Using UAVs to Monitor Algal Blooms

This study focused on using Unmanned Arial Vehicles (UAVs) to monitor algal blooms. This study was done based on data collated from Fernandez et. al (Fernandez-Figueroa et al., 2022). An orthomosaic was the final product of each drone flight. Orthomosaics are essentially a bunch of individual photos that are stitched together by similarities and create a full picture. Using the data provided, I created two orthomosaics and calculated the vegetation indices for both compared to actual water samples collected. The RGB imagery was collected using a Phantom 4 drone and the multispectral imagery was collected using a Parrot Sequoia sensor. The vegetation indices used were the Color Index of Vegetation Extraction (CIVE) and Normalized Difference Vegetation Index (NDVI) (Fernandez-Figueroa et al., 2022; Kislik et al., 2018). We found that the most accurate representation for chlorophyll a was using multispectral imagery and comparing it to the point the water sample was taken rather than the whole pond or a buffer around the point. The point provides a more accurate determination of chlorophyll a likely because scum within the ponds saturated the indices and skewed the results.

Presentation Preference: Poster

Primary Presenter: Caitlyn Walley, University of Tampa (cewalley1046@gmail.com)

Authors:

Caitlyn Walley, University of Tampa  (cewalley1046@gmail.com)

EDNA Fernandez-Figueroa, University of Tampa  (efernandezfigueroa@ut.edu)

Josie Hoffman, University of Tampa  (josiehoffman12@gmail.com)