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Special Issue "Advanced Applications of Remote Sensing in Monitoring Marine Environment"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 5404

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Special Issue Editors

Prof. Dr. Zhe-Wen Zheng

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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

Prof. Dr. Jiayi Pan

E-Mail Website
Guest Editor

1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: coastal and lake remote sensing; coastal ocean dynamics; marine remote sensing physics
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Special Issue Information

Dear Colleagues,

Remote sensing in a marine environment is the use of sensors to retrieve remote, non-contact observations of the ocean to obtain images or data related to certain oceanic phenomena or processes. In recent years, advancements in remote sensing technology have enabled data collection with much higher spatial and temporal resolutions from either passive or active sensors. These new applications offer new opportunities and incredible new insights and interpretations for practical implementation in certain oceanic phenomena/processes. This Special Issue invites the most up-to-date applications on the hot topic of “Remote Sensing for Marine Environment Monitoring”, especially new observations, analytical methods, data, and modeling that can significantly improve our understanding of marine environmental sciences.

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 2500 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

Published Papers (6 papers)

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Research

Open AccessArticle

Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery

and

Remote Sens. 2023, 15(8), 2196; https://doi.org/10.3390/rs15082196 - 21 Apr 2023

Viewed by 637

Abstract

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km². Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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Open AccessArticle

Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province

Xinzhe Wang

Linlin Tan

and

Jianchao Fan

Remote Sens. 2023, 15(5), 1386; https://doi.org/10.3390/rs15051386 - 01 Mar 2023

Cited by 1 | Viewed by 902

Abstract

Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data. Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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Open AccessArticle

A Method for Long-Term Target Anti-Interference Tracking Combining Deep Learning and CKF for LARS Tracking and Capturing

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Remote Sens. 2023, 15(3), 748; https://doi.org/10.3390/rs15030748 - 28 Jan 2023

Cited by 1 | Viewed by 785

Abstract

Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, such as poor tracking and detection. To solve these problems, we propose a long-term target anti-interference tracking (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks and online learning ideas. Meanwhile, we propose using the cubature Kalman filter (CKF) for optimization and prediction of the position. We constructed a launch and recovery system (LARS) tracking and capturing the AUV. The system consists of the following parts: First, images are acquired via binocular cameras. Next, the relative position between the AUV and the end of the LARS was estimated based on the pixel positions of the tracking AUV feature points and binocular camera data. Finally, using a discrete proportion integration differentiation (PID) method, the LARS is controlled to capture the moving AUV via a CKF-optimized position. To verify the feasibility of our proposed system, we used the robot operating system (ROS) platform and Gazebo software to simulate the system for experiments and visualization. The experiment demonstrates that in the tracking process when the AUV makes a sinusoidal motion with an amplitude of 0.2 m in the three-dimensional space and the relative distance between the AUV and LARS is no more than 1 m, the estimated position error of the AUV does not exceed 0.03 m. In the capturing process, the final capturing error is about 28 mm. Our results verify that our proposed system has high robustness and accuracy, providing the foundation for future AUV recycling research. Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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Open AccessArticle

Research on Self-Noise Suppression of Marine Acoustic Sensor Arrays

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Remote Sens. 2022, 14(24), 6186; https://doi.org/10.3390/rs14246186 - 07 Dec 2022

Viewed by 709

Abstract

Marine acoustic sensors can detect underwater acoustic information. The cilium micro-electro-mechanical system (MEMS) vector hydrophone (CVH) is the core component of the ocean noise measurement system. The performance of the CVH, especially its self-noise, has received widespread attention. In this paper, we propose a solution to improve the performance of the CVH using an array to detect environmental noise in a complex deep-water environment. We analyzed the self-noise source of the CVH and the noise suppression principle of the four-unit MEMS vector hydrophone (FUVH). In addition, we designed the pre-circuit of the FUVH, completed the cross-beam structure by the MEMS processing, and packaged a FUVH. Then, we tested the performance of a packaged FUVH. Finally, the experimental results show that the FUVH reduces the self-noise voltage power spectrum by 6 dB compared to the CVH structure. The FUVH achieves better linearity at low frequencies without reducing the bandwidth and sensitivity. In addition, it minimizes the equivalent self-noise levels by 5.18 and 5.14 dB in the X and Y channels, respectively. Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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Open AccessCommunication

Improved Understanding of Typhoon-Induced Immediate Chlorophyll-A Response Using Advanced Himawari Imager (AHI) Onboard Himawari-8

Jia-Yi Lin

Hua Ho

and

Zhe-Wen Zheng

Remote Sens. 2022, 14(23), 6055; https://doi.org/10.3390/rs14236055 - 29 Nov 2022

Viewed by 599

Abstract

The biological response triggered by a tropical cyclone (TC) passage has attracted much attention due to its possible impacts on regional oceanic, ecological environment, and regional climate balance. However, the detailed progress of TC-induced chlorophyll-a (Chl-a) responses (TICRs) remains unclear due to the inherent limitation of observations in ocean color with polar-orbiting satellites as used in previous studies. The appearance of the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite opens the opportunity of correcting all our understanding of TICRs due to its hyper temporal image acquisition capability. In this study, the more real relationship between Chl-a response and TC is further clarified. Results show an essentially different reacting progress of TICRs given by AHI/Himawari-8. It shows a much quicker response relative to previous understanding. Chl-a concentrations reached the highest value on the first day under the severe influences of typhoons. The averaged Chl-a response (0–3 days behind TC passage) observed by AHI is approximately three (2.95) times stronger than that observed by the Moderate Resolution Imaging Spectrometer onboard the National Aeronautics and Space Administration Terra/Aqua satellites. The spatial characteristics of TICRs by AHI show marked differences. Overall, the rapid and strong response sheds new light on the role of TICRs in influencing the regional oceanic environment, marine ecosystem, and local climate. Whole new estimations for the impacts of TICRs on the aforementioned issues are needed urgently. Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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Open AccessArticle

Variability of Chl a Concentration of Priority Marine Regions of the Northwest of Mexico

Carlos Manuel Robles-Tamayo

Ricardo García-Morales

José Raúl Romo-León

Gudelia Figueroa-Preciado

María Cristina Peñalba-Garmendia

and

Luis Fernando Enríquez-Ocaña

Remote Sens. 2022, 14(19), 4891; https://doi.org/10.3390/rs14194891 - 30 Sep 2022

Cited by 1 | Viewed by 1087

Abstract

Priority Marine Regions (PMR) are important areas for biodiversity conservation in the Northwest Pacific Ocean in Mexico. The oceanographic dynamics of these regions are very important to understand their variability, generate analyses, and predict climate change trends by generating an adequate management of marine resources and their ecological characterization. Chlorophyll a (Chl a) is important to quantify phytoplankton biomass, consider the main basis of the trophic web in marine ecosystems, and determine the primary productivity levels and trends of change. The objective of this research is to analyze the oceanographic variability of 24 PMR through monthly 1-km satellite image resolution Chl a data from September 1997 to October 2018. A cluster analysis of Chl a data yielded 18 regions with clear seasonal variability in the Chl a concentration in the South-Californian Pacific (maximum values in spring-summer and minimum ones in autumn-winter) and Gulf of California (maximum values in winter-spring and minimum ones in summer-autumn). Significant differences (p < 0.05) were observed in Chl a concentration analyses for each one of the regions when climate patterns—El Niño/La Niña Southern Oscillation (ENSO) and normal events—were compared for all the seasons of the year (spring, summer, autumn, and winter). Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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