The Phycocyanin Content algorithm (PCY) was used to quantify cyanobacteria pigments on land. The PYC was originally developed to quantify blooms of the toxic cyanobacteria, Microcystis aeruginosa, in Lake Erie. However, there were large, unexplained highlighted areas on land in the Lake Erie PYC images. The PYC algorithm, when applied on land, is negatively affected by the averaging effect on each pixel of vegetation and minerals. Filters to reduce this image noise on a terrestrial setting were developed by using a conceptual idealized dataset to establish the hypothesized relationship between chlorophyll and phycocyanin. The ratio of LANDSAT TM bands 4/3 was used as a vegetation filter and the 3/2 ratio was used to lessen the effects of iron oxides on the application on land of the PYC.
The PYC performed successfully in areas of very low vegetation as the vegetation and mineral filters worked as designed. However, further calibration is necessary for this algorithm to function as a quantification tool. The mineral filter was based on the iron oxide mineral as it is the prevalent mineral in the region. Unfortunately, the iron oxide filter also detects areas of senescent vegetation and inversely indicates areas with green vegetation, complicating the performance of PYC in these areas.
|Commitee:||Lowe, Rex, Vincent, Robert|
|School:||Bowling Green State University|
|School Location:||United States -- Ohio|
|Source:||MAI 57/05M(E), Masters Abstracts International|
|Subjects:||Biology, Geography, Geology, Organic chemistry, Remote sensing|
|Keywords:||Fluorescence, Modeling, Phycocyanin, Regression, Remote sensing, Soil|
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