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Fishes
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Application of Remote Sensing to Fisheries
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Application of Remote Sensing to Fisheries

A special issue of Fishes (ISSN 2410-3888).

Deadline for manuscript submissions: 24 August 2026 | Viewed by 2430

Editors

Dr. Fenghua Tang

E-Mail Website
Guest Editor
East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
Interests: fisheries; remote sensing
Dr. Guoqing Zhao

E-Mail Website
Guest Editor Assistant
East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
Interests: changes in fishing grounds; resource assessment; marine ecology; environmental impact

Special Issue Information

Dear Colleagues,

As a critical global sector for food production and economic activity, fisheries are undergoing a profound transformation. Currently, they face severe challenges, such as the overexploitation of resources, the deterioration of ecological environments, and the intensification of the impacts of climate change, making sustainable development fraught with obstacles. Meanwhile, rapid technological progress is bringing about new opportunities, especially the growing and deepening application of remote sensing in fisheries, which strongly supports solving traditional issues and advancing modernization. With large-scale, real-time, dynamic monitoring advantages, remote sensing shows great potential in resource assessment, fishing ground detection, aquaculture, and ecological monitoring, gradually changing production and management patterns.

This Special Issue, entitled "Application of Remote Sensing to Fisheries", solicits the submission of relevant original research papers, reviews, and case studies, including, but not limited to, the following contents:

  • Quantitative fishery resource assessment models using remote sensing data;
  • Satellite remote sensing for monitoring fishing ground environments and predicting fishing grounds;
  • Innovative use of remote sensing in precision aquaculture management;
  • Monitoring and analyzing fishing activities via night-light remote sensing;
  • Integrated applications of multi-source remote sensing data fusion with GIS and GPS in fisheries;
  • Remote sensing progress in fishery ecological protection and restoration.

Dr. Fenghua Tang
Guest Editor

Dr. Guoqing Zhao
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fishes is an international peer-reviewed open access monthly 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

  • remote sensing
  • resource assessment
  • spatial–temporal dynamics
  • climate change
  • environmental factor
  • night-light remote sensing
  • electronic monitoring (EM)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

22 pages, 3822 KB  
Article
Research on Fish Recognition in Complex Backgrounds Using ViT-Enhanced YOLOv11
by Xiangshuo He, Shenglong Yang, Wei Wang, Kai Zhu, Shengmao Zhang, Yang Dai, Keji Jiang and Fei Wang
Fishes 2026, 11(7), 385; https://doi.org/10.3390/fishes11070385 (registering DOI) - 27 Jun 2026
Viewed by 124
Abstract
To address the common challenges in fish recognition tasks under complex backgrounds, such as target overlap, occlusion, and chaotic spatial distribution, an improved YOLOv11 recognition model based on the Vision Transformer (ViT) is proposed. Traditional Convolutional Neural Networks (CNNs) and the YOLO series [...] Read more.
To address the common challenges in fish recognition tasks under complex backgrounds, such as target overlap, occlusion, and chaotic spatial distribution, an improved YOLOv11 recognition model based on the Vision Transformer (ViT) is proposed. Traditional Convolutional Neural Networks (CNNs) and the YOLO series models are limited by their local receptive fields, making it difficult to capture global semantic correlations in dense and heavily occluded fish target detection, which often leads to feature confusion and false detections. By embedding ViT modules at the beginning of the Head and at the end of the Backbone of YOLOv11, the self-attention mechanism of ViT is leveraged to capture global dependencies in the image, re-integrate and enhance multi-scale features from the Backbone and Neck, thus constructing two improved ViT models. Comparative experiments are conducted on the FishRecognition-2025 dataset, which contains 955 high-resolution RGB images covering nine common coastal fish species across four categories: single fish species, multiple classes separated, slight overlap of multiple fish species, and severe overlap of multiple fish species. Under identical training strategies and evaluation metrics, the four models—original YOLOv11, traditional CNN, ViT-Head, and ViT-Backbone—are compared. The results show that the second improved ViT model (with ViT placed at the end of the Backbone) outperformed the first improved model (with ViT placed at the beginning of the Head) in terms of mAP50 and mAP50-95. Moreover, its overall accuracy across the four test data categories (single fish species, multiple classes separated, slight overlap of multiple fish species, and severe overlap of multiple fish species) surpassed that of YOLOv11, CNN, and the first ViT model. Although its accuracy in single fish species and multiple classes separated scenarios was slightly lower than that of the CNN model, it demonstrated significant advantages in scenarios with slight overlap of multiple fish species and severe overlap of multiple fish species. These findings validate the effectiveness of the ViT module in global feature modeling and adaptability to complex backgrounds, suggesting a promising technical direction for future real-time recognition in fishery field operations. Full article
(This article belongs to the Special Issue Application of Remote Sensing to Fisheries)
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21 pages, 12908 KB  
Article
Spatiotemporal Analysis of Light-Fishing Vessel Operations in the Arabian Sea Based on Nighttime Light Remote Sensing
by Tianfei Cheng, Shenglong Yang, Fei Wang, Wanbing Ren, Dongxu Yang and Shengmao Zhang
Fishes 2026, 11(6), 324; https://doi.org/10.3390/fishes11060324 - 28 May 2026
Viewed by 204
Abstract
A comprehensive understanding of the spatial dynamics and operational characteristics of fishing activities in the Arabian Sea is critical for effective marine management and regional resource conservation. Based on VIIRS/DNB nighttime light imagery from 2017 to 2022 and the YOLOv11 model, this study [...] Read more.
A comprehensive understanding of the spatial dynamics and operational characteristics of fishing activities in the Arabian Sea is critical for effective marine management and regional resource conservation. Based on VIIRS/DNB nighttime light imagery from 2017 to 2022 and the YOLOv11 model, this study presents an applied observational pipeline for the spatial extraction of fishing vessel positions. Spatial statistical methods were employed to analyze the operational patterns of light-fishing fleets, and habitat niches were identified by integrating marine environmental data. The results indicate that: (1) The YOLOv11 model achieved a precision (P) of 0.966, a recall (R) of 0.954, and a mean average precision (mAP) of 0.969. Under clear-sky and thin-cloud conditions, it demonstrated superior detection accuracy compared to existing VBD (VIIRS Boat Detection) products. (2) Through Kernel Density Hotspot Analysis (KDHSA), the primary spatial distribution of the light-fishing fleet was delineated. Fishing Operation Areas (FOAs) exhibited a pronounced seasonal “clustering–diffusion–re-clustering” pattern. The Center of Effort (CoE) generally followed a counter-clockwise migration trajectory, though a clockwise shift was observed during the 2019–2020 fishing season. (3) Random Forest analysis identified dissolved oxygen at 200 m (DO200), sea surface height (SSH), and temperature at 200 m (T200) as the primary predictive environmental features associated with vessel distribution. The core spatial ranges associated with high vessel density were 9.5–14.9 mmol⋅m−3 for DO200, 0.24–0.36 m for SSH, and 17.3–18.0 °C for T200. Notably, the statistical contribution of subsurface factors significantly exceeded that of sea surface temperature (SST). Future research should integrate ship position data with fishery biological data to further explore the drivers of FOA variations. This study provides a scientific basis for the sustainable management and rational development of marine resources in the Northwest Indian Ocean. Full article
(This article belongs to the Special Issue Application of Remote Sensing to Fisheries)
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20 pages, 2623 KB  
Article
Prediction of Fishing Effort Intensity and Identification of Key Environmental Factors in Northwest Pacific Squid Fishing Grounds Using a Multi-Mechanism Integrate 3DCNN Model
by Guangyao Li, Chunlei Feng, Yongchuang Shi, Keji Jiang and Shenglong Yang
Fishes 2026, 11(5), 270; https://doi.org/10.3390/fishes11050270 - 30 Apr 2026
Viewed by 391
Abstract
To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and [...] Read more.
To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and three fusion models incorporating residual, attention, and Transformer mechanisms. Using the 2017–2024 AIS fishing data and ocean environmental variables from the North Pacific squid fishing industry, the models’ performance is compared at 12 different temporal and spatial scales, and key core environmental variables are identified. The results show that the ResNet3D model exhibits the best overall performance, achieving an F1 score of 0.7909 at the 1.0°-7 days temporal–spatial scale. The residual connections effectively mitigate the gradient vanishing problem, balancing prediction accuracy and stability. The optimal spatial resolution is 1.0°, and the key environmental variables include S100, Chl-a100, PP100, and DO100. S100 is the core driving variable, consistently exhibiting the highest feature importance value at all time scales. It should be noted that Chl-a is considered an indirect indicator of primary productivity, which may influence squid distribution through trophic transfer processes rather than direct biological effects. This study demonstrates the prediction accuracy and applicability of the multi-mechanism fusion 3DCNN model, reveals the temporal and spatial distribution patterns of fishing intensity in the Northwest Pacific squid fishing grounds, and provides scientific methods and technical support for dynamic monitoring, intelligent management, and sustainable utilization of squid resources. Full article
(This article belongs to the Special Issue Application of Remote Sensing to Fisheries)
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24 pages, 20988 KB  
Article
Monitoring of Oyster Reef Spatial Distribution with Thermal Infrared Band Data
by Xirui Xu, Fei Wang, Weimin Quan, Ruiliang Fan, Wei Fan and Sanling Yuan
Fishes 2026, 11(4), 209; https://doi.org/10.3390/fishes11040209 - 1 Apr 2026
Viewed by 522
Abstract
The spatial distribution of oyster reefs is an important indicator for assessing environmental changes in nearshore fishery habitats. However, due to tidal fluctuations, images of oyster reef distribution acquired under low-light conditions such as early morning or evening often exhibit common issues such [...] Read more.
The spatial distribution of oyster reefs is an important indicator for assessing environmental changes in nearshore fishery habitats. However, due to tidal fluctuations, images of oyster reef distribution acquired under low-light conditions such as early morning or evening often exhibit common issues such as bright spots and shadows. Thermal infrared (TIR) images, which are unaffected by external lighting conditions, can effectively address this problem. Aerial imaging of Liya Mountain, Haimen, Jiangsu Province, China, was conducted in this study. Based on unmanned aerial vehicles (UAVs) imagery acquired in 2025 using multispectral and TIR sensors, the total oyster reef area was estimated to be 6.61 ha. When compared with the oyster reef distribution derived from visible light aerial imagery collected in 2023 under favorable environmental conditions, this represents a decrease of 0.36 ha (5.4%), with the largest individual reef measuring 3388.17 m2. To demonstrate the improvement in extraction accuracy achieved by integrating TIR data with multispectral imagery, the research team compared the extraction accuracy for oyster reefs of different sizes: a 1.91% improvement was observed for small reefs, a 9.02% improvement for middle reefs, and an 18.98% improvement for large reefs. Experimentally, the emissivity of oyster reefs was determined to be 0.982 ± 0.002 using an isothermal method in the laboratory. The emissivity derived from in situ measurements showed similar values, supporting the reliability of the laboratory result and providing a crucial parameter for the inversion of reef surface temperature. Experimental results demonstrate that the TIR band can effectively enhance the spatial accuracy of oyster reef measurements under low-light conditions. Full article
(This article belongs to the Special Issue Application of Remote Sensing to Fisheries)
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16 pages, 1780 KB  
Article
Spatiotemporal Dynamics and Multi-Scale Fishing Effort of Squid Jigging Fleets in the Southeast Pacific Ocean
by Jiashu Shi, Yu Zhang, Yongchuang Shi, Guangyao Li, Wei Wang and Shenglong Yang
Fishes 2025, 10(12), 637; https://doi.org/10.3390/fishes10120637 - 10 Dec 2025
Viewed by 652
Abstract
The dynamic monitoring of fishing activities is fundamental to fishery management. Leveraging multi-year (2020–2023) AIS data from squid jigging vessels, this study employed a multi-level data mining and spatial statistical approach to decode the spatiotemporal patterns of fishing effort in the Southeast Pacific [...] Read more.
The dynamic monitoring of fishing activities is fundamental to fishery management. Leveraging multi-year (2020–2023) AIS data from squid jigging vessels, this study employed a multi-level data mining and spatial statistical approach to decode the spatiotemporal patterns of fishing effort in the Southeast Pacific Ocean. Our analysis reveals a highly concentrated and cyclical operation model: temporally, 20% of days contributed 46% of the total effort; spatially, 30% of the fishing grounds accounted for 80% of the effort, forming four persistent hotspots. Vessels exhibited a distinct bimodal speed distribution, enabling clear behavioral differentiation. While no fishing was detected inside the seasonal no-take zone, activities were observed near its boundaries and Exclusive Economic Zones, highlighting compliance and potential risks. The significant spatial aggregation, strongest in June, underscores the tight linkage between fleet operations and resource distribution. These findings provide a scientific basis for spatially explicit management strategies to ensure the sustainable harvesting of squid resources. Full article
(This article belongs to the Special Issue Application of Remote Sensing to Fisheries)
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