Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis

Guffogg, Jenna A.; Blades, Samantha M.; Soto‐Berelov, Mariela; Bellman, Chris; Skidmore, A.K.; Jones, Simon

doi:10.3390/rs13224548

Cited by 13 publications

(18 citation statements)

References 46 publications

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“…According to research by Guffogg et al [88], spectral feature analysis enables detection of synthetic material at a sub-pixel. The minimum surface cover required to detect plastic on a sandy surface was found to be merely 2-8% for different polymer types.…”

Section: Challenges In Plastic Detection and Future Opportunities For...mentioning

confidence: 99%

Detection of River Plastic Using UAV Sensor Data and Deep Learning

et al. 2022

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Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

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Section: Challenges In Plastic Detection and Future Opportunities For...mentioning

confidence: 99%

Detection of River Plastic Using UAV Sensor Data and Deep Learning

et al. 2022

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“…Several studies have been looking for a comprehensive study on this topic. For example, not only Gallo F et al (2018) research shows 8% million tons of plastic debris entering the ocean [8] but also Guffogg J A et al (2020) and Borelle S B et al (2020) showed 0.8-2.7 million of ocean plastics [9,10]. Jambeck et al (2015) estimated that 4.8 to 12.7 million metric tons of plastic from land-based activities entered the world's oceans [11].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the issue of marine debris, several fundamental studies explore the potential to detect plastic pollution with surveys and observations in laboratories [8,12]. However, assessing marine plastic debris was time-consuming and costly.…”

Section: Introductionmentioning

confidence: 99%

Potential Marine Plastic Debris Detection using Sentinel-2 Multi-Spectral Instrument (MSI)

Damayanti

Hanami

Hirose³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Plastic debris has a long-term and quite severe impact on the marine ecosystem. Population growth in the developing area, Makassar coastal zone, will contribute significantly to the land’s waste. Consequently, floating plastic debris comes directly from the land’s plastic leaks to rapidly increase marine plastic debris. Due to being a major environmental issue in the coastal zone and reducing options for removal are very limited, this study becomes to detect the potential marine plastic debris in Makassar by Sentinel-2 Multi-Spectral Instrument (MSI) using Kernel Normalization Vegetation Index (KNDVI) and Floating Debris Index (FDI) for satellite image processing. The plastic debris was corrected atmospheric effect by Sen2Cor and processed by Sentinel Application Platform (SNAP). The resolution of images was set to 10 x 10 meters. The target area was calculated as 173.47 Ha. The results show that KNDVI and FDI combined are a developed and powerful approach to detecting plastic debris. KNDVI has more representative indices (SE 0.38) than FDI indices (SE 0.47). Through the discriminant analysis were verified pixels of KNDVI (3506 pixels) and FDI indices (701 pixels). Meanwhile, the Sensitivity Analysis Value (SAV) was well performed to detect the potential of marine plastic debris by KNDVI with SAV 7.5 than FDI indices with SAV 2.9.

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“…Guffogg et al [20] noted that an obstacle for spectral detection on some beaches was that a large percentage of the plastic debris was shoes, predominantly flip flops, with polyurethane foam not having received as much focus as other plastics. They found that between 2-8%, depending on the plastic polymer, of a unit area must be covered in plastics before the material can be spectrally separated from a non-contaminated area only containing sand.…”

Section: Introductionmentioning

confidence: 99%

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

Lavender¹

2022

Preprint

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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.

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Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis

Cited by 13 publications

References 46 publications

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Potential Marine Plastic Debris Detection using Sentinel-2 Multi-Spectral Instrument (MSI)

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

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