Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil

D. Santos; A. Teodoro; A. Lima; J. Cardoso-Fernandes

doi:10.1117/12.2532744

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3 October 2019 Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil

D. Santos, A. Teodoro, A. Lima, J. Cardoso-Fernandes

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Proceedings Volume 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X; 111561F (2019) https://doi.org/10.1117/12.2532744
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Lithium (Li) is defined as an alkaline metal which does not exist in nature in its free form. Moreover, it has properties that make it possible to be applied in several manners, such as industrial use, especially in the ceramic and glass industries, as well as the battery industry which has had an increase in this element consumption. It is crucial to use less expensive and faster techniques, compared to classical and intrusive methods, to identify new Li deposits. Remote sensing as proved to be a powerful tool to identify areas with potential for Li exploration. The objective of this work is to apply several images processing techniques, such as RGB band combinations, Band Ratios and Principal Components Analysis (PCA) to identify potential areas for Li prospection in the pegmatite district of São João Del Rei, located in the south of the state of Minas Gerais, Brazil. In these areas, two study zones were defined: the zone A, with approximately 323 km² in the pegmatite district of São João Del Rei and the zone B with approximately 90 km² in the pegmatite district of Araçuaí. The results of the techniques applied in this study are very promising since, in addition to ease and low cost, these techniques can be applied to several locations. This approach is highly valuable for the Li mining industry.

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D. Santos, A. Teodoro, A. Lima, and J. Cardoso-Fernandes "Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111561F (3 October 2019); https://doi.org/10.1117/12.2532744

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