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
Kanhai, L. D. K., Asmath, H., and Gobin, J. F. (2022) The status of marine debris/litter and plastic pollution in the Caribbean Large Marine Ecosystem (CLME): 1980–2020. Environmental Pollution, v. 300, p. 118919, https://doi.org/10.1016/j.
Karakuş O (2023), On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring. Front. Remote Sens., 4, 1302384, https://doi.org/10.3389/frsen.2023.1302384
Kataoka, T., Hinata, H., and Kako, S. i. (2012). A new technique for detecting colored macro plastic debris on beaches using webcam images and CIELUV, Mar. Pollut. Bull., 64, 1829-1836, https://doi.org/10.1016/j.marpolbul.2012.06.006.
Kataoka, T., Murray, C. C., and Isobe, A. (2018). Quantification of marine macro-debris abundance around Vancouver Island, Canada, based on archived aerial photographs processed by projective transformation, Mar. Pollut. Bull., 132, 44-51, https://doi.org/10.1016/j.marpolbul.2017.08.060
Kikaki, A., Karantzalos, K., Power, C. A., and Raitsos, D.A. (2020). Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea). Remote Sens., 12, 1727, https://doi.org/10.3390/rs12111727
Kikaki, K., Kakogeorgiou, I., Mikeli, P., Raitsos, DE., Karantzalos, K. (2022). MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data. PLoS ONE 17(1): e0262247. https://doi.org/10.1371/journal.pone.0262247
Kim, J., and Kim, C. W. (2023) Improving the efficiency of marine debris collection policies using drone technology, J. Coastal Res., 116, 353-357, https://doi.org/10.2112/JCR-SI116-072.1
Knaeps, E., Sterckx, S., Strackx, G., Mijnendonckx, J., Moshtaghi, M., Garaba, S. P., and Meire, D. (2021). Hyperspectral reflectance dataset of dry, wet and submerged marine litter, Earth Syst. Sci. Data, 13, 713–730, https://doi.org/10.5194/essd-13-713-2021
Koestner, D.. Foster, R. and El-Habashi, A. (2023). On the potential for optical detection of microplastics in the ocean. Oceanography, 36, https://doi.org/10.5670/oceanog.2023.s1.15
Kremezi, M., Kristollari, V., Karathanassi, V., Topouzelis, K., Kolokoussis, P., Taggio, N., Aiello, A., Ceriola, G., Barbone, E., Corradi, P. (2021). Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes. IEEE Access,9, 61955-61971, https://doi.org/10.1109/ACCESS.2021.3073903.
Kremezi, M., Kristollari, V., Karathanassi, V., Topouzelis, K., Kolokoussis, P., Taggio, N., Aiello, A., Ceriola, G., Barbone, E., Corradi, P. (2022). Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques. Marine Pollution Bulletin, Volume 182, 2022, 113974, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2022.113974 .
Kruse, C., Boyda, E., Chen, S., Karra, K., Bou-Nahra, T., Hammer, D., Mathis, J., Maddalene, T., Jambeck, J. and Laurier, F. (2023) Satellite monitoring of terrestrial plastic waste. PLoS One, 18, (1), 1-20, e0278997, https://doi.org/10.1371/journal.pone.0278997
Kühn, F., Oppermann, K., and Hörig, B. (2004). Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons, Int. J. Remote Sens., 25(12), 2467-2473, https://doi.org/10.1080/01431160310001642287
Lavender, S. (2022) Detection of waste plastics in the environment: Application of Copernicus earth observation data. Remote Sensing, v. 14, no. 19, p. 4772, https://doi.org/10.3390/rs14194772
Leone, G., Catarino, A. I., De Keukelaere, L., Bossaer, M., Knaeps, E. and Everaert, G. (2023). Hyperspectral reflectance dataset of pristine, weathered, and biofouledplastics. Earth Syst. Sci. Data, 2, 745–52, https://doi.org/10.5194/essd-15-745-2023
Li, J., Liu, H., Liao, R., Wang, H., Chen, Y., Xiang, J., Xu, X. and Ma, H. (2023). Recognition of microplastics suspended in seawater via refractive index by Mueller matrix polarimetry. Mar. Pollut. Bull., 188, 114706, https://doi.org/10.1016/j.marpolbul.2023.114706
Liu, H., Wang, M., Tang, H., and Zhang, H. (2024) Progress in research on marine litter-related monitoring technologies, J. Phys.: Conf. Ser., 2679, 012055, https://doi.org/10.1088/1742-6596/2679/1/012055
Ma, J., Ma, R., Pan, Q., Liang, X., Wang, J., and Ni, X. (2023) A global review of progress in remote sensing and monitoring of marine pollution, Water, 15, 3491, https://doi.org/10.3390/w15193491
Ma, Y., Qu, X., Yu, C., Wu, L., Zhang, P., Huang, H., Gui, F. and Feng, D.(2022). Automatic extraction of marine aquaculture zones from optical satellite images by R3Det with piecewise linear stretching. Remote Sens., 14, (18), 4430, https://doi.org/10.3390/rs14184430
Maharjan, N., Miyazaki, H., Pati, B. M.; Dailey, M. N.; Shrestha, S. and Nakamura, T. (2022). Detection of river plastic using UAV sensor data and deep learning. Remote Sens., 14, (13), 3049, https://doi.org/10.3390/rs14133049
Martin, C., Parkes, S., Zhang, Q., Zhang, X., McCabe, M. F., and Duarte, C. M. (2018). Use of unmanned aerial vehicles for efficient beach litter monitoring, Mar. Pollut. Bull., 131, 662-673, https://doi.org/10.1016/j.marpolbul.2018.04.045
Martinez-Vicente, V., Biermann, L., and Mata, A. (2020). Optical methods for marine litter detection (OPTIMAL) – Final Report. Zenodo, https://doi.org/10.5281/zenodo.3748797
Martínez-Vicente, V., Clark, J. R., Corradi, P., Aliani, S., Arias, M., Bochow, M., Bonnery, G., Cole, M., Cózar, A., Donnelly, R., Echevarría, F., Galgani, F., Garaba, S. P., Goddijn-Murphy, L., Lebreton, L., Leslie, H. A., Lindeque, P. K., Maximenko, N., Martin-Lauzer, F.-R., Moller, D., Murphy, P., Palombi, L., Raimondi, V., Reisser, J., Romero, L., Simis, S. G. H., Sterckx, S., Thompson, R. C., Topouzelis, K. N., van Sebille, E., Veiga, J. M., and Vethaak, A. D. (2019). Measuring marine plastic debris from space: Initial assessment of observation requirements, Remote Sens. (Basel), 11, 2443, doi:10.3390/rs11202443, https://www.mdpi.com/2072-4292/11/20/2443
Matthews, J. P., Ostrovsky, L., Yoshikawa, Y., Komori, S. and Tamura, H. (2017) Dynamics and early post-tsunami evolution of floating marine debris near Fukushima Daiichi. Nature Geosci., 10, 598-603, https://doi.org/10.1038/ngeo2975
Maximenko, N., Arvesen, J., Asner, G., Carlton, J., Castrence, M., Centurioni, L., Chao, Y., Chapman, J., Chirayath, V., Corradi, P., Crowley, M., Dierssen, H. M., Dohan, K., Eriksen, M., Galgani, F., Garaba, S. P., Goni, G., Griffin, D., Hafner, J., Hardesty, D., Isobe, A., Jacobs, G., Kamachi, M., Kataoka, T., Kubota, M., Law, K. L., Lebreton, L., Leslie, H. A., Lumpkin, R., Mace, T. H., Mallos, N., McGillivary, P. A., Moller, D., Morrow, R., Moy, K. V., Murray, C. C., Potemra, J., Richardson, P., Robberson, B., Thompson, R., van Sebille, E., and Woodring, D (2016). Remote sensing of marine debris to study dynamics, balances and trends, Community White Paper Produced at the Workshop on Mission Concepts for Marine Debris Sensing, 22 pp, 2016.
Maximenko, N., Corradi, P., Law, K. L., Van Sebille, E., Garaba, S. P., Lampitt, R. S., Galgani, F., Martinez-Vicente, V., Goddijn-Murphy, L., Veiga, J. M., Thompson, R. C., Maes, C., Moller, D., Löscher, C. R., Addamo, A. M., Lamson, M. R., Centurioni, L. R., Posth, N. R., Lumpkin, R., Vinci, M., Martins, A. M., Pieper, C. D., Isobe, A., Hanke, G., Edwards, M., Chubarenko, I. P., Rodriguez, E., Aliani, S., Arias, M., Asner, G. P., Brosich, A., Carlton, J. T., Chao, Y., Cook, A.-M., Cundy, A. B., Galloway, T. S., Giorgetti, A., Goni, G. J., Guichoux, Y., Haram, L. E., Hardesty, B. D., Holdsworth, N., Lebreton, L., Leslie, H. A., Macadam-Somer, I., Mace, T., Manuel, M., Marsh, R., Martinez, E., Mayor, D. J., Le Moigne, M., Molina Jack, M. E., Mowlem, M. C., Obbard, R. W., Pabortsava, K., Robberson, B., Rotaru, A.-E., Ruiz, G. M., Spedicato, M. T., Thiel, M., Turra, A., and Wilcox, C. (2019). Toward the Integrated Marine Debris Observing System, Front. Mar. Sci., 6, doi:10.3389/fmars.2019.00447. https://www.frontiersin.org/articles/10.3389/fmars.2019.00447/full
Merlino, S., Paterni, M., Berton, A., and Massetti, L. (2020). Unmanned aerial vehicles for debris survey in coastal areas: Long-term monitoring programme to study spatial and temporal accumulation of the dynamics of beached marine litter. Remote Sens.(Basel), 12(8), 1260, https://doi.org/10.3390/rs12081260
Merlino, S., Paterni, M., Locritani, M., Andriolo, U., Gonçalves, G., Massetti, L. (2021). Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images. Water, 13(23):3349. https://doi.org/10.3390/w13233349
Mifdal, J., Longépé, N. and Rußwurm, M. (2021) Towards detecting floating objects on a global scale with learned spatial features using Sentinel 2. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 285-293, https://doi.org/10.5194/isprs-annals-V-3-2021-285-2021
Mohsen, A., Kiss, T. and Kovács, F. (2023) Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery. Environ. Sci. Pollut. Res., 30, 67742–67757, https://doi.org/10.1007/s11356-023-27068-0
Moshtaghi, M., Knaeps, E., Sterckx, S., Garaba, S., and Meire, D. (2021) Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment, Sci. Rep., 11, 5436(1-12), https://doi.org/10.1038/s41598-021-84867-6
Moy, K., Neilson, B., Chung, A., Meadows, A., Castrence, M., Ambagis, S., and Davidson, K. (2018). Mapping coastal marine debris using aerial imagery and spatial analysis, Mar. Pollut. Bull., 132, 52-59, https://doi.org/10.1016/j.marpolbul.2017.11.045
Murata, H., Komatsu, T., Yonezawa, C. (2019) Detection and discrimination of aquacultural facilities in Matsushima Bay, Japan, for integrated coastal zone management and marine spatial planning using full polarimetric L-band airborne synthetic aperture radar. Int. J. Remote Sens., 40 (13), 5141-5157, DOI: https://doi.org/10.1080/01431161.2019.1579380
Nagy, M., Istrate, L., Simtinică, M., Travadel, S. and Blanc, P. (2022). Automatic detection of marine litter: A general framework to leverage synthetic data. Remote Sens., 14, (23), 6102, https://doi.org/10.3390/rs14236102
Nakashima, E., Isobe, A., Magome, S., Kako, S. i., and Deki, N. (2011). Using aerial photography and in situ measurements to estimate the quantity of macro-litter on beaches, Mar. Pollut. Bull., 62, 762-769, https://doi.org/10.1016/j.marpolbul.2011.01.006.
Olyaei, M., & Ebtehaj, A. (2023). Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms. Remote Sensing, 16(1), 172. https://doi.org/10.3390/rs16010172
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Palombi, L., Raimondi, V., (2022). Experimental Tests for Fluorescence LIDAR Remote Sensing of Submerged Plastic Marine Litter, Remote Sens. 2022, 14, 5914. https://doi.org/10.3390/
Papachristopoulou, I., Filippides, A., Fakiris, E., and Papatheodorou, G. (2020). Vessel-based photographic assessment of beach litter in remote coasts. A wide scale application in Saronikos Gulf, Greece, Mar. Pollut. Bull., 150, 110684, https://doi.org/10.1016/j.marpolbul.2019.110684
Papageorgiou, D., Topouzelis, K., Suaria, G., Aliani, S., and Corradi, P.(2022). Sentinel-2 Detection of Floating Marine Litter Targets with Partial Spectral Unmixing and Spectral Comparison with Other Floating Materials (Plastic Litter Project 2021), Remote Sens., 14(23), 5997, https://doi.org/10.3390/rs14235997
Papakonstantinou, A., Batsaris, M., Spondylidis, S., and Topouzelis, K. (2021). A citizen science unmanned aerial system data acquisition protocol and deep learning techniques for the automatic detection and mapping of marine litter concentrations in the coastal zone, Drones, 5(1), 6, https://doi.org/10.3390/drones5010006
Papakonstantinou, A., Moustakas, A., Kolokoussis, P., Papageorgiou, D., de Vries, R. and Topouzelis, K. (2023). Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment. Data, 8, (1), 19, https://doi.org/10.3390/data8010019
Park, Y.-J., Garaba, S. P., and Sainte-Rose, B. (2021) Detecting the Great Pacific Garbage Patch floating plastic litter using WorldView-3 satellite imagery. Opt. Express, 29 (22), 35288-35298, https://doi.org/10.1364/OE.
Parrish, C. E., Winans, W. R., Battista, T., Uhrin, A. V., Herrera, K., Murphy, P., Simpson, C. and Slocum, R. (2023) Uncrewed aircraft systems, machine learning, and polarimetric imaging for enhanced marine debris shoreline surveys. NOAA Technical Memorandum NOS NCCOS 312, Marine Spatial Ecology Division, Silver Spring, MD, USA, 31 p., https://doi.org/10.25923/337h-k518
Pathira Arachchilage, K. R. L., Tang, D., Yu, J., and Wang, S. (2022) A preliminary analysis towards detecting floating marine macro plastics using an index developed for sentinel 2 acolite and sen2cor images, Journal of Geospatial Surveying, 2, 1-10, http://doi.org/10.4038/jgs.v2i2.37
Pichel, W. G., Veenstra, T. S., Churnside, J. H., Arabini, E., Friedman, K. S., Foley, D. G., Brainard, R. E., Kiefer, D., Ogle, S., Clemente-Colón, P. and Li, X. (2012) GhostNet marine debris survey in the Gulf of Alaska – Satellite guidance and aircraft observations. Mar. Pollut. Bull., 65(1–3), 28-41, https://doi.org/10.1016/j.marpolbul.2011.10.009
Piehl, S., Atwood, E.C., Bochow, M., Imhof, H.K., Franke, J., Siegert, F., Laforsch, C. (2020). Can Water Constituents Be Used as Proxy to Map Microplastic Dispersal Within Transitional and Coastal Waters? Frontiers in Environmental Science, 8:92, https://doi.org/10.3389/fenvs.2020.00092
Pinto, L., Andriolo, U., and Gonçalves, G. (2021). Detecting stranded macro-litter categories on drone orthophoto by a multi-class Neural Network, Mar. Pollut. Bull., 169, 112594, https://doi.org/10.1016/j.marpolbul.2021.112594
Qi, L., M. Wang, C. Hu, and B. Holt (2022). On the capacity of Sentinel-1 synthetic aperture radar in detecting floating macroalgae and other floating matters. Remote Sens. Environ., 280, 113188, https://doi.org/10.1016/j.rse.2022.113188.
Ryan, P. G.(2020). Using photographs to record plastic in seabird nests, Mar. Pollut. Bull., 156, 111262, https://doi.org/10.1016/j.marpolbul.2020.111262