Protecting Water Safety
Harmful algal blooms (HABs) are increasing worldwide due to nutrient pollution and warming waters. These events produce toxins that threaten drinking water supplies, fisheries, and recreational waters.
Our satellite-based monitoring system provides early detection of bloom formation, enabling water managers and public health officials to take protective action before toxins reach dangerous levels.
Detection Methodology
Ocean Color Imaging
Acquire multi-spectral satellite imagery from Sentinel-3 OLCI, MODIS, and Landsat sensors to measure ocean surface reflectance and chlorophyll concentrations.
Chlorophyll Analysis
Apply bio-optical algorithms to estimate chlorophyll-a concentrations, phycocyanin levels, and other pigment indicators associated with harmful algal species.
Bloom Classification
Machine learning models distinguish harmful blooms from benign phytoplankton by analyzing spectral signatures, spatial patterns, and temporal dynamics.
Environmental Integration
Combine satellite observations with sea surface temperature, salinity, nutrients, and wind data to understand bloom drivers and predict intensification.
Risk Communication
Generate automated advisories with bloom location, species identification probability, toxin risk levels, and recommended actions for public health officials.
Key Features
Early Detection
Identify bloom formation 24-48 hours before they become visible, enabling proactive response measures.
Species Identification
Spectral analysis helps identify dominant phytoplankton types including cyanobacteria, dinoflagellates, and diatoms.
Trajectory Prediction
Combine with ocean current models to predict bloom movement and potential impact on beaches and aquaculture.
Water Quality Index
Calculate comprehensive water quality scores integrating turbidity, chlorophyll, and temperature for drinking water sources.
Scientific Foundation
Our HAB detection methods are built on decades of ocean color research and validated through collaboration with water quality agencies.
Remote Sensing of Harmful Algal Blooms: A Review
Kudela, R.M., Palacios, S.L., Austerberry, D.C., et al.
Satellite Detection of Cyanobacterial Harmful Algal Blooms
Urquhart, E.A., Schaeffer, B.A., Stumpf, R.P., et al.
Machine Learning for Harmful Algal Bloom Prediction
Hill, P.R., Kumar, A., Temimi, M., Bull, D.R.
Global Expansion of Harmful Algal Blooms: Status, Trends and Projections
Gobler, C.J.
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