UTILIZATION OF SENTINEL-2 IMAGERY IN THE ESTIMATION OF PLASTICS AMONG FLOATING DEBRIS ALONG THE COAST OF MANILA BAY

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. remote sensing and spatial information sciences., 2021-11, Vol.XLVI-4/W6-2021, p.177-184
• Main Authors: Gonzaga, M. L. R., Wong, M. T. S., Blanco, A. C., Principe, J. A.
• Format: Article
• Language: English
• Subjects: Archives & records; Debris; Economic activities; Environmental cleanup; Fieldwork; Floating; Harbors; Imagery; Industrial areas; Marine pollution; Methods; Oceans; Packaging; Photogrammetry; Plastic debris; Plastic pollution; Polyethylene terephthalate; Population growth; Remote sensing; Rivers; Satellite imagery; Shipping; Spaceborne remote sensing; Styrofoam; Training; Unmanned aerial vehicles; Vegetation index
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVI-4-W6-2021-177-2021

With the Philippines ranking as the third largest source of plastics that end up in the oceans, there is a need to further explore methodologies that will become an aid in plastic waste removal from the ocean. Manila Bay is a natural harbor in the Philippines that serves as the center of different economic activities. However, the bay is also threatened with plastic pollution due to increasing population and industrial activities. BASECO is one of the areas in Manila Bay where clean-up activities are focused as this is where trash accumulates. Sentinel-2 images are provided free of charge by the European Commission's Copernicus Programme. Satellite images from June 2019 to May 2020 were inspected, then cloud-free images were downloaded. After downloading and pre-processing, spectral data of different types of plastic such as shipping pouch, bubble wrap, styrofoam, PET bottle, sando bag and snack packaging that were measured by a spectrometer during a fieldwork by the Development of Integrated Mapping, Monitoring, and Analytical Network System for Manila Bay and Linked Environments (project MapABLE) were utilized in the selection of training data. Then, indices such as the Normalized Vegetation Index (NDVI), Floating Debris Index (FDI) and Plastic Index (PI) from previous studies were analyzed for further separation of classes used as training data. These training data served as an input to the two supervised classification methods, Naive Bayes and Mixture Tuned Matched Filtering (MTMF). Both methods were validated by reports and articles from Philippine agencies indicating the spots where trash frequently accumulates.