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., 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., 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
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
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/
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
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
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., 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
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
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
de Vries, R. V. F., Garaba, S. P., and Royer, S. J.(2023) Hyperspectral reflectance of pristine, ocean weathered and biofouled plastics from a dry to wet and submerged state, Earth Syst. Sci. Data, 15, 5575-5596, https://doi.org/10.5194/essd-15-5575-2023
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
Felício, J. M., Costa, T. S., Vala, M., Leonor, N., Costa, J. R., Marques, P., Moreira, A. A., Caldeirinha, R. F. S., Matos, S. A., Fernandes, C. A., Fonseca, N. J. G., and Maagt, P. D. (2024) Feasibility of radar-based detection of floating macroplastics at microwave frequencies, IEEE Trans. Antennas Prop., 1-1, https://doi.org/10.1109/TAP.2023.3347031
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