Spatio-Temporal Determination of Small-Scale Vessels’ Fishing Grounds Using a Vessel Monitoring System in the Southeastern Gulf of Mexico

Torres‐Irineo, Edgar; Salas, Silvia; Euán-Ávila, Jorge Iván; Palomo, Leopoldo; Quiñones, Daniel R. Quijano; Coronado, Eva; Joo, Rocío

doi:10.3389/fmars.2021.643318

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…The model exhibits strong predictive ability and permits the expansion of geographical assessments of fishing footprints to pertinent, though as-yet-undiscovered, fleet segments [39]. Torres-Irineo et al [40] discover the role of environmental factors in the distribution of viable fishing grounds for this fleet using data from a pilot VMS, where a study was tested on a small fleet in the southeastern Gulf of Mexico (SGoM). For seven months, fishing vessels operating in four states provided tracking data for 1,608 daily trips.…”

Section: Vessel Monitoring Systemmentioning

confidence: 99%

A review on features and methods of potential fishing zone

Ya’acob,

Nik Dzulkefli,

Abdul Aziz

et al. 2024

IJECE

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This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.

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Section: Vessel Monitoring Systemmentioning

confidence: 99%

A review on features and methods of potential fishing zone

Ya’acob,

Nik Dzulkefli,

Abdul Aziz

et al. 2024

IJECE

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“…Recently, authors in [51] applied a model technique for optimal fishing, by using fishing location data, chlorophyll-a and sea surface temperature, to forecast the spatio-temporal distribution of the Indian mackerel. Also, in [46] a correlative modelling approach, combining VMS and environmental variables, was used to identify potential fishing grounds of smallscale fishery. Finally, authors in [16] applied statistical and process-based models to predict the changes in fish abundance and distribution correlated to climate change.…”

Section: Fishing Activities Forecastmentioning

confidence: 99%

“…Literature on the use of predictive methods to forecast fish catch in space and time is limited. Recent paper [46] is perhaps the closest to our work as it presents a correlative method to predict spatio-temporal presence of fish for small-scale fisheries using environmental and VMS (Vessel Monitoring System) data. One significant difference with our work is that [46] uses VMS instead of AIS.…”

Section: Introductionmentioning

confidence: 99%

“…Recent paper [46] is perhaps the closest to our work as it presents a correlative method to predict spatio-temporal presence of fish for small-scale fisheries using environmental and VMS (Vessel Monitoring System) data. One significant difference with our work is that [46] uses VMS instead of AIS. VMS data have some limitations, such as long time between the transmission of two consecutive signals (low temporal resolution), as well as the difficulty to obtain data.…”

Section: Introductionmentioning

confidence: 99%

“…VMS data have some limitations, such as long time between the transmission of two consecutive signals (low temporal resolution), as well as the difficulty to obtain data. Moreover, catch reports are not available in [46], so spatio-temporal catch prediction is not considered.…”

Section: Introductionmentioning

confidence: 99%

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From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea

et al. 2022

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In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.

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