NOVEL & RAPID PROCESSING OF UAS IMAGERY FOR TARGETED CYANOBACTERIAL HARMFUL ALGAL BLOOM SAMPLING​

William Reckling , Jay Levine, Joel Sánchez-Gallego, Hwa Huang, Troy Walton, Haley Plaas , Kimberly Popendorf, Ryan Paerl 

Unmanned aerial systems (UAS) are an efficient way to monitor and sample algal biomass including cyanobacterial harmful algal blooms (CyanoHABs). However, conventional methods to create a UAS orthophoto of homogeneous water surfaces often produce a patchy, smoothed, or spatially inaccurate output. In this study, we developed a novel method to interpolate a spectral index from a central pixel in individual aerial photos to rapidly map the distribution of algal biomass. The results of our interpolation method align well with orthomosaic-derived indices and UAS-collected water samples analyzed in the laboratory, affirming the reliability of our approach. This alignment further strengthens our ability to monitor, detect, and alert the public of algal growth via rapid system-wide assessment as well as targeted sampling of low and high-biomass regions. Last, we demonstrate how simple UAS sampling associated with imagery analyses enables genetic- and microscopy-based assessment of collected algal biomass as well as cyanotoxin concentrations.