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JOMAFISH
Published by PT. Global Research Media Group
ISSN : -     EISSN : 31646581     DOI : https://doi.org/10.67710/jomafish
Core Subject :
JOMAFISH: Journal of Marine and Fisheries Science is a scholarly journal published by GRM Academic Press, an academic publisher managed by PT Global Research Media Group. The journal publishes original research in marine science, fisheries, aquaculture, oceanography, coastal studies, marine biodiversity, conservation, aquatic resources, remote sensing and GIS, fishing technology, and related fields. JOMAFISH provides a platform for researchers, academics, practitioners, and professionals to share scientific findings that support the sustainable management and development of marine, coastal, fisheries, and aquatic resources. The journal is published twice a year, in May and November.
Arjuna Subject : -
Articles 5 Documents
 1 
Artificial Light Color and Fish Catch Performance in Marine Fisheries: A Review Muhammad Anur Rosyid; Luhur Moekti Prayogo
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.25

Abstract

Artificial light is widely used in nocturnal fisheries to aggregate fish, improve encounter probability, and influence capture selectivity. Light-emitting diodes (LEDs) allow precise control of spectral output, yet published evidence does not identify one universally optimal color. This review synthesizes recent studies on the effects of artificial light color on fish attraction, aggregation, catch performance, species composition, and selectivity in marine fisheries. A structured narrative review was conducted using peer-reviewed literature published mainly during 2016–2025, emphasizing field experiments and physiological studies relevant to light-assisted capture. Blue, cyan, and green wavelengths frequently produced strong behavioral or catch responses because they propagate efficiently in seawater and overlap with the visual sensitivity of many marine organisms. However, outcomes were strongly dependent on species, fishing gear, irradiance, depth, water optical properties, and operational context. Indonesian studies on anchovy and lift-net fisheries reported advantages of blue, white, green, or mixed-color LEDs under different experimental conditions, while recent fixed lift-net trials found strong aggregation and catch under green LEDs. International studies similarly reported contrasting responses in trammel nets, pots, squid-related fisheries, and illuminated nets. Light may also increase catch indirectly by attracting prey organisms or improve selectivity by altering bycatch interactions. Therefore, lamp color should be optimized together with irradiance, deployment geometry, exposure duration, lunar phase, and target-species behavior. Future studies should prioritize replicated field trials, spectral measurements, acoustic or video verification, standardized CPUE, and reporting of energy use and bycatch.
Remote Sensing for Mangrove Mapping and Monitoring: Sensors, Indices, Algorithms, and Applications Luhur Moekti Prayogo; Perdana Ixbal Spanton M; Jumiati
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.26

Abstract

Mangrove forests are spatially dynamic intertidal ecosystems whose monitoring is constrained by difficult field access, tidal variation, cloud cover, spectral similarity with terrestrial vegetation, and rapid land-use change. Remote sensing has therefore become a central tool for mapping mangrove extent, condition, species composition, structural attributes, and temporal change. This review synthesizes recent developments in optical, synthetic aperture radar (SAR), hyperspectral, unmanned aerial vehicle (UAV), and LiDAR approaches for mangrove mapping, with emphasis on freely available Landsat and Sentinel data and applications relevant to tropical coasts. Studies published mainly during 2016–2026 were examined across four analytical dimensions: sensor characteristics, spectral or structural features, classification algorithms, and accuracy assessment. The evidence shows that Landsat remains valuable for long-term change analysis, whereas Sentinel-2 improves detection of narrow and fragmented stands through 10-m observations. Combining Sentinel-1 SAR and Sentinel-2 optical time series generally improves robustness in persistently cloudy regions. Mangrove-specific indices such as the Mangrove Vegetation Index can simplify extent mapping, while hyperspectral imagery, UAV data, and LiDAR are increasingly important for species discrimination, canopy height, biomass, and restoration assessment. However, no sensor or index removes the need for representative training data and independent validation. Tidal stage, mixed pixels, seasonal compositing, class imbalance, and spatially non-independent validation remain major sources of uncertainty. For Indonesia, an operational strategy should integrate multi-temporal Sentinel-1/2 data, machine learning, field observations, and standardized accuracy reporting to support repeatable mapping for conservation, restoration, blue-carbon accounting, and coastal management.
Google Earth Engine for Sea Surface Temperature Mapping in Marine Environments: A Review Jihan Nur Fadhilah; Luhur Moekti Prayogo
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.27

Abstract

Sea surface temperature (SST) is a fundamental oceanographic variable for describing air–sea exchange, ocean circulation, fisheries habitat, coral thermal stress, coastal processes, and climate variability. The rapid expansion of satellite archives has improved SST observation, but conventional desktop workflows remain constrained by data volume, preprocessing requirements, and reproducibility. This review evaluates the role of Google Earth Engine (GEE) as a cloud-computing environment for SST mapping in marine and coastal research. A structured narrative review was conducted using peer-reviewed literature and authoritative dataset documentation published mainly between 2017 and 2026, with emphasis on studies that employed GEE, satellite-derived SST products, validation procedures, or marine applications. The synthesis shows that GEE substantially reduces data-handling barriers and supports scalable time-series analysis through readily accessible products such as MODIS Aqua/Terra Ocean Color L3, NOAA OISST v2.1, NOAA WHOI SST, and JAXA GCOM-C/SGLI SST. However, dataset choice remains application-dependent. Coarse, gap-filled products are effective for climate-scale trends and anomalies, whereas thermal infrared sensors with kilometre- to hectometre-scale observations are more appropriate for coastal gradients but require stricter cloud screening, atmospheric correction, and validation. The principal methodological risks are confusion between land surface temperature and true marine SST, inadequate quality-flag filtering, nearshore mixed pixels, inconsistent temporal compositing, and weak validation. A reproducible workflow is proposed that integrates dataset selection, quality assurance, temporal compositing, anomaly analysis, in-situ or multi-product validation, and uncertainty reporting. GEE is therefore best regarded as an analytical infrastructure rather than an SST algorithm itself; scientific reliability ultimately depends on sensor physics, product quality, validation design, and transparent code-based processing.
Satellite-Derived Bathymetry in Tropical Shallow Waters: Methods, Sensors, Accuracy, and Applications Wartono; Jumiati; Ade Hanie Nurfahanie; Luhur Moekti Prayogo
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.28

Abstract

Accurate bathymetry is fundamental to coastal navigation, habitat assessment, shoreline management, hydrodynamic modelling, and fisheries planning, yet conventional hydrographic surveys remain costly and difficult in remote or very shallow tropical waters. Satellite-derived bathymetry (SDB) provides a complementary approach by estimating water depth from optical reflectance, wave properties, or spaceborne laser altimetry. This review synthesizes methodological developments in SDB with emphasis on tropical shallow-water environments and studies published from 2018 to August 2026, while retaining foundational algorithms required to explain current practice. The literature was organized according to sensor, preprocessing, calibration data, retrieval model, validation metric, and environmental limitation. Sentinel-2 has become the principal freely available optical source because its 10-m visible bands, five-day revisit, and global coverage are well matched to reef flats, lagoons, and island coasts. Landsat remains important for historical reconstruction, whereas PlanetScope and very-high-resolution imagery improve representation of narrow channels and small geomorphic features. ICESat-2 has substantially changed SDB by supplying independent along-track depth observations that can calibrate empirical and machine-learning models where field soundings are unavailable. Band-ratio and log-linear approaches remain robust baselines, but Random Forest, Support Vector Regression, neural networks, and multi-temporal fusion increasingly improve performance in complex waters. Accuracy nevertheless depends more strongly on optical depth, turbidity, bottom heterogeneity, sun glint, tide, temporal mismatch, and reference-data quality than on algorithm complexity alone. For tropical archipelagic regions such as Indonesia, an operational workflow should combine aquatic atmospheric correction, glint and cloud screening, tidal normalization, spatially independent validation, and explicit uncertainty reporting. SDB should therefore be treated as a scalable complement to hydrographic surveying rather than an unrestricted replacement for echo sounding.
Remote Sensing for Seagrass Mapping and Monitoring: Methods, Sensors, and Applications Luhur Moekti Prayogo; Perdana Ixbal Spanton M; Ade Hanie Nurfahanie
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.29

Abstract

Seagrass meadows are highly productive coastal ecosystems, yet their submerged position, patchy distribution, seasonal variability, and exposure to turbidity make consistent mapping difficult. Remote sensing provides a practical means to extend field observations across space and time, but mapping performance depends strongly on sensor characteristics, water-column conditions, image preprocessing, classification design, and validation quality. This review synthesizes recent progress in remote sensing for seagrass mapping and monitoring, emphasizing studies published from 2017 to August 2026 and applications relevant to tropical and Indonesian waters. The literature was evaluated according to sensor type, preprocessing strategy, classification or regression method, ecological variable, and accuracy assessment. Landsat remains important for multi-decadal change detection, while Sentinel-2 has become the principal freely available sensor for contemporary mapping because its 10-m visible bands and frequent revisit provide a useful compromise between spatial detail and temporal coverage. PlanetScope and WorldView imagery improve delineation of small or fragmented meadows, whereas UAV and hyperspectral systems enable centimetre-scale mapping, species discrimination, and detailed biomass assessment. Machine-learning approaches, particularly Random Forest, Support Vector Machine, and gradient-boosting methods, are increasingly used, but classifier choice cannot compensate for poor atmospheric correction, sun-glint contamination, depth effects, mixed pixels, or weak reference data. Recent Indonesian studies demonstrate applications ranging from seagrass extent and benthic habitat mapping to aboveground carbon estimation. A robust operational workflow should therefore combine aquatic atmospheric correction, tidal and cloud screening, representative field data, depth-aware feature engineering, independent spatial validation, and explicit uncertainty reporting. Such standardization is essential for reproducible monitoring, restoration assessment, fisheries habitat management, and blue-carbon accounting.

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