Abstract
Abstract. Over the last few decades in coastal areas, the occurrence of Harmful Algal Blooms (HAB) has increased. The phenomenon is harmful to the health of coastal residents as well as marine organisms and can cause damage to the economy of the region. In this article, considering the need of a method for detecting red tide phenomenon using high spatial resolution satellite images, we tried to test the capability of spectral features, which can be generated using Sentinel-2 satellite images, in detecting red tide phenomenon. For this purpose, we generated an algorithm for detecting spectral features, which the red tide phenomenon causes a noticeable change in their value compared with the non-blooming condition. The ability of the selected spectral features in detecting HABs has been evaluated using statistical methods such as type I and II error, overall accuracy, kappa coefficient, and ROC curves. The best case, for the spectral feature, is (R4−R8A)/(R4+R8A), 5% for type I and 6% for type II error were achieved where R4 stands for reflectance in band 4 and R8A is the reflectance in band 8A of a Sentinel-2 satellite image.
Highlights
According to (Ricardson, 1997), the definition of an algal bloom is “the rapid growth of one or more species which leads to an increase in biomass of the species”
We have examined the ability of some spectral features, generated using band 2, 3, 4, 8 and 8A of Sentinel-2 satellite images, in detecting of Harmful Algal Bloom (HAB) using statistical methods
The methods were applied to three Sentinel-2/MSI satellite images that were taken from the study area on July 14, 2017, July 28, 2017 and August 8, 2017
Summary
According to (Ricardson, 1997), the definition of an algal bloom is “the rapid growth of one or more species which leads to an increase in biomass of the species”. (Kurekin et al, 2014) used an almost similar way to detect the red tide phenomenon occurred in west waters of Netherlands. There are different ways to detect and monitor HABs such as applying anomaly detecting algorithms to satellite images and products or visual analysis of ocean color data. He used field data along with visual analysis of MODIS ocean color data to classify different types of the area in bloom, no-bloom or harmful bloom categories.
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