1. A Regional Lake Clarity Assessment Using Landsat Steve Kloiber Randy Anhorn

2. Outline Describe the remote sensing method Describe the remote sensing method Describe the application to TCMA lakes Describe the application to TCMA lakes Present regional lake clarity status and trends Present regional lake clarity status and trends

3. Generalized Spectral-Radiometric Response 450500550600650700750800850900 Wavelength (nm) Reflectance or Brightness Low Clarity Lake High Clarity Lake TM1TM2TM3TM4 MSS1MSS2MSS3 Red Spectral Region

4. Remote Sensing Method Steps Data Acquisition Data Acquisition Preprocessing Preprocessing Data Extraction Data Extraction Regression Modeling Regression Modeling Model Application Model Application

5. Landsat Data Acquisition Cloud-free (<10%) Cloud-free (<10%) Peak productivity Peak productivity –mid-July through early September About $500 per image About $500 per image One image covers TCMA One image covers TCMA

6. Ground Observation Data Sampled within 3 days of overpass Sampled within 3 days of overpass Water clarity measured by Secchi disk Water clarity measured by Secchi disk Sources include Met Council and MPCA Sources include Met Council and MPCA Paired with average lake brightness values from satellite images Paired with average lake brightness values from satellite images

7. Data Extraction Unsupervised cluster classification used to mask off terrestrial portions of scene Unsupervised cluster classification used to mask off terrestrial portions of scene Shoreline, littoral, and macrophytes avoided using unsupervised classification Shoreline, littoral, and macrophytes avoided using unsupervised classification Automated signature extraction using vector layer of lakes Automated signature extraction using vector layer of lakes Histogram trim leaving darkest 50% Histogram trim leaving darkest 50%

8. Correlation of Landsat TM and Secchi y = -15.583x + 4.6742 R 2 = 0.8431 -2 -1.5 -0.5 0 0.5 1 1.5 2 0.150.20.250.30.350.40.45 TM3:TM1 ln(SDT)

9. Regression Modeling cTMb aSDT  1 3 1 )log( r-squared ranges from 0.70 to 0.80 SE ranges from 0.30 to 0.40

10. Translating Synoptic Data Images are from late summer, but not same day each year Images are from late summer, but not same day each year To compare data from year to year, results should be tranlated to a common scale: Growing Season Mean SDT To compare data from year to year, results should be tranlated to a common scale: Growing Season Mean SDT Common methods of estimating GSM assume the data are not serially correlated Common methods of estimating GSM assume the data are not serially correlated Normalized ground observations were fit to a season model based on a sine function Normalized ground observations were fit to a season model based on a sine function

11. Seasonal Clarity Model -0.8 -0.4 0 0.4 0.8 130155180205230255 Day of Year Percent Difference from Lake-Year Mean SDTSDT trend 1991 Ground1991 Satellite SDT rel =a [sin(2π (j - 90)/182.5)] + b The sine model is a strong predictor of GSM SDT (R^2 = 0.75)

12. Map of 2005 Results

13. YEAR 2005 2004 2003 1998 1996 1995 1993 1991 1988 1986 1983 1975 1973 6 4 2 0 Growing Seaon Mean SDT (m) Typical range Typical range –0.6 – 3.4 m Regional median Regional median –1.4 m Above normal Above normal –1975, 1993, 1996 Below normal Below normal –1973, 1988, 2003 Regional Growing Season Mean SDT

14. Lake Clarity Trends 1973 - 2005 517 lakes evaluated 517 lakes evaluated 61 lakes with increasing trends* 61 lakes with increasing trends* 32 lakes with decreasing trends* 32 lakes with decreasing trends* * Kendall Tau (P < 0.05) 0 5 10 15 20 25 -12 to -16 -8 to -12 -4 to -8 0 to -4 0 to 44 to 8 8 to 12 12 to 16 Change in SDT (cm/yr) Number of Lakes

15. Trend Results More lakes had increasing clarity (61) than decreasing (32) More lakes had increasing clarity (61) than decreasing (32) Some improvements are related to point source removal or septic system controls Some improvements are related to point source removal or septic system controls –Minnetonka, Tanager, Coon Some improvements are due to stormwater treatment Some improvements are due to stormwater treatment –Josephine, Stieger

16. Trends for Coon Lake 0 1 2 3 4 5 19701975198019851990199520002005 Year Growing Season Mean SDT (m) Satellite-estimated SDT Ground-based SDT

17. Trends for Fish Lake Connection of trunk storm sewer 0 1 2 3 4 5 6 19701975198019851990199520002005 Year Growing Season Mean SDT (m) Satellite-estimated SDT Ground-based SDT

18. Trends for Lake Josephine Stormwater diversion to wetland treatment 0 1 2 3 4 19701975198019851990199520002005 Year Growing Season Mean SDT (m) Satellite-estimated SDT Ground-based SDT

19. Conclusions Valuable tool for comparative limnology Valuable tool for comparative limnology Benefits Benefits –complete spatial coverage –consistent method from lake to lake –cost-effective Uses Uses –mapping regional lake clarity (hotspots) –identifying trends

20. Acknowledgements Water Resources Center, UM ( Pat Brezonik) Water Resources Center, UM ( Pat Brezonik) Remote Sensing Lab, UM ( Marv Bauer, Leif Olmanson) Remote Sensing Lab, UM ( Marv Bauer, Leif Olmanson) MPCA (Bruce Wilson) MPCA (Bruce Wilson) Metropolitan Council Metropolitan Council

21. Other Efforts Statewide water clarity assessment - Landsat Statewide water clarity assessment - Landsat Imperviousness assessment – Landsat Imperviousness assessment – Landsat River water quality assessment – hyperspectral (AISA) River water quality assessment – hyperspectral (AISA)

22. More Info http://es.metc.state.mn.us/eims http://water.umn.edu