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


ACAR U., Yilmaz O. S., BALIK ŞANLI F., Ozcimen D.

Advances in Space Research, vol.73, no.6, pp.2955-2968, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 73 Issue: 6
  • Publication Date: 2024
  • Doi Number: 10.1016/j.asr.2023.12.053
  • Journal Name: Advances in Space Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Artic & Antarctic Regions, Communication Abstracts, Compendex, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.2955-2968
  • Keywords: AlgaeBlooms, Antarctica, Image Processing, Remote Sensing, Sentinel-2
  • Yıldız Technical University Affiliated: Yes

Abstract

The 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.