Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308647 
Year of Publication: 
2022
Citation: 
[Journal:] Mineral Economics [ISSN:] 2191-2211 [Volume:] 35 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 495-502
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Hyperspectral remote sensing already is important in geoscientific research in the fields of geology, soil, exploration and mining. New hyperspectral satellite systems are already in operation (e.g. PRISMA and DESIS; Caporusso et al. 2020; Alonso et al. (Sensors 19(20):4471–4515, 1 )) and more systems are planned e.g. the European Copernicus Next Generation Hyperspectral Satellite CHIME (Nieke and Rast 2018). The German system EnMAP was successfully launched into space on 1 st of April 2022 (DLR 5 ). The potential of hyperspectral airborne and satellite borne data for mining-related applications is discussed. Investigated are the information contents of hyperspectral data for exploration target recognition and their dependency on spatial resolutions of different sensor platforms. Airborne data offer high spatial resolution of 2.5 m with limited areal data acquisition, whereas hyperspectral spaceborne sensors guaranty nearly worldwide data availability with the same spectral characteristics but medium spatial resolution (30 m). The aspects of high spectral resolution and high versus medium spatial resolution targeted mineral mapping is investigated. The methodological concept includes processing aspects, standardized data availability for mineral mapping and mineralization targeting for operational application, to maintain/allow application of hyperspectral data even for non-remote sensing experts. Based on hyperspectral airborne data acquired in the Aggeneys region in South Africa, spaceborne hyperspectral data are simulated following the EnMAP specifications, and the concept for targeted mapping of surface alterations of a lead zinc deposit is discussed.
Subjects: 
Exploration
Mineral mapping
Hyperspectral imaging
EnMAP
VNIR-SWIR
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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