Early Detection of Red Tides in the Strait of Hormuz Using Satellite Data

Red tide can adversely affect coastal activities, marine aquaculture, and economy of the affected waters. The Strait of Hormuz at the northeast part of the Persian Gulf is highly prone to the red tide because of receiving considerable nutrients either from its coastal regions or from pollutant currents from Oman Sea. Early detection of the algae bloom (AB) is of high importance in this strategic region. This study aims at the investigation of spatio-temporal variations of the red tide as well as the sea surface temperature (SST) and salinity (SSS) as the two key driving forces. Three main events were studied including the longest AB of the area between September 2008 to May 2009 and the two events on January 2012 and December 2014, each lasting for more than one month. Several algorithms using MODIS satellite data were assessed to extract the chlorophyll-a (Chl-a) concentration, SST and SSS maps before, during and after the red tide formation. Moreover, three algorithms were applied to detect the red tide including Red Band Difference (RBD), Chlorophyll Anomaly (CA) , Back Scattering (BS), and Floating Algal Index (FAI), which RBD algorithm outperformed (Having a Probability of Detection=0.67 and False Alarm Ratio=0) other techniques when compared to the in situ data. Direct correlation analysis between SST and SSS with Chl-a concentration did not lead to a meaningful relation either at the onset or during the red tide events. However, temporal analysis of the maps indicates that the formation and development of the bloom occur within the specific ranges of temperature (19-26 ℃) and salinity (36-39 psu), which are well consistent with the results of experimentally determined thresholds for the peak density population of dominant cyanobacteria. Classification and regression tree analysis (CART) was also implemented to detect the red tide formation and its warning state (Chl-a>5 mg/m³). Results revealed that the model can recognize non-red tide and red tide affected areas as well as the Chl-a hotspots with the probability of 92% , 89%, and 98%, respectively. Findings of this study can provide promising insights to develop a satellite-based early alarm system for this region, which can prohibit the expansion of the algae bloom and its negative and costly consequences.