Assessing Lake Clarity...from SPACE...

Transcription

1 Assessing Lake Clarity...from SPACE... Taylor Long Peter Marinello December 10, 2012

2 Why look at lakes from space?... Lake Clarity Indicative of Water Quality Monitoring time consuming and expensive Satellite Imagery Offers Broader Perspective

3 Secchi Disk Transparency (SDT) - Easy to collect (MPCA Volunteers) - Measures clarity by depth (m) - Used to calculate Trophic State Index

4 Carlson's Trophic State Index (TSI) Photosynthetic Productivity on a Scale of Lake Trophic State Oligotrophic Lakes Mesotrophic Lakes Eutrophic Lakes Hypereutrophic Lakes Water Quality Extremely High Moderate Poor Extremely Poor Photosynthetic Productivity (TSI) Low (<30-40) Intermediate (40-50) High (50-70) Extremely High (>70) Nutrient Levels Low Intermediate High Extremely High Typical Lake Very clear, deep lakes Seasonal algae blooms, various lake depths Green water, shallow lakes Green water, shallow lakes, summer blooms of blue-green algae (toxic) and surface scum. Secchi Transparency High Intermediate Low Very Low

5 Knight and Voth, 2012 "Application of MODIS Imagery for Intra-Annual Water Clarity Assessment of Minnesota Lakes" Analysed 6 MODIS Images from 2006

6 Our Questions 1. How does lake clarity change from year to year? Mid-August Images 2001, 2006, How does lake clarity differ inside protected boundaries? - BWCA - National Forest Land - State Forest Land - "Other"

7 Study Area

8 Analysis Regions

9 Analysis Regions

10 Analysis Regions

11 METHODS

12 Image Preparation / Classification MODIS 8-Day Composite (fewer clouds) Clip Images for each year Unsupervised Classification, 20 clusters

13 Identifying Clear Water Pixels Class 2: Clear Water Clipped Band 1 (Red) and Band 3 (Blue)

14 Selecting SDT Data (MPCA) Points recorded August 13th-20th, 2001, 2006, Points over clear lake pixels Points over lakes >400 Acres

15 Regression Analysis Image Date Regression Equation R2 Standard Error 2001 (n=206) Y = (-.025 x Band 1) + (0.28 x Band 3) (n=254) Y = (-.023 x Band 1) + (0.14 x Band 3) (n=177) Y = (-.026 x Band 1) + (0.26 x Band 3) Low R2 values

16 RESULTS! Color TSI Category Lake Clarity Grey >100 Considered invalid. No quality inferences made. Red Very Low Yellow Low Green Low Blue Moderate Dark Blue 0-40 High Legend: Five TSI Categories

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23 Year-to-Year Comparison 2001, 2006, 2011

24 Analysis Region Comparison

25 Analysis Region Comparison

26 Change Analysis

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29 Change in TSI Category by Year

30 Change in TSI Category by Region

31 Change in TSI Category by Region

32 Accuracy Assessment Image 2006 Image (n=254) R2 Standard Error Range SDT (M) Subset A (n=127) Subset B (n=127) Image (n=254) Trophic Class Accuracy Within 1 TSI Within 2 TSI Within 5 TSI Within 10 TSI Subset A (n=127) 55% 12% 25% 64% 92% Subset B (n=127) 54% 21% 34% 69% 94% 2006 Averages 54.5% 16.5% 29.5% 66.5% 93%

33 Comparison Results TEMPORAL How does lake clarity change from year to year? General decreasing trend in lake clarity over a 10 year period. SPATIAL How does lake clarity differ inside protected boundaries? General higher clarity in protective boundaries. Trend: More restrictive = Higher clarity

34 TSI Change Results: Temporal and Spatial Temporal Trend: '01-'06, '06-'11 TSI suggests lake clarity average in ecoregion from '01-'06 to '06-'11 has Improved. Spatial Trend: Protective Boundaries Improved clarity: National, State, and Other Worsened clarity: BWCAW (a small # of lakes worsened)

35 Accuracy Assessment Results Selected only 2006 image (1/3 years) following Knight & Voth (2012) selection of (2/6 years) for assessment. 54.5% of Pixels classified same as TSI classification. 93% of pixels classified within 10 TSI (range of clarity class) in Trophic State Index

36 Discussion Accuracy Assessment Results - Low R2 Omissions in analysis process to fit project timeframe: -Not hand picking SDT points Image Only + MODIS 8-day composites: -Cloud/Haze residuals = More Error Introduced

37 Discussion We see justification for future studies examining: Larger Temporal Study (30 years): -same trend of Decreasing water clarity between years, and regions? Possible Causes: -Yearly Weather Effects? -Human Effects? -Water Quality Standards?

38 Conclusion Remote Sensing is complex and highlyinvolved process. Our study produced interesting trends worth follow-on study. Temporal change analysis, and spatial change comparison are valuable for further water resource management.

39 Sources Carlson, R. E. (1977) Kloiber, S. M., Brezonik, P. L., Olmanson, L. G., & Bauer, M. E. (2002) Knight, J. F., & Voth, M. L. (2012) Lake Access. TSI (2012) Minnesota Pollution Control Agency (2012) NASA (2012) Olmanson, L. G. (2008) Sawaya, K. E., Olmanson, L. G., Heinert, N. J., Brezonik, P. L., & Bauer, M. E. (2003) Yale University (2012)

40 Questions?