Multispectral remote sensing of harmful algal blooms in Lake Champlain, USA.

Abstract

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.