Remote Sensing Near real-time monitoring of algal blooms

Throughout the world today, increases in nutrient and pollutant-laden runoff from urban population growth and agricultural production are contributing to the degradation and eutrophication (excess algal growth and… oxygen depletion) of surface waters.

The ecological impact of these stressors is clearly evident in western Lake Erie as seen through the intensive re-emergence of expansive algal blooms from the mid-1990s, which coincides with influxes of dissolved reactive phosphorus into the lake. Algal blooms containing Microcystis, a cyanobacterium that produces the hepatotoxin microcystin, threaten the stability of the ecosystem, contaminate fish catches, and require tap water treatment utilities to filter out the toxin.

To safeguard the environment and human health, as well as support remediation plans, it is essential to develop a reliable method for the daily monitoring of harmful algal bloom (HAB) formation, movement, and differentiation from nontoxic algal blooms. Manually assessing toxic zones through sampling is time consuming and expensive, but we have developed a near-real-time early warning system based on integrated data fusion and mining (IDFM) techniques to provide a detailed toxicity map the moment daily satellite imagery becomes available.

Our system predicts microcystin distributions by measuring the water’s surface reflectance using fused multispectral satellite imagery, bolstered by a suite of wireless ground sensor networks. The fundamental advantage of IDFM is the ability to aggregate the spatial, temporal, and spectral properties of multiple satellite sensors into a single synthetic image that possesses the most useful characteristics of the input images, thereby enhancing the reliability of the data for post processing and data mining.

Photo caption: An integrated data fusion and mining technique predicts spatiotemporal microcystin distributions produced by harmful algal bloom.

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