Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment
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 uncon...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/8/2020 |
| _version_ | 1797603667054952448 |
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| author | Toni Schmidt Theres Kuester Taylor Smith Mathias Bochow |
| author_facet | Toni Schmidt Theres Kuester Taylor Smith Mathias Bochow |
| author_sort | Toni Schmidt |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:35:14Z |
| format | Article |
| id | doaj.art-a8e44b42e9d04e339dd8f58a3347fbc4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:35:14Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a8e44b42e9d04e339dd8f58a3347fbc42023-11-17T21:10:56ZengMDPI AGRemote Sensing2072-42922023-04-01158202010.3390/rs15082020Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the EnvironmentToni Schmidt0Theres Kuester1Taylor Smith2Mathias Bochow3Department Remote Sensing, Helmholtz-Centre for Environmental Research—UFZ, 04318 Leipzig, GermanyRemote Sensing and Geoinformatics Section, Helmholtz Centre Potsdam—GFZ German Research Centre for Geosciences, 14473 Potsdam, GermanyInstitute of Geosciences, University of Potsdam, 14476 Potsdam, GermanyRemote Sensing and Geoinformatics Section, Helmholtz Centre Potsdam—GFZ German Research Centre for Geosciences, 14473 Potsdam, GermanyPlastics 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.https://www.mdpi.com/2072-4292/15/8/2020plastic mappingplastic debrisspectroscopysensor simulationoptical remote sensingmultispectral sensors |
| spellingShingle | Toni Schmidt Theres Kuester Taylor Smith Mathias Bochow Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment Remote Sensing plastic mapping plastic debris spectroscopy sensor simulation optical remote sensing multispectral sensors |
| title | Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment |
| title_full | Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment |
| title_fullStr | Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment |
| title_full_unstemmed | Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment |
| title_short | Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment |
| title_sort | potential of optical spaceborne sensors for the differentiation of plastics in the environment |
| topic | plastic mapping plastic debris spectroscopy sensor simulation optical remote sensing multispectral sensors |
| url | https://www.mdpi.com/2072-4292/15/8/2020 |
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