A satellite remote sensing method for detecting marine plastic debirs

doi:10.13474/j.cnki.11-2246.2023.0163

Abstract

Abstract: Marine floating plastic debris is widely distributed in surface waters such as coastal waters and ocean gyres, which seriously endangers marine life and the sustainable development of human society. Limited by the small-scale, small number of marine plastic target samples and the spatial resolution of satellite remote sensing sensors, accurately detect the spatial and temporal distribution characteristics of marine plastic debris has an important practical significance. Based on the known spectral characteristics of plastic and other floating objects in the sea, this study proposes a reflectance feature classification method based on Sentinel-2 satellite imagery. By combining the reflectance threshold and peak characteristics of different bands, it can effectively detect and identify floating plastic in multiple regions of the world, with an overall accuracy of 98% and an F-score of 0.85, which is better than the traditional machine learning classification method, and is beneficial for the change detection and impact mechanism research of marine plastic debris.

Key words: marine plastic debris, spectral reflectance, coastal zone, Sentinel-2, machine learning

CLC Number:

P237

LI Peng, ZHOU Hongli, LIN Shicong, WANG Houjie, LI Zhenhong. A satellite remote sensing method for detecting marine plastic debirs[J]. Bulletin of Surveying and Mapping, 2023, 0(6): 20-26.

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