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

Details

• Journal Title:
  Harmful Algae
• Personal Author:
  Pokrzywinski, Kaytee ; Johansen, Richard ; Reif, Molly ; Bourne, Scott ; Hammond, Shea ; Fernando, Brianna
• NOAA Program & Office:
  NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)
• 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:
  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
• File Type:
  [PDF - 4.80 MB ]