Monitoring cyanobacterial harmful algal blooms by unmanned aerial vehicles in aquatic ecosystems

Abstract

The spatial distribution of harmful algal blooms may change rapidly due to variations in wind, water current, and population dynamics. Traditional sampling methods may not be enough for an effective local risk assessment. Unmanned aerial vehicles can gather data at appropriate spatial and temporal resolutions, with full control over data collection timing and rapid result availability. We show that data can be correlated with chlorophyll (r² = 0.79) and cyanobacteria (r² = 0.77) concentration at the water surface, leading to spatial distribution maps capable of spotting cyanobacteria patches in water bodies precisely. In addition, climatic change may benefit various species of harmful cyanobacteria by increasing their geographic distributions and modifying population dynamics. Thus, new methods with a high spatial and temporal resolution for monitoring blooms come handy under changing climate scenarios. The method presented here provides valuable information that is complementary to risk assessment data derived from other monitoring methods available.