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Satellite Remote Sensing and Crowd Sourcing to Monitor and Predict Cyanobacteria Blooms
Schaeffer, B., R. Lunetta, AND R. Stumpf. Satellite Remote Sensing and Crowd Sourcing to Monitor and Predict Cyanobacteria Blooms. 9th National Monitoring Conference, Cincinnati, OH, April 28 - May 02, 2014.
Cyanobacterial blooms occur worldwide and are associated with human respiratory irritation, undesirable taste and odor of potable water, increased drinking water treatment costs, loss of revenue from recreational use, and human illness as a result of ingestion or skin exposure during recreational activities.
Cyanobacterial blooms occur worldwide and are associated with human respiratory irritation, undesirable taste and odor of potable water, increased drinking water treatment costs, loss of revenue from recreational use, and human illness as a result of ingestion or skin exposure during recreational activities. Satellite technology allows for the development of harmful algal bloom indicators at the local scale with national coverage. Cyanobacteria can successfully be monitored using remote sensing technologies based on algorithms to retrieve chlorophyll-a and phycocyanin. In this project, cyanobacteria cell count data from Ohio, Florida, New Hampshire, Vermont, Rhode Island, Connecticut, and Massachusetts were derived with data from the Medium Resolution Imaging Spectrometer (MERIS). MERIS data on the European Space Agency’s Envisat-1 satellite were used in a case study with 300 m data available in the region from 2009 to 2012. The goal of this project was to develop a stakeholder tool with the capability to monitor cyanobacteria blooms near real-time, and potentially provide predictive capability. Crowd sourcing was a unique opportunity to pool the problem solving skills of >500,000 people worldwide to develop these capabilities. The predictive algorithm was targeted to forecast the status of cyanobacteria bloom events in 7, 14, and 28 day intervals. First, the model identified lakes likely to attain cyanobacteria cell counts greater than 10,000 cells/mL during the forecast period. Next, the model focused on freshwater systems identified by satellite observations to have low cell counts (10,000 - 109,999 cells/mL) and predicted the potential for future development to either Medium (109,000 - 299,999), High (300,000 - 1,000,000), or Very High (> 1,000,000) cell count ranges. The combined use of satellite technology with crowd sourcing provided a sophisticated stakeholder tool that may allow for more holistic management to reduce exposure risk to the public.
Record Details:Record Type: DOCUMENT (PRESENTATION/SLIDE)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY