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

Kalani Randima Lakshani PATHIRA ARACHCHILAGE; Danling TANG; Sufen WANG

doi:10.1007/s00343-024-3152-7

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

Physics | Updated：2025-05-27

- Detection of floating marine macro plastics using a new index with remote sensing data
- Journal of Oceanology and Limnology Vol. 43, Issue 3, Pages: 723-734(2025)
- Affiliations：
  
  1.Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
  2.Guangdong Key Lab of Ocean Remote Sensing, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 511548, China
  3.Sabaragamuwa University of Sri Lanka, P. O. Box 02, Belihuloya 70140, Sri Lanka
  4.School of Engineering, College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia
- Author bio：
  
  lingzistdl@126.com
- Funds：
- DOI：10.1007/s00343-024-3152-7
  CLC：
- Received：09 August 2023，
  
  Online First：20 May 2024，
  
  Published：01 May 2025
- Accepted：
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PATHIRA ARACHCHILAGE Kalani Randima Lakshani,TANG Danling,WANG Sufen.Detection of floating marine macro plastics using a new index with remote sensing data[J].Journal of Oceanology and Limnology,2025,43(03):723-734.
DOI：

PATHIRA ARACHCHILAGE Kalani Randima Lakshani,TANG Danling,WANG Sufen.Detection of floating marine macro plastics using a new index with remote sensing data[J].Journal of Oceanology and Limnology,2025,43(03):723-734. DOI： 10.1007/s00343-024-3152-7.

摘要

Abstract

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

to detect clusters of floating macro plastics in the ocean using satellite imagery. The index

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

and 9 were plotted against index

calculated and convolution high-pass filtered Level 1C (CHPIC) images. The plastic pixels

clustering in the three scatter plots

showed positive ‘

’

i.e.

CHPIC image value and ‘

’

i.e.

each band 5

and 9 reflectance values

along with a CHPIC image value exceeding 0.05. Using the index

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 cla

ssification 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.

关键词

Keywords

references

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