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Toxins 2015, 7(4), 1065-1078; doi:10.3390/toxins7041065

Harmful Algal Bloom Characterization at Ultra-High Spatial and Temporal Resolution Using Small Unmanned Aircraft Systems

1,†,* and 2,†,‡
1
Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University, 1800 Denison Ave, Manhattan, KS 66506, USA
2
Department of Agronomy, College of Agriculture, Kansas State University, 2004 Throckmorton Plant Science Center, Manhattan, KS 66506, USA
These authors contributed equally to this work.
Current Address: EVP Research and Technology Development, AgPixel LLC, 5530 West Parkway, Suite 300, Johnston, IA 50131, USA.
*
Author to whom correspondence should be addressed.
Received: 1 December 2014 / Revised: 17 February 2015 / Accepted: 18 March 2015 / Published: 27 March 2015
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Abstract

Harmful algal blooms (HABs) degrade water quality and produce toxins. The spatial distribution of HAbs may change rapidly due to variations wind, water currents, and population dynamics. Risk assessments, based on traditional sampling methods, are hampered by the sparseness of water sample data points, and delays between sampling and the availability of results. There is a need for local risk assessment and risk management at the spatial and temporal resolution relevant to local human and animal interactions at specific sites and times. Small, unmanned aircraft systems can gather color-infrared reflectance data at appropriate spatial and temporal resolutions, with full control over data collection timing, and short intervals between data gathering and result availability. Data can be interpreted qualitatively, or by generating a blue normalized difference vegetation index (BNDVI) that is correlated with cyanobacterial biomass densities at the water surface, as estimated using a buoyant packed cell volume (BPCV). Correlations between BNDVI and BPCV follow a logarithmic model, with r2-values under field conditions from 0.77 to 0.87. These methods provide valuable information that is complimentary to risk assessment data derived from traditional risk assessment methods, and could help to improve risk management at the local level. View Full-Text
Keywords: HAB; sUAS; cyanotoxins; cyanobacteria; blue-green algae; remote sensing HAB; sUAS; cyanotoxins; cyanobacteria; blue-green algae; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Van der Merwe, D.; Price, K.P. Harmful Algal Bloom Characterization at Ultra-High Spatial and Temporal Resolution Using Small Unmanned Aircraft Systems. Toxins 2015, 7, 1065-1078.

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