Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie

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@Article{Chang:2014:ieeeSTAEORS,

• author = "Ni-Bin Chang and Benjamin Vannah and Y. Jeffrey Yang",
• journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
• title = "Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie",
• year = "2014",
• month = jun,
• volume = "7",
• number = "6",
• pages = "2426--2442",
• abstract = "Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. 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. The hepatotoxin microcystin 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 study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations.",
• keywords = "genetic algorithms, genetic programming, Harmful algal bloom, image fusion, machine learning, microcystin, remote sensing",
• DOI = "doi:10.1109/JSTARS.2014.2329913",
• ISSN = "1939-1404",
• notes = "Also known as \cite{6851120}",

}

Genetic Programming entries for Ni-Bin Chang Benjamin W Vannah Y Jeffrey Yang

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