Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Ocean Monitoring Based on Artificial Intelligence and Remote Sensing
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Ocean Monitoring Based on Artificial Intelligence and Remote Sensing

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

Deadline for manuscript submissions: 26 April 2024 | Viewed by 7026

Special Issue Editors

Prof. Dr. Xiao-Hai Yan

E-Mail Website
Guest Editor
Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA
Interests: physical oceanography; ocean remote sensing; climate change; air-sea interaction; ocean circulation; image processing; environmental monitoring; deep learning/big data/data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hua Su

E-Mail Website
Guest Editor
The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
Interests: ocean remote sensing; coastal remote sensing; deep ocean remote sensing; global climate change; AI oceanography
Special Issues, Collections and Topics in MDPI journals
Dr. Wenfang Lu

E-Mail Website
Guest Editor
School of Marine Sciences, Sun Yat-Sen University, Guangzhou, China
Interests: physical oceanography; ocean remote sensing; AI oceanography; data science; bio-physical coupling

Special Issue Information

Dear Colleagues,

Ocean environmental monitoring plays a crutial role in understanding and utilization of the ocean, involving many fields such as ocean’s role in climate change, ocean energy development and protection, ocean transportation, fisheries, and ocean disaster prevention, etc. Due to the dynamic nature of the ocean and sparcity of in-situ ocean observation, monitoring the ocean from space has been a difficult task. With the continuous development of remote sensing and artificial intelligence technologies during recent years, ocean monitoring has entered the big-data era. More and more ocean satellites equipped with broad sensors have been deployed to view the ocean from a large scale and high-resolution perspective.

Moreover, the combination of the two technologies has unleased great potential in dealing with complex remote sensing retrival, feature/pattern recognition, and reconstruction problems. By combining remote sensing technology, existing rules of value and hidden correlation can be discovered from the data, to better observe the ocean and coastal environment. This can effectively avoid the defects faced by traditional ocean monitoring and provide a new data-driven direction for the development of AI-based ocean monitoring.

The main goal of this Special Issue is to provide a scientific platform to discuss recent advances in the application of remote sensing and AI techniques to monitor the ocean and coastal environment. Papers of both theoretical and applicative nature, as well as contributions regarding new advanced AI/Machine learning, deep learning and data science techniques for the remote sensing research community, are welcome.

Prof. Dr. Xiao-Hai Yan
Prof. Dr. Hua Su
Dr. Wenfang Lu
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 monitoring
  • remote sensing
  • artificial intelligence
  • machine learning and deep learning
  • big data
  • data-driven model
  • ocean processes
  • sea level, altimetry, ocean color and environment
  • coastal environment and disaster

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

17 pages, 3028 KiB  
Article
Evaluating Ecosystem Service Value Changes in Mangrove Forests in Guangxi, China, from 2016 to 2020
by Kedong Wang, Mingming Jia, Xiaohai Zhang, Chuanpeng Zhao, Rong Zhang and Zongming Wang
Remote Sens. 2024, 16(3), 494; https://doi.org/10.3390/rs16030494 - 27 Jan 2024
Viewed by 713
Abstract
Mangrove forests play a vital role in maintaining ecological balance in coastal regions. Accurately assessing changes in the ecosystem service value (ESV) of these mangrove forests requires more precise distribution data and an appropriate set of evaluation methods. In this study, we accurately [...] Read more.
Mangrove forests play a vital role in maintaining ecological balance in coastal regions. Accurately assessing changes in the ecosystem service value (ESV) of these mangrove forests requires more precise distribution data and an appropriate set of evaluation methods. In this study, we accurately mapped the spatial distribution data and patterns of mangrove forests in Guangxi province in 2016 and 2020, using 10 m spatial resolution Sentinel-2 imagery, and conducted a comprehensive evaluation of ESV provided by mangrove forests. The results showed that (1) from 2016 to 2020, mangrove forests in Guangxi demonstrated a positive development trend and were undergoing a process of recovery. The area of mangrove forests in Guangxi increased from 6245.15 ha in 2016 to 6750.01 ha in 2020, with a net increase of 504.81 ha, which was mainly concentrated in Lianzhou Bay, Tieshan Harbour, and Dandou Bay; (2) the ESV of mangrove forests was USD 363.78 million in 2016 and USD 390.74 million in 2020; (3) the value of fishery, soil conservation, wave absorption, and pollution purification comprises the largest proportions of the ESV of mangrove forests. This study provides valuable insights and information to enhance our understanding of the relationship between the spatial pattern of mangrove forests and their ecosystem service value. Full article
(This article belongs to the Special Issue Ocean Monitoring Based on Artificial Intelligence and Remote Sensing)
►▼ Show Figures

Figure 1

19 pages, 5115 KiB  
Article
Gas Plume Target Detection in Multibeam Water Column Image Using Deep Residual Aggregation Structure and Attention Mechanism
by Wenguang Chen, Xiao Wang, Binglong Yan, Junjie Chen, Tingchen Jiang and Jialong Sun
Remote Sens. 2023, 15(11), 2896; https://doi.org/10.3390/rs15112896 - 02 Jun 2023
Cited by 2 | Viewed by 1293
Abstract
A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in an image have no obvious contour information and are susceptible to the influence of underwater environments, equipment noises, [...] Read more.
A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in an image have no obvious contour information and are susceptible to the influence of underwater environments, equipment noises, and other factors, resulting in varied shapes and sizes. Compared with traditional detection methods, this paper proposes an improved YOLOv7 (You Only Look Once vision 7) network structure for detecting gas plume targets in a WCI. Firstly, Fused-MBConv is used to replace all convolutional blocks in the ELAN (Efficient Layer Aggregation Networks) module to form the ELAN-F (ELAN based on the Fused-MBConv block) module, which accelerates model convergence. Additionally, based on the ELAN-F module, MBConv is used to replace the 3 × 3 convolutional blocks to form the ELAN-M (ELAN based on the MBConv block) module, which reduces the number of model parameters. Both ELAN-F and ELAN-M modules are deep residual aggregation structures used to fuse multilevel features and enhance information expression. Furthermore, the ELAN-F1M3 (ELAN based on one Fused-MBConv block and three MBConv blocks) backbone network structure is designed to fully leverage the efficiency of the ELAN-F and ELAN-M modules. Finally, the SimAM attention block is added into the neck network to guide the network to pay more attention to the feature information related to the gas plume target at different scales and to improve model robustness. Experimental results show that this method can accurately detect gas plume targets in a complex WCI and has greatly improved performance compared to the baseline. Full article
(This article belongs to the Special Issue Ocean Monitoring Based on Artificial Intelligence and Remote Sensing)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

12 pages, 2509 KiB  
Technical Note
Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data
by Zhongqiang Wu, Shulei Wu, Haixia Yang, Zhihua Mao and Wei Shen
Remote Sens. 2023, 15(20), 4955; https://doi.org/10.3390/rs15204955 - 13 Oct 2023
Viewed by 1169
Abstract
Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning [...] Read more.
Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, specifically focusing on the Baidu Easy DL model for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using ACOLITE software, this research compares the performance of several machine learning algorithms, including the Stumpf model, Log-Linear model, and the Baidu Easy DL model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0–11 m depth range. This study showcases the substantial potential of machine learning in remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research opens avenues for the further exploration of machine learning applications in remote sensing and highlights the promising prospects of online model APIs when streamlining remote sensing data processing. Full article
(This article belongs to the Special Issue Ocean Monitoring Based on Artificial Intelligence and Remote Sensing)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop