Comparing unoccupied aerial systems (UAS) for monitoring harmful algal blooms in small inland waterbodies

Keywords: harmful algal blooms (HABs), remote sensing, phycocyanin, cyanobacteria, unmanned aerial vehicles, unoccupied aerial vehicles
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 02:00 PM (Eastern Time (US & Canada)) - 2/26/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 5

Abstract

Cyanobacterial harmful algal blooms (CyanoHABs) threaten surface water sources worldwide. Some species of cyanobacteria poison drinking water sources, aquatic organisms, domesticated animals, and humans, thus it is critical to understand and study their spatiotemporal dynamics. Traditionally, satellite imagery has been used for detection of CyanoHABs, specifically phytoplankton (i.e., chlorophyll-a) and cyanobacteria (i.e., phycocyanin), in large water bodies and coastal areas. However, coarse spatial resolutions limit the ability to monitor small inland systems, which are critical for drinking supply, agriculture, and recreation. This study investigates the use of unoccupied aerial systems (UAS) as a high-resolution alternative for the rapid assessment of cyanobacterial abundance in small inland systems. In this study, four sensors were investigated for their effectiveness in estimating cyanobacterial abundance: 1) RGB sensor on Phantom 4, 2) RGB sensor on Phantom 4 Professional, 3) MAPIR Survey3W modified multispectral sensor, and 4) Parrott Sequoia multispectral sensor. The performance of each sensor was determined by comparing 26 vegetation indices to chlorophyll-a and phycocyanin measurements from 54 ponds across Alabama. Vegetation indices incorporating the red and near-infrared wavelengths generated from Parrot Sequoia aerial images provided the best chlorophyll-a (i.e., NDVI, r^2 = 0.78, p <0.0001) and phycocyanin (i.e., EVI2, r^2 = 0.57, p <0.0001) estimates. The RGB sensors were moderately effective for estimating chlorophyll-a, whereas the MAPIR Survey3W generated poor estimates of both pigments due to differences in recorded wavelengths. Results indicate that the Parrot Sequoia sensor provides the best results for estimating phytoplankton abundance in small inland systems.