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Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: 28 November 2024 | Viewed by 2000

Special Issue Editors


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Guest Editor
Institute of Marine Environmental Science and Technology, Department of Earth Science, National Taiwan Normal University, Taipei 106, Taiwan
Interests: remote sensing of oceanic environment; physical oceanography; typhoon-ocean Interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing in a marine environment involves using sensors to collect non-contact ocean observations, providing crucial data and images related to various oceanic phenomena and processes. Recent advancements in remote sensing technology have significantly enhanced our ability to gather data at higher spatial and temporal resolutions, leveraging both passive and active sensor capabilities. These innovations present new opportunities for gaining groundbreaking insights and interpretations essential for practical implementations in marine environmental sciences.

This Special Issue, “Advanced Remote Sensing for Marine Environment Monitoring”, serves as a continuation of Volume 1, aiming to build upon previous contributions and further advance the field. We invite cutting-edge applications focusing on new observations, analytical methods, data, and modeling techniques that promise to enhance our understanding of marine environmental processes. We look forward to receiving your high-quality contributions, which will continue to push the boundaries of our understanding in this dynamic and critical area of research.

Prof. Dr. Zhe-Wen Zheng
Prof. Dr. Jiayi Pan
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

  • ocean remote sensing
  • ocean environment
  • environment monitoring
  • remote sensing techniques
  • satellite data

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Related Special Issue

Published Papers (3 papers)

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Research

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18 pages, 16650 KiB  
Article
Mapping Seagrass Distribution and Abundance: Comparing Areal Cover and Biomass Estimates Between Space-Based and Airborne Imagery
by Victoria J. Hill, Richard C. Zimmerman, Dorothy A. Byron and Kenneth L. Heck, Jr.
Remote Sens. 2024, 16(23), 4351; https://doi.org/10.3390/rs16234351 - 21 Nov 2024
Viewed by 343
Abstract
This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification [...] Read more.
This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km2 of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which each pixel was classified as seagrass. Seagrass patches encompassing at least nine pixels (~200 m2) were almost always detected by our classification algorithm. Using an OF threshold equal to or greater than >60% provided a high level of confidence in seagrass presence while effectively reducing the impact of small misclassifications, often of individual pixels, that appeared sporadically in individual images. The image-to-image uncertainty in seagrass retrieval from the satellite images was 0.1 km2 or 2.3%, reflecting the robustness of our classification method and allowing confidence in the accuracy of the seagrass area estimate. The satellite-retrieved leaf area index (LAI) was consistent with previous in situ measurements, leading to the estimate that 2700 tons of carbon per year are produced by the Santa Rosa Sound seagrass ecosystem, equivalent to a drawdown of approximately 10,070 tons of CO2. This satellite-based approach offers a cost-effective, semi-automated, and scalable method of assessing the distribution and abundance of submerged aquatic vegetation that provides numerous ecosystem services. Full article
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23 pages, 19437 KiB  
Article
Impact of Turbidity on Satellite-Derived Bathymetry: Comparative Analysis Across Seven Ports in the South China Sea
by Chunzhu Wei, Yaqi Xiao, Dongjie Fu and Tingting Zhou
Remote Sens. 2024, 16(23), 4349; https://doi.org/10.3390/rs16234349 - 21 Nov 2024
Viewed by 229
Abstract
This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal [...] Read more.
This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal correlation with satellite-based turbidity indicators across seven Chinese port areas. Results indicate that both Sentinel-2 and Landsat 8, using a three-band combination, achieved comparable performance in SDB estimation, with R2 values exceeding 0.85. However, turbidity showed a negative correlation with SDB accuracy, and higher turbidity levels limited the maximum retrievable water depth, resulting in SDB variances ranging from 0 to 15 m. Landsat 8 was more accurate in low to moderate turbidity environments (12–15), where SDB variance was lower, while higher turbidity (above 15) led to greater SDB variance and reduced accuracy. Sentinel-2 outperformed Landsat 8 in moderate to high turbidity environments (36–203), delivering higher R2 values and more consistent SDB estimates, making it a more reliable tool for areas with variable turbidity. These findings suggest that SDB is a viable method for bathymetric and turbidity mapping in diverse port settings, with the potential for broader application in coastal monitoring and marine management. Full article
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Review

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29 pages, 2184 KiB  
Review
A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
by Andrea Mazzeo, Alfredo Renga and Maria Daniela Graziano
Remote Sens. 2024, 16(20), 3775; https://doi.org/10.3390/rs16203775 - 11 Oct 2024
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Abstract
The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the [...] Read more.
The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field. Full article
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