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Remote Sensing
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Remote Sensing for Mapping and Monitoring Anthropogenic Debris
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Remote Sensing for Mapping and Monitoring Anthropogenic Debris

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 30633

Special Issue Editors

Dr. Gil Rito Gonçalves

E-Mail Website
Guest Editor
Department of Mathematics, Faculty of Sciences and Technology, University of Coimbra, 3001-454 Coimbra, Portugal
Interests: unmanned aerial systems; satellite image processing; satellite image analysis; geoinformation; mapping; spatial analysis; geospatial science; digital mapping; remote sensing; geographical information systems; environment
Special Issues, Collections and Topics in MDPI journals
Dr. Umberto Andriolo

E-Mail Website
Guest Editor
Institute for Systems Engineering and Computers (INESC), University of Coimbra, Coimbra, Portugal
Interests: remote sensing for coastal studies; beach-dune morphodynamics; nearshore hydrodynamics; UAV for marine litter mapping marine geology; environmental hydraulics; coastal engineering; sediments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Anthropogenic debris abundance has become a global issue for marine, coastal, and terrestrial environments, as it represents a threat for species, ecosystems, and, potentially, human health. Innovative and robust remote sensing tools, methods, and techniques are beneficial for improving the current anthropogenic debris monitoring programs. For instance, remote sensing provides a reliable source of data collection to widen observations, which are usually limited in traditional surveys, and to monitor inaccessible areas. These improvements are essential in finding the appropriate mitigation measures and to optimize the removal of anthropogenic debris.

This Special Issue proposes to include research on anthropogenic debris detection, mapping, and monitoring in the environment using different remote sensing techniques. We welcome original contributions on all possible types of remote sensing platforms, such as satellite, airborne, unmanned aerial systems, and terrestrial and underwater robotic systems, such as remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs). Research on all environmental domains is welcome, with emphasis on marine and ocean litter; coastal litter, including beaches and dunes; and riverine litter.

We look forward to receiving your submissions for this Special Issue.

 

Dr. Gil Rito Gonçalves
Dr. Umberto Andriolo
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

  • anthropogenic debris monitoring
  • coastal litter
  • riverine litter
  • urban litter
  • terrestrial and underwater robotic systems
  • coastal and terrestrial environments
  • plastic
  • marine litter
  • floating litter
  • marine pollution
  • urban pollution
  • beach litter
  • river pollution
  • ocean pollution
  • micro-plastic
  • macro-plastic
  • machine learning
  • marine litter detection

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Published Papers (7 papers)

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Research

18 pages, 8680 KiB  
Article
Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination
by Sílvia Almeida, Marko Radeta, Tomoya Kataoka, João Canning-Clode, Miguel Pessanha Pais, Rúben Freitas and João Gama Monteiro
Remote Sens. 2023, 15(1), 84; https://doi.org/10.3390/rs15010084 - 23 Dec 2022
Cited by 13 | Viewed by 3167
Abstract
Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing [...] Read more.
Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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21 pages, 4236 KiB  
Article
Sentinel-2 Detection of Floating Marine Litter Targets with Partial Spectral Unmixing and Spectral Comparison with Other Floating Materials (Plastic Litter Project 2021)
by Dimitris Papageorgiou, Konstantinos Topouzelis, Giuseppe Suaria, Stefano Aliani and Paolo Corradi
Remote Sens. 2022, 14(23), 5997; https://doi.org/10.3390/rs14235997 - 26 Nov 2022
Cited by 22 | Viewed by 3215
Abstract
Large-area, artificial floating marine litter (FML) targets were deployed during a controlled field experiment and data acquisition campaign: the Plastic Litter Project 2021. A set of 22 Sentinel-2 images, along with UAS data and ancillary measurements were acquired. Spectral analysis of the FML [...] Read more.
Large-area, artificial floating marine litter (FML) targets were deployed during a controlled field experiment and data acquisition campaign: the Plastic Litter Project 2021. A set of 22 Sentinel-2 images, along with UAS data and ancillary measurements were acquired. Spectral analysis of the FML and natural debris (wooden planks) targets was performed, along with spectral comparison and separability analysis between FML and other floating materials such as marine mucilage and pollen. The effects of biofouling and submersion on the spectral signal of FML were also investigated under realistic field conditions. Detection of FML is performed through a partial unmixing methodology. Floating substances such as pollen exhibit similar spectral characteristics to FML, and are difficult to differentiate. Biofouling is shown to affect the magnitude and shape of the FML signal mainly in the RGB bands, with less significant effect on the infrared part of the spectrum. Submersion affects the FML signal throughout the range of the Sentinel-2 satellite, with the most significant effect in the NIR part of the spectrum. Sentinel-2 detection of FML can be successfully performed through a partial unmixing methodology for FML concentrations with abundance fractions of 20%, under reasonable conditions. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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10 pages, 15242 KiB  
Communication
Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak
by Oscar Bryan, Roy Edgar Hansen, Tom S. F. Haines, Narada Warakagoda and Alan Hunter
Remote Sens. 2022, 14(11), 2619; https://doi.org/10.3390/rs14112619 - 31 May 2022
Cited by 7 | Viewed by 2494
Abstract
The disposal of unexploded ordnance (UXOs) at sea is a global problem. The mapping and remediation of historic UXOs can be assisted by autonomous underwater vehicles (AUVs) carrying sensor payloads such as synthetic aperture sonar (SAS) and optical cameras. AUVs can image large [...] Read more.
The disposal of unexploded ordnance (UXOs) at sea is a global problem. The mapping and remediation of historic UXOs can be assisted by autonomous underwater vehicles (AUVs) carrying sensor payloads such as synthetic aperture sonar (SAS) and optical cameras. AUVs can image large areas of the seafloor in high resolution, motivating an automated approach to UXO detection. Modern methods commonly use supervised machine learning which requires labelled examples from which to learn. This work investigates the often-overlooked labelling process and resulting dataset using an example historic UXO dumpsite at Skagerrak. A counterintuitive finding of this work is that optical images cannot be relied on for ground truth as a significant number of UXOs visible in SAS images are not in optical images, presumed buried. Given the lack of ground truth, we use an ordinal labelling scheme to incorporate a measure of labeller uncertainty. We validate this labelling regime by quantifying label accuracy compared to optical labels with high confidence. Using this approach, we explore different taxonomies and conclude that grouping objects into shells, bombs, debris, and natural gave the best trade-off between accuracy and discrimination. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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16 pages, 8603 KiB  
Article
Marine Litter Detection by Sentinel-2: A Case Study in North Adriatic (Summer 2020)
by Achille Carlo Ciappa
Remote Sens. 2022, 14(10), 2409; https://doi.org/10.3390/rs14102409 - 17 May 2022
Cited by 16 | Viewed by 3250
Abstract
Aggregates of floating materials detected in North Adriatic in six Sentinel-2 scenes of August 2020 have been investigated. Most of the floating materials were identified by the chlorophyll red edge and consisted of vegetal materials, probably conveyed by rivers and exchanged with the [...] Read more.
Aggregates of floating materials detected in North Adriatic in six Sentinel-2 scenes of August 2020 have been investigated. Most of the floating materials were identified by the chlorophyll red edge and consisted of vegetal materials, probably conveyed by rivers and exchanged with the lagoons. Traces of marine litter were looked for in the spectral anomalies of the Red Edge bands, assuming changes of the red edge in pixels where marine litter was mixed with vegetal materials. About half of the detected patches were unclassified due to the weakness of the useful signal (pixel filling percentage < 25%). The classification produced 59% of vegetal materials, 16% of marine litter mixed with vegetal materials and 22% of intermediate cases. A small percentage (2%) was attributed to submerged vegetal materials, found in isolated patches. The previous percentages were obtained with a separation criterion based on arbitrary thresholds. The patches were more concentrated at the mouths of the northern rivers, less off the Venice lagoon, and very few outside the Po River, with the minimal river outflow during the period. Sentinel-2 is a valid tool for the discrimination of marine litter in aggregates of floating matter. The proposed method requires validation, and the North Adriatic is an excellent site for field work, as in summer many patches of floating matter form in proximity to the coast. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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12 pages, 1173 KiB  
Communication
Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences
by Umberto Andriolo, Odei Garcia-Garin, Morgana Vighi, Asunción Borrell and Gil Gonçalves
Remote Sens. 2022, 14(6), 1336; https://doi.org/10.3390/rs14061336 - 9 Mar 2022
Cited by 32 | Viewed by 4334
Abstract
The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and [...] Read more.
The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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23 pages, 7556 KiB  
Article
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
by Jenna A. Guffogg, Samantha M. Blades, Mariela Soto-Berelov, Chris J. Bellman, Andrew K. Skidmore and Simon D. Jones
Remote Sens. 2021, 13(22), 4548; https://doi.org/10.3390/rs13224548 - 12 Nov 2021
Cited by 11 | Viewed by 6869
Abstract
Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, [...] Read more.
Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, within the water column, in coastal waters, on the benthic layer and on beaches. While research on detecting MPD using remote sensing is increasing, most of it focuses on detecting floating debris in open waters, rather than detecting MPD on beaches. However, beaches present challenges that are unique from other parts of the marine environment. In order to better understand the spectral properties of beached MPD, we present the SWIR reflectance of weathered MPD and virgin plastics over a sandy substrate. We conducted spectral feature analysis on the different plastic groups to better understand the impact that polymers have on our ability to detect synthetic debris at sub-pixel surface covers that occur on beaches. Our results show that the minimum surface cover required to detect MPD on a sandy surface varies between 2–8% for different polymer types. Furthermore, plastic composition affects the magnitude of spectral absorption. This suggests that variation in both surface cover and polymer type will inform the efficacy of beach litter detection methods. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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Graphical abstract

19 pages, 5311 KiB  
Article
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
by Gil Gonçalves, Umberto Andriolo, Luísa Gonçalves, Paula Sobral and Filipa Bessa
Remote Sens. 2020, 12(16), 2599; https://doi.org/10.3390/rs12162599 - 12 Aug 2020
Cited by 60 | Viewed by 5780
Abstract
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS [...] Read more.
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
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