Remote sensing data in lithium (Li) exploration: A new approach for the detection of Li-bearing pegmatites

Highlights

• Developed innovative remote sensing methodologies capable of identifying Li-pegmatites through alteration mapping.

• Direct identification of Li-bearing minerals.

• Different remote sensing data used (Landsat-5, Landsat-8, Sentinel-2 and ASTER images).

• Comparison of the results provided for the different remote sensing sensors.

• New self-proposed RGB combinations, band ratio and subsets for selective PCA.

Abstract

Remote sensing has proved to be a powerful resource in geology capable of delineating target exploration areas for several deposit types. Only recently, these methodologies have been used for the detection of lithium (Li)-bearing pegmatites. This happened because of the growing importance and demand of Li for the construction of Li-ion batteries for electric cars. The objective of this study was to develop innovative and effective remote sensing methodologies capable of identifying Li-pegmatites through alteration mapping and through the direct identification of Li-bearing minerals. For that, cloud free Landsat-5, Landsat-8, Sentinel-2 and ASTER images with low vegetation coverage were used. The image processing methods included: RGB (red, green, blue) combinations, band ratios and selective principal component analysis (PCA). The study area of this work is the Fregeneda (Salamanca, Spain)-Almendra (Vila Nova de Foz Côa, Portugal) region, where different known types of Li-pegmatites have been mapped. This study proposes new RGB combinations, band ratios and subsets for selective PCA capable of differentiating the spectral signatures of the Li-bearing pegmatites from the spectral signatures of the host rocks. The potential and limitations of the methodologies proposed are discussed, but overall there is a great potential for the identification of Li-bearing pegmatites using remote sensing. The results obtained could be improved using sensors with a better spatial and spectral resolution.

Introduction

It is impossible to ignore the current growing importance and demand of lithium (Li) for several industrial applications among which the production of rechargeable Li-ion batteries stands outs, mainly for the construction of electric vehicles in the pursuit for more environment-friendly means of transportation. To address that demand, the identification of new Li deposits is crucial and less expensive and faster exploration methods (when compared with classic techniques) are in order.

Remote sensing could be the answer to this problem, since it has already proved itself as a powerful resource to delineate target exploration areas for several deposit types. Worldwide, remote sensing has been used in mineral exploration since the 70′s. Most of the studies concern the application of remote sensing in hydrothermal gold exploration (e.g. Moradi et al., 2014). Other publications detail the use of remote sensing in other deposit types, such as: epithermal gold deposits (e.g.Crósta et al., 2003), porphyry copper deposits (e.g. Pour and Hashim, 2015), brine deposits (e.g. Sabins and Miller, 1994), Carlin-type gold deposits (e.g. Rockwell and Hofstra, 2008), skarn deposits (e.g. Rowan and Mars, 2003) and volcanogenic massive sulfide ore (VMS) deposits (e.g. Berger et al., 2003). The potential of the use of Sentinel-2 images for geological applications has also been documented (van der Meer et al., 2014). Only recently, the first steps on the application of remote sensing to Li mineralizations were made: Perrotta et al. (2005) used ASTER images for a trial mapping of Li-bearing pegmatites in Vale do Jequitinhonha region, Brazil; and Mendes et al. (2017) applied a similar methodology in the identification of Li minerals’ spectral signatures in future identification of unknown ore deposits. Cardoso-Fernandes et al. (2018), in a preliminary stage of this work, presented the potential of Sentinel-2 in Li-mapping. There are then several developments to be made in the field of remote sensing applied to Li-bearing pegmatites.

The possible detection of Li-bearing pegmatites using remote sensing can be of great interest to exploration and mining companies, since they could lead to a decrease of the impacts of the early stages of exploration on the populations and an increased efficiency and sustainability of mineral exploration. Taking this into account, the main objective of this study was to develop a new methodology considering remotely sensed data, capable of identifying Li-pegmatites. This recognition was made based on two approaches: the identification of hydrothermally altered zones associated with the pegmatites (through the discrimination of iron oxides and clay minerals) and the direct identification of Li-bearing minerals. For that, a set of well-known processing methods were applied using well-established and also innovative remote sensing algorithms. The methodologies allowed to predict the occurrence of iron oxides and clay minerals, and to discriminate between hydrothermally altered zones and non-altered zones. Self-proposed RGB (red, green, blue) combinations for ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Landsat-5 and 8 were capable of discriminating the Li-bearing pegmatites from the host rocks. Self-proposed band ratios and selective principal component analysis (PCA) were also able to highlight the Li-bearing minerals in ASTER, Landsat-5, Landsat-8 and Sentinel-2 images.