COMPUTER ANIMATION OF CYANOBACTERIA BLOOMS IN LAKE ERIE FROM JULY-OCTOBER, 2003 AS MAPPED FROM SEAWIFS DATA WITH A NEW PHYCOCYANIN ALGORITHM

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1 COMPUTER ANIMATION OF CYANOBACTERIA BLOOMS IN LAKE ERIE FROM JULY-OCTOBER, 2003 AS MAPPED FROM SEAWIFS DATA WITH A NEW PHYCOCYANIN ALGORITHM Padmanava Dash, Graduate Student Robert K. Vincent, Professor Department of Geology Bowling Green State University Bowling Green, OH, pdash@bgnet.bgsu.edu rvincen@bgnet.bgsu.edu ABSTRACT This paper is the first application of a SeaWiFS phycocyanin algorithm to map cyanobacterial blooms for observing Lake Erie during the growing season of 2003, a year of known large cyanobacterial blooms. Vincent et al. (2004) have developed a set of algorithms that employ LANDSAT TM remote sensing technology to map cyanobacterial blooms in fresh water lakes from space by quantitatively mapping phycocyanin, the pigment most uniquely associated with cyanobacterial blooms. Although the LANDSAT TM sensor has a 16-day revisit cycle and a spatial resolution of 28.5 m, the SeaWiFS sensor has a daily revisit cycle and a coarse spatial resolution of 1.1 km for LAC (local area coverage) and 4.5 km for GAC (global area coverage). Hence, the SeaWiFS sensor can be applied with higher temporal frequency and can be used to document large scale changes in the region, due to its larger synoptic coverage (Dash et al., 2005). An algorithm has been developed from SeaWiFS data for mapping incipient cyanobacterial blooms, (Dash et al, 2005) that is also based on phycocyanin pigment. To investigate intra-annual variability of cyanobacterial blooms in the entirety of Lake Erie this algorithm was applied to the whole of Lake Erie for all the cloud free days from July 01- October 31, The SeaWiFS images after applying the algorithm for the above dates were used to create an animation to monitor the changes in phycocyanin concentration throughout that time period. This animation shows that phycocyanin concentration continually increases from July to October in Lake Erie. The algorithm may serve as a tool for monitoring of cyanobacterial blooms, thereby assisting future assessment of water quality and the aquatic ecosystem health of large fresh water lakes around the globe. INTRODUCTION Powerful remote sensing techniques have become available in the last two decades that facilitate the study of large scale biological processes in difficult environments (Vincent et al., 2004). Anbazhagan et al. (2003) claimed that remote sensing techniques can be extensively used for studying the environment. There are three major algal pigment groups found in phytoplankton and bacteria: chlorophylls, carotenoids and phycobilins (MacColl & Guard- Friar, 1987). The phycobilins are phycoerythrin and phycocyanin. The blue phycobilin, phycocyanin, occurs in the cells of cyanobacteria and is the dominant accessory pigment in the three major types of cyanobacteria, which are Anabaena, Microcystis, and Aphanizomenon (MacColl & Guard-Friar, 1987) that grow in freshwater systems. The unique spectral signature produced by phycocyanin can be used for detecting cyanobacteria in remotely sensed data (Dekker, 1993). Over the past decade, the western basin of Lake Erie has experienced toxin-producing blooms of the cyanobacterium Microcystis on a number of occasions (Vincent et al., 2004). Makarewicz (1993) studied the phytoplankton biomass and species composition in Lake Erie and reported several massive blooms of Microcystis. During September, 1995 several authors, such as Taylor (1997) and Budd et al. (2001), observed an abundance of cyanobacterial blooms in Lake Erie. Taylor (1997) described a Microcystis bloom resembling a thick slick of green paint that extended over the entire surface of the western basin of Lake Erie, whereas Budd et al. (2001) demonstrated that there was an intense bloom of Microcystis aeruginosa that was visible from shore and satellite imagery as a surface scum that covered much of the western basin of Lake Erie in September, Along the same line, Brittain et al. (2000) documented a bloom of Cyanobacteria dominated by Microcystis aeruginosa in the western basin of Lake Erie during the summer of 1996, when the toxin content was equivalent to as much as 1 µg October 23-27, 2005* Sioux Falls, South Dakota

2 microcystin/l. Another notable bloom was reported in September, 1998 (Lake Erie LaMP, 2002). Eventually, Henry (2002) reported on September 17, 2002 that a substantial bloom of Microcystis had enveloped parts of the western basin in the vicinity of the Lake Erie islands. Therefore, it is evident that the common bloom-forming species of cyanobacteria, Microcystis, has been dominant in the water of Lake Erie. Hence, Lake Erie was considered as the study area for developing and applying an algorithm to detect phycocyanin for monitoring cyanobacterial blooms. Microcystins are a group of cyclic heptapeptide liver toxins produced by several genera of cyanobacteria (Carmichael, 1997). The microcystins are potent tumor promoters (Nishiwaki-Matushima et al., 1992) and are also carcinogenic (Ohta et al., 1994). In addition, Microcystins are related to animal deaths (Skulberg et al., 1989), human sickness (Falconer et al., 1983 & Carmichael et al., 1993) and Caruaru syndrome, which caused the deaths of over 60 hemodialysis patients from liver toxicosis (Jochimsen et al., 1998). Henry (2002) reported that although the algae never have been linked to any death in the Great Lakes region, the U. S. Centers for Disease Control linked it to as many as 75 deaths in Brazil in Therefore, potentially toxic cyanobacterial blooms can be serious environmental problems. To investigate intra-annual variability of cyanobacterial blooms in the entirety of Lake Erie, a recently developed SeaWiFS phycocyanin algorithm (Dash et al., 2005) was applied to the whole of Lake Erie for all the cloud free days from July 01- October 31, The SeaWiFS images produced with the phycocyanin algorithm for the above dates were used to create an animated video to monitor the changes in phycocyanin concentration throughout that time period. MATERIALS AND METHODS The new algorithm for mapping of cyanobacteria (Dash et al., 2005) uses SeaWiFS data as input and outputs an image with brightness proportional to the concentration of phycocyanin pigment in the water in units of micrograms per liter. NASA s Goddard Space Flight Center s SeaWiFS project website provides both Chlorophyll Content and Quasi True Color satellite data. From this website, prepackaged 1.1 km pixel resolution, level-1, MLAC (merged LAC) data for all 8 bands of SeaWiFS from July 01- October 31, 2003, were downloaded and unzipped through bunzip software in the DOS (Disc Operating System) environment. The SeaWiFS data for the above dates were georeferenced using ENVI remote sensing software and processed using the ERMAPPER image processing software. The SeaWiFS phycocyanin algorithm uses three visible and two infrared spectral bands of SeaWiFS, which renders it useless for mapping cyanobacterial blooms except on cloud-free days. From all of the four month s of data (July 01- October 31, 2003), only 31 days were found to be cloud-free; hence, only those 31 SeaWiFS frames were chosen for application of the algorithm. A remote sensing method known as dark-object subtraction (Vincent, 1997) was applied to each band of SeaWiFS imagery to reduce the effect of atmospheric haze. The minimum digital number (DN) found in all of the pixels in the image were determined for each spectral band. One less than the minimum DN is taken to be the value of the dark object (Vincent, 1972). The dark objects for eight spectral bands of SeaWiFS were extracted from the image data. An algorithm has recently been developed from SeaWiFS data for mapping incipient cyanobacterial blooms, (Dash et al., 2005) that is based on phycocyanin pigment, using the same procedures as Vincent et al. (2004) employed. The SeaWiFS phycocyanin algorithm was then applied to all of the 31 days SeaWiFS data to yield the phycocyanin images for all those dates. While applying the algorithm, the dark objects were subtracted from all of the five spectral bands that participated in the algorithm. Finally, all the 31 phycocyanin images were animated to investigate intra-annual variability of cyanobacterial blooms in the entirety of Lake Erie. RESULTS AND DISCUSSIONS The recently developed SeaWiFS phycocyanin algorithm by Dash et al. (2005) for predicting the phycocyanin concentration with the SeaWiFS data had an R 2 (adjusted) value of 64%, and it passed the DW test with a DW statistic of This model was first applied to July 01, 2000 SeaWiFS data (from which the model was constructed) and the predicted phycocyanin concentration agreed with the actual phycocyanin concentration within a root mean square error of 0.68 µg/l. This is an error that represents approximately 17% of the total range in phycocyanin for that date. When this phycocyanin model was applied to the SeaWiFS data of September 27, 2000, the predicted phycocyanin concentration agreed with the actual phycocyanin concentration within a root mean square error of 3.43 µg/l, which represents approximately 29% of the total range in phycocyanin for that date. It was interesting to note that the phycocyanin concentration was higher in the September 27, 2000 than that of the July 01,

3 Figure 1. Phycocyanin concentration displayed as red (highest phycocyanin content) to blue (lowest phycocyanin content) from the SeaWiFS phycocyanin model applied to the SeaWiFS data for 31 cloud-free days from July 01 October 31, 2003.

4 2000, which reflects the fact that cyanobacterial blooms usually peak in late September in western Lake Erie (Dash et al., 2005). Vincent et al. (2004) have developed a spectral ratio algorithm for detecting cyanobacterial blooms by quantitatively mapping phycocyanin, employing LANDSAT TM remote sensing technology. Their algorithm, applied to LANDSAT TM frames of the same two dates are in excellent agreement with the phycocyanin algorithm (which also employs spectral ratios) for SeaWiFS data in the common part of the LANDSAT TM and SeaWiFS images (the western part of Lake Erie), as shown by Dash et al. (2005). After comparison of the error terms for both the LANDSAT TM spectral ratio model applied to the September 27, 2000 (with a root mean square error of 3.1 µg/l, which is an error that represents approximately 26% of the total range in phycocyanin) and SeaWiFS phycocyanin model (with a root mean square error of 3.43 µg/l, which is an error that represents approximately 29% of the total range in phycocyanin), we can conclude that the SeaWiFS phycocyanin model predicts phycocyanin concentration only a little worse than the LANDSAT TM spectral ratio model (Dash et al., 2005). Dash et al. (2005) have more recently developed a model for chlorophyll a (Chl a) from SeaWiFS data to investigate relationships between phycocyanin and Chl a. The Chl a model was then applied to the same dates of SeaWiFS data to which the phycocyanin model was applied and the two models results were compared, which brought to view that no correlation exists between phycocyanin and Chl a concentration (Dash et al., 2005). Fig. 1 shows the images that resulted when this SeaWiFS phycocyanin model was applied to the SeaWiFS images for all the cloud free days from July 01- October 31, 2003 (total of 31 days), where blue to red color represents lower to higher concentration of phycocyanin, respectively. From all the 31 days, 14 days (July 01, July 14, July 29, July 30, August 7, August 14, August 19, September 11, September 17, October 07, October 08, October 11, October 12 and October 13) were found to be completely cloud free and dates July 16, July 17, July 24, July 25, August 13, August 17, August 18, August 23, August 27, September 07, September 12, September 13, September 16, September 21, October 06, October 24 and October 30 were found to be partially covered by cloud. SeaWiFS data of all other dates of the four month period from July 01- October 31, 2003 were found to be fully cloudy; hence, they could not be used. From all of the phycocyanin images, it is observed that the phycocyanin concentration in Lake Erie as a whole continually increases from the month of July towards the month of October. In most of the images, it is found that cyanobacteria blooms start near river mouths, particularly the Maumee River in the SW corner of Lake Erie. The Western Basin Lake Erie cyanobacterial bloom peak indicated by the images in the animation (shown during the oral presentation of this paper) coincides in time with cited references of their peak recorded in the Toledo Blade newspaper. There are many phenomenological implications of this animation for the understanding of cyanobacterial blooms. For instance, it appears as though on consecutive days in August, 2003, that the cyanobacteria sunk beneath the surface, then rose back to the surface a few days later. Cyanobacteria are known to have the capability of changing their buoyancy, owing to gas vacuoles inside them, with changing sea states. It will take considerable study by numerous biologists to understand everything that these images imply. It will also be interesting to apply this algorithm to other freshwater lakes in the world. CONCLUSIONS In this research, a phycocyanin-mapping algorithm (Dash et al., 2005) from SeaWiFS data, with best-case root mean square error that represents approximately 17% of the total range in phycocyanin for the same data from which the algorithm was derived, has been applied to the whole of Lake Erie for all the cloud free days from July 01- October 31, It has performed reasonably well in predicting phycocyanin values (with root mean square error of 3.43 µg/l, which is an error that represents approximately 29% of the total range in phycocyanin) on September 27, 2000 (Dash et al., 2005). SeaWiFS phycocyanin images were compared with LANDSAT TM phycocyanin phycocyanin images of Vincent et al. (2004) and it was observed that they are in excellent agreement with each other, even though they used similar algorithms for different satellite data. After comparison of phycocyanin images with Chl a images, no correlation was found between phycocyanin and Chl a concentration. The animated video for 31 cloud-free days from July 01 October 31, 2003 shows that cynaobacterial blooms continually increase from the month of July towards the month of October in the whole of Lake Erie. This SeaWiFS phycocyanin algorithm can serve as a tool for monitoring of cyanobacterial blooms in Lake Erie and similar freshwater lakes. The animation shown in this paper will require a host of biologists to explain and critique it, especially regarding the correlation of archived wind and wave data with possible submergence and reemergence of cyanobacteria at the surface, due to the ability of cyanobacteria to change buoyancy with changing surface sea states. It would be instructive, also, to apply this algorithm to freshwater lakes on other continents.

5 ACKNOWLEDGEMENTS We gratefully acknowledge the partial support of the NASA Glenn Research Center for funding this research through OhioView, a remote sensing consortium of 12 universities in Ohio. We are also thankful to the University of Toledo s Lake Erie Center for the use of their research vessel to collect water samples in Lake Erie, as well as for the help of Mamoon Al Rshaidat and Dr. R. Michael McKay in the Dept. of Biological Sciences at Bowling Green State University. REFERENCES Anbazhagan, S., and Dash, P. (2003). Environmental case study of Cauvery river flood plain. GIS Development, December 2003, 7, 12, Brittain, S. M., Wang, J., Babcock-Jackson, L., Carmichael, W. W., Rinehart, K. L., & Culver, D. A. (2000). Isolation and characterization of microcystins, cyclic heptapeptide hepatotoxins from a Lake Erie strain of Microcystis aeruginosa. Journal of Great Lakes Research, 26, Budd, J. W., Beeton, A. M., Stumpf, R. P., Culver, D. A., & Kerfoot, W. C. (2002). Satellite observations of Microcystis blooms in Western Lake Erie. Verhandlungen-Internationale Vereinigung fu r Theoretische und Angewandte Limnologie, 27, Carmichael, W. W. & Falconer, I. A. (1993). Diseases related to fresh water blue-green algal toxins, and control measures. In Falconer I. R. (Eds.), Algal toxins in seafood and drinking water. London: Academic Press Carmichael, W. W. (1997). The cyanotoxins. In. J. A. Callow (Eds.), Advances in Botanical Research. London: Academic Press Dash, P., Vincent R. K., and. Al-Rshaidat M. M. D. (2005). SeaWiFS Algorithm for Mapping Phycocyanin in Incipient Freshwater Cyanobacterial Blooms. Submitted to Remote Sensing of Environment. Dekker, A. G. (1993). Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing. PhD thesis. Amsterdam: Free University. Falconer, I. R., & Humpage, A. R. (1996). Tumour production by cyanobacterial toxins. Phycologia, 35(Suppl. 6), Henry, T. (2002). Toxic algae thrive in summer s heat, article ID: , Published on September 17, Toledo, OH: The Blade. Jochimen, E. M., Carmichael, W. W., An, J., Cardo, D. M., Cookson, S. T., Holmes, C. E. M., Antunes, M. B. C., de Melo Filho., Lyra, D. A., Barreto, V. S. T., Azavedo, S. M. F. O. & Jarvis, W. R. (1998). Liver failure and death following exposure to Microcystins at a hemodialysis center in Brazil. New England Journal of Medical. 338, Lake Erie LaMP. (2002). In J. Letterhos, & J. Vincent (Eds.), The Lake Erie lakewide management plan. Environment Canada, Ontario Region and U.S. Environmental Protection Agency, Region 5. MacColl, R., & Guard-Friae, D. (1987). Phycobiliproteins. Boca Raton, FL: CRC Press. Makarewicz, J. C. (1993). Phytoplankton biomass and species composition in Lake Erie, 1970 to Journal of Great Lakes Research, 19, Nishiwaki-Matushima, R., Ohta, T., Nishiwaki, S., Suganuma, M., Kohyama, K., Ishikawa, T., Carmichael, W. W. & Fujiki, H. (1992). Liver tumor promotion by the cyanobacterial cyclic peptide toxin microcystin. Journal of Cancer Research. 118, Ohta, T., Sueoka, E., Iida, N., Komora, A., Suganuma, M., Nishiwaki, K., Tatematsu, M., Kim, S. J., Carmichael, W. W. & Fujiki, H. (1994). Nodularian, a potent inhibitor of protein phosphates 1 and 2A, is a new environmental carcinogen in male F344 rat liver. Cancer Research. 54, Skulberg, O. M., Codd, G. A. & Carmichael, W. W. (1989). Toxic blue-green algal blooms in Europe: a growing problem. Ambio. 13, Taylor, R. (1997). That bloomin Microcystis: Where d it come from? Where d it go? Twine Line, 19, 1. Vincent, R. K. (1997). Fundamentals of geological and environmental remote sensing. Upper Saddle River, NJ: Prentice-Hall Vincent, R. K., Qin, X., McKay, R. M., Miner J., Czajkowski, K., Savino, J., & Bridgeman T. (2004). Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment, 89,