Evaluation of cyanobacteria cell count detection derived from MERIS imagery across the eastern USA

Abstract Inland waters across the United States (US) are at potential risk for increased outbreaks of toxic cyanobacteria blooms events resulting from elevated water temperatures and extreme hydrologic events attributable to climate change and increased nutrient loadings associated with intensive agricultural practices. Current monitoring efforts are limited in scope due to resource limitations, analytical complexity, and data integration efforts. The goals of this study were to validate an algorithm for satellite imagery that could potentially be used to monitor surface cyanobacteria events in near real-time to provide a compressive monitoring capability for freshwater lakes (> 100 ha). The algorithm incorporated narrow spectral bands specific to the European Space Agency's (ESA's) MEdium Resolution Imaging Spectrometer (MERIS) instrument that were optimally oriented at phytoplankton pigment absorption features including phycocyanin at 620 nm. A validation of derived cyanobacteria cell counts was performed using available in situ data assembled from existing monitoring programs across eight states in the eastern US over a 39-month period (2009–2012). Results indicated that MERIS provided robust estimates for low (10,000–109,000 cells/mL) and very high (> 1,000,000 cells/mL) cell enumeration ranges (approximately 90% and 83%, respectively). However, the results for two intermediate ranges (110,000–299,000 and 300,000–1,000,000 cells/mL) were substandard, at approximately 28% and 40%, respectively. The confusion associated with intermediate cyanobacteria cell count ranges was largely attributed to the lack of available taxonomic data and distinction of natural counting units for the in situ measurements that would have facilitated conversions between cell counts and cell volumes. The results of this study document the potential for using MERIS-derived cyanobacteria cell count estimates to monitor freshwater lakes (> 100 ha) across the eastern US.

[1]  L. Backer,et al.  Cyanobacterial Harmful Algal Blooms (CyanoHABs): Developing a Public Health Response , 2002 .

[2]  Morton Lippmann,et al.  Exposure science in the 21st century: a vision and a strategy , 2013, Journal of Exposure Science and Environmental Epidemiology.

[3]  Anu Reinart,et al.  Detecting cyanobacterial blooms in large North European lakes using the Maximum Chlorophyll Index , 2010 .

[4]  W. Dodds,et al.  Eutrophication of U.S. freshwaters: analysis of potential economic damages. , 2009, Environmental science & technology.

[5]  Ronghua Ma,et al.  Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China , 2012 .

[6]  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.

[7]  H. Paerl,et al.  Blooms Like It Hot , 2008, Science.

[8]  Chuanmin Hu.  A novel ocean color index to detect floating algae in the global oceans , 2009 .

[9]  H. Hudnell,et al.  The state of U.S. freshwater harmful algal blooms assessments, policy and legislation. , 2010, Toxicon : official journal of the International Society on Toxinology.

[10]  Antonio Ruiz-Verdú,et al.  An evaluation of algorithms for the remote sensing of cyanobacterial biomass , 2008 .

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

[12]  R. Bukata,et al.  An assessment of MERIS algal products during an intense bloom in Lake of the Woods , 2011 .

[13]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[14]  Ragnar Elmgren,et al.  Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability , 2007 .

[15]  J. Burkholder,et al.  Occurrence of Cyanobacterial Harmful Algal Blooms Workgroup report. , 2008, Advances in experimental medicine and biology.

[16]  H. Paerl,et al.  Nutrient and other environmental controls of harmful cyanobacterial blooms along the freshwater-marine continuum. , 2008, Advances in experimental medicine and biology.

[17]  John S. Iiames,et al.  An ASSESSMENT OF GROUND TRUTH VARIABILITY USING a AQUOT;VIRTUAL FIELD REFERENCE DATABASEQ , 2005 .

[18]  D. Mishra,et al.  Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data , 2011 .

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

[20]  Kevin Winter,et al.  Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS , 2010 .

[21]  Tiit Kutser,et al.  Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters , 2009 .

[22]  J. Bartram,et al.  Health risks caused by freshwater cyanobacteria in recreational waters. , 2000, Journal of toxicology and environmental health. Part B, Critical reviews.

[23]  José Antonio Domínguez Gómez,et al.  Remote sensing as a tool for monitoring water quality parameters for Mediterranean Lakes of European Union water framework directive (WFD) and as a system of surveillance of cyanobacterial harmful algae blooms (SCyanoHABs) , 2011, Environmental monitoring and assessment.

[24]  H. Paerl,et al.  Climate change: links to global expansion of harmful cyanobacteria. , 2012, Water research.

[25]  J. Graham,et al.  Environmental factors influencing microcystin distribution and concentration in the Midwestern United States. , 2004, Water research.

[26]  L. Schuster,et al.  Monitoring and Evaluation of Cyanobacteria in Lake Champlain , 2005 .

[27]  Stefan G. H. Simis,et al.  Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water , 2005 .

[28]  R. Vincent,et al.  Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie , 2004 .

[29]  Melanie A. Riedinger-Whitmore,et al.  Cyanobacterial Proliferation is a Recent Response to Eutrophication in Many Florida Lakes: A Paleolimnological Assessment , 2005 .

[30]  C. Donlon,et al.  The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission , 2012 .

[31]  Judith Wells Budd,et al.  Remote sensing of biotic effects: Zebra mussels (Dreissena polymorpha) influence on water clarity in Saginaw Bay, Lake Huron , 2001 .

[32]  M. Loessner,et al.  Rhamnose-Inducible Gene Expression in Listeria monocytogenes , 2012, PloS one.

[33]  Mohamed Sultan,et al.  Mapping Cyanobacterial Blooms in the Great Lakes Using MODIS , 2009 .

[34]  R. Stumpf,et al.  Adjustment of ocean color sensor calibration through multi-band statistics. , 2010, Optics express.

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

[36]  Peter D. Hunter,et al.  Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes , 2010 .

[37]  R. Lunetta,et al.  Barriers to adopting satellite remote sensing for water quality management , 2013 .

[38]  Patricia A. Soranno,et al.  Factors affecting the timing of surface scums and epilimnetic blooms of blue-green algae in a eutrophic lake , 1997 .

[39]  David P. Hamilton,et al.  New Zealand Guidelines for cyanobacteria in recreational fresh waters: Interim Guidelines , 2009 .

[40]  Ian Stewart,et al.  Recreational and occupational field exposure to freshwater cyanobacteria – a review of anecdotal and case reports, epidemiological studies and the challenges for epidemiologic assessment , 2006, Environmental health : a global access science source.

[41]  J. Gower,et al.  Global monitoring of plankton blooms using MERIS MCI , 2008 .

[42]  Jukka Seppälä,et al.  Ship-of-opportunity based phycocyanin fluorescence monitoring of the filamentous cyanobacteria bloom dynamics in the Baltic Sea , 2007 .

[43]  Yang Shao,et al.  Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[44]  Jamie Bartram,et al.  Toxic Cyanobacteria in Water: a Guide to Their Public Health Consequences, Monitoring and Management Chapter 2. Cyanobacteria in the Environment 2.1 Nature and Diversity 2.1.1 Systematics , 2022 .

[45]  William Philpot,et al.  The derivative ratio algorithm: avoiding atmospheric effects in remote sensing , 1991, IEEE Trans. Geosci. Remote. Sens..

[46]  M. Matthews,et al.  A new algorithm for detecting trophic status ( chlorophyll-a ) , cyanobacterial-dominanance , surface scums and floating vegetation in coastal and inland waters from MERIS , 2012 .

[47]  T. Smayda,et al.  What is a bloom? A commentary , 1997 .

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