VNIR-SWIR Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications

Hyperspectral imaging technology holds great potential for various stages of the mining life cycle, both in active and abandoned mines, from exploration to reclamation. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite imagery yields high spectral resolution, a high signal-to-noise ratio (SNR), and global availability with breakthrough systems like EnMAP, EMIT, GaoFen-5, PRISMA, and Tanager-1, limited spatial and temporal resolution poses challenges for the mining sectors, which require decimetre-to-centimetre-scale spatial resolution for applications such as reconciliation and environmental monitoring and daily temporal revisit times, such as for ore/waste estimates and geotechnical assessments. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high-spatial-resolution data relevant to the pit/mine scale, with the capability for frequent, user-defined re-visit times for areas of limited extent. Areas of interest can be defined by the user and targeted explicitly. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers the detection of different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. This is related to but not exclusive to detecting deleterious minerals for different processes (e.g., clays, iron oxides, talc), secondary iron oxides indicating the leakage of acid mine drainage for rehabilitation efforts, swelling clays potentially affecting rock integrity and stability, and alteration minerals used to vector toward economic mineralisation (e.g., dickite, jarosite, alunite). In this paper, we review applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging datasets in or appropriate to the mining sector, with a particular focus on hyperspectral VNIR-SWIR UASs. Complementarily, we draw on previous insights from airborne, satellite, and ground-based imaging systems. We also discuss common practises for UAS survey planning and ground sampling considerations to aid in data interpretation.

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Publication Details

Journal
Mining
Published
2024-11-29
DOI
https://doi.org/10.3390/mining4040057
Citations
9
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
2.88

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article

VNIR-SWIR Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications

Ekaterina Savinova, Miranda Lehman, Friederike Koerting, Konstantinos G. Nikolakopoulos et al.
9 citations
Mining
Remote-Sensing Image Classification
2.88
article

VNIR-SWIR Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications

Ekaterina Savinova, Miranda Lehman, Friederike Koerting, Konstantinos G. Nikolakopoulos, Saeid Asadzadeh, Steven Micklethwaite, J. Hildebrand, Daniel R. Schlapfer, David Lindblom, Simon John Buckley, Evlampia Kouzeli, Nicole Köllner
article en
9 citations

Abstract

Hyperspectral imaging technology holds great potential for various stages of the mining life cycle, both in active and abandoned mines, from exploration to reclamation. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite imagery yields high spectral resolution, a high signal-to-noise ratio (SNR), and global availability with breakthrough systems like EnMAP, EMIT, GaoFen-5, PRISMA, and Tanager-1, limited spatial and temporal resolution poses challenges for the mining sectors, which require decimetre-to-centimetre-scale spatial resolution for applications such as reconciliation and environmental monitoring and daily temporal revisit times, such as for ore/waste estimates and geotechnical assessments. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high-spatial-resolution data relevant to the pit/mine scale, with the capability for frequent, user-defined re-visit times for areas of limited extent. Areas of interest can be defined by the user and targeted explicitly. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers the detection of different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. This is related to but not exclusive to detecting deleterious minerals for different processes (e.g., clays, iron oxides, talc), secondary iron oxides indicating the leakage of acid mine drainage for rehabilitation efforts, swelling clays potentially affecting rock integrity and stability, and alteration minerals used to vector toward economic mineralisation (e.g., dickite, jarosite, alunite). In this paper, we review applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging datasets in or appropriate to the mining sector, with a particular focus on hyperspectral VNIR-SWIR UASs. Complementarily, we draw on previous insights from airborne, satellite, and ground-based imaging systems. We also discuss common practises for UAS survey planning and ground sampling considerations to aid in data interpretation.

MiningVol. 4(4)
The University of Queensland (AU), Colorado School of Mines (US), University of Patras (GR), Norsk Elektro Optikk (Norway) (NO), Systems, Applications & Products in Data Processing (Switzerland) (CH), GFZ Helmholtz Centre for Geosciences (DE)
European Commission, HORIZON EUROPE Framework Programme, Staatssekretariat für Bildung, Forschung und Innovation
Responsible consumption and production
Openalex Percentile: Top 8%
Remote-Sensing Image Classification
2.88
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