Special Issue "Remote Sensing and Artificial Intelligence in Inland Waters Monitoring"
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 1 February 2024 | Viewed by 5646
Special Issue Editors
Interests: geoinformatics; spatial databases; GeoAI; remote sensing; data analytics; big data; water resources monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: natural resources monitoring; remote sensing; geoinformatics
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; artificial intelligence; plastic litter; water body monitoring
Special Issue Information
Dear Colleagues,
Water is the main fundamental element for life. Aquatic ecosystems are under great pressure due to different natural and anthropogenic factors increasing water crises, including water shortage, water pollution, and other water-related issues. Because of this, the comprehensive monitoring of water status on a local, regional, and global scale is needed to provide efficient and sustainable management of water resources, which is critical to the targets of the 2030 Agenda for Sustainable Development.
Remote sensing technologies and conjunction with in situ data can be used to reflect the spatial distribution and dynamic changes in water quality and quantity. Owing to the high frequency of data acquisition, large-scale coverage and different types of sensors combined with artificial intelligence and cloud computing can be used to understand complex and interconnected changes in aquatic environments.
This Special Issue focuses on papers describing how to improve inland water monitoring in terms of accuracy, and frequency, and add user value to derived data from remote sensing. In particular, this issue was designed to highlight currently applied research using optical, thermal and radar satellite images, LiDAR and UAV data, in situ instrumentation, GeoAI, deep and machine-learning algorithms, cloud computing, and big data processing application to better understand the current status and prevent feature degradation of water resources. Therefore, potential topics include, but are not limited to, the following:
- Water flow dynamic monitoring;
- Remote sensed monitoring of water quality parameters;
- Water surface level monitoring;
- GeoAI;
- Plastic pollution;
- Time-series analysis.
Prof. Dr. Miro Govedarica
Prof. Dr. Flor Alvarez-Taboada
Dr. Gordana Jakovljević
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
- remote sensing
- GeoAI
- artificial intelligence
- inland water bodies
- water dynamic
- water quality
- time-series analysis
- plastic pollution