1. Using Satellite Imagery to Analyze Lake Quality Matthew J. Kucharski Under the direction of Stefan Falke And CAPITA Washington University in St. Louis REU Program August 6, 2004

2. Idea behind the Project Satellite monitoring of the earth has risen dramatically in the last years allowing coverage of large regions daily. Purpose of Project SeaWiFS satellite imagery data processing Understand the processing of SeaWiFS data for imaging Convert raw data to usable formats for air and water quality analysis Application of satellite imagery to lake water quality monitoring Compare SeaWiFS and MODIS surface reflectance (color) data to lake clarity monitoring data

3. Spectral Characteristics of soil, vegetation, and water Soil Reflects Red and wavelengths with higher frequency Vegetation High in green (~555 nm) and infrared wavelengths Water Absorbs most of the light reflected upon it Reflects mostly the lower frequency wavelengths i.e. blue Vegetation Soil Water Visible Spectrum

4. Passive remote sensing from Satellite Use of the sun’s radiance Absorption and scattering of sunlight in the atmosphere, on the land, and on the water

5. CAPITA and Processing SeaWiFS data At Center for Air Pollution Impact and Trend Analysis (CAPITA), SeaWiFS data is being processed for aerosol optical thickness across the United States

6. The SeaWiFS data Process Georeferencing Splicing and Mosaicing Rayleigh correction Scattering angle correction * Process Created by Sean Raffuse at Washington University

7. Other Manipulations for SeaWiFS data: Create Time Series Plots and Color Time Series Jan. JuneNov.

8. Previous attempts at using Satellite for Lake monitoring Environment Remote Sensing Center at University of Wisconsin Uses Landsat and MODIS imagery to monitor Water clarity i.e. Secchi Depth. Correlation found between Secchi Depth and Blue to Red ratio of the reflectance

9. Using SeaWiFS imagery for Lake Monitoring Missed Algae Event? Apr.Oct. June

10. Data Used Wisconsin Department of Natural Resources and the Self-Help Lake Monitoring Volunteers extensively monitors the numerous lakes of Wisconsin. SeaWiFS surface reflectance data for 2000, 2001, and 2002 Green Lake Shawano Lake Koshkonong Wind Lake Tomahawk North Twin Lake Fence Lake

11. Results of Wisconsin Lakes Green Lake Shawano Lake Koshkonong Wind Lake Tomahawk North Twin Lake Fence Lake

12. Spectral Characteristics of Lakes No Two Lakes are the Same Lake size Lake chemistry Difficult to derive a universal model

13. Problems and Potential with SeaWiFS for Lake Monitoring One km Resolution creates a wide area to monitor Not functional with smaller or narrower lakes Temporal Alignment Atmosphere remaining after processing Haze, clouds ect. Possible to analyze seasonal changes of lakes Color time series gives visual aid to current monitoring techniques

14. MODIS vs. SeaWiFS Imagery Table Rock Lake MODIS Image 250m resolution SeaWiFS Image 1 km resolution

15. Table Rock Lake Current research project involving Wash U. High phosphorus levels Creates Eutrophication Monitored using by the Lakes of Missouri Volunteer Program every 20 days Of eight time spans for gathering Lake data, MODIS had only five Cloud-free days

16. Results of Table Rock Lake At 250m resolution Examined relationships by day and by site

17. 500m Resolution

18. Personal Secchi Depth Sampling On July 12-13 2004, I recorded my own Secchi depth measurement. Secchi disk provided by David Caseletto Boat Provided by Table Rock Lake Water Quality, Inc. Days were perfectly clear for Satellite Results Definite Human Error No longer just a place on a image

19. Future research opportunities of Lake Monitoring Lake monitoring via satellite on regional or individual scale Explore other factors affecting the lake’s spectral characteristics Extent MODIS imagery analysis with similar tools as SeaWiFS

20. Acknowledgements I would like to acknowledge Dr. Stefan Falke, Dr. Rudolf Husar, and Erin Robinson at CAPITA. Dr. Lars Angenent and Dr. Dan Giammar for valuable insight about lake properties. Also, Gene Bulfin with technical support.

21. References Giammar, Daniel and Angenent, Lars, 2004. Evaluation of Chemical and Biological Tracers for Source Appointment of phosphorus in Table Rock Lake, on the Missouri- Arkansas Border. Proposal Introduction to Remote Sensing Environment. 2004. www.microimage.com. Lincoln: MicroImages, Inc. http://www.microimages.com/getstart/pdf/introrse.pdfwww.microimage.comhttp://www.microimages.com/getstart/pdf/introrse.pdf Kaufman, Y. J., Tanre, D., Gordon, H. R., Nakajima, T., Lenoble, J., Frouin, R., Grassl, H., Herman, B. M., King, M. D., and Teillet, P.M., (1997), Passive remote sensing of tropospheric aerosoland atmospheric correction for the aerosol effect, J. Geophys. Res. 102:16,815-16,830. Li, F., and Husar, R. B., (1999), Pre-processing of SeaWiFS satellite data for aerosol retrieval [Online]. Center for Air Pollution Impact and Trend Analysis. Available from: http://capita.wustl.edu/capita/capitareports/CoRetrieval/SeaWiFSPreProcessinghtm. [Accessed 27 July 2004]. http://capita.wustl.edu/capita/capitareports/CoRetrieval/SeaWiFSPreProcessinghtm Lillesand, Thomas M. Combining Satellite Remote Sensing and Volunteer Secchi Disk Measurement for Lake Transparency Monitoring. University of Wisconsin Radiative Transfer Theory, Atmospheric Correction, and Ocean Color. University of Miami. June 25, 2004 http://www.physics.miami.edu/~chris/envr_optics.html Raffuse, Sean M. 2003. Estimation of Daily Surface Reflectance over the United States from the SeaWifS Sensor. Thesis. Washington University In St. Louis Schultz, Gert A. Ed., Engman, Edwin T., Ed. Remote Sensing in Hydrology and Water Management. Springer: Heidelberg 2000 Wisconsin Department of Natural Resources. Self-Help Lake Monitoring. July 6, 2004 http://www.dnr.state.wi.us/org/water/fhp/lakes/selfhelp/index.htm http://www.dnr.state.wi.us/org/water/fhp/lakes/selfhelp/index.htm