Version 1
: Received: 16 July 2024 / Approved: 17 July 2024 / Online: 17 July 2024 (10:56:29 CEST)
How to cite:
Koerting, F. M.; Asadzadeh, S.; Hildebrand, J. C.; Savinova, E.; Kouzeli, E.; Nikolakopoulos, K. G.; Lindblom, D.; Koellner, N.; Buckley, S. J.; Lehman, M. R.; Schläpfer, D.; Micklethwaite, S. Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications. Preprints2024, 2024071407. https://doi.org/10.20944/preprints202407.1407.v1
Koerting, F. M.; Asadzadeh, S.; Hildebrand, J. C.; Savinova, E.; Kouzeli, E.; Nikolakopoulos, K. G.; Lindblom, D.; Koellner, N.; Buckley, S. J.; Lehman, M. R.; Schläpfer, D.; Micklethwaite, S. Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications. Preprints 2024, 2024071407. https://doi.org/10.20944/preprints202407.1407.v1
Koerting, F. M.; Asadzadeh, S.; Hildebrand, J. C.; Savinova, E.; Kouzeli, E.; Nikolakopoulos, K. G.; Lindblom, D.; Koellner, N.; Buckley, S. J.; Lehman, M. R.; Schläpfer, D.; Micklethwaite, S. Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications. Preprints2024, 2024071407. https://doi.org/10.20944/preprints202407.1407.v1
APA Style
Koerting, F. M., Asadzadeh, S., Hildebrand, J. C., Savinova, E., Kouzeli, E., Nikolakopoulos, K. G., Lindblom, D., Koellner, N., Buckley, S. J., Lehman, M. R., Schläpfer, D., & Micklethwaite, S. (2024). Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications. Preprints. https://doi.org/10.20944/preprints202407.1407.v1
Chicago/Turabian Style
Koerting, F. M., Daniel Schläpfer and Steven Micklethwaite. 2024 "Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications" Preprints. https://doi.org/10.20944/preprints202407.1407.v1
Abstract
Hyperspectral imaging data holds great potential for various stages of the mining life cycle in active and abandoned mines. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite im-agery yields high spectral resolution, high signal-to-noise ratio (SNR), and global availability with breakthrough satellite sys-tems like EnMAP, EMIT, and PRISMA, limited spatial resolution poses challenges for mining sectors, which require decimeter- to centimeter-scale spatial resolution for applications such as reconciliation, ore/waste estimates, geotechnical assessments, and en-vironmental monitoring. 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. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers to detect different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we review of applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging in or appropriate to the mining sector, especially for hyperspectral VNIR-SWIR UASs. If directly applicable, draw on previous insights derived from airborne, satellite, and ground-based imaging systems. We also discuss common practices for UAS survey planning and sampling considerations for 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.
