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
Abimbola, I.; McAfee, M.; Creedon, L.; Gharbia, S. (2024) In-situ detection of microplastics in the aquatic environment: A systematic literature review. Sci. Total Environ., vol. 934, pp 173111, https://doi.org/10.1016/j.scitotenv.2024.173111
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.
Ahmed Dar, A., Chen, Z., Sardar, M. F. and An, C. (2024) Navigating the nexus: climate dynamics and microplastics pollution in coastal ecosystems. Environ. Res., 252, 118971, https://doi.org/10.1016/j.envres.2024.118971
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., Topouzelis, K., van Emmerik, T. H. M., Papakonstantinou, A., Monteiro, J. G., Isobe, A., Hidaka, M., Kako, S. i., Kataoka, T., and Gonçalves, G. (2023) Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution, Mar. Pollut. Bull., 195, 115521, https://doi.org/10.1016/j.marpolbul.2023.115521
Andriolo, U., Gonçalves, G., Hidaka, M., Gonçalves, D., Gonçalves, L. M., Bessa, F.,Kako, S. i. (2024) Marine litter weight estimation from UAV imagery: Three potential methodologies to advance macrolitter reports. Mar. Pollut. Bull. 202, 116405, https://doi.org/10.1016/j.marpolbul.2024.116405
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
Anggraini, N., Tawakkal, I., Rachman, I., and Matsumoto, T. (2024) Object detection of macroplastic waste using unmanned aerial vehicles in urban canal. Ecological Engineering & Environmental Technology, 25(8), pp. 271-284, https://doi.org/10.12912/27197050/189888
Antara, I. M. O. G., Nuarsa, I. W., Sudarma, I. M., Hendrawan, I. G., Cordova, M. R., (2024) Developing beach litter monitoring system based on reflectance characteristics and its abundance. Ecol. Eng. Technol. 25, (5), 61-78, https://doi.org/10.12912/27197050/184253
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
Arias, M., Sumerot, R., Delaney, J., Coulibaly, F., Cozar, A., Aliani, S., Suaria, G., Papadopoulou, T., and Corradi, P. (2021) Advances on remote sensing of windrows as proxies for marine litter based on Sentinel-2/MSI datasets, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium 2021, 1126-1129, https://doi.org/10.1109/IGARSS47720.2021.9555139
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
Bak, S., Kim, H.-M., Kim, Y., Lee, I., Park, M., Kim, T.-Y. and Jang Seon, W. (2024) High-resolution mapping techniques for coastal debris using YOLOv8 and unmanned aerial vehicle. KJRS, vol 40 (2), pp 151-166, https://doi.org/10.7780/kjrs.2024.40.2.3
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
Bansal, K. and Tripathi, A. K. (2024) WasteNet: A novel multi-scale attention-based U-Net architecture for waste detection in UAV images. Remote Sens. Appl. Soc. Environ., vol. 35, p. 101220, https://doi.org/10.1016/j.rsase.2024.101220
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/
Beck, A. J., Kaandorp, M., Hamm, T., Bogner, B., Kossel, E., Lenz, M., Haeckel, M., and Achterberg, E. P. (2023) Rapid shipboard measurement of net-collected marine microplastic polymer types using near-infrared hyperspectral imaging, Anal. Bioanal. Chem., 415, 2989-2998, https://doi.org/10.1007/s00216-023-04634-6
Bekova, R., and Prodanov, B. (2023) Assessment of beach macrolitter using unmanned aerial systems: A study along the Bulgarian Black Sea Coast, Mar. Pollut. Bull., 196, 115625, https://doi.org/10.1016/j.marpolbul.2023.115625
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
Bojesomo, A., Liatsis, P., and Almarzouqi, H.(2023) Marine debris segmentation using a parameter efficient octonion-based architecture, IEEE Geosci. Remote Sens. Lett., 20, 1-5, https://doi.org/10.1109/LGRS.2023.3321177
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
Bouchelaghem, S., Tibermacine, I. E., Balsi, M., Moroni, M. and Napoli, C. (2024) In Cross-domain machine learning approaches using hyperspectral imaging for plastics litter detection, 2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Oran, Algeria, 15-17 April, pp 36-40. https://doi.org/10.1109/M2GARSS57310.2024.10537535
Campillo, A., Almeda, R., Vianello, A., Gómez, M., Martínez, I., Navarro, A., and Herrera, A. (2023) Searching for hotspots of neustonic microplastics in the Canary Islands, Mar. Pollut. Bull., 192, 115057, https://doi.org/10.1016/j.marpolbul.2023.115057
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
Chun, S., Gopal, J. and Muthu, M. (2024) Portable analytics as a contemporary environmental microplastic research tool: The Pitches and Hitches. TrEAC 2024, p. e00234, https://doi.org/10.1016/j.teac.2024.e00234
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., Capodici, F., Ciraolo, G., and Topouzelis, K. (2023) Marine plastic detection using PRISMA hyperspectral satellite imagery in a controlled environment, Int. J. Remote Sens., 44, 6845-6859, https://doi.org/10.1080/01431161.2023.2275324
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/
Cortesi, I., Masiero, A., De Giglio, M., Tucci, G., and Dubbini, M. (2021) Random forest-based river plastic detection with a handheld multispectral camera, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 9-14, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-9-2021
Cortesi, I., Masiero, A., Pfeifer, N. and Tucci, G. (2023) On the co-registration of asynchronous multi-spectral and thermal images. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLVIII-1/W1-2023, pp 101-106, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-101-2023
Cortesi, I., Masiero, A., Tucci, G., and Topouzelis, K. (2022) UAV-based river plastic detection with a multispectral camera, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 855-861, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-855-2022
Cortesi, I., Mugnai, F., Angelini, R., and Masiero, A. (2023) Mini UAV-based litter detection on river banks, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4/W1-2022, 117-122, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-117-2023
Costa, A., Sans, E., Pereira-Sánchez, I., Duran, J. and Navarro, J. (2024) In Improving marine litter segmentation with limited resolution satellite imagery, 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), 8-10 April 2024, 1-3.; https://doi.org/10.1109/MIGARS61408.2024.10544681
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
da Costa, T. S., Felício, J. M., Vala, M., Leonor, N., Costa, J. R., Marques, P., Moreira, A. A., Caldeirinha, R., Matos, S. A., Fernandes, C. A., Fonseca, N. J. G., and Maagt, P. d. (2023) Detection of low permittivity floating plastic sheets at microwave frequencies, in 2023 17th European Conference on Antennas and Propagation (EuCAP), 1-5, https://doi.org/10.23919/EuCAP57121.2023.10133107
da Costa, T. S., Felício, J. M., Vala, M., Leonor, N., Costa, J. R., Marques, P., Moreira, A. A., Caldeirinha, R., Matos, S. A., Fernandes, C. A., Fonseca, N. J. G., Maagt, P. D. (2024) In Feature selection for identifying optimal microwave frequencies to detect floating macroplastic litter in C and X Bands, 2024 18th European Conference on Antennas and Propagation (EuCAP), Glasgow, United Kingdom, 17-22 March 2024, Glasgow, United Kingdom, 2024, pp 1-5,
https://doi.org/10.23919/EuCAP60739.2024.10501024
The fifth International Ocean Colour Science (IOCS) meeting took place in St. Petersburg, FL, USA from 14 – 17 November 2023, and was convened by the International Ocean Colour Coordinating Group (IOCCG) in partnership with the University of South Florida, and thanks to sponsorship from several sponsors.