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Cyanobacteria Assessment Network (CyAN)

An EPA, NASA, NOAA, and USGS Project

Cyanobacteria Assessment Network (CyAN), graphic identifier

CyAN is a multi-agency project among the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), U.S. Geological Survey (USGS), and EPA to develop an early warning indicator system using historical and current satellite data to detect algal blooms in U.S. freshwater systems. This research supports federal, state, and local partners in their monitoring efforts to assess water quality to protect aquatic and human health.

 
  • Goals
    • Develop a uniform and systematic approach for identifying cyanobacteria blooms using ocean satellites across the contiguous United States.
    • Create a strategy for evaluation and refinement of algorithms across satellite platforms.
    • Identify landscape linkage postulated causes of chlorophyll a and cyanobacteria blooms in freshwater systems.
    • Characterize exposure and human health effects using ocean color satellites in drinking water sources and recreational waters.
    • Characterize behavior responses and economic value of the early warning system using ocean satellites and mobile dissemination platform.
    • Disseminate satellite data through an Android mobile application and EnviroAtlas.
  • Mission Statement and Objectives

    Mission Statement:

    Support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying algal blooms and related water quality using satellite data records.

    Objectives:

    • Create a standard and uniform approach for early identification of algal blooms that is useful and accessible to stakeholders of freshwater systems using the new set of satellites: Ocean Land Colour Instrument (OLCI) on Sentinel-3, Sentinel-2, Landsat and future NASA missions.
    • Develop an information dissemination system for expedient public health advisory postings.
    • Better understand the connections between health, economic, and environmental conditions to cyanobacteria and phytoplankton blooms.
  • Project Components and Fiscal Year 2017 Updates

    Validation

    In situ validation data will primarily come from our federal and state collaborators. Sources of data will include, but are not limited to, federal, state, and local government agencies, universities, private research groups, and published peer-reviewed journals. Minimum data reporting requirements include sample station identification, cyanobacteria counts, abundance, or phycocyanin pigment concentration, latitude, longitude, depth, and date. Additional information that are not required but are considered beneficial include chlorophyll a concentration (especially), temperature, secchi depth, turbidity, and other available water quality measures. Data sets will undergo quality review by confirming that all methods used were documented and widely accepted.

    Fiscal Year 2017 Update

    As seen in Figure 1, the CyAN Field Integrated Exploratory Lakes Database (CyAN FIELD) functionality has been expanded to include integrated tools for interactive quality control and basic exploratory analysis to evaluate data trends. Automated quality control queries have been added to evaluate internal consistency of sample location information, and that interrelated chemical, biological, and physical data are internally consistent.

    Figure 1Figure 1. CONUS streetmap with site data from CyAN Field Integrated Exploratory Lakes Database.

    Satellite Algorithms

    A strategy for evaluation of algorithm updates has been established in large part through the open source availability of the NASA ocean color processing software (l2gen) and the SeaWiFS Data Analysis System (SeaDAS). This project will perform a complementary effort by using existing products for MERIS (Medium Resolution Imaging Spectrometer) and OLCI that have shown management value to establish algorithm development and data processing infrastructure. We propose to adopt second derivative spectral shape algorithms, which have been shown to be robust in the presence of poor atmospheric correction. For MERIS data, the bands at 620, 665, 681, 709, and 754 nanometers are used. The Cyanobacteria Index algorithm estimates cyanobacteria concentrations and the algorithm has been successfully transferred to MODIS (Moderate Resolution Imaging Spectroradiometer).

    Fiscal Year 2017 Update

    Standardized algorithm intercomparison metrics have been developed and were presented and incorporated into recommendations at the biannual International Ocean Colour Science team meeting. Toxin distributions are a concern to managers, however remote sensing cannot detect toxins. A strategy for estimating toxin levels with satellite data has been identified. In addition, the relative sensitivity of phycocyanin against chlorophyll for cyanobacteria detection was determined: two to four times as much phycocyanin is needed to detect the same amount of cyanobacteria biomass as can be detected with chlorophyll. 

    Cross Satellite Platforms

    The reliable application of any remote-sensing algorithm over a large area requires a strategy for its evaluation, validation, and refinement on multiple spatial and temporal scales using field reference data. As our knowledge of the statistical and analytical relationships within the algorithm improve with time, successes and failures need to be understood, as does the ongoing need for refinement of algorithm parameterizations. Using in situ data as reference and data from multiple ocean color satellite instruments, we will compare (1) model output from in situ radiometry vs. in situ metrics for cyanobacteria, (2) satellite radiometry vs. in situ radiometry and model output from satellite radiometry vs. in situ metrics for cyanobacteria, and (3) model outputs from multiple satellite instruments (MERIS and Landsat).

    Fiscal Year 2017 Update

    As seen in Figure 2, CyAN generated MERIS CI composites for the full mission at 300 m for the full continental United States (CONUS). Temporal scales for these composites are 14-days for 2002-2007, when MERIS CONUS coverage was incomplete, and 7-days for 2008-2012. These MERIS CI data are available for project collaborators evaluation. As of August 30, 2017, CyAN is processing Sentinel-3 OLCI in forward stream.

    Figure 2. Example of how CyAN divides the satellite images into smaller files. The numbered tiles each represent a spatial area that will be made into a separate file. The data file name will use the row and column number to identify the tile location. SaFigure 2. Example of how CyAN divides the satellite images into smaller files. The numbered tiles each represent a spatial area that will be made into a separate file. The data file name will use the row and column number to identify the tile location. Satellite data is from the European Space Agency Envisat MERIS and Copernicus Sentinel-3 Ocean and Land Colour Imager (OLCI) sensors.

    Environmental Assessment

    The Environmental Component of the CyAN project focuses on the evaluation of the existing satellite data to document changes in land-cover composition, land-use activities, chlorophyll a, and cyanobacteria concentrations.

    Fiscal Year 2017 Update

    An approach for identifying lakes that can be resolved by any satellite given different pixel resolutions has been defined, and a method to quantify the frequency of bloom occurrence in recreational and surface drinking water sites has been developed. A method for examining temporal changes in cyanobacteria harmful algal bloom spatial extent for state level assessment, transferable to different spatial areas was also developed.

    Human Health

    Remote sensing of cyanobacteria blooms offers a unique opportunity to estimate human exposure to cyanotoxins over specific geographic areas.The health of those communities with a past history of cyanobacteria blooms detected via satellite may be evaluated retrospectively by the analysis of existing health records.

    Fiscal Year 2017 Update

    Analysis of a study of human health effects associated with exposure to recreational water at a Great Lakes beach is underway. Although this prospective epidemiology study was conducted in 2003, for this project, we retrospectively evaluated beachgoer’s potential exposure to phytoplankton using two different remote sensing methods: the MODIS ocean chlorophyll-a (OC4) algorithm and the MERIS cyanobacteria index (CI).

    Economics

    Across the U.S., many states are developing programs to monitor bloom events. However, monitoring can be expensive, takes time, and results are often not available in enough time for management decisions. Automated detection of events based on remote sensing has the potential to improve the quality and timing of HAB-related data delivery to those who need it. The costs associated with monitoring algal blooms, and the economic value of early bloom event detection using remote sensing data are being identified.

    Fiscal Year 2017 Update

    CyAN quantified the benefits of using remote sensing and field-based monitoring data to detect chlorophyll a as a general indicator of harmful algal blooms. This effort investigates the spatial and temporal coverage of both satellite and field based observations across US lakes and potential costs and trade-offs. This work examines the extent of chlorophyll a observations across the CONUS for both the Landsat (OLI) and Sentinel 3 (OLCI) satellites and makes comparisons with the spatial and temporal extent of existing national programs collecting in-situ field observations of chlorophyll a.

    Decision Support

    Satellite data that is accessible to scientists is not typically processed and delivered to the public in a manner that demonstrates its practical value to daily life. The CyAN Android mobile application is the first platform for immediate decision support. The second platform is the EnviroAtlas for longer-term trend analysis. Additional software packages for data analysis include RS Tools for GIS and the open source SeaDAS software.

    Fiscal Year 2017 Update

    Figure 3. Main splash page of CyAN app for dropping pin locations and navigating to the My Location, Compare, Notification, and Geographic Coordinates tabs. Selection of a location in the My Location tab allows the user to visualize the thumbnail archive Figure 3. Main splash page of CyAN app for dropping pin locations and navigating to the My Location, Compare, Notification, and Geographic Coordinates tabs. Selection of a location in the My Location tab allows the user to visualize the thumbnail archive of Sentinel-3 satellite imagery. 

    The CyAN Science team held multiple training sessions in FY17. The first session was an introduction to SeaDAS for state and federal stakeholders, the second was an introduction to the RSTools toolkit for ArcGIS and basics of the products being produced through the project. A smaller-scale training was held for collaborators asked to beta test the CyAN mobile application.

    RSTools, the package for ArcGIS, was modified to support both NASA and NOAA naming conventions for MERIS and OLCI. It supports Sentinel-3A, as well as Envisat (MERIS) and MODIS. RSTools allows calculation of all composites and extraction of time series data for point and shape polygons by any agency using ArcGIS.

    The CyAN mobile app (Figure 3) is operational and providing weekly OLCI data for the CONUS to collaborators. It is currently available to any state regulatory agency or health department for beta testing (see "Project Contacts" below).

Project Contacts

Contact Us about the CyAN Project.

Leads for each Agency:
Blake Schaeffer, EPA
Jeremy Werdell, NASA
Keith Loftin, USGS
Richard Stumpf, NOAA