This study developed satellite remote sensing models to detect cyanobacterial blooms via chlorophyll a in Lake Champlain. Landsat Enhanced Thematic Mapper Plus data was used to retrieve chlorophyll a concentrations, phytoplankton, and cyanobacteria biovolume by calibrating and validating with coincident observation data. Correlation analysis results showed that band 2 (green band) and the band ratio of 2/1 (green/blue) were most highly correlated to chlorophyll a concentration (r = 0.76 and 0.82, respectively). Multiple regression results identified band 2 and 3 (red), and band ratio of 2/1 and 3/1 (red/blue) as critical information to estimate chlorophyll a concentrations. The regression models accounted for 72 to 83% of the variability in chlorophyll a observations, allowing for estimates of phytoplankton and cyanobacteria levels in the lake. Satellite image processing results successfully showed the temporal and spatial distribution of chlorophyll a, phytoplankton, and cyanobacteria in the lake. This information can be used to evaluate the effect of pollution sources and weather conditions, and assist decision making for water management.

Lake Champlain is significantly impaired by excess phosphorus loading, requiring frequent lake-wide monitoring for eutrophic conditions and algal blooms. Satellite remote sensing provides regular, synoptic coverage of algal production over large areas with better spatial and temporal resolution compared with in situ monitoring. This study developed two algal production models using Landsat Enhanced Thematic Mapper Plus (ETM(+)) satellite imagery: a single band model and a band ratio model. The models predicted chlorophyll a concentrations to estimate algal cell densities throughout Lake Champlain. Each model was calibrated with in situ data compiled from summer 2006 (July 24 to September 10), and then validated with data for individual days in August 2007 and 2008. Validation results for the final single band and band ratio models produced Nash-Sutcliffe efficiency (NSE) coefficients of 0.65 and 0.66, respectively, confirming satisfactory model performance for both models. Because these models have been validated over multiple days and years, they can be applied for continuous monitoring of the lake.

The objective of this study was to develop cyanobacteria remote sensing models using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) as an alternative to shipboard monitoring efforts in Lake Champlain. The approach allowed for estimation of cyanobacteria directly from satellite images, calibrated and validated with 4 years of in situ monitoring data from Lake Champlain's Long-Term Water Quality and Biological Monitoring Program (LTMP). The resulting stepwise regression model was applied to entire satellite images to provide distribution of cyanobacteria throughout the surface waters of Lake Champlain. The results demonstrate the utility of remote sensing for estimating the distribution of cyanobacteria in inland waters, which can be further used for presenting the initiation and propagation of cyanobacterial blooms in Lake Champlain.

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