Identification of Tyre and Plastic Waste from Combined Copernicus Sentinel-1 and -2 Data
| Autor: | Emmanuel Udugbezi, Jennifer Best, Katie Berry, Gillian Fowler, Samantha Lavender, Susan Stevens, Robert Page, Iain Brockie, Dean Thomas, Mohammed Haq |
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| Rok vydání: | 2020 |
| Předmět: |
random forests
010504 meteorology & atmospheric sciences 0211 other engineering and technologies 02 engineering and technology Land cover 01 natural sciences land cover lcsh:Science land use & 021101 geological & geomatics engineering 0105 earth and related environmental sciences Copernicus business.industry Scale (chemistry) Environmental resource management Random forest Statistical classification Identification (information) Thematic map General Earth and Planetary Sciences Environmental science lcsh:Q Plastic waste plastics business EARSeL |
| Zdroj: | Remote Sensing, Vol 12, Iss 2824, p 2824 (2020) |
| ISSN: | 2072-4292 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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