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Advances in Remote Sensing and Geoinformatics for Sustainable Aquaculture and...
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Advances in Remote Sensing and Geoinformatics for Sustainable Aquaculture and Fisheries

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3571

Special Issue Editors

Dr. Lin Yan

E-Mail Website
Guest Editor
Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA
Interests: land cover land use change; remote sensing of aquaculture; satellite data fusion
Dr. Ben Belton

E-Mail Website
Guest Editor
1. International Food Policy Research Institute, Dhaka, Bangladesh
2. Department of Agricultural, Food and Resource Economics, Michigan State University, East Lansing, MI, USA
Interests: aquaculture & capture fisheries development; agrarian change; food systems
Dr. Juhua Luo

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing 210008, China
Interests: remote sensing of aquaculture; land cover and land use change; wetland remote sensing; remote sensing of water environment and water ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aquaculture and fisheries industries make substantial contributions to international food security. Due to increasing demand, aquatic food produced by aquaculture and fisheries firms significantly in the last few decades and is expected to achieve further growth. However, the aquatic ecosystems in which aquaculture and fishery practices take place are under stress due to climate change, overfishing, pollution, and environmental degradation.

Remote sensing Earth observation (EO) techniques provide temporally and spatially comprehensive measurements of the Earth’s surface. Due to its integration into geographic information systems (GIS), it is widely used to support monitoring, planning, management, and policy making related to aquaculture and fisheries.

Recent advances in active and passive remote sensing sensors provide improved EO data with high spatial resolutions, high revisit cycles, more spectral bands, and wider wavelength ranges. These advances create unprecedented opportunities to monitor rapid and fine-scale changes in the Earth’s surface. This Special Issue requests papers that demonstrate enhanced remote sensing capabilities that can support sustainable aquaculture and fisheries.

Responsive research topic examples include, but are not limited to, the following subjects:

  • Land cover land use changes associated with aquaculture in brackish and freshwater areas;
  • Monitoring of fisheries activities and fishing efforts;
  • Monitoring of marine aquaculture installations;
  • Monitoring of oceanic or lacustrine plankton using Lidar;
  • Monitoring of the environmental impacts of aquaculture and fisheries (e.g., water quality, land degradation, toxic algal blooms, and greenhouse gas emissions);
  • Protection and management of aquatic resources and ecosystems;
  • Climate change adaptions in aquaculture and fisheries;
  • Human activity in marine protected areas;
  • Integrated coastal zone management;
  • Monitoring of aquaculture areas and types using multisource satellite fusion (e.g., UAV, SAR, and optical satellites);
  • Use of machine or deep learning in large-scale mapping aquaculture types and areas;
  • The drivers, effects, and results of changes in the ecosystems and environments devoted to aquaculture.

Dr. Lin Yan
Dr. Ben Belton
Dr. Juhua 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

  • remote sensing
  • aquaculture
  • fisheries
  • sustainability
  • climate change
  • environment
  • land use change
  • carbon cycle/sequestration

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Published Papers (2 papers)

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Research

21 pages, 6039 KiB  
Article
An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province
by Yingwen Hu, Li Zhang, Bowei Chen and Jian Zuo
Remote Sens. 2024, 16(7), 1217; https://doi.org/10.3390/rs16071217 - 29 Mar 2024
Cited by 2 | Viewed by 1254
Abstract
Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement [...] Read more.
Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement of Sustainable Development Goals (SDGs). Consequently, extracting aquaculture has become crucial and valuable. However, due to the limitations of remote sensing image spatial resolution and traditional extraction methods, most research studies focus on aquaculture areas containing dikes rather than individually separable aquaculture ponds (ISAPs). This is not an accurate estimation of these aquaculture areas’ true size. In our study, we propose a rapid and effective object-based method of extracting ISAPs. We chose multi-scale segmentation to generate semantically meaningful image objects for various types of land cover, and then built a decision tree classifier according to the unique features of ISAPs. The results show that our method can remove small rivers and other easily confused features, which has thus far been difficult to accomplish with conventional methods. We obtained an overall precision value of 85.61% with a recall of 84.04%; compared to the support vector machine’s (SVM) overall precision value of 78.85% and recall rate of 61.21%, our method demonstrates greater accuracy and efficiency. We used this method to test the transferability of the algorithm to nearby areas, and the obtained accuracy exceeded 80%. The method proposed in this study could provide a readily available solution for the simple and efficient extracting of ISAPs and shows high spatiotemporal transferability. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and Geoinformatics for Sustainable Aquaculture and Fisheries)
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20 pages, 12906 KiB  
Article
Freshwater Aquaculture Mapping in “Home of Chinese Crawfish” by Using a Hierarchical Classification Framework and Sentinel-1/2 Data
by Chen Wang, Genhou Wang, Geli Zhang, Yifeng Cui, Xi Zhang, Yingli He and Yan Zhou
Remote Sens. 2024, 16(5), 893; https://doi.org/10.3390/rs16050893 - 2 Mar 2024
Cited by 1 | Viewed by 1468
Abstract
The escalating evolution of aquaculture has wielded a profound and far-reaching impact on regional sustainable development, ecological equilibrium, and food security. Currently, most aquaculture mapping efforts mainly focus on coastal aquaculture ponds rather than diverse inland aquaculture areas. Recognizing all types of aquaculture [...] Read more.
The escalating evolution of aquaculture has wielded a profound and far-reaching impact on regional sustainable development, ecological equilibrium, and food security. Currently, most aquaculture mapping efforts mainly focus on coastal aquaculture ponds rather than diverse inland aquaculture areas. Recognizing all types of aquaculture areas and accurately classifying different types of aquaculture areas remains a challenge. Here, on the basis of the Google Earth Engine (GEE) and the time-series Sentinel-1 and -2 data, we developed a novel hierarchical framework extraction method for mapping fine inland aquaculture areas (aquaculture ponds + rice-crawfish fields) by employing distinct phenological disparities within two temporal windows (T1 and T2) in Qianjiang, so-called “Home of Chinese Crawfish”. Simultaneously, we evaluated the classification performance of four distinct machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Boosting (GTB), as well as 11 feature combinations. Following an exhaustive comparative analysis, we selected the optimal machine learning classifier (i.e., the RF classifier) and the optimal feature combination (i.e., feature combination after an automated feature selection method) to classify the aquaculture areas with high accuracy. The results underscore the robustness of the proposed methodology, achieving an outstanding overall accuracy of 93.8%, with an F1 score of 0.94 for aquaculture. The result indicates that an area of 214.6 ± 10.5 km2 of rice-crawfish fields, constituting approximately 83% of the entire aquaculture area in Qianjiang, followed by aquaculture ponds (44.3 ± 10.7 km2, 17%). The proposed hierarchical framework, based on significant phenological characteristics of varied aquaculture types, provides a new approach to monitoring inland freshwater aquaculture in China and other regions of the world. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and Geoinformatics for Sustainable Aquaculture and Fisheries)
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