Special Issue “New Trends on Remote Sensing Applications to Mineral Deposits”

Prof. Ana Cláudia Teodoro
Guest Editor
Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto; Institute of Earth Sciences (ICT), Pole of University of Porto
Interests: image acquisition and processing for environmental, coastal, geological and health applications; machine learning algorithms; GIS

 

MS. Joana Cardoso-Fernandes
Guest Editor

Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto; Institute of Earth Sciences (ICT), Pole of University of Porto
Interests: Remote Sensing, Macine learning algorithms; Geological exploration, Li mineralizations

 

Dr. Alexandre Lima
Guest Editor

Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto; Institute of Earth Sciences (ICT), Pole of University of Porto
Interests: Geological Exploration. Research in mineral resources, principally in the development of Au exploration and in the industrial rocks and minerals based in pegmatites as their possible metals: Li, Sn, Ta, Nb and W. Public understanding of Earth Science

 

Special Issue Information

Remote sensing data, in particular, satellite-acquired images, played a determinant role in the early stages of mineral exploration since the 1970s. For the last four decades, different product types and numerous image processing algorithms have allowed to target exploration areas all over the world. Among the most successful applications are the porphyry copper and gold deposits, often associated with hydrothermal alteration minerals that can be detected by following well-known procedures and algorithms. However, current paradigm shifts in the global markets and technological advances lead to high demand for other raw materials. Nevertheless, the possible contribution of remote sensing to target these mineral commodities is often not entirely assessed.

On the other hand, non-parametric methods such as machine and deep learning algorithms have gain popularity in several remote sensing-based applications during recent years. One example is their application in land-use/land-cover (LULC) problems. Similar results could and are being obtained in lithological mapping and mineral exploration, but the number of applications is still very small in comparison. Moreover, due to the inherently different nature of mineral exploration studies when compared to LULC applications, some difficulties should be expected when trying to apply machine and deep learning algorithms to real-life exploration problems.

Therefore, in this Special Issue of Remote Sensing, we are looking for new remote sensing approaches whether applied to non-traditional geological applications (such as diamond, bauxite, evaporite minerals, lithium, and rare earth elements (REE) exploration, etc.) or that make use of trending techniques such as machine and deep learning algorithms. Ultimately, the goal is to find any research study that can contribute to the current state of the art and that may help assess the challenges and potentials of new applications in the field of geological remote sensing.

We look forward to your contributions.

Prof. Ana Cláudia Teodoro
MS. Joana Cardoso-Fernandes
Dr. Alexandre Lima
Guest Editors

 

Manuscript Submission Information

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