A new method for fully automated detection of algae blooms in Antarctica using Sentinel-2 satellite images

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dc.contributor.authorAcar U.
dc.contributor.authorYilmaz O.S.
dc.contributor.authorBalik Sanli F.
dc.contributor.authorOzcimen D.
dc.date.accessioned2024-07-22T08:01:27Z
dc.date.available2024-07-22T08:01:27Z
dc.date.issued2024
dc.description.abstractThe melting of Antarctic glaciers has become a significant issue as a result of global climate change. Algae on the Antarctic ice/snow is an important part of terrestrial photosynthetic organisms. Monitoring and tracking these algal blooms is crucial for understanding the melting of glaciers in the region. Due to the climatic and natural conditions of the region, traveling to and arranging logistics for monitoring and observing snow algae in the Antarctic continent becomes extremely challenging. To overcome these challenges, a novel algorithm has been developed and designed to automatically detect and analyze green algae (Chlorella sp.) from satellite images. Leveraging the vast and free available data from the Sentinel-2 satellite, the algorithm utilizes its high spectral resolution capabilities, capturing invaluable information from various spectral bands. The algorithm was formulated based on the image obtained on February 28, 2017, where green algae formations were intensively seen in the Ryder Bay. The algorithm was developed based on rule-based detection of algae, with the usage of reflection values from the areas where ground truth was established on this date. The developed algorithm was coded and tested using Python version 3.9. The accuracy analysis of the algorithm was conducted using overall accuracy (OA), F1 score, and Kappa statistical test. As a result of the analysis, the OA, F1 score, and Kappa statistic values were calculated as %91, %88.82-%95.27, and 0.901, respectively. © 2023 COSPAR
dc.identifier.DOI-ID10.1016/j.asr.2023.12.053
dc.identifier.issn02731177
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11462
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectAlgae
dc.subjectClimate change
dc.subjectImage processing
dc.subjectSatellites
dc.subjectSpectral resolution
dc.subjectAlgaebloom
dc.subjectAntarctica
dc.subjectF1 scores
dc.subjectFully automated
dc.subjectGreen alga
dc.subjectImages processing
dc.subjectOverall accuracies
dc.subjectRemote-sensing
dc.subjectSatellite images
dc.subjectSentinel-2
dc.subjectRemote sensing
dc.titleA new method for fully automated detection of algae blooms in Antarctica using Sentinel-2 satellite images
dc.typeArticle

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