QUALITATIVE AND QUANTITATIVE REMOTE SENSING TECHNIQUES TO IDENTIFY MICROALGAE AND THEIR MAIN CAUSES OF GROWTH IN THE SOUTH SHETLAND ISLANDS
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Keywords

Remote sensing
NDVI
South Shetland Islands
Microalgae
Albedo
Algal biomass
Sentinel-2

How to Cite

QUALITATIVE AND QUANTITATIVE REMOTE SENSING TECHNIQUES TO IDENTIFY MICROALGAE AND THEIR MAIN CAUSES OF GROWTH IN THE SOUTH SHETLAND ISLANDS. (2025). Brotes Científicos: Revista De Investigaciones Escolares, 7, 14-24. https://doi.org/10.35588/bc.7.%x

Abstract

In this study, we present vegetation estimates for the South Shetland Islands based on remote sensing. Using the combination of bands provided by the Sentinel-2 satellite, we calculated a Normalised Difference Vegetation Index (NDVI), which is a numerical indicator strongly associated with vegetation content. Higher NDVI values correspond to areas that reflect more in the near-infrared spectrum, as greater reflectance in this band indicates higher vegetation cover.

Algal biomass on snow results in a reduction in albedo compared to clean snow. Visible green patches reduce snow albedo by approximately 40 %. In comparison, red patches reduce it by 20 %, since algal pigments not only act as protective compounds but also play a functional role in snow and ice melt (Khan et al., 2021).

Based on these remote sensing results, we aim to identify areas with higher NDVI values to analyse the main drivers of microalgae growth and bloom.

PDF (Spanish)

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