Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Pokrzywinski, Kaytee; Johansen, Richard; Reif, Molly; Bourne, Scott; Hammond, Shea; Fernando, Brianna

doi:10.1016/j.hal.2022.102268

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Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

2022
By Pokrzywinski, Kaytee ; Johansen, Richard ; Reif, Molly ; ...
Source: Harmful Algae, 117, 102268

[PDF-4.80 MB]

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Details:

Journal Title:

Harmful Algae
Personal Author:

Pokrzywinski, Kaytee ; Johansen, Richard ; Reif, Molly ; Bourne, Scott ; Hammond, Shea ; ... More +

Pokrzywinski, Kaytee ; Johansen, Richard ; Reif, Molly ; Bourne, Scott ; Hammond, Shea ; Fernando, Brianna Less -
NOAA Program & Office:

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) Less -
Description:

Remote sensing technologies offer a consistent, spatiotemporal approach to assess water quality, which includes the detection, monitoring, and forecasting of cyanobacteria harmful algal blooms. In this study, a series of ex-situ mesoscale experiments were conducted to first develop and then monitor a Microcystis sp. bloom using a hyperspectral sensor mounted on an unmanned aircraft system (UAS) along with coincident ground sampling efforts including laboratory analyses and in-situ field probes. This approach allowed for the simultaneous evaluation of both bloom physiology (algal growth stages/life cycle) and data collection method on the performance of a suite of 41 spectrally-derived water quality algorithms across three water quality indicators (chlorophyll a, phycocyanin and turbidity) in a controlled environment. Results indicated a strong agreement between Lab and Field-based methods for all water quality indicators independent of growth phase, with regression R2-values above 0.73 for mean absolute percentage error (MAPE) and 0.87 for algorithm R2 values. Three of the 41 algorithms evaluated met predetermined performance criteria (MAPE and algorithm R2 values); however, in general, algal growth phase had a substantial impact on algorithm performance, especially those with blue and violet wave bands. This study highlights the importance of co-validating sensor technologies with appropriate ground monitoring methods to gain foundational knowledge before deploying new technologies in large-scale field efforts.
Keywords:

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Aquatic Science Plant Science
Source:

Harmful Algae, 117, 102268
DOI:

https://doi.org/10.1016/j.hal.2022.102268
ISSN:

1568-9883
Format:

PDF
Publisher:

Elsevier BV
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:d65bc74fa70a143904eaa740312046930ad3405a365bc3e93c3fe49e0c623e7d
Download URL:

https://repository.library.noaa.gov/view/noaa/49205/noaa_49205_DS1.pdf
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