Remote Sensing for Maritime and Water Monitoring

Image courtesy of Gabriele Pieri and Marco Reggiannini

Special Issue Editors


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Guest Editor
Institute of Information Science and Technologies, Pisa, Italy
Interests: Image processing for marine environment; multi-source data fusion; environmental decision support systems; marine information systems; machine learning methods; multimedia data integration
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Signals and Images Laboratory, Institute of Information Science and Technologies, National Research Council of Italy, Pisa, Italy
Interests: ICT applied to underwater cultural heritage preservation and safeguard; image processing for scene understanding purposes; ICT applied to maritime surveillance purposes; multi-sensor multi-source data analysis and integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing plays a crucial role in the supervision of extended maritime areas. In particular, multisensor multisource imagery is collected daily through satellite platforms moving on earth-centered orbits. This massive amount of data, including optical images and radar maps, can be exploited for relevant purposes, such as the implementation of maritime surveillance tasks. These tasks typically address security and safety issues (vessel traffic monitoring, search and rescue) as well as environmental sustainability aspects (fishery, pollution). Satellite missions also serve the purpose of remotely measuring meaningful properties of the water column (e.g., salinity, turbidity, pollutants concentration), enabling chemophysical surveys of maritime areas of interest. Publications in this Special Issue will aim at composing a comprehensive overview of the several aspects that emerge in the implementation of maritime observation platforms through data processing.

With these topics in mind, among various other subjects, the authors are invited to discuss theoretical issues and methods concerning remote sensing and data processing over the marine and maritime environment (such as software platforms for surveillance or monitoring, processing algorithms for radar or optical imagery, sea-level assessment through satellite altimetry, integrated methods for the observation of the maritime environment, etc.), not excluding application case studies focused on remote sensing within the maritime context.

To this purpose, authors are invited to submit contributions that take into consideration the following topics:

  • Maritime data representation, analysis, and learning;
  • Ocean observation applied to fishing sustainability or biodiversity;
  • Techniques for data processing applied to maritime observation;
  • Target detection, classification, and identification in maritime data;
  • Marine pollution monitoring along with sea environment monitoring issues;
  • Open water analysis and trends based on remote sensing.

Dr. Gabriele Pieri
Dr. Marco Reggiannini
Guest Editors

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Keywords

  • Machine learning applied to maritime observation
  • Vessel detection and classification
  • Marine pollution and biological monitoring
  • Multisource multisensor maritime data processing
  • Maritime traffic monitoring
  • Water quality assessment

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Published Papers (1 paper)

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Research

21 pages, 10508 KiB  
Article
The Contribution of Multispectral Satellite Image to Shallow Water Bathymetry Mapping on the Coast of Misano Adriatico, Italy
by Anselme Muzirafuti, Giovanni Barreca, Antonio Crupi, Giancarlo Faina, Diego Paltrinieri, Stefania Lanza and Giovanni Randazzo
J. Mar. Sci. Eng. 2020, 8(2), 126; https://doi.org/10.3390/jmse8020126 - 16 Feb 2020
Cited by 35 | Viewed by 6493
Abstract
The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by [...] Read more.
The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by relying on limited in situ water depth data to extract and calibrate bathymetry from a QuickBird satellite image acquired on a highly dynamic coastal environment. The image was processed using the log-band ratio and optimal band ratio analysis (OBRA) methods. Preprocessing steps included the conversion of the raw satellite image into top of atmosphere reflectance, spatial filtering, land and water classification, the determination of the optimal OBRA spectral band pairs, and the estimation of relative SDB. Furthermore, calibration and vertical referencing were performed via in situ bathymetry acquired in November 2007. The relative bathymetry obtained from different band ratios were vertically referenced to the local datum using in situ water depth in order to obtain absolute SDB. The coefficient of determination (R2) and vertical root mean square error (RMSE) were computed for each method. A strong correlation with in situ field bathymetry was observed for both methods, with R2 = 0.8682 and RMSE = 0.518 m for the log-band ratio method and R2 = 0.8927–0.9108 and RMSE = 0.35 m for the OBRA method. This indicated a high degree of confidence of the SDB results obtained for the study area, with a high performance of the OBRA method for SDB mapping in turbid water. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime and Water Monitoring)
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