Anthropogenic marine debris over beaches: Spectral characterization for remote sensing applications

Acuña-Ruz, Tomás; Uribe, Diego; Taylor, Richard; Amézquita, Lucas; Guzmán, María Cristina; Merrill, Javier; Martínez, Paula; Voisin, Leandro; Mattar, B Cristian

doi:10.1016/j.rse.2018.08.008

Cited by 86 publications

(62 citation statements)

References 65 publications

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“…However, the RGB images do not provide information on the physical and chemical composition of the litter. The potential of new "machine learning" techniques, combined with hyperspectral information, demonstrated recently by Acuña- Ruz et al (2018) in their pilot study on the shoreline, will increase the value of high-resolution visible imaging in the future. Costs related to managing "big data" might be an operational challenge for such very high temporal and geo-spatial resolution RGB imagery.…”

Section: High Spatial Resolution Imagingmentioning

confidence: 99%

“…For the bottom trawls litter, the database used the experience of ICES DATRAS 47 but added reports from some of the 43 https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=434&titre_chap=TG %2520Marine%2520Litter 44 http://www.helcom.fi/ 45 http://www.blacksea-commission.org/ 46 http://www.emodnet-chemistry.eu/welcome 47 http://www.ices.dk/marine-data/data-portals/Pages/DATRAS.aspx International Bottom Trawl Surveys in the Mediterranean (MEDITS) 48 as well as general data submitted according to the Marine Strategy Framework Directive (MSFD 49 . )…”

Section: Networkingmentioning

confidence: 99%

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Toward the Integrated Marine Debris Observing System

Maximenko¹,

Corradi²,

Law³

et al. 2019

Front. Mar. Sci.

221

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Plastics and other artificial materials pose new risks to the health of the ocean. Anthropogenic debris travels across large distances and is ubiquitous in the water and on shorelines, yet, observations of its sources, composition, pathways, and distributions in the ocean are very sparse and inaccurate. Total amounts of plastics and other man-made debris in the ocean and on the shore, temporal trends in these amounts under exponentially increasing production, as well as degradation processes, vertical fluxes, and time scales are largely unknown. Present ocean circulation models are not able to accurately simulate drift of debris because of its complex hydrodynamics. In this paper we discuss the structure of the future integrated marine debris observing system (IMDOS) that is required to provide long-term monitoring of the state of this anthropogenic pollution and support operational activities to mitigate impacts on the ecosystem and on the safety of maritime activity. The proposed observing system integrates remote sensing and in situ observations. Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. Remote sensing technologies will provide spatially coherent coverage and consistent surveying time series at local to global scale. Optical sensors, including high-resolution imaging, multi-and hyperspectral, fluorescence, and Raman technologies, as well as SAR will be used to measure different types of debris. They will be implemented in a variety of platforms, from hand-held tools to ship-, buoy-, aircraft-, and satellite-based sensors. A network of in situ observations, including reports from volunteers, citizen scientists and ships of opportunity, will be developed to provide data for calibration/validation of remote sensors and to monitor the spread of plastic pollution and other marine debris. IMDOS will interact with other observing systems monitoring physical, chemical, and biological processes in the ocean and on shorelines as well as the state of the ecosystem, maritime activities and safety, drift of sea ice, etc. The synthesized data will support innovative multidisciplinary research and serve a diverse community of users.

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Section: High Spatial Resolution Imagingmentioning

confidence: 99%

Section: Networkingmentioning

confidence: 99%

Toward the Integrated Marine Debris Observing System

Maximenko¹,

Corradi²,

Law³

et al. 2019

Front. Mar. Sci.

221

View full text Add to dashboard Cite

show abstract

“…It is very likely that with the advent of hyperspectral imaging spectroscopy from space (EnMap, PRISMA, SHALOM, HyspIRI or HYPXIM sensors), the synergy between hyperspectral and multispectral images can be used to enrich co-located images. Acuña-Ruz et al (2018) [87] demonstrated that a combination of in situ quantification using hyperspectral radiometric measurements with very high spatial resolution imagers (1 m) could be used for remote quantification of marine plastic debris on a beach. Guanter et al (2018) [88] have recently reviewed the techniques and potential for synergy between hyperspectral imagers, multispectral imagers, ocean colour radiometry and LIDAR techniques.…”

Section: Challenges and Opportunities For Remote Sensing Detection Ofmentioning

confidence: 99%

Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements

et al. 2019

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Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.

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“…The detection of plastics can be improved using cameras extended beyond the visible band 37,[45][46][47][48] . Recently, several researchers have studied the application of near-infrared hyperspectral cameras to identify plastics in the environment [45][46][47][48] . If hyperspectral imaging is available in rivers, macro-plastics can be distinguished from the macro-debris flowing on the river surface.…”

Section: Discussionmentioning

confidence: 99%

Quantification of floating riverine macro-debris transport using an image processing approach

Kataoka

Nihei

2020

Sci Rep

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A new algorithm has been developed to quantify floating macro-debris transport on river surfaces that consists of three fundamental techniques: (1) generating a difference image of the colour difference between the debris and surrounding water in the CIELuv colour space, (2) detecting the debris pixels from the difference image, and (3) calculating the debris area flux via the template matching method. Debris pixels were accurately detected from the images taken of the laboratory channel and river water surfaces and were consistent with those detected by visual observation. The area fluxes were statistically significantly correlated with the mass fluxes measured through debris collection. The mass fluxes calculated by multiplying the area fluxes with the debris mass per unit area (M/A) were significantly related to the flood rising stage flow rates and agreed with the mass fluxes measured through debris collection. In our algorithm, plastic mass fluxes can be estimated via calibration using the mass percentage of plastics to the total debris in target rivers. Quantifying riverine macro-plastic transport is essential to formulating countermeasures, mitigating adverse plastic pollution impacts and understanding global-scale riverine macro-plastic transport. Quantifying the transport of macro-debris floating on the world's rivers, which are major sources of ocean debris, is essential in formulating countermeasures to mitigate the adverse impacts of land-based loads. In particular, the adverse impacts on aquatic ecosystems of plastics containing toxic chemicals (e.g., persistent organic pollutants (POPs)) 1-4 are recognized as a serious concern in the global aquatic environment 5-8. Many of the plastics in the oceans originate from land 9 , and thereafter, macro-plastics (>25 mm in diameter) 10 are evenly broken down into smaller plastic fragments known as meso-(5−25 mm in diameter) and micro-plastics (<5 mm in diameter) 10 due to photo-and thermo-oxidative degradation 6. Micro-plastics are rarely removed from the aquatic environment when released and are thus gradually transported far away due to ocean currents. Hence, to formulate countermeasures against oceanic plastic pollution, the macro-plastics in rivers must be efficiently captured before being fragmented into smaller pieces and/or being released to the oceans. In particular, because most macro-plastics float on the surface, it is important to understand how floating macro-debris is transported via rivers and then released into the oceans. Recently, a few studies have attempted to estimate the plastic waste emissions from land 9,11. Jambeck et al. 9 , for instance, estimated that 4.8 to 12.7 million tonnes of mismanaged plastic waste could be entering the oceans from 192 coastal countries in 2010 by considering the waste management level in each country and the coastal population. In their estimate, 0.02 to 0.06 million MT of plastic waste can enter the Pacific Ocean from Japan. Moreover, Lebreton et al. 11 estimated that between 1.15 and 2.41 million tonnes ...

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Anthropogenic marine debris over beaches: Spectral characterization for remote sensing applications

Cited by 86 publications

References 65 publications

Toward the Integrated Marine Debris Observing System

Toward the Integrated Marine Debris Observing System

Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements

Quantification of floating riverine macro-debris transport using an image processing approach

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