{"id":2307,"date":"2020-05-14T11:14:10","date_gmt":"2020-05-14T11:14:10","guid":{"rendered":"http:\/\/www.icterra.pt\/?p=2307"},"modified":"2020-05-14T11:26:36","modified_gmt":"2020-05-14T11:26:36","slug":"special-issue-new-trends-on-remote-sensing-applications-to-mineral-deposits","status":"publish","type":"post","link":"https:\/\/www.icterra.pt\/legacy\/index.php\/2020\/05\/14\/special-issue-new-trends-on-remote-sensing-applications-to-mineral-deposits\/","title":{"rendered":"Special Issue “New Trends on Remote Sensing Applications to Mineral Deposits”"},"content":{"rendered":"
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Prof. Ana Cl\u00e1udia Teodoro<\/b> <\/span>
\nGuest Editor<\/i><\/span><\/div>\n
Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto; Institute of Earth Sciences (ICT), Pole of University of Porto<\/span><\/div>\n
Interests:<\/b> image acquisition and processing for environmental, coastal, geological and health applications; machine learning algorithms; GIS<\/span><\/div>\n<\/div>\n
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MS. Joana Cardoso-Fernandes<\/span><\/strong><\/span>
\nGuest Editor<\/i><\/span><\/p>\n<\/div>\n

Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto; Institute of Earth Sciences (ICT), Pole of University of Porto<\/span><\/div>\n
Interests:<\/b> Remote Sensing, Macine learning algorithms; Geological exploration, Li mineralizations<\/span><\/div>\n<\/div>\n
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Dr. Alexandre Lima<\/span><\/strong><\/span>
\nGuest Editor<\/i><\/span><\/p>\n<\/div>\n

Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto; Institute of Earth Sciences (ICT), Pole of University of Porto<\/span><\/div>\n
Interests:<\/b> 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<\/span><\/div>\n<\/div>\n
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Special Issue Information<\/span><\/strong><\/p>\n

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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.<\/span><\/p>\n

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.<\/span><\/p>\n

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.<\/span><\/p>\n

We look forward to your contributions.<\/span><\/p>\n

Prof. Ana Cl\u00e1udia Teodoro<\/span>
\nMS. Joana Cardoso-Fernandes<\/span>
\nDr. Alexandre Lima<\/span>
\nGuest Editors<\/em><\/span><\/p>\n<\/div>\n

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 <\/p>\n

Manuscript Submission Information<\/span><\/strong><\/span><\/p>\n

Manuscripts should be submitted online at www.mdpi.com<\/a> by registering<\/a> and logging in to this website<\/a>. Once you are registered, click here to go to the submission form<\/a>. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.<\/span><\/p>\n

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors<\/a> page. Remote Sensing<\/em><\/a> is an international peer-reviewed open access semimonthly journal published by MDPI.<\/span><\/p>\n

Please visit the Instructions for Authors<\/a> page before submitting a manuscript. The Article Processing Charge (APC)<\/a> for publication in this open access<\/a> journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service<\/a> prior to publication or during author revisions.<\/span><\/p>\n

More information here<\/a>.<\/span><\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Prof. Ana Cl\u00e1udia 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 […]<\/p>\n","protected":false},"author":3,"featured_media":2221,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[361,360,358,359,278],"_links":{"self":[{"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/posts\/2307"}],"collection":[{"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/comments?post=2307"}],"version-history":[{"count":8,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/posts\/2307\/revisions"}],"predecessor-version":[{"id":2315,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/posts\/2307\/revisions\/2315"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/media\/2221"}],"wp:attachment":[{"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/media?parent=2307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/categories?post=2307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.icterra.pt\/legacy\/index.php\/wp-json\/wp\/v2\/tags?post=2307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}