Identification of Tyre and Plastic Waste from Combined Copernicus Sentinel-1 and -2 Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-09, Vol.12 (17), p.2824
• Main Authors: Page, Robert, Lavender, Samantha, Thomas, Dean, Berry, Katie, Stevens, Susan, Haq, Mohammed, Udugbezi, Emmanuel, Fowler, Gillian, Best, Jennifer, Brockie, Iain
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Contamination; Copernicus; Crime; EARSeL; Environmental health; Environmental regulations; Identification methods; Image classification; Land cover; land use & Livestock; Noise; Plastic debris; Plastics; Polymers; random forests; Remote sensing; Stockpiling; Tires; Vegetation; Waste materials; Water pollution; West Nile virus
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12172824

As a result of tightened waste regulation across Europe, reports of waste crime have been on the rise. Significant stockpiles of tyres and plastic materials have been identified as a threat to both human and environmental health, leading to water and livestock contamination, providing substantial fuel for fires, and cultivating a variety of disease vectors. Traditional methods of identifying illegal stockpiles usually involve laborious field surveys, which are unsuitable for national scale management. Remotely-sensed investigations to tackle waste have been less explored due to the spectrally variable and complex nature of tyres and plastics, as well as their similarity to other land covers such as water and shadow. Therefore, the overall objective of this study was to develop an accurate classification method for both tyre and plastic waste to provide a viable platform for repeatable, cost-effective, and large-scale monitoring. An augmented land cover classification is presented that combines Copernicus Sentinel-2 optical imagery with thematic indices and Copernicus Sentinel-1 microwave data, and two random forests land cover classification algorithms were trained for the detection of tyres and plastics across Scotland. Testing of the method identified 211 confirmed tyre and plastic stockpiles, with overall classification accuracies calculated above 90%.