Finding Plastic Patches in Coastal Waters using Optical Satellite Data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
23 04 2020
23 04 2020
Historique:
received:
17
12
2019
accepted:
09
03
2020
entrez:
25
4
2020
pubmed:
25
4
2020
medline:
25
4
2020
Statut:
epublish
Résumé
Satellites collecting optical data offer a unique perspective from which to observe the problem of plastic litter in the marine environment, but few studies have successfully demonstrated their use for this purpose. For the first time, we show that patches of floating macroplastics are detectable in optical data acquired by the European Space Agency (ESA) Sentinel-2 satellites and, furthermore, are distinguishable from naturally occurring materials such as seaweed. We present case studies from four countries where suspected macroplastics were detected in Sentinel-2 Earth Observation data. Patches of materials on the ocean surface were highlighted using a novel Floating Debris Index (FDI) developed for the Sentinel-2 Multi-Spectral Instrument (MSI). In all cases, floating aggregations were detectable on sub-pixel scales, and appeared to be composed of a mix of seaweed, sea foam, and macroplastics. Building first steps toward a future monitoring system, we leveraged spectral shape to identify macroplastics, and a Naïve Bayes algorithm to classify mixed materials. Suspected plastics were successfully classified as plastics with an accuracy of 86%.
Identifiants
pubmed: 32327674
doi: 10.1038/s41598-020-62298-z
pii: 10.1038/s41598-020-62298-z
pmc: PMC7181820
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5364Commentaires et corrections
Type : ErratumIn
Références
Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015).
doi: 10.1126/science.1260352
Simon, N. & Schulte, M. L. Stopping global plastic pollution: The case for an international convention. Ecology Publication Series 43 (2017).
Goddijn-Murphy, L. & Dufaur, J. Proof of concept for a model of light reflectance of plastics floating on natural waters. Marine pollution bulletin 135, 1145–1157 (2018).
doi: 10.1016/j.marpolbul.2018.08.044
Hartley, B. L., Thompson, R. C. & Pahl, S. Marine litter education boosts children’s understanding and self-reported actions. Marine pollution bulletin 90, 209–217 (2015).
doi: 10.1016/j.marpolbul.2014.10.049
Gall, S. C. & Thompson, R. C. The impact of debris on marine life. Marine pollution bulletin 92, 170–179 (2015).
doi: 10.1016/j.marpolbul.2014.12.041
Maximenko, N. et al. Remote sensing of marine debris to study dynamics, balances and trends. White Paper, Decadal Survey for Earth Science and Applications from Space, 22 (2016).
Beaumont, N. J. et al. Global ecological, social and economic impacts of marine plastic. Marine pollution bulletin 142, 189–195 (2019).
doi: 10.1016/j.marpolbul.2019.03.022
Lebreton, L., Egger, M. & Slat, B. A global mass budget for positively buoyant macroplastic debris in the ocean. Scientific reports 9, 1–10 (2019).
doi: 10.1038/s41598-019-49413-5
Pham, C. K. et al. Marine litter distribution and density in european seas, from the shelves to deep basins. PloS one 9, e95839 (2014).
doi: 10.1371/journal.pone.0095839
Li, W. C., Tse, H. & Fok, L. Plastic waste in the marine environment: A review of sources, occurrence and effects. Science of the Total Environment 566, 333–349 (2016).
doi: 10.1016/j.scitotenv.2016.05.084
Solomon, O. O. & Palanisami, T. Microplastics in the marine environment: current status, assessment methodologies, impacts and solutions. Journal of Pollution Effects & Control, 1–13 (2016).
Barboza, L. G. A. et al. Macroplastics pollution in the marine environment. In World Seas: an Environmental Evaluation, 305–328 (Elsevier, 2019).
Napper, I. E. & Thompson, R. C. Marine plastic pollution: Other than microplastic. In Waste, 425–442 (Elsevier, 2019).
Garaba, S. P. & Dierssen, H. M. 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 (2018).
doi: 10.1016/j.rse.2017.11.023
Moy, K. et al. Mapping coastal marine debris using aerial imagery and spatial analysis. Marine pollution bulletin 132, 52–59 (2018).
doi: 10.1016/j.marpolbul.2017.11.045
Goddijn-Murphy, L., Peters, S., Van Sebille, E., James, N. A. & Gibb, S. Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics. Marine pollution bulletin 126, 255–262 (2018).
doi: 10.1016/j.marpolbul.2017.11.011
Aoyama, T. Monitoring of marine debris in the sea of japan using multi-spectral satellite images. In Ocean Remote Sensing and Monitoring from Space, vol. 9261, 92611E (International Society for Optics and Photonics, 2014).
Hu, C., Feng, L., Hardy, R. F. & Hochberg, E. J. Spectral and spatial requirements of remote measurements of pelagic sargassum macroalgae. Remote Sensing of Environment 167, 229–246 (2015).
doi: 10.1016/j.rse.2015.05.022
Aoyama, T. Extraction of marine debris in the sea of japan using high-spatial-resolution satellite images. In Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challenges, vol.9878, 987817 (International Society for Optics and Photonics, 2016).
Garaba, S. P. et al. Sensing ocean plastics with an airborne hyperspectral shortwave infrared imager. Environmental science & technology 52, 11699–11707 (2018).
Topouzelis, K., Papakonstantinou, A. & Garaba, S. P. Detection of floating plastics from satellite and unmanned aerial systems (plastic litter project 2018). International Journal of Applied Earth Observation and Geoinformation 79, 175–183 (2019).
doi: 10.1016/j.jag.2019.03.011
Maximenko, N. et al. Towards the integrated marine debris observing system. Frontiers in marine science 6, 447 (2019).
doi: 10.3389/fmars.2019.00447
Hafeez, S. et al. Detection and monitoring of marine pollution using remote sensing technologies. In Monitoring of Marine Pollution (IntechOpen, 2018).
Martínez-Vicente, V. et al. Measuring marine plastic debris from space: Initial assessment of observation requirements. Remote Sensing 11, 2443 (2019).
doi: 10.3390/rs11202443
D’Asaro, E. A. et al. Ocean convergence and the dispersion of flotsam. Proceedings of the National Academy of Sciences, 201718453 (2018).
Möhlenkamp, P., Purser, A. & Thomsen, L. Plastic microbeads from cosmetic products: an experimental study of their hydrodynamic behaviour, vertical transport and resuspension in phytoplankton and sediment aggregates. Elem Sci Anth 6, 61 (2018).
doi: 10.1525/elementa.317
Brooks, M. T., Coles, V. J. & Coles, W. C. Inertia influences pelagic sargassum advection and distribution. Geophysical Research Letters (2019).
Thiel, M., Hinojosa, I. A., Joschko, T. & Gutow, L. Spatio-temporal distribution of floating objects in the german bight (north sea). Journal of Sea Research 65, 368–379 (2011).
doi: 10.1016/j.seares.2011.03.002
Mustard, J. F. & Sunshine, J. M. Spectral analysis for earth science: investigations using remote sensing data. Remote sensing for the earth sciences: Manual of remote sensing 3, 251–307 (1999).
Shanmugam, S. & SrinivasaPerumal, P. Spectral matching approaches in hyperspectral image processing. International Journal of Remote Sensing 35, 8217–8251 (2014).
doi: 10.1080/01431161.2014.980922
Van Dyck, I. P., Nunoo, F. K. & Lawson, E. T. An empirical assessment of marine debris, seawater quality and littering in ghana. Journal of Geoscience and Environment Protection 4, 21 (2016).
doi: 10.4236/gep.2016.45003
Davis, W. III & Murphy, A. G. Plastic in surface waters of the inside passage and beaches of the salish sea in washington state. Marine pollution bulletin 97, 169–177 (2015).
doi: 10.1016/j.marpolbul.2015.06.019
Martins, V. et al. Assessment of atmospheric correction methods for sentinel-2 msi images applied to amazon floodplain lakes. Remote Sensing 9, 322 (2017).
doi: 10.3390/rs9040322
Wang, D., Ma, R., Xue, K. & Loiselle, S. A. The assessment of landsat-8 oli atmospheric correction algorithms for inland waters. Remote Sensing 11, 169 (2019).
doi: 10.3390/rs11020169
Vanhellemont, Q. & Ruddick, K. Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications. Remote sensing of environment 216, 586–597 (2018).
doi: 10.1016/j.rse.2018.07.015
Hu, C. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment 113, 2118–2129 (2009).
doi: 10.1016/j.rse.2009.05.012
Wang, M. & Hu, C. Mapping and quantifying sargassum distribution and coverage in the central west atlantic using modis observations. Remote sensing of environment 183, 350–367 (2016).
doi: 10.1016/j.rse.2016.04.019
Liu, D., Keesing, J. K., Xing, Q. & Shi, P. World’s largest macroalgal bloom caused by expansion of seaweed aquaculture in china. Marine Pollution Bulletin 58, 888–895 (2009).
doi: 10.1016/j.marpolbul.2009.01.013
Koepke, P. Effective reflectance of oceanic whitecaps. Applied optics 23, 1816–1824 (1984).
doi: 10.1364/AO.23.001816
Goddijn-Murphy, L., Woolf, D. K. & Callaghan, A. H. Parameterizations and algorithms for oceanic whitecap coverage. Journal of Physical Oceanography 41, 742–756 (2011).
doi: 10.1175/2010JPO4533.1
Prasannarai, K. & Sridhar, K. bundance and diversity of marine fungi on intertidal woody litter of the west coast of india on prolonged incubation. Fungal Divers 14, 127–141 (2003).
Storry, K. A., Weldrick, C. K., Mews, M., Zimmer, M. & Jelinski, D. E. Intertidal coarse woody debris: A spatial subsidy as shelter or feeding habitat for gastropods? Estuarine, Coastal and Shelf Science 66, 197–203 (2006).
doi: 10.1016/j.ecss.2005.08.005
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).