The IOCCG bibliography is updated periodically when new references are submitted by readers. Another useful ocean colour bibliography is the searchable Historic Ocean Colour Archive assembled by Marcel Wernand, with articles and books written between the 17th and early 20th century.
If you would like to submit a peer-reviewed publication to be included in the IOCCG Bibliography, please send the reference to Raisha Lovindeer using the following format: Lastname1, Initials1., Lastname2, Initials2., etc. (DATE). Full title of publication, Journal Abbreviation, Volume, Page numbers, DOI (if available). Please also check to see if the reference is not already in the database (search by first author). It is not necessary to send the PDF file as an attachment. Note that only peer-reviewed articles will be accepted.
If you would like to view recently-published papers, enter the current year in “Search by Keyword”. You can also search the database using keywords or the author’s last name. For papers dealing with Remote Sensing of Marine Litter and Debris, use the keyword “RSMLD”. You can also view the Datasets Bibliography for remote sensing and marine litter and debris.
Bibliography
Acuña-Ruz, T., Uribe, D., Amézquita, L., Guzmán, C., Taylor R., Merrill, J., Martínez P., Voisin, Mattar, C. (2018). Anthropogenic marine debris over beaches: Spectral characterization for remote sensing applications. Remote Sensing of Environment, 217: 309-322, https://doi.org/10.1016/j.rse.2018.08.008.
Aguilar, M. A., Jiménez-Lao, R., Ladisa, C., Aguilar, F. J., and Tarantino, E. (2022). Comparison of spectral indices extracted from Sentinel-2 images to map plastic covered greenhouses through an object-based approach, GIsci. Remote Sens., 59, 822-842, https://doi.org/10.1080/15481603.2022.2071057.
Alboody, A., Vandenbroucke, N., Porebski, A., Sawan, R., Viudes, F., Doyen, P., and Amara, R. (2023) A new remote hyperspectral imaging system embedded on an unmanned aquatic drone for the detection and identification of floating plastic litter using machine learning. Remote Sens.(Basel), 15(14), 3455, https://doi.org/10.3390/rs15143455
Andriolo, U., Garcia-Garin, O., Vighi, M., Borrell, A., and Gonçalves, G. (2022) . Beached and floating litter surveys by unmanned aerial vehicles: operational analogies and differences, Remote Sens. (Basel), 14, 1336(1331-1312), https://doi.org/10.3390/rs14061336.
Andriolo, U., Gonçalves, G., Bessa, F., Sobral, P. (2020). Mapping marine litter on coastal dunes with unmanned aerial systems: A showcase on the Atlantic Coast. Science of the Total Environment, 736: https://doi.org/10.1016/j.scitotenv.2020.139632
Andriolo, U., Gonçalves, G., Rangel-Buitrago, N., Paterni, M., Bessa, F., Gonçalves, L., Sobral, P., Bini, M., Duarte, D., Fontan-Bouzas, A., Gonçalves, D., Kataoka, T., Luppichini, M., Pinto, L., Topouzelis, K., V ´ elez-Mendoza, A., Merlino, S., (2021). Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images. Marine Pollution Bulletin, 169, 112542. https://doi.org/10.1016/j.marpolbul.2021.112542
Andriolo, U., Gonçalves, G., Sobral, P., and Bessa, F.(2021) Spatial and size distribution of macro-litter on coastal dunes from drone images: A case study on the Atlantic coast, Mar. Pollut. Bull., 169, 112490, https://doi.org/10.1016/j.marpolbul.2021.112490.
Andriolo, U., Gonçalves, G., Sobral, P., Fontán-Bouzas, Á., Bessa, F. (2020). Beach-dune morphodynamics and marine macro-litter abundance: An integrated approach with Unmanned Aerial System. Science of the Total Environment, 749, 141474. https://doi.org/10.1016/j.scitotenv.2020.141474
Aoyama, T. (2016). Extraction of marine debris in the Sea of Japan using satellite images, Proc. SPIE 9878, Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challenges, 987817 (7 May 2016); https://doi.org/10.1117/12.2220370
Arii, M., Koiwa, M., and Aoki, Y. (2014) Applicability of SAR to marine debris surveillance after the Great East Japan Earthquake. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7(5), 1729-1744, https://doi.org/10.1109/JSTARS.2014.2308550
Asamoah, B.O., Uurasjärvi, E., Räty, J., Koistinen, A., Roussey, M., Peiponen, K.-E. (2021). Towards the development of portable and in situ optical devices for detection of micro-and nanoplastics in water: A review on the current status. Polymers, 13, 730. https://doi.org/10.3390/polym13050730
Atwood, E.C., Falcieri, F.M., Piehl, S., Bochow, M. Matthies, M., Franke, J., Carniel, S., Sclavo, M., Laforsch, C., Siegert, F. (2019). Coastal accumulation of microplastic particles emitted from the Po River, Northern Italy: Comparing remote sensing and hydrodynamic modelling with in situ sample collections. Marine Pollution Bulletin, 138, 561-574. https://doi.org/10.1016/j.marpolbul.2018.11.045
Balsi, M., Moroni, M., Chiarabini, V., and Tanda, G.(2021). High-resolution aerial detection of marine plastic litter by hyperspectral sensing, Remote Sens. (Basel), 13, 1557, https://doi.org/10.3390/rs13081557
Bancud, G. E., Labanon, A. J., Abreo, N. A. and Kobayashi, V. (2023). Combining image enhancement techniques and deep learning for shallow water benthic marine litter detection. International Workshops of ECML PKDD 2022, 137-149, https://doi.org/10.1007/978-3-031-23618-1_9
Bao, Z., Sha, J., Li, X., Hanchiso, T., and Shifaw, E. (2018). Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method, Mar. Pollut. Bull., 137, 388-398. https://doi.org/10.1016/j.marpolbul.2018.08.009
Basu, B, Sannigrahi, S, Sarkar Basu, A., and Pilla, F. (2021) Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery. Remote Sens.(Basel), 13(8), 1598, https://doi.org/10.3390/
Biermann, L., Clewley, D., Martinez-Vicente, V., and Topouzelis, K. (2020). Finding plastic patches in coastal waters using optical satellite data, Sci. Rep., 10, 5364, https://doi.org/10.1038/s41598-020-62298-z
Blondeau-Patissier, D., Schroeder, T., Suresh, G., Li, Z., Diakogiannis, F. I., Irving, P., Witte, C., & Steven, A. D. (2023). Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park. Marine Pollution Bulletin, 188, 114598. https://doi.org/10.1016/j.marpolbul.2023.114598
Booth, H., Ma, W., and Karakuş, O. (2023) High-precision density mapping of marine debris and floating plastics via satellite imagery. Sci. Rep., 13(1), 6822, https://doi.org/10.1038/s41598-023-33612-2
Chaturvedi, S., Yadav, B.P., Siddique, N.A., and Chaturvedi, S.K. (2020). Mathematical modelling and analysis of plastic waste pollution and its impact on the ocean surface, JOES, 5, 136–163, https://doi.org/10.1016/j.joes.2019.09.005
Chia, K. Y., Chin, C. S., and See, S. (2023), Deep transfer learning application for intelligent marine debris detection, in Proceedings EANN 2023: Engineering Applications of Neural Networks, 14–17 June, León, Spain, 1826, 479-490, https://doi.org/10.1007/978-3-031-34204-2_39
Ciappa, A. C. (2021) Marine plastic litter detection offshore Hawai’i by Sentinel-2, Mar. Pollut. Bull., 168, 112457, https://doi.org/10.1016/j.marpolbul.2021.112457
Ciappa, A. C. (2022). Marine litter detection by Sentinel-2: A case study in North Adriatic (Summer 2020), Remote Sens. (Basel), 14, 2409, https://doi.org/10.3390/rs14102409.
Cocking, J., Narayanaswamy, B. E., Waluda, C. M., and Williamson, B. J. (2022) Aerial detection of beached marine plastic using a novel, hyperspectral short-wave infrared (SWIR) camera. ICES J. Mar. Sci, 79(3), 648-660, https://doi.org/10.1093/icesjms/fsac006
Colkesen, I., Kavzoglu, T., Sefercik, U. G. and Ozturk, M. Y. (2023). Automated mucilage extraction index (AMEI): a novel spectral water index for identifying marine mucilage formations from Sentinel-2 imagery. Int. J. Remote Sens., 44, (1), 105-141, https://doi.org/10.1080/01431161.2022.2158049
Corbari, L., Maltese, A., Capodici, F., Mangano, M. C., Sarà, G., & Ciraolo, G. (2020). Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery. Scientific Reports, 10(1), 19850. https://doi.org/10.1038/
Cózar, A., Aliani, S., Basurko, O.C., Arias, M., Isobe, A., Topouzelis, K., Rubio, A., and Morales-Caselles, C. (2021). Marine litter windrows: A strategic target to understand and manage the ocean plastic pollution, Front. Mar. Sci., 8, 571796, https://doi.org/10.3389/fmars.2021.571796
Dasgupta, S., Sarraf, M., and Wheeler, D. (2022) Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data. Sci. Total Environ., v. 839, p. 156319, DOI: https://doi.org/10.1016/j.scitotenv.2022.156319
Deidun, A., Gauci, A., Lagorio, S., and Galgani, F. (2018). Optimising beached litter monitoring protocols through aerial imagery, Mar. Pollut. Bull., 131, 212-217, https://doi.org/10.1016/j.marpolbul.2018.04.033
de Vries, R., Egger, M., Mani, T. and Lebreton, L. (2021). Quantifying floating plastic debris at sea using vessel-based optical data and artificial intelligence. Remote Sensing, 13(17), 3401, https://doi.org/10.3390/rs13173401
Dierssen, H. M., and Garaba, S. P.: (2020). Bright Oceans: Spectral Differentiation of Whitecaps, Sea Ice, Plastics, and Other Flotsam, in: Recent Advances in the Study of Oceanic Whitecaps: Twixt Wind and Waves, edited by: Vlahos, P., and Monahan, E. C., Springer International Publishing, Cham, 197-208, 2020. https://doi.org/10.1007/978-3-030-36371-0_13
Duarte, D., Andriolo, U., Gonçalves, G. (2020). Addressing the class imbalance problem in the automatic image classification of coastal litter from orthophotos derived from uas imagery, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 439–445, https://doi.org/10.5194/isprs-annals-V-3-2020-439-2020
Duarte, M. M., and Azevedo, L. (2023) Automatic detection and identification of floating marine debris using multi-spectral satellite imagery. IEEE Trans. Geosci. Remote Sens., 1-15, https://doi.org/10.1109/TGRS.2023.3283607
Evans, M. C. and C. S. Ruf (2021). Towards the detection and imaging of ocean microplastics with a spaceborne radar. IEEE Transactions on Geoscience and Remote Sensing, 1-9,https://doi.org/10.1109/TGRS.2021.3081691
Fallati, L., Polidori, A., Salvatore, C., Saponari, L., Savini, A., and Galli, P.(2019). Anthropogenic marine debris assessment with unmanned aerial vehicle imagery and deep learning: A case study along the beaches of the Republic of Maldives, Sci. Total Environ., 693, 133581, https://doi.org/10.1016/j.scitotenv.2019.133581
Freitas, S., Silva, H., and Silva, E. (2021). Remote hyperspectral imaging acquisition and characterization for marine litter detection, Remote Sens., 13, 2536(2531-2522), https://doi.org/10.3390/rs13132536.
Freitas, S., Silva, H., and Silva, E. (2022). Hyperspectral imaging zero-shot learning for remote marine litter detection and classification, Remote Sens. (Basel), 14, 5516(5511-5518), https://doi.org/10.3390/rs14215516.
Garaba S.P.and Dierssen H.M. (2018). An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro- and microplastics. Remote Sensing of Environment 205:224-235. doi:10.1016/j.rse.2017.11.023. www.sciencedirect.com/science/article/pii/S0034425717305722
Garaba, S. P. and Dierssen, H. M. (2020). Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics, Earth Syst. Sci. Data, 12, 77–86, https://doi.org/10.5194/essd-12-77-2020
Garaba, S. P., Acuña-Ruz, T., and Mattar, C. B. (2020). Hyperspectral longwave infrared reflectance spectra of naturally dried algae, anthropogenic plastics, sands and shells, Earth Syst. Sci. Data, 12, 2665-2678, https://doi.org/10.5194/essd-12-2665-2020
Garaba, S. P., and Harmel, T. (2022) Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth. Opt. Express, 30 (10), 16553-16571, https://doi.org/10.1364/OE.
Garaba, S. P., Arias, M., Corradi, P., Harmel, T., de Vries, R., and Lebreton, L. (2021) Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics, J. Hazard. Mater., 406, 124290, https://doi.org/10.1016/j.jhazmat.2020.124290
Garaba, S. P., Aitken, J., Slat, B., Dierssen, H.M., Lebreton, L., Zielinski, O. and Reisser, J. (2018). Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager. Environmental Science & Technology. DOI: 10.1021/acs.est.8b02855
Garcia-Garin, O., Aguilar, A., Borrell, A., Gozalbes, P., Lobo, A., Penadés-Suay, J., Raga, J. A., Revuelta, O., Serrano, M., and Vighi, M. (2020). Who’s better at spotting? A comparison between aerial photography and observer-based methods to monitor floating marine litter and marine mega-fauna, Environ. Pollut., 258, 113680, https://doi.org/10.1016/j.envpol.2019.113680.
Garcia-Garin, O., Monleón-Getino, T., López-Brosa, P., Borrell, A., Aguilar, A., Borja-Robalino, R., Cardona, L., and Vighi, M. (2021). Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R, Environ. Pollut., 273, 116490,
https://doi.org/10.1016/j.envpol.2021.116490.
Ge, Z., Shi, H., Mei, X., Dai, Z., and Li, D. (2016). Semi-automatic recognition of marine debris on beaches, Sci. Rep., 6, 25759, https://doi.org/10.1038/srep25759.
Geraeds, M.; van Emmerik, T.; de Vries, R. and bin Ab Razak, M.S. (2019). Riverine Plastic Litter Monitoring Using Unmanned Aerial Vehicles (UAVs). Remote Sens. 11, 2045, https://doi.org/10.3390/rs11172045
Gnann, N., Baschek, B., Ternes, T. A. (2022). Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review. Water Research: 222, 118902, https://doi.org/10.1016/j.watres.2022.118902 .
Goddijn-Murphy, L., Williamson, B. (2019). On Thermal Infrared Remote Sensing of Plastic Pollution in Natural Waters, Remote Sens., 11, 18: 2159. https://doi.org/10.3390/rs11182159
Goddijn-Murphy, L., Williamson, B.J., McIlvenny, J., Corradi, P. (2022). Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sens. 14, 13:3179. https://doi.org/10.3390/rs14133179
The fifth International Ocean Colour Science (IOCS) meeting will take place in St. Petersburg, FL, USA from 14 – 17 November 2023, and will be convened by the International Ocean Colour Coordinating Group (IOCCG) in partnership with the University of South Florida, and thanks to sponsorship from several sponsors.