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Water
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Conservation and Monitoring of Marine Ecosystem
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Conservation and Monitoring of Marine Ecosystem

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".

Deadline for manuscript submissions: closed (10 April 2024) | Viewed by 4238

Special Issue Editors

Prof. Dr. Hui Zhao

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Guest Editor
College of Chemistry and Environmental Science, Guangdong Ocean University, Zhanjiang, China
Interests: marine ecology; marine environment; natural resource management; phytoplankton; conservation biology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiuying Han

E-Mail Website
Guest Editor
Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, China
Interests: nitrogen and carbon circle in marine environment; marine ecosystem management; marine ecosystem healthy evaluation; marine ecological disaster; marine ecosystem restoration
Prof. Dr. Na Liu

E-Mail
Guest Editor
First Institute of Ocanography, Ministry of Natural Resources, Beijing, China
Interests: the polar physical oceanography; the polar climate change

Special Issue Information

Dear Colleagues,

The coastal/offshore marine ecosystem is one of the most distinctive features of the global ecosystem. The conservation and monitoring of marine ecosystems are important means and measures for protecting marine biodiversity and the sustainability of marine living resources, as well as maintaining a marine ecological balance. Monitoring the marine ecological environment is the basic basis for objectively assessing environmental quality, reflecting pollution control effectiveness, and implementing environmental management and decision making. It is an important foundational work for marine ecological environment protection, marine environmental management, and even the development of the entire marine enterprise. With the development of the economy and the intensification of human activities, environmental pollution, biodiversity reduction, and habitat degradation in coastal regions, estuaries, and bays are becoming increasingly aggravated. Multiple monitoring methods are used to obtain multi-parameter, long-term, three-dimensional, and real-time monitoring data in the aforementioned areas. Based on the comprehensive analysis of the above data, a valuable evaluation can be provided for the ecological system status of the coastal regions, estuaries, and bays. Reasonable environmental protection measures need to be formulated, making significant contributions to the protection of the marine ecological environment, the development of marine undertakings, and the coastal economy.

Prof. Dr. Hui Zhao
Prof. Dr. Qiuying Han
Prof. Dr. Na Liu
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. Water 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 2600 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

  • coastal marine ecosystem
  • marine ecological environment monitoring
  • marine ecological environment protection

Published Papers (3 papers)

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Research

20 pages, 6401 KiB  
Article
Effects of Aquaculture and Thalassia testudinum on Sediment Organic Carbon in Xincun Bay, Hainan Island
by Qiuying Han, Wenxue Che, Hui Zhao, Jiahui Ye, Wenxuan Zeng, Yufeng Luo, Xinzhu Bai, Muqiu Zhao and Yunfeng Shi
Water 2024, 16(2), 338; https://doi.org/10.3390/w16020338 - 19 Jan 2024
Viewed by 948
Abstract
Eutrophication due to aquaculture can cause the decline of seagrasses and impact their carbon storage capacity. This study explored the effects of aquaculture on the sediment organic carbon (SOC) in Thalassia testudinum seagrass beds using enzyme activity and microorganisms as indicators. Our results [...] Read more.
Eutrophication due to aquaculture can cause the decline of seagrasses and impact their carbon storage capacity. This study explored the effects of aquaculture on the sediment organic carbon (SOC) in Thalassia testudinum seagrass beds using enzyme activity and microorganisms as indicators. Our results showed that the distance to aquaculture significantly increased the SOC and TN of sediments; the C/N ratio of sediments was reduced by the distance to aquaculture. Distance to aquaculture and seagrasses significantly impacted the δ13C of sediments, and their significant interactive effects on the δ13C of sediments were found. Distance to aquaculture and seagrasses had significantly interactive effects on the cellulase activity of sediments. Distance to aquaculture and seagrasses separately reduced the invertase activity of sediments. SOC in the seagrass bed was significantly positively impacted by cellulase activity and polyphenol oxidase activity in sediments. Firmicutes, Desulfobacterota and Chloroflexi were the dominant taxa in the S1 and S2 locations. From the S1 location to the S2 location, the relative abundances of Firmicutes and Desulfobacterota increased. The functional profiles of COG were relatively similar between the S1 and S2 locations. BugBase phenotype predictions indicated that the microbial phenotypes of all the seagrass sediment samples were dominated by anaerobic bacteria in terms of oxygen utilizing phenotypes. FAPROTAX functional predictions indicated that aquaculture affects functions associated with seagrass bed sediment bacteria, particularly those related to carbon and nitrogen cycling. This study can provide an important basis for understanding the response mechanism of global carbon sink changes to human activities such as aquaculture and supply more scientific data for promoting the conservation and management of seagrass beds. Full article
(This article belongs to the Special Issue Conservation and Monitoring of Marine Ecosystem)
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14 pages, 9403 KiB  
Article
A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning
by You Zeng, Tianlong Liang, Donglin Fan and Hongchang He
Water 2023, 15(21), 3864; https://doi.org/10.3390/w15213864 - 6 Nov 2023
Viewed by 1376
Abstract
Chlorophyll a (Chla) is a crucial pigment in phytoplankton, playing a vital role in determining phytoplankton biomass and water nutrient status. However, in optically complex water bodies, Chla concentration is no longer the primary factor influencing remote sensing spectral reflectance signals, leading to [...] Read more.
Chlorophyll a (Chla) is a crucial pigment in phytoplankton, playing a vital role in determining phytoplankton biomass and water nutrient status. However, in optically complex water bodies, Chla concentration is no longer the primary factor influencing remote sensing spectral reflectance signals, leading to significant errors in traditional Chla concentration estimation methods. With advancements in in situ measurements, synchronized satellite data, and computer technology, machine learning algorithms have become popular in Chla concentration retrieval. Nevertheless, when using machine learning methods to estimate Chla concentration, abrupt changes in Chla values can disrupt the spatiotemporal smoothness of the retrieval results. Therefore, this study proposes a two-stage approach to enhance the accuracy of Chla concentration estimation in optically complex water bodies. In the first stage, a one-dimensional convolutional neural network (1D CNN) is employed for precise Chla retrieval, and in the second stage, the regression layer of the 1DCNN is replaced with support vector regression (SVR). The research findings are as follows: (1) In the first stage, the performance metrics (R2, RMSE, RMLSE, Bias, MAE) of the 1D CNN outperform state-of-the-art algorithms (OCI, SVR, RFR) on the test dataset. (2) After the second stage, the performance further improves, with the metrics achieving values of 0.892, 11.243, 0.052, 1.056, and 1.444, respectively. (3) In mid- to high-latitude regions, the inversion performance of 1D CNN\SVR is superior to other algorithms, exhibiting richer details and higher noise tolerance in nearshore areas. (4) 1D CNN\SVR demonstrates high inversion capabilities in water bodies with medium-to-high nutrient levels. Full article
(This article belongs to the Special Issue Conservation and Monitoring of Marine Ecosystem)
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14 pages, 4423 KiB  
Article
A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning
by Qianguo Xing, Hailong Liu, Jinghu Li, Yingzhuo Hou, Miaomiao Meng and Chunli Liu
Water 2023, 15(17), 3080; https://doi.org/10.3390/w15173080 - 28 Aug 2023
Cited by 3 | Viewed by 1284
Abstract
Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green [...] Read more.
Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available. Full article
(This article belongs to the Special Issue Conservation and Monitoring of Marine Ecosystem)
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