Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing

Sayers, Michael J.; Fahnenstiel, Gary L.; Shuchman, Robert A.; Whitley, M. A.

doi:10.1080/01431161.2016.1207265

Cited by 59 publications

(25 citation statements)

References 54 publications

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“…Remote sensing provides information on broad spatial and temporal scales unattainable by other means, and is an attractive tool to monitor short-and long-term change in lakes (e.g., Binding et al, 2007;Hu et al, 2010;Mouw et al, 2015;Sayers et al, 2016). The quality of remote sensing products is critical to interpreting ecological trends and linkages to underlying drivers.…”

Section: Introductionmentioning

confidence: 99%

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

et al. 2017

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There is a growing use of remote sensing observations for detecting and quantifying freshwater cyanobacteria populations, yet the inherent optical properties of these communities in natural settings, fundamental to bio-optical algorithms, are not well known. Toward bridging this knowledge gap, we measured a full complement of optical properties in western Lake Erie during cyanobacteria blooms in the summers of 2013 and 2014. Our measurements focus attention on the optical uniqueness of cyanobacteria blooms, which have consequences for remote sensing and bio-optical modeling. We found the cyanobacteria blooms in the western basin during our field work were dominated by Microcystis, while the waters in the adjacent central basin were dominated by Planktothrix. Chlorophyll concentrations ranged from 1 to over 135 µg/L across the study area with the highest concentrations associated with Microcystis in the western basin. We observed large, amorphous colonial Microcystis structures in the bloom area characterized by high phytoplankton absorption and high scattering coefficients with a mean particle backscatter ratio at 443 nm > 0.03, which is higher than other plankton types and more comparable to suspended inorganic sediments. While our samples contained mixtures of both, our analysis suggests high contributions to the measured scatter and backscatter coefficients from cyanobacteria. Our measurements provide new insights into the optical properties of cyanobacteria blooms, and indicate that current semi-analytic models are likely to have problems resolving a closed solution in these types of waters as many of our observations are beyond the range of existing model components. We believe that different algorithm or model approaches are needed for these conditions, specifically for phytoplankton absorption and particle backscatter components. From a remote sensing perspective, this presents a challenge not only in terms of a need for new algorithms, but also for determining when to apply the best Moore et al. Lake Erie IOPs algorithm for a given situation. These results are new in the sense that they represent a complete description of the optical properties of freshwater cyanobacteria blooms, and are likely to be representative of bloom conditions for other systems containing Microcystis cells and colonies.

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Section: Introductionmentioning

confidence: 99%

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

et al. 2017

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“…Notable inland water quality applications of remote sensing include large-scale quality and clarity surveys [1][2][3][4] and real-time tracking and forecasting of nuisance algal blooms (NABs) or harmful algal blooms (HABs) [5,6]. The general process of developing an empirical remote sensing model for algal blooms typically involves: downloading and processing of remote sensing imagery (which may include atmospheric correction and conversion from digital numbers to reflectance at the near-surface of the water body), collecting coincident (or near-coincident) field measurements of chlorophyll-a (or other parameters related to biomass or levels of toxins), and using regression or other statistical modeling techniques to develop a relationship between the field-measured concentrations and remotely sensed reflectance from the corresponding pixel or group of pixels.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models

et al. 2017

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Abstract:This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1) selecting sensors which are suitable for the spatial and temporal variability in the water body; (2) determining appropriate uses of near-coincident data in empirical model calibration; and (3) recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake) through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships between the surface and water column conditions illustrate potential issues with near-surface remote sensing, particularly when there are events that cause mixing in the water column.

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“…Cultures were stored for a maximum of 2 wk, and if they were not used in that time, they were discarded. Cyanobacterial cultures were used at a density of 100 to 200 µg/L chlorophyll a , which is within the reported range for dense cyanobacterial blooms worldwide (Yuan et al ; Sayers et al ; Duan et al ). Microcystis aeruginosa (UTEX) was purchased through the Culture Collection of Algae at The University of Texas at Austin, M. aeruginosa (GLERL) was provided by the Great Lakes Environmental Research Lab, and Anabaena flos‐aquae , Aphanizomenon flos‐aquae , Dolichospermum lemmermannii , Gloeotrichia echinulata , M. aeruginosa (BQ11‐02, ZUR‐HINDAK, and LSC‐13‐02), M. wesenbergii , and Planktothrix suspensa were provided by Environment and Climate Change Canada.…”

Section: Methodsmentioning

confidence: 92%

Cyanobacteria reduce motility of quagga mussel (Dreissena rostriformis bugensis) sperm

Boegehold

Alame

Johnson

et al. 2019

Enviro Toxic and Chemistry

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The temporal expansion of harmful algal blooms, primarily associated with cyanobacteria, may impact aquatic organisms at vulnerable life-history stages. Broadcast spawning species release gametes into the water column for external fertilization, directly exposing sperm to potential aquatic stressors. To determine if cyanobacteria can disrupt reproduction in freshwater broadcast spawners, we evaluated sublethal effects of cyanobacteria exposure on quagga mussel (Dreissena rostriformis bugensis) sperm. In laboratory studies, sperm were collected after inducing mussels to spawn using serotonin and exposed to 11 cultures of cyanobacteria including Anabaena flos-aquae, Aphanizomenon flos-aquae, Dolichospermum lemmermannii, Gloeotrichia echinulata, 5 cultures of Microcystis aeruginosa, M. wesenbergii, and Planktothrix suspensa. Sperm motility, using endpoints of cumulative distance traveled and mean velocity, was calculated for a minimum of 10 individual sperm using a novel optical biotracking assay method. The distance and velocity at which sperm traveled decreased when exposed to Aphanizomenon flos-aquae and 2 M. aeruginosa cultures. Our findings indicate that cyanobacteria impede the motility of quagga mussel sperm, which can potentially result in reproductive impairments to mussels and potentially other broadcast spawning species. Environ Toxicol Chem 2019;38:368-374. C 2018 SETAC

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Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing

Cited by 59 publications

References 54 publications

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models

Cyanobacteria reduce motility of quagga mussel (Dreissena rostriformis bugensis) sperm

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