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<p>A massive amount of plastic waste has presented an immense management challenge. This escalating ecological damage, coupled with the detrimental effects of plastics infiltrating the marine food web, poses a significant threat to human livelihoods. To combat this, there is a call for the development of plastic detection algorithms using remote sensing data. Here we tested a new index, referred to index<sub>MP</sub>, to detect clusters of floating macro plastics in the ocean using satellite imagery. The index<sub>MP</sub> was applied to convolution high-pass filtered (3×3) Sentinel 2 Level 1C images, showing the potential to reduce atmospheric interference and enhance the object edges, thereby improving the clarity of detection. In the analysis, we used three scatter plots to identify and assess plastic pixels. To differentiate the common features of plastic from non-plastic objects, the Sentinel 2 bands 5, 8, and 9 were plotted against index<sub>MP</sub> calculated and convolution high-pass filtered Level 1C (CHPIC) images. The plastic pixels, clustering in the three scatter plots, showed positive ‘<i>X</i>’, i.e., CHPIC image value and ‘<i>Y</i>’, i.e., each band 5, 8, and 9 reflectance values, along with a CHPIC image value exceeding 0.05. Using the index<sub>MP</sub> and scatter plot analysis, we identified plastic pixels containing 14% or more plastic bottles. Detection of other types of plastics, such as fishing nets and plastic bags, required pixel proportions greater than 50%. Hence, plastic bottles were notably responsive even at a low pixel fraction. We further explored the classification of plastic and non-plastic objects by analyzing reed (plant) pixels; the differentiation between plastic and reed was conducted in the band 5 and 9 scatter plots.</p>

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Detection of floating marine macro plastics using a new index with remote sensing data

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Kalani Randima Lakshani Pathira Arachchilage,
  • Danling Tang,
  • Sufen Wang

摘要

A massive amount of plastic waste has presented an immense management challenge. This escalating ecological damage, coupled with the detrimental effects of plastics infiltrating the marine food web, poses a significant threat to human livelihoods. To combat this, there is a call for the development of plastic detection algorithms using remote sensing data. Here we tested a new index, referred to indexMP, to detect clusters of floating macro plastics in the ocean using satellite imagery. The indexMP was applied to convolution high-pass filtered (3×3) Sentinel 2 Level 1C images, showing the potential to reduce atmospheric interference and enhance the object edges, thereby improving the clarity of detection. In the analysis, we used three scatter plots to identify and assess plastic pixels. To differentiate the common features of plastic from non-plastic objects, the Sentinel 2 bands 5, 8, and 9 were plotted against indexMP calculated and convolution high-pass filtered Level 1C (CHPIC) images. The plastic pixels, clustering in the three scatter plots, showed positive ‘X’, i.e., CHPIC image value and ‘Y’, i.e., each band 5, 8, and 9 reflectance values, along with a CHPIC image value exceeding 0.05. Using the indexMP and scatter plot analysis, we identified plastic pixels containing 14% or more plastic bottles. Detection of other types of plastics, such as fishing nets and plastic bags, required pixel proportions greater than 50%. Hence, plastic bottles were notably responsive even at a low pixel fraction. We further explored the classification of plastic and non-plastic objects by analyzing reed (plant) pixels; the differentiation between plastic and reed was conducted in the band 5 and 9 scatter plots.