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Advances on Remote Sensing, Modeling, and Trajectory Prediction of Marine Oil Spill

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1135

Special Issue Editors

Faculty of Information Technology, Beijing University of Technology, No. 100 PingLeYuan Road, Chaoyang District, Beijing 100124, China
Interests: SAR; polarimetry; electromagnetic; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Engineering Department, University of Naples “Parthenope”, 80143 Naples, Italy
Interests: electromagnetic modeling; SAR; polarimetry; ocean; coastal areas
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
Interests: SAR; InSAR; PSInSAR; deformation monitoring; oil spill detection; change detection; geohazards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Oil spill accidents seriously pollute the marine environment, threaten the safety of marine ecosystems and coastal residents, and cause huge economic losses to industries such as marine fisheries, aquaculture, and tourism. Timely and accurate monitoring and early warning of oil spills on the sea can not only ensure the timely and effective disposal of oil spills and effectively reduce the negative impact of accidents, but also provide a basis for charging companies and individuals responsible for oil spill accidents.

The recent development of remote sensing technology provides powerful tools for detecting marine oil spills and retrieving their detailed properties. However, many challenges remain for the diversity of oil film properties, the complicated marine environment, and the affection of marine oil spill look-alikes. Moreover, it is also very important to model and predict the trajectory of marine oil spills for efficient clean-up and damage evaluation.

The main scope of this Special Issue includes, but is not limited to, the detection of marine oil spills by optical and radar platforms, the retrieval of oil spill type and parameters as well as the modeling and prediction of oil spill trajectory and its dispersion. It is hoped that this Special Issue may boost the exchange and interconnection between the recent studies related to marine oil spill remote sensing and eventually contribute to the protection of the marine environment.

Dr. Yu Li
Dr. Andrea Buono
Dr. Qingli Luo
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

  • marine oil spill
  • SAR
  • optical
  • remote sensing
  • modeling
  • prediction

Published Papers (1 paper)

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Research

18 pages, 5847 KiB  
Article
Combined Retrieval of Oil Film Thickness Using Hyperspectral and Thermal Infrared Remote Sensing
by Junfang Yang, Yabin Hu, Yi Ma, Meiqi Wang, Ning Zhang, Zhongwei Li and Jie Zhang
Remote Sens. 2023, 15(22), 5415; https://doi.org/10.3390/rs15225415 - 18 Nov 2023
Viewed by 804
Abstract
An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression [...] Read more.
An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Dear Colleagues,

Oil spill accidents seriously pollute the marine environment, threaten the safety of marine ecosystems and coastal residents, and cause huge economic losses to industries such as marine fisheries, aquaculture, and tourism. Timely and accurate monitoring and early warning of oil spills on the sea can not only ensure the timely and effective disposal of oil spills and effectively reduce the negative impact of accidents, but also provide a basis for charging companies and individuals responsible for oil spill accidents.

The recent development of remote sensing technology provides powerful tools for detecting marine oil spills and retrieving their detailed properties. However, many challenges remain for the diversity of oil film properties, the complicated marine environment, and the affection of marine oil spill look-alikes. Moreover, it is also very important to model and predict the trajectory of marine oil spills for efficient clean-up and damage evaluation.

The main scope of this Special Issue includes, but is not limited to, the detection of marine oil spills by optical and radar platforms, the retrieval of oil spill type and parameters as well as the modeling and prediction of oil spill trajectory and its dispersion. It is hoped that this Special Issue may boost the exchange and interconnection between the recent studies related to marine oil spill remote sensing and eventually contribute to the protection of the marine environment.

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