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    Topic review

    Detecting Lithium Mineralizations from Space

    Subjects: Others
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    Definition

    Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A review of the application studies and developments in this field was also made. The addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration.

    1. Introdcution

    Optical and thermal remote sensing data, namely satellite-acquired images, have been an important tool in geological exploration allowing to target exploration areas for more than four decades. The major contribution that remote sensing offers to mineral exploration was reviewed in several works dedicated to this topic [[1][2][3][4][5]]: it provides information in a fairly quick, inexpensive, and non-intrusive way, which favors mining and exploration companies especially in inaccessible remote areas. However, Rajesh [3], in an overview of the use of remote sensing and Geographic Information Systems (GIS) in mineral exploration, points out the difficulty of directly pinpointing mineralizations using only remote sensing data, highlighting the importance of the integration with other types of geological data.

    Sabins [[6]], in one of the first reviews about the types of data and image processing methods for mineral exploration, describes two main approaches to target mineral deposits: (i) structural and lithological mapping; and (ii) hydrothermal alteration mapping. Later, Rajesh [[3]] proposed three approaches: (i) lithological mapping; (ii) structural mapping; and (iii) alteration mapping. These approaches have been applied since the 1970s to identify very distinct types of mineral deposits. The more common applications include porphyry copper [[7][8][9][10][11][12][13][14][15][16][17]] and gold [[18][19][20][21][22][23][24][25][26][27][28]] deposits. Other applications may include: iron ore deposits [[19],[29][30][31][32]], volcanogenic massive sulfide ore (VMS) deposits [[33][34][35]], several skarn-hosted deposits [[36][37][38][39]], chromite deposits [[40][41][42][43]], uranium deposits [[39],[44]], rare earth elements (REE) exploration [36][45], brine and evaporite deposits [[46][47]], porphyry molybdenum deposits [[48][49]], zinc-lead (Zn-Pb) deposits [50], diamond [[51]] and bauxite exploration [[52]].

    Regarding the types of data used, multispectral products, namely Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery, played an important part in geological remote sensing [[4],[5][53]. Abrams and Yamaguchi [[54]] reviewed ASTER’s contributions to mineral exploration and lithological mapping. The success of the ASTER sensor in geological exploration was mainly due to a higher spectral resolution in the short-wave infrared (SWIR) and thermal infrared (TIR), specially designed for geological applications, which improved its lithological and mineral mapping capabilities, particularly in the identification of alteration minerals [4][55][54],[55]]. The importance of TIR (including ASTER’s TIR subsystem) in the discrimination of minerals and rocks was revised by Ninomiya and Fu [[55]]. These authors highlight several studies in which the TIR region was fundamental for mineral discrimination, namely where silicate minerals occur, and reviewed the spectral indices proposed for lithological mapping using ASTER-TIR data. Nonetheless, the advent of hyperspectral remote sensing allowed the direct identification and quantification of specific minerals which represented a key contribution to mineral exploration [3][53]. Cudahy [[4]] in a review on mineral mapping projects for exploration led by Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia, pointed out that hyperspectral remote sensing can be less popular among exploration companies because of limited spatial coverage, the relatively high-cost of quality datasets, and the inherent complexity of hyperspectral data.

    2. Influence and application

    Despite the successful application of different types of remote sensing data to distinct mineral deposits, the current growing economic and technological advances, which rely on other mineral commodities, highlights the need to use and adapt remote sensing data and image processing techniques on new deposit types. Nowadays, green technologies, like electric vehicles, represent an important sector of the economy [[56]] and lithium (Li) has become a critical metal to the green-power industry [[57][58]]. However, Li exploration with the resource of remote sensing data and techniques represents an emergent field, with several difficulties and unknown possibilities. Taking this into account, this review aims at (i) providing information about what can be accomplished in Li exploration using remote sensing methods; (ii) identifying the main difficulties associated with this kind of deposits; and (iii) providing insights on how to overcome these difficulties as well as future research perspectives. This paper presents the first summary of the developments made in the field of remote sensing applied to Li exploration and we consider it to be timely and appropriate due to the high global demand of this metal to the production of Li-ion batteries [58]. Additionally, this review can help to promote new applications and to solve new exploration problems.

    In general, past application studies relied on four distinct approaches: (i) geobotanical mapping; (ii) lithological mapping; (iii) mineral alteration mapping; and (iv) Li minerals/Li pegmatite discrimination. Different types of satellite products with distinct characteristics were employed as well as diverse image processing algorithms ranging from simple logical or mathematical operations (band ratios) to more evolved and complex algorithms like machine learning algorithms.

    Despite the early attempt to target Li mineralizations with the launch of the Landsat missions, there is an unequivocal exponential growth on the publication numbers in the last decades. Nonetheless, since it still is an emergent field, the studies are limited to a small number of research groups, mainly based in Europe. However, considering the market demand for this raw material, we expect that many other studies will flourish in the near future all around the globe.

    This entry is adapted from 10.3390/app10051785

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