Advances in Remote Sensing Used in Mineral Exploration
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 28 June 2025 | Viewed by 373
Special Issue Editors
Interests: inversion of TEM data; geophysical investigation of mineral resources; joint inversion of multi-geophysical data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing has emerged as a pivotal tool within the mining sector and mineral exploration, offering advanced methodologies for data acquisition and analysis over extensive and frequently hard-to-reach regions. Initially based on aerial photography, remote sensing technology has progressed into highly advanced satellite and drone systems that can capture intricate spectral data across diverse wavelengths. This advancement facilitates the recognition of geological characteristics that signify mineral deposits, markedly improving exploration efficiency and informed decision-making. The significance of this research domain is highlighted by its capacity to enhance resource management, mitigate environmental consequences, and heighten safety during mining operations, aligning with global sustainability objectives.
This Special Issue intends to gather state-of-the-art research and innovations in remote sensing technologies pertinent to the mining industry, encompassing satellite imagery, UAV applications, and hyperspectral imaging. It aims to investigate how these technologies can bolster mineral exploration, optimize mining processes, and support environmental monitoring. By emphasizing both theoretical developments and practical implementations, this issue will enrich the journal's scope by promoting interdisciplinary collaboration and inspiring innovative strategies to tackle the challenges encountered by the mining industry. Submissions should embody original research or thorough reviews that advance this dynamic field. Prospective authors are invited to contribute articles on a broad spectrum of topics, including, but not limited to, the following:
- Innovative techniques for mineral detection;
- Environmental monitoring through optical spectroscopy;
- Geospatial data fusion;
- Real-world applications of remote sensing in the mining sector;
- Development and validation of remote sensing algorithms;
- Challenges and future directions for remote sensing in the mining industry.
Dr. Hai Li
Prof. Dr. Zhenwei Guo
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are 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.
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 page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- mineral exploration
- geospatial analysis
- data integration
- geological mapping
- machine learning
- hyperspectral imaging
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