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Remote Sensing of Plastic Pollution

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 56824

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Special Issue Editors

Dr. Mathias Bochow

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Section 1.4 Remote Sensing and Geoinformatics, Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: applied remote sensing; digital image processing and classification; imaging spectroscopy; spectroscopy of synthetic polymers; geomatics and GIS; remote sensing of vegetation; water and urban areas; microplastic pollution of the environment

Prof. Heidi M. Dierssen

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Department of Marine Sciences/Geography, University of Connecticut, 1080 Shennecossett Road, Groton, CT 06340, USA
Interests: hyperspectral remote sensing; sea surface optical properties; air-sea interactions; atmospheric correction of ocean color imagery
Special Issues, Collections and Topics in MDPI journals

Dr. Jonas Franke

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Remote Sensing Solutions GmbH, Dingolfinger Str. 9, 81673 Munich, Germany
Interests: environmental monitoring; geospatial data-driven decision support; identifying and tracking plastic debris; satellite data time series; spectrometry

Dr. Lonneke Goddijn-Murphy

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Guest Editor

Environmental Research Institute, North Highland College, University of the Highlands and Islands, Ormlie Road, Thurso KW14 7EE, UK
Interests: ocean surface remote sensing; water quality; marine renewable energy; air-sea gas exchange

Dr. Theres Kuester

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Guest Editor

Section 1.4 Remote Sensing and Geoinformatics, Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: optical remote sensing; imaging spectroscopy; methodological developments for hyperspectral and multispectral data of environmental topics such as the detection of plastics; VIS/NIR/SWIR spectroscopy; spatial and spectral modeling of reflectance and radiance signals; geomatics and GIS

Ms. Laia Romero

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Guest Editor

Lobelia Earth, Barcelona Advanced Industry Park, Marie Curie, 8-14, 08042 Barcelona, Spain
Interests: applied remote sensing; Synthetic Aperture Radar processing (SAR) and SAR altimetry; artificial intelligence for Earth science; climate diagnostics and prediction; Earth observation for hydrology and oceanography; extreme events and ocean plastic pollution

Special Issue Information

Dear Colleagues,

The problems that plastic litter causes in the environment are manifold, as are the disciplines conducting research on it. What is the contribution that remote sensing can make? This Special Issue calls for papers presenting both first success stories as well as clear indications (based on simulations, measurements or fundamental knowledge) of what is technically not (yet) possible by means of remote sensing, in order to form a clearer picture of the contribution remote sensing can provide now and in the future.

Papers may present results from measurements (lab, field), simulations and modeling, as well as remote sensing data analysis across all scales (local to global) and platforms (e.g., vessel-based, UAV-based, airborne or spaceborne) using any types of sensors (e.g., optical or radar). Due to the ubiquitous nature of the problem, studies in any type of environment (e.g., aquatic, terrestrial, urban) are welcome, and any object sizes from micro- to mega-plastics may be studied.

Dr. Mathias Bochow
Prof. Heidi M. Dierssen
Dr. Jonas Franke
Dr. Lonneke Goddijn-Murphy
Dr. Theres Kuester
Ms. Laia Romero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
plastic pollution
plastic litter
microplastics
macroplastics
mapping
monitoring
oceans
rivers
beaches
terrestrial environment
sources
sinks
pathways

Published Papers (10 papers)

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Research

23 pages, 75814 KiB

Open AccessArticle

A New Remote Hyperspectral Imaging System Embedded on an Unmanned Aquatic Drone for the Detection and Identification of Floating Plastic Litter Using Machine Learning

by Ahed Alboody, Nicolas Vandenbroucke, Alice Porebski, Rosa Sawan, Florence Viudes, Perine Doyen and Rachid Amara

Remote Sens. 2023, 15(14), 3455; https://doi.org/10.3390/rs15143455 - 08 Jul 2023

Cited by 3 | Viewed by 2421

Abstract

This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d’Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic. Finally, the ability of our original system RHIS to automatically recognize different types of plastic litter was assessed by applying different supervised machine learning methods on a set of representative image patches of marine litter. The obtained results highlighted the plastic litter classification capability with an overall accuracy close to 90%. This paper showed that the newly presented RHIS coupled with the UAD is a promising approach to identify plastic waste in aquatic environments. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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29 pages, 9267 KiB

Open AccessArticle

Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment

by Toni Schmidt, Theres Kuester, Taylor Smith and Mathias Bochow

Remote Sens. 2023, 15(8), 2020; https://doi.org/10.3390/rs15082020 - 11 Apr 2023

Cited by 1 | Viewed by 2618

Abstract

Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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31 pages, 10802 KiB

Open AccessArticle

Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images

by Marian-Daniel Iordache, Liesbeth De Keukelaere, Robrecht Moelans, Lisa Landuyt, Mehrdad Moshtaghi, Paolo Corradi and Els Knaeps

Remote Sens. 2022, 14(22), 5820; https://doi.org/10.3390/rs14225820 - 17 Nov 2022

Cited by 7 | Viewed by 2713

Abstract

The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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26 pages, 14232 KiB

Open AccessArticle

Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data

by Samantha Lavender

Remote Sens. 2022, 14(19), 4772; https://doi.org/10.3390/rs14194772 - 23 Sep 2022

Cited by 7 | Viewed by 5060

Abstract

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning-based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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16 pages, 11120 KiB

Open AccessCommunication

A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data

by Nicolò Taggio, Antonello Aiello, Giulio Ceriola, Maria Kremezi, Viktoria Kristollari, Polychronis Kolokoussis, Vassilia Karathanassi and Enrico Barbone

Remote Sens. 2022, 14(15), 3606; https://doi.org/10.3390/rs14153606 - 28 Jul 2022

Cited by 18 | Viewed by 3333

Abstract

A significant amount of the produced solid waste reaching the oceans is made of plastics. The amount of plastic debris in the ocean and coastal areas is steadily increasing and is now a major global environmental issue. The monitoring of marine plastic litter, ground-based monitoring systems and/or field campaigns are time-consuming, expensive, require great organisational efforts, and provide very limited information in terms of the spatial and temporal dynamics of marine debris. Earth Observation (EO) by satellite can contribute significantly to marine plastic litter detection. In 2019, a new hyperspectral satellite, called PRISMA, was launched by the Italian Space Agency. The high spectral resolution of PRISMA may allow for better detection of floating plastic materials. At the same time, Machine Learning (ML) algorithms have the potential to find hidden patterns and identify complex relations among data and are increasingly employed in EO. This paper presents the development of a new method of identifying floating plastic objects in coastal areas by exploiting pan-sharpened hyperspectral PRISMA data, based on the combination of unsupervised and supervised ML algorithms. The study consisted of a configuration phase, during which the algorithms were trained in a fully controlled test, and a validation phase, in which the pre-trained algorithms were applied to satellite data collected at different sites and in different periods of the year. Despite the limited input data, results suggest that the tested ML approach, applied to pan-sharpened PRISMA data, can effectively recognise floating objects and plastic targets. The study indicates that increasing input datasets can help achieve higher-quality results. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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18 pages, 8146 KiB

Open AccessArticle

Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning

by Sophie Armitage, Katie Awty-Carroll, Daniel Clewley and Victor Martinez-Vicente

Remote Sens. 2022, 14(14), 3425; https://doi.org/10.3390/rs14143425 - 16 Jul 2022

Cited by 14 | Viewed by 5946

Abstract

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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25 pages, 5811 KiB

Open AccessEditor’s ChoiceArticle

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

by Lonneke Goddijn-Murphy, Benjamin J. Williamson, Jason McIlvenny and Paolo Corradi

Remote Sens. 2022, 14(13), 3179; https://doi.org/10.3390/rs14133179 - 02 Jul 2022

Cited by 14 | Viewed by 4267

Abstract

In recent years, the remote sensing of marine plastic litter has been rapidly evolving and the technology is most advanced in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) wavelengths. It has become clear that sensing using VIS-SWIR bands, based on the surface reflectance of sunlight, would benefit from complementary measurements using different technologies. Thermal infrared (TIR) sensing shows potential as a novel method for monitoring macro plastic litter floating on the water surface, as the physics behind surface-leaving TIR is different. We assessed a thermal radiance model for floating plastic litter using a small UAV-grade FLIR Vue Pro R 640 thermal camera by flying it over controlled floating plastic litter targets during the day and night and in different seasons. Experiments in the laboratory supported the field measurements. We investigated the effects of environmental conditions, such as temperatures, light intensity, the presence of clouds, and biofouling. TIR sensing could complement observations from VIS, NIR, and SWIR in several valuable ways. For example, TIR sensing could be used for monitoring during the night, to detect plastics invisible to VIS-SWIR, to discriminate whitecaps from marine litter, and to detect litter pollution over clear, shallow waters. In this study, we have shown the previously unconfirmed potential of using TIR sensing for monitoring floating plastic litter. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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16 pages, 65711 KiB

Open AccessCommunication

Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence

by Robin de Vries, Matthias Egger, Thomas Mani and Laurent Lebreton

Remote Sens. 2021, 13(17), 3401; https://doi.org/10.3390/rs13173401 - 27 Aug 2021

Cited by 12 | Viewed by 8603

Abstract

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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19 pages, 4193 KiB

Open AccessEditor’s ChoiceArticle

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

by Paolo Tasseron, Tim van Emmerik, Joseph Peller, Louise Schreyers and Lauren Biermann

Remote Sens. 2021, 13(12), 2335; https://doi.org/10.3390/rs13122335 - 15 Jun 2021

Cited by 34 | Viewed by 8898

Abstract

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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17 pages, 4123 KiB

Open AccessArticle

Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

by Konstantinos Topouzelis, Dimitris Papageorgiou, Alexandros Karagaitanakis, Apostolos Papakonstantinou and Manuel Arias Ballesteros

Remote Sens. 2020, 12(12), 2013; https://doi.org/10.3390/rs12122013 - 23 Jun 2020

Cited by 58 | Viewed by 7372

Abstract

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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