Version 1
: Received: 23 March 2024 / Approved: 24 March 2024 / Online: 25 March 2024 (08:47:40 CET)
How to cite:
Koerting, F.; Asadzadeh, S.; Hildebrand, J. C.; Savinova, E.; Kouzeli, E.; Nikolakopoulos, K.; Lindblom, D.; Koellner, N.; Buckley, S. J.; Lehman, M.; Schläpfer, D.; Micklethwaite, S. Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications. Preprints2024, 2024031430. https://doi.org/10.20944/preprints202403.1430.v1
Koerting, F.; Asadzadeh, S.; Hildebrand, J. C.; Savinova, E.; Kouzeli, E.; Nikolakopoulos, K.; Lindblom, D.; Koellner, N.; Buckley, S. J.; Lehman, M.; Schläpfer, D.; Micklethwaite, S. Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications. Preprints 2024, 2024031430. https://doi.org/10.20944/preprints202403.1430.v1
Koerting, F.; Asadzadeh, S.; Hildebrand, J. C.; Savinova, E.; Kouzeli, E.; Nikolakopoulos, K.; Lindblom, D.; Koellner, N.; Buckley, S. J.; Lehman, M.; Schläpfer, D.; Micklethwaite, S. Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications. Preprints2024, 2024031430. https://doi.org/10.20944/preprints202403.1430.v1
APA Style
Koerting, F., Asadzadeh, S., Hildebrand, J. C., Savinova, E., Kouzeli, E., Nikolakopoulos, K., Lindblom, D., Koellner, N., Buckley, S. J., Lehman, M., Schläpfer, D., & Micklethwaite, S. (2024). Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications. Preprints. https://doi.org/10.20944/preprints202403.1430.v1
Chicago/Turabian Style
Koerting, F., Daniel Schläpfer and Steven Micklethwaite. 2024 "Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications" Preprints. https://doi.org/10.20944/preprints202403.1430.v1
Abstract
Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.