongoing assessment of mining activities to mitigate environmental impact

- Survey of post mining landscape rehabilitation.

Dr. Richard Gloaguen
Dr. Moritz Kirsch
Guest Editors

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Published Papers (6 papers)

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Research

15 pages, 3784 KiB

Open AccessArticle

Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face

by Stephane Boubanga-Tombet, Alexandrine Huot, Iwan Vitins, Stefan Heuberger, Christophe Veuve, Andreas Eisele, Rob Hewson, Eric Guyot, Frédérick Marcotte and Martin Chamberland

Remote Sens. 2018, 10(10), 1518; https://doi.org/10.3390/rs10101518 - 21 Sep 2018

Cited by 27 | Viewed by 7457

Abstract

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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31 pages, 12258 KiB

Open AccessFeature PaperArticle

Integration of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Mining Monitoring

by Moritz Kirsch, Sandra Lorenz, Robert Zimmermann, Laura Tusa, Robert Möckel, Philip Hödl, René Booysen, Mahdi Khodadadzadeh and Richard Gloaguen

Remote Sens. 2018, 10(9), 1366; https://doi.org/10.3390/rs10091366 - 28 Aug 2018

Cited by 79 | Viewed by 12831

Abstract

Mapping lithology and geological structures accurately remains a challenge in difficult terrain or in active mining areas. We demonstrate that the integration of terrestrial and drone-borne multi-sensor remote sensing techniques significantly improves the reliability, safety, and efficiency of geological activities during exploration and mining monitoring. We describe an integrated workflow to produce a geometrically and spectrally accurate combination of a Structure-from-Motion Multi-View Stereo point cloud and hyperspectral data cubes in the visible to near-infrared (VNIR) and short-wave infrared (SWIR), as well as long-wave infrared (LWIR) ranges acquired by terrestrial and drone-borne imaging sensors. Vertical outcrops in a quarry in the Freiberg mining district, Saxony (Germany), featuring sulfide-rich hydrothermal zones in a granitoid host, are used to showcase the versatility of our approach. The image data are processed using spectroscopic and machine learning algorithms to generate meaningful 2.5D (i.e., surface) maps that are available to geologists on the ground just shortly after data acquisition. We validate the remote sensing data with thin section analysis and laboratory X-ray diffraction, as well as point spectroscopic data. The combination of ground- and drone-based photogrammetric and hyperspectral VNIR, SWIR, and LWIR imaging allows for safer and more efficient ground surveys, as well as a better, statistically sound sampling strategy for further structural, geochemical, and petrological investigations. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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17 pages, 6755 KiB

Open AccessArticle

Virtual Structural Analysis of Jokisivu Open Pit Using ‘Structure-from-Motion’ Unmanned Aerial Vehicles (UAV) Photogrammetry: Implications for Structurally-Controlled Gold Deposits in Southwest Finland

by Mohammad Sayab, Domingo Aerden, Markku Paananen and Petri Saarela

Remote Sens. 2018, 10(8), 1296; https://doi.org/10.3390/rs10081296 - 16 Aug 2018

Cited by 37 | Viewed by 7142

Abstract

Unmanned aerial vehicles (UAVs) are rapidly growing remote sensing platforms for capturing high-resolution images of exposed rock surfaces. We used a DJI Phantom 3 Professional (P3P) quadcopter to capture aerial images that were used to generate a high-resolution three-dimensional (3-D) model of the Jokisivu open-pit gold deposit that is located in southwestern Finland. 158 overlapping oblique and nadir images were taken and processed with Agisoft Photoscan Pro to generate textured 3-D surface models. In addition, 69 overlapping images were taken from the steep faces of the open pit. We assessed the precision of the 3-D model by deploying ground control points (GCPs) and the average errors were found minimal along X (2.0 cm), Y (1.2 cm), and Z (5.0 cm) axes. The steep faces of the open pit were used for virtual structural measurements and kinematic analyses in CloudCompare and ArcGIS to distinguish the orientation of different fracture sets and statistical categorization, respectively. Three distinct fracture sets were observed. The NW-SE and NE-SW striking fractures form a conjugate geometry, whereas the NNW-SSE striking fractures cut the conjugate fracture set. The orientation of conjugate fractures match well with the resource model of the deposit and NW- and NE-trending segments of regional-scale anastomosing shear zones. Based on the conjugate geometry of fracture sets I and II, and the regional pattern of anastomosing shear system lead us to interpret an origin of gold mineralization in two stages. An early N-S or NNW-SSE crustal shortening, corresponding to the regional D4 (ca. 1.83–1.81 Ga) or pre-D4 (ca. 1.87–1.86 Ga) Svecofennian tectonic event(s) that produced anastomosing shear zones. Subsequent E-W directed D5 contraction (ca. 1.79–1.77 Ga) partly reactivated the anastomosing shear zones with the formation of conjugate system, which controlled the migration of fluids and gold mineralization in SW Finland. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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23 pages, 21330 KiB

Open AccessFeature PaperArticle

Drone-Borne Hyperspectral Monitoring of Acid Mine Drainage: An Example from the Sokolov Lignite District

by Robert Jackisch, Sandra Lorenz, Robert Zimmermann, Robert Möckel and Richard Gloaguen

Remote Sens. 2018, 10(3), 385; https://doi.org/10.3390/rs10030385 - 02 Mar 2018

Cited by 53 | Viewed by 18416

Abstract

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. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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9934 KiB

Open AccessArticle

UAV Remote Sensing Surveillance of a Mine Tailings Impoundment in Sub-Arctic Conditions

by Anssi Rauhala, Anne Tuomela, Corine Davids and Pekka M. Rossi

Remote Sens. 2017, 9(12), 1318; https://doi.org/10.3390/rs9121318 - 15 Dec 2017

Cited by 40 | Viewed by 8137

Abstract

Mining typically involves extensive areas where environmental monitoring is spatially sporadic. New remote sensing techniques and platforms such as Structure from Motion (SfM) and unmanned aerial vehicles (UAVs) may offer one solution for more comprehensive and spatially continuous measurements. We conducted UAV campaigns in three consecutive summers (2015–2017) at a sub-Arctic mining site where production was temporarily suspended. The aim was to monitor a 0.5 km² tailings impoundment and measure potential subsidence of tailings. SfM photogrammetry was used to produce yearly topographical models of the tailings surface, which allowed the amount of surface displacement between years to be tracked. Ground checkpoints surveyed in stable areas of the impoundment were utilized in assessing the vertical accuracy of the models. Observed surface displacements were linked to a combination of erosion, tailings settlement, and possible compaction of the peat layer underlying the tailings. The accuracy obtained indicated that UAV-assisted monitoring of tailings impoundments is sufficiently accurate for supporting impoundment management operations and for tracking surface displacements in the decimeter range. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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19605 KiB

Open AccessFeature PaperArticle

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

by Sandra Jakob, Robert Zimmermann and Richard Gloaguen

Remote Sens. 2017, 9(1), 88; https://doi.org/10.3390/rs9010088 - 18 Jan 2017

Cited by 152 | Viewed by 19600

Abstract

Drone-borne hyperspectral imaging is a new and promising technique for fast and precise acquisition, as well as delivery of high-resolution hyperspectral data to a large variety of end-users. Drones can overcome the scale gap between field and air-borne remote sensing, thus providing high-resolution and multi-temporal data. They are easy to use, flexible and deliver data within cm-scale resolution. So far, however, drone-borne imagery has prominently and successfully been almost solely used in precision agriculture and photogrammetry. Drone technology currently mainly relies on structure-from-motion photogrammetry, aerial photography and agricultural monitoring. Recently, a few hyperspectral sensors became available for drones, but complex geometric and radiometric effects complicate their use for geology-related studies. Using two examples, we first show that precise corrections are required for any geological mapping. We then present a processing toolbox for frame-based hyperspectral imaging systems adapted for the complex correction of drone-borne hyperspectral imagery. The toolbox performs sensor- and platform-specific geometric distortion corrections. Furthermore, a topographic correction step is implemented to correct for rough terrain surfaces. We recommend the c-factor-algorithm for geological applications. To our knowledge, we demonstrate for the first time the applicability of the corrected dataset for lithological mapping and mineral exploration. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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