Spatio-temporal analysis of harmful algal blooms in tropical crater-lake from MODIS data (2003-2020)




MODIS, Machine Learning algorithms, Harmful algal bloom, Turquoise lake


The crater lake of Santa María del Oro in Nayarit, presents Algal Blooms (AB) in a cyclical annual manner, the blooming and subsequent decline of these populations creates color changes in the water, generally in the first half of the year. This work evaluated supervised classification algorithms that allow these changes to be identified using data from the MOD09GQ and MYD09GQ products of MODIS sensor in the period from January 2003 to December 2020. Based on a review of AB recorded in the literature and statistical analysis of dispersion graphs, a database of spectral information and lake color state labels were built to evaluate the different classification algorithms. The best classifier was Random Forest with an accuracy of 87.1%. The temporal analysis and spatial evaluation of the blooms incidence showed that may, april and march are the months with the greatest presence of color changes related to AB in the lake. The spatial analysis found that the highest incidence of blooms occurs in the southeast region of the lake and the largest amounts of events occurred in the years 2011, 2008 and 2012 respectively. The influence of the El Niño-Southern Oscillation (ENSO) phenomenon on the incidence of algal blooms in the crater lake is determined due to the temporal pattern between the anomalies in the AB and the Multivariate ENSO Index, where the greater number of AF events occurred in the cold phases of the ENSO.


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