Viz Lab Summer Grant 2004

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Water Quality Visualization in Lake Superior Based on GIS and RS

Yuhu Yan Graduate Student
under Professor Michael Sydor, Physics

The water quality monitoring is a very important issue for the research of Lake Superior. The traditional methods of measurement in the field are inefficient and limited in a small area. Water quality detection with remote sensing (RS) can continuously cover the whole lake and it is easy to get real-time monitoring data. But it is also limited by poor accuracy and weather condition.

Chlorophyll-a concentration [Chl-a] is one of the most important indices of the water quality and the health of water body. The concentration of phytoplankton near-surface of water can be derived from satellite observation and quantification of water color. This is due to the fact that the color in most of the world’s waters in the visible light region (wavelengths of 400-760 nm) varies with the concentration of chlorophyll and other plant pigments present in the water, i.e., the more phytoplankton present, the greater the concentration of plant pigments and the greener the water.

We downloaded satellite images (June2004—August 2004) from ocean-color sensors (Sea-viewing Wide Field-of-view Sensor - SeaWiFS, Moderate Resolution Imaging Spectroradiometer - MODIS) which have more precise detection ability. Before we applied in-water [Chl-a] deriving algorithms, we pre-analyzed the raw data for georectification and atmospheric correction with SeaDAS. The image processing software ERDAS in VDIL is also very helpful for the satellite images preprocessing.

We applied some empirical in-water retrieving algorithms from National Aeronautics and Space Administration (NASA) on the pre-processed images in SeaDAS to get the distribution of Chlorophyll-a in Lake Superior. For example, we applied OC2V2 algorithm (Ocean Chlorophyll two-band Algorithm Version 2) for SeaWiFS, which is where X = log10[Rrs(490)/Rrs(555) ], Rrs(490) and Rrs(555) are Normalized Remote Sensing Reflectance at wavelength of 490 nm and 555 nm from satellite images.

This graph shows the distribution of [Chl-a] in Lake Superior in summer of 2004. We can see the West Arm of Lake Superior has the highest concentration of Chl-a.

To test the accuracy of remote sensing detection we compared the satellite images based results and in situ data for the Western Arm of Lake Superior. The field data were taken by AqualFluor Handheld Fluorometer-Turbidimeter, which detection limit is 0.25 mg/m3 for [Chl-a]. We found out the satellite-derived data about 3 times of the true in field data with reasonable good linear relation.

Supported by Geographical information system (GIS) technology, 3D (latitude, longitude, time) dynamic water quality data visualization can be presented. This work can lead to real time image processing of satellite data to be used in forecasting sports fishery and warm water stratification offshore, and to assess pollutant accumulation in Lake Superior. The visualization results will be published online for public use.