An adaptive approach to detect high-biomass algal blooms from EO chlorophyll-a data in support of harmful algal bloom monitoring

Abstract

High-biomass harmful algal blooms can kill farmed fish through toxicity, physical effects or de-oxygenation of the water column. These blooms often form over spatially large areas meaning that Earth observation is well placed to monitor and study them. In this letter, we present a statistical-based background subtraction technique that has been modified to detect high-biomass algal blooms. The method builds upon previous work and uses a statistical framework to combine spatial and temporal information to produce maps of bloom extent. Its statistical nature allows the approach to characterize the region of interest meaning that region-specific tuning is not needed. The accuracy of the approach has been evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) data and an in situ cell concentration dataset, resulting in a correct classification rate of 68.0% with a false alarm rate of 0.24 (n = 25). The method is then used to study the surface coverage of a large high-biomass harmful algal bloom of Karenia mikimotoi. The approach shows promise for the early warning of spatially large high-biomass algal blooms, providing valuable information to support in situ sampling campaigns.

Acknowledgements

All MODIS data were kindly provided by the UK Natural Environment Research Council (NERC), Earth Observation Data Acquisition and Analysis Service (NEODAAS). All SeaWiFS data were kindly received and processed by the NEODAAS through the NASA SeaWiFS project (code 970.2). This work was carried out within (i) the Crown Estate funded project ‘Predicting the progression of the harmful dinoflagellate K. mikimotoi along the Scottish coast and the potential impact for fish farming’ to provide water quality bulletins to aquaculture; (ii) the UK Environment Agency pilot project Algal bloom monitoring in support of Bathing Water quality (AlgaRisk) which was initiated by the UK National Centre for Ocean Forecasting (NCOF); and (iii) the Plymouth Marine Laboratory and the Scottish Association for Marine Science' contribution to the UK strategic marine research programme, Oceans 2025.