Spatial and temporal scales of variability of cyanobacteria harmful algal blooms from NOAA GLERL airborne hyperspectral imagery

Abstract NOAA GLERL has routinely flown a hyperspectral imager to detect cyanobacteria harmful algal blooms (cyanoHABs) over the Great Lakes since 2015. Three consecutive years of hyperspectral imagery over the Great Lakes warn drinking water intake managers of the presence of cyanoHABs. Western basin imagery of Lake Erie contributes to a weekly report to the Ohio Environmental Protection Agency using the cyanobacteria index (CI) as an indicator of the presence of cyanoHABs. The CI is also used for the weekly NOAA NCCOS cyanoHAB Lake Erie bulletin applied to satellite data. To date, there has not been a sensor comparison to look at the variability between the satellite and hyperspectral imagery on a pixel-by-pixel basis, as well as a time scale comparison between measurements from buoys and shipboard surveys. The spatial scale is a measure of size of a cyanobacteria bloom on a scale of meters to kilometers. The change in the spatial scale or spatial variability has been quantified from satellite and airborne imagery using a decorrelation scale analysis to find the point at which the values are not changing or are not correlated with each other. The decorrelation scales were also applied to the buoy and shipboard survey data to look at temporal scales or changes in time on hourly to daytime scales for blue-green algae, chlorophyll and temperature. These scales are valuable for ecosystem modelers and for those initiating sampling efforts to optimize sampling plans and to infer a potential mechanism in an observational study from a synoptic viewpoint.

[1]  T. Kutser,et al.  A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes. , 2001, The Science of the total environment.

[2]  Richard P. Stumpf,et al.  Spatial and Temporal Patterns in the Seasonal Distribution of Toxic Cyanobacteria in Western Lake Erie from 2002–2014 , 2015, Toxins.

[3]  Richard P. Stumpf,et al.  Interannual Variability of Cyanobacterial Blooms in Lake Erie , 2012, PloS one.

[4]  David J. Schwab,et al.  Evolution of a cyanobacterial bloom forecast system in western Lake Erie: Development and initial evaluation , 2013 .

[5]  R. P. Stumpf,et al.  Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes , 2008 .

[6]  Alexander A Gilerson,et al.  Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. , 2010, Optics express.

[7]  J. Bruce,et al.  GREAT LAKES WATER QUALITY AGREEMENT , 1978 .

[8]  R. Kudela,et al.  Nearshore retention of upwelled waters north and south of Point Reyes (northern California)- : Patterns of surface temperature and chlorophyll observed in CoOP WEST , 2006 .

[9]  K. Denman,et al.  Time scales of pattern evolution from cross‐spectrum analysis of advanced very high resolution radiometer and coastal zone color scanner imagery , 1994 .

[10]  Bo Yang,et al.  Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations , 2017, Remote. Sens..

[11]  Anna M. Michalak,et al.  Challenges in tracking harmful algal blooms: A synthesis of evidence from Lake Erie , 2015 .

[12]  Ricardo M. Letelier,et al.  Decorrelation scales of chlorophyll as observed from bio-optical drifters in the California Current , 1998 .

[13]  Wayne W. Carmichael,et al.  Zebra mussel (Dreissena polymorpha) selective filtration promoted toxic Microcystis blooms in Saginaw Bay (Lake Huron) and Lake Erie , 2001 .

[14]  Raphael M. Kudela,et al.  Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters , 2015 .

[15]  T. Wynne,et al.  Characterizing a cyanobacterial bloom in Western Lake Erie using satellite imagery and meteorological data , 2010 .

[16]  J. Conroy,et al.  From River to Lake: Phosphorus partitioning and algal community compositional changes in Western Lake Erie , 2012 .

[17]  T. Smayda,et al.  Harmful algal blooms: Their ecophysiology and general relevance to phytoplankton blooms in the sea , 1997 .

[18]  Nathan S. Bosch,et al.  Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions , 2013, Proceedings of the National Academy of Sciences.

[19]  M. Rowe,et al.  Vertical distribution of buoyant Microcystis blooms in a Lagrangian particle tracking model for short‐term forecasts in Lake Erie , 2016 .

[20]  S. Watson,et al.  Effects of increasing nitrogen and phosphorus concentrations on phytoplankton community growth and toxicity during Planktothrix blooms in Sandusky Bay, Lake Erie. , 2015, Environmental science & technology.

[21]  W. Carmichael,et al.  Isolation and Characterization of Microcystins, Cyclic Heptapeptide Hepatotoxins from a Lake Erie Strain of Microcystis aeruginosa , 2000 .

[22]  R. Stumpf,et al.  Satellite observations of Microcystis blooms in western Lake Erie , 2001 .

[23]  H. Paerl,et al.  Climate change: a catalyst for global expansion of harmful cyanobacterial blooms. , 2009, Environmental microbiology reports.

[24]  C. Stow,et al.  Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts , 2014 .

[25]  C. Gobler,et al.  The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms , 2009 .

[26]  W. L. Chadderton,et al.  Predicting spread of aquatic invasive species by lake currents , 2017 .