Title

Fusion Of Hyperspectral Remote Sensing Data For Near Real-Time Monitoring Of Microcystin Distribution In Lake Erie

Keywords

Data fusion; harmful algal bloom; machine-learning; microcystin; remote sensing; surface reflectance

Abstract

Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophic zones. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. Microcystis has a unique advantage over its competition as a result of the invasive zebra mussel population that filters algae out of the water column except for the toxic Microcystis. The toxin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This presentation demonstrates the prototype of a near real-time early warning system using Integrated Data Fusion techniques with the aid of both hyperspectral remote sensing data to determine spatiotemporal microcystin concentrations. The temporal resolution of MODIS is fused with the higher spatial and spectral resolution of MERIS to create synthetic images on a daily basis. As a demonstration, the spatiotemporal distributions of microcystin within western Lake Erie are reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the this type of algorithm has better potential to accurately estimating microcystin concentrations in the lake, which is better than current two band models and other computational intelligence models. © 2013 Copyright SPIE.

Publication Date

1-1-2013

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

8871

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.2026933

Socpus ID

84887919449 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84887919449

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