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
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., and Park, Y.-J. (2024) Riverine litter monitoring from multispectral fine pixel satellite images, Environ. Adv., 15, 100451, https://doi.org/10.1016/j.envadv.2023.100451
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
Goddijn-Murphy, L.M., Dufaur, J. (2018). Proof of concept for a model of light reflectance of plastics floating on natural waters, Mar. Pollut. Bull., 135, 1145-1157. https://doi.org/10.1016/j.marpolbul.2018.08.044
Goddijn-Murphy, L.M., Peters, S., Van Sebille, E., James, N. A., Gibb, S. (2018). Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics, Mar. Pollut. Bull., 126, 255–262. https://doi.org/10.1016/j.marpolbul.2017.11.011
Gómez, À. S., Scandolo, L., and Eisemann, E. (2022) A learning approach for river debris detection. Int. J. Appl. Earth Obs. Geoinformation, 107, 102682, https://doi.org/10.1016/j.jag.2022.102682
Gonçalves, G. and Andriolo, U. (2022). Operational use of multispectral images for macro-litter mapping and, categorization by Unmanned Aerial Vehicle. Marine Pollution Bulletin. 176, March 2022, 113431 https://doi.org/10.1016/j.marpolbul.2022.113431
Gonçalves, G., Andriolo, U., Gonçalves, L., Sobral, P., Bessa, F. (2020). Quantifying marine macro litter abundance on a sandy beach using unmanned aerial systems and object-oriented machine learning methods. Remote Sensing, 12: 2599. https://doi.org/10.3390/rs12162599
Gonçalves, G., Andriolo, U., Gonçalves, L., Sobral, P., Bessa, F. (2022). Beach litter survey by drones: Mini-review and discussion of a potential standardization. Environmental Pollution, 315, 120370. https://doi.org/10.1016/j.envpol.2022.120370
Gonçalves, G., Andriolo, U., Pinto, L., and Bessa, F. (2020). Mapping marine litter using UAS on a beach-dune system: a multidisciplinary approach. Sci. Total Environ., 706, 135742, https://doi.org/10.1016/j.scitotenv.2019.135742
Gonçalves, G., Andriolo, U., Pinto, L., Duarte, D. (2020). Mapping marine litter with Unmanned Aerial Systems : A showcase comparison among manual image screening and machine learning techniques. Marine Pollution Bulletin. 155, 111158. https://doi.org/10.1016/j.marpolbul.2020.111158
Gonzaga, M. L. R., Wong, M. T. S., Blanco, A. C., and Principe, J. A. (2021) Utilization of Sentinel-2 imagery in the estimation of plastics among floating debris along the coast of Manila Bay, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 177-184, https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-177-2021
Guffogg, J. A., Blades, S. M., Soto-Berelov, M., Bellman, C. J., Skidmore, A. K., and Jones, S. D. (2021). Quantifying marine plastic debris in a beach environment using spectral analysis, Remote Sens. (Basel), 13, 4548, https://doi.org/10.3390/rs13224548.
Guffogg, J. A., Soto-Berelov, M., Jones, S. D., Bellman, C. J., Lavers, J. L., and Skidmore, A. K.(2021). Towards the spectral mapping of plastic debris on beaches, Remote Sens. (Basel), 13, 1850, https://doi.org/10.3390/rs13091850.
Guo, X. and Li, P. (2020). Mapping plastic materials in an urban area: Development of the normalized difference plastic index using WorldView-3 superspectral data, ISPRS J. Photogramm. and Remote Sens., 169, 214-226, https://doi.org/10.1016/j.isprsjprs.2020.09.009
Hamill, M., Magee, B., and Millar, P.(2020) Application of remote sensing for automated litter detection and management, in: Advances in Computer Vision, in Computer Vision Conference (CVC 2019), 2-3 May 2019, Las Vegas, USA, 2020, 157-168, https://doi.org/10.1007/978-3-030-17798-0_15
Hanke, G. and González-Fernández, D. (2014). Longterm deployment of the JRC Sealittercam on the Western Mediterranean Sea. In: Giannoudi, L., Streftaris, N. and Papathanassiou, E. (eds.) Policy‐oriented marine Environmental Research for the Southern European (PERSEUS) 2nd Scientific Workshop – Marrakesh 2014. Book of Abstracts.
Hengstmann, E., Gräwe, D., Tamminga, M., and Fischer, E. K.(2017). Marine litter abundance and distribution on beaches on the Isle of Rügen considering the influence of exposition, morphology and recreational activities, Mar. Pollut. Bull., 115, 297-306, https://doi.org/10.1016/j.marpolbul.2016.12.026
Herman, A., and Węsławski, J. M. (2022). Typical and anomalous pathways of surface-floating material in the Northern North Atlantic and Arctic Ocean, Sci. Rep., 12, 20521, https://doi.org/10.1038/s41598-022-25008-5.
Hidaka, M., Matsuoka, D., Sugiyama, D., Murakami, K., Kako, S. (2022). Pixel-level image classification for detecting beach litter using a deep learning approach, Mar. Pollut. Bull, 175, 113371, https://doi.org/10.1016/j.
Hörig, B., Kühn, F., Oschütz, F., and Lehmann, F. (2001). HyMap hyperspectral remote sensing to detect hydrocarbons, Int. J. Remote Sens., 22(8), 1413-1422, https://doi.org/10.1080/01431160120909
Hu, C. (2021) Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: Challenges and potentials. Remote Sensing of Environment, 259, 112414, https://doi.org/10.1016/j.rse.2021.112414
Hu, C. (2022). Sea Snots in the Marmara Sea as Observed From Medium-Resolution Satellites. IEEE Geosci. & Remote Sens. Lett., 19, 1504905, doi:10.1109/LGRS.2022.3173997.
Hu, C. (2022). Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations. Earth Syst. Sci. Data, 14, 1183–1192, https://doi.org/10.5194/essd-14-1183-2022.
Hu, C., Feng, L., Hardy, R. F., and Hochberg, E. J. (2015). Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae, Remote Sens. Environ., 167, 229-246, http://dx.doi.org/10.1016/j.rse.2015.05.022
Hu, C., L. Qi, Y. Xie, S. Zhang, and B. B. Barnes (2022). Spectral characteristics of sea snot reflectance observed from satellites: Implications for remote sensing of marine debris. Remote Sens., Environ, 269, 112842, https://doi.org/10.1016/j.rse.
Hu, C., Qi, L., Wang, M. and Park, Y.-J. (2023) Floating debris in the Northern Gulf of Mexico after Hurricane Katrina. Environ. Sci. Technol., 57(28), 10373−10381, https://doi.org/10.1021/acs.est.3c01689
Hueni, A., and Bertschi, S. (2020). Detection of sub-pixel plastic abundance on water surfaces using airborne imaging spectroscopy, in IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, 26 Sept.-2 Oct, Waikoloa, HI, USA, 6325-6328, https://doi.org/10.1109/IGARSS39084.2020.9323556
Iordache, M.-D., De Keukelaere, L., Moelans, R., Landuyt, L., Moshtaghi, M., Corradi, P., and Knaeps, E. (2022). Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images, Remote Sens., 14(22), 5820, https://doi.org/10.3390/rs14225820
Jakovljevic, G., Govedarica, M., and Alvarez-Taboada, F.(2020). A deep learning model for automatic plastic mapping using unmanned aerial vehicle (UAV) data, Remote Sens. (Basel), 12, 1515 ( 1511-1521),
http://dx.doi.org/10.3390/rs12091515
Janssens, N., Schreyers, L., Biermann, L., van der Ploeg, M., Le Bui, T.-K. and van Emmerik, T. (2022). Rivers running green: water hyacinth invasion monitored from space. Environmental Research Letters. 17 (4), https://doi.org/10.1088/1748-9326/ac52c
Jayasiri, H.B., Purushothaman, C.S., and Vennila, A (2013). Quantitative analysis of plastic debris on recreational beaches in Mumbai, India, Mar. Pollut. Bull., 77, 107-112, https://doi.org/10.1016/j.marpolbul.2013.10.024
Jia, T., Kapelan, Z., de Vries, R., Vriend, P., Peereboom, E. C., Okkerman, I. and Taormina, R., (2023). Deep learning for detecting macroplastic litter in water bodies: A review. Water Res., 231, 119632, https://doi.org/10.1016/j.watres.2023.119632
Jia, T., Vallendar, A. J., de Vries, R., Kapelan, Z., and Taormina, R. (2023) Advancing deep learning-based detection of floating litter using a novel open dataset, Front. Water, 5, 1298465, https://doi.org/10.3389/frwa.2023.1298465
Kako, S. i., Isobe, A., and Magome, S. (2012). Low altitude remote-sensing method to monitor marine and beach litter of various colors using a balloon equipped with a digital camera, Mar. Pollut. Bull., 64(6), 1156-1162, https://doi.org/10.1016/j.marpolbul.2012.03.024
Kako, S. i., Isobe, A., Kataoka, T., and Hinata, H. (2014). A decadal prediction of the quantity of plastic marine debris littered on beaches of the East Asian marginal seas, Mar. Pollut. Bull., 81, 174-184, https://doi.org/10.1016/j.marpolbul.2014.01.057
Kako, S. i., Isobe, A., Kataoka, T., Yufu, K., Sugizono, S., Plybon, C., and Murphy, T. A. (2018). Sequential webcam monitoring and modeling of marine debris abundance, Mar. Pollut. Bull., 132, 33-43, https://doi.org/10.1016/j.marpolbul.2018.04.075
Kako, S. i., Morita, S., and Taneda, T.(2020). Estimation of plastic marine debris volumes on beaches using unmanned aerial vehicles and image processing based on deep learning, Mar. Pollut. Bull., 155, 111127, https://doi.org/10.1016/j.marpolbul.2020.111127.
Kaladharan, P., Vijayakumaran, K., Singh, V., Prema, D., Asha, P. S., Sulochanan, B., Hemasankari, P., Edward, L., Padua, S., Shettigar, V., Anasukoya, A., and Bhint, H. (2017). Prevalence of marine litter along the Indian beaches: A preliminary account on its status and composition, J. Mar. biol. Ass. India, 59, 19-24, https://doi.org/10.6024/jmbai.2017.59.1.1953-03
Kalogirou, E., Makri, D., Kountouri, J., Stylianou, T., Themistokleous, K., Papoutsa, C., Melillos, G., and G. Hadjimitsis, D. (2023) Detect plastic litter in Cyprus region using Sentinel-2, in Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 3-5 April, Ayia Napa, Cyprus, 7, https://doi.org/10.1117/12.2681679