TÉCNICAS PARA CLASIFICAR SEDIMENTOS DEL FONDO MARINO UTILIZANDO IMÁGENES SATELITALES: UNA REVISIÓN SISTEMÁTICA DE LA LITERATURA
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https://doi.org/10.66104/nxfq0t06Palabras clave:
sedimentos marinos; aprendizaje automático; imágenes satelitales.Resumen
Esta revisión sistemática de la literatura tiene como objetivo analizar las principales técnicas para clasificar sedimentos del fondo marino a partir de imágenes satelitales. La investigación se realizó en las bases de datos IEEE Xplore, SciELO, PubMed y Google Scholar, abarcando publicaciones entre 2015 y 2025. Los resultados destacan avances significativos en metodologías de teledetección marina, particularmente la integración de sensores ópticos y altimétricos (Sentinel-2, Landsat 8 e ICESat-2) y el uso combinado de modelos empíricos, regresiones espectrales y algoritmos supervisados como Random Forest, SVM y redes neuronales convolucionales (CNN). Si bien predominan los estudios centrados en batimetría, se observó un aumento gradual en la investigación dedicada a la clasificación óptica de sedimentos. Los enfoques basados en aprendizaje profundo mostraron ser prometedores, aunque su rendimiento aún está limitado por la turbidez del agua y la escasez de datos etiquetados. Se concluye que la fusión multisensorial, asociada a la integración entre modelos empíricos y técnicas de aprendizaje profundo, representa la principal tendencia futura para mejorar la precisión, reducir la dependencia de los estudios in situ y expandir el uso de la teledetección en el monitoreo y gestión ambiental de las zonas costeras.
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BRENAN, C.; KIENAST, M.; MASELLI, V.; ALGAR, C. K.; MISIUK, B.; BROWN, C. J. Seafloor sediment characterization improves estimates of organic carbon standing stocks: an example from the Eastern Shore Islands, Nova Scotia, Canada. EGUsphere, 2024. DOI: https://doi.org/10.5194/egusphere-2024-5. DOI: https://doi.org/10.5194/egusphere-2024-5-supplement
CAPIZZANO, C. W.; RHOADS, A. C.; CROTEAU, J. A.; TAYLOR, B. G.; GUARINELLO,
M. L.; SHUMCHENIA, E. J. Mapping seafloor sediment distributions using public geospatial data and machine learning to support regional offshore renewable energy development. Geosciences, v. 14, p. 186, 2024. DOI: https://doi.org/10.3390/geosciences14070186. DOI: https://doi.org/10.3390/geosciences14070186
CARRERETTE, O. et al. Macrobenthic assemblages across deep-sea pockmarks and carbonate mounds at Santos Basin, SW Atlantic. Ocean and Coastal Research, v. 70, p. e22045, 2022. DOI: https://doi.org/10.1590/2675-2824070.22081oc. DOI: https://doi.org/10.1590/2675-2824070.22081oc
CHEN, B.; MILLER, K.; BERTOZZI, A. L.; SCHWENK, J. Graph-based active learning for surface water and sediment detection in multispectral imagery. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2023. arXiv:2306.10440. DOI: https://doi.org/10.48550/arXiv.2306.10440. DOI: https://doi.org/10.1109/IGARSS52108.2023.10282009
HILGERT, S.; SOTIRI, K.; FUCHS, S. Review of sediment detection methods in reservoirs. International Journal of Sediment Research, v. 39, n. 1, p. 28–43, 2024. DOI: https://doi.org/10.1016/j.ijsrc.2023.12.004. DOI: https://doi.org/10.1016/j.ijsrc.2023.12.004
INÁCIO, M. et al. Mapping and assessing marine ecosystem services supply in the Baltic Sea. Science of the Total Environment, v. 950, p. 175199, 2024. DOI: https://doi.org/10.1016/j.scitotenv.2024.175199. DOI: https://doi.org/10.1016/j.scitotenv.2024.175199
INNANGI, S.; INNANGI, M.; DI FEBBRARO, M.; DI MARTINO, G.; SACCHI, M.;
TONIELLI, R. Continuous, high-resolution mapping of coastal seafloor sediment distribution. Remote Sensing, v. 14, n. 5, p. 1268, 2022. DOI: https://doi.org/10.3390/rs14051268. DOI: https://doi.org/10.3390/rs14051268
KIM, K.-L.; WOO, H.-J.; JOU, H.-T.; JUNG, H. C.; LEE, S.-K.; RYU, J.-H. Surface sediment classification using a deep learning model and unmanned aerial vehicle data of tidal flats. Marine Pollution Bulletin, v. 198, p. 115823, 2024. DOI: https://doi.org/10.1016/j.marpolbul.2023.115823. DOI: https://doi.org/10.1016/j.marpolbul.2023.115823
KUTSER, T.; HEDLEY, J.; GIARDINO, C.; ROELFSEMA, C.; BRANDO, V. E. Remote sensing of shallow waters: a 50 year retrospective and future directions. Remote Sensing of Environment, v. 240, p. 111619, 2020. DOI: https://doi.org/10.1016/j.rse.2019.111619. DOI: https://doi.org/10.1016/j.rse.2019.111619
LI, S.; WANG, X. H.; MA, Y.; YANG, F. Satellite-derived bathymetry with sediment classification using ICESat-2 and multispectral imagery: case studies in the South China Sea and Australia. Remote Sensing, v. 15, n. 4, p. 1026, 2023. DOI: https://doi.org/10.3390/rs15041026. DOI: https://doi.org/10.3390/rs15041026
LOPES, [iniciais do nome]. Classificação de sedimentos da margem equatorial brasileira utilizando algoritmos. 2024. 67 f. Dissertação (Mestrado em Engenharia de Eletricidade) – Universidade Federal do Maranhão, São Luís, 2024. Disponível em: https://tedebc.ufma.br/jspui/handle/tede/5313. Acesso em: 9 out. 2025.
MENANDRO, P. S. et al. Multispectral multibeam backscatter response of heterogeneous rhodolith beds. Scientific Reports, v. 13, p. 20220, 2023. DOI: https://doi.org/10.1038/s41598-023-46240-7. DOI: https://doi.org/10.1038/s41598-023-46240-7
NETO, J. C. Geodiversidade da plataforma continental de Icapuí, Ceará: uma proposta de identificação de áreas chave em ambientes marinhos. 2017. 125 f. Tese (Doutorado em Geologia) – Universidade Federal do Ceará, Fortaleza, 2017. Disponível em: http://repositorio.ufc.br/handle/riufc/29898. Acesso em: 8 out. 2025.
OLIVEIRA, R. G. Mapeamento de habitats marinhos epibentônicos da porção nordeste da Baía de Todos os Santos – Bahia – Brasil. 2017. Dissertação (Mestrado em Geologia) – Universidade Federal da Bahia, Salvador, 2017. Disponível em: https://repositorio.ufba.br/handle/ri/23634. Acesso em: 8 out. 2025.
QIN, X.; LUO, X.; WU, Z.; SHANG, J. Optimizing the sediment classification of small side-scan sonar images based on deep learning. IEEE Access, v. 9, p. 1–?, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3052206. DOI: https://doi.org/10.1109/ACCESS.2021.3052206
VIÑA-BORJA, S. P.; FERNÁNDEZ-MORA, A.; STUMPF, R. P.; NAVARRO, G.; CABALLERO, I. Semi-automated bathymetry using Sentinel-2 for coastal monitoring in the Western Mediterranean. International Journal of Applied Earth Observation and Geoinformation, v. 120, p. 103328, 2023. DOI: https://doi.org/10.1016/j.jag.2023.103328. DOI: https://doi.org/10.1016/j.jag.2023.103328
VAN AN, N.; QUANG, N. H.; HOANG FILHO, T. P.; AN, T. T. Mapeamento de habitat bentônico de alta resolução a partir de aprendizado de máquina em imagens PlanetScope e dados ICESat-2. Journal of Applied Remote Sensing, 2023. DOI: https://doi.org/10.1080/10106049.2023.2184875. DOI: https://doi.org/10.1080/10106049.2023.2184875
WEI, C.; XIAO, Y.; FU, D.; ZHOU, T. Impact of turbidity on satellite-derived bathymetry: comparative analysis across seven ports in the South China Sea. Remote Sensing, v. 16, n. 23, p. 4349, 2024. DOI: https://doi.org/10.3390/rs16234349. DOI: https://doi.org/10.3390/rs16234349
ZHAO, Y.; ZHU, K.; ZHAO, T.; ZHENG, L.; DENG, X. Small-sample seabed sediment classification based on deep learning. Remote Sensing, v. 15, n. 8, p. 2178, 2023. DOI: https://doi.org/10.3390/rs15082178. DOI: https://doi.org/10.3390/rs15082178
ZHANG, N.; LI, H.; ZHANG, J.; CHEN, J.; WU, H.; GONG, Z. Study of the spatial and temporal distributions of tidal flat surface sediment based on multitemporal remote sensing. Frontiers in Marine Science, v. 10, p. 1109146, 2023. DOI: https://doi.org/10.3389/fmars.2023.1109146. DOI: https://doi.org/10.3389/fmars.2023.1109146
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Derechos de autor 2026 Diego de Oliveira Dantas, Jailly Aparecida do Rosario Silva, Marta de Oliveira Barreiros , Jonathan Araujo Queiroz, Allan Kardec Duailibe Barros Filho
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