Temporal Relationship Between Landsat 8 Spectral Reflectance and Transparency in Grand Lake O' the Cherokee

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Hubert Tyler York
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1 Temporal Relationship Between Landsat 8 Spectral Reflectance and Transparency in Grand Lake O' the Cherokee 37 TH Annual Oklahoma Governor s Water Conference October 11 th 12 th, 2016 Norman, Oklahoma Abu Mansaray OSU Environmental Science Graduate Program Dr. Andrew Dzialowski, Dr. Scott Stoodley, Dr. Daniel Storm Oklahoma State University Dr. Nate Torbick, Applied Geosolutions Dr. Darrell Townsend, Steve Nikolai, Dr. Rich Zamor Grand River Dam Authority Funding Provided by Grand River Dam Authority

Grand Lake O the Cherokees Located in Northeast Oklahoma in the foothills of the Ozark Mountain Range Administered by Grand River Dam Authority (GRDA)

2 Grand Lake O the Cherokees Located in Northeast Oklahoma in the foothills of the Ozark Mountain Range Administered by Grand River Dam Authority (GRDA) Pensacola Dam constructed in ,500 surface acres Designated Uses Hydroelectric power Flood control Water supply Recreation

3 Grand Lake Water Quality Issues Cyanobacteria Algae Bloom, 2011 Elevated Microcystin over 350 µg/l World Health Organization (WHO) Adverse Health Effects: Microcystin >20 µg/ Oklahoma Department of Environmental Quaity (ODEQ) issued alert GRDA shut down the reservoir on July 4 th, 201 GRDA Water Quality Monitoring Program has grown significantly in recent years (Townsend, Oklahoma Clean Lakes and Watershed Association (OCLWA)

4 Project Objectives Relate in situ water quality and spectral reflectance data Develop algorithms to predict water quality parameters using empirical model and semianalytical shape derivative approach Landsat Multispectral Satellite Imagery Spectral Data (30 m) Temporally and spatially coincident Landsat 8 OLI (Operational Land Imager) Historical Landsat 5 TM (Thematic Mapper) Proba CHRIS satellite Water Quality Data Remotely Sensed Data Temporally & Spatially Coincident

5 Presentation Objective Determine whether change in Landsat 8 Surface Reflectance (SR) with Secchi depth (SD) is temporally consistent 7 bands Landsat 8 (July 13, 29, Aug. 14, Sept ) Temporally coincident Secchi depth 13 sites Justification: Time is crucial in estimating future/past water quality

6 Literature Review Han & Rundquist (1997) NIR/RED (Band 5/Band 4) comparison Arenz Jr. & Saunders III (1996) NIR/Green (Band 5/Band 3) comparison Strong relationship (R 2 = 0.98) Pattiaratchi et al. (2007) Combined Band 1 & Band 3 High predictive confidence Torbick et al. (2013) Lake Water Quality Mapping Band ratio models performed well (R 2 = )

7 Data Acquisition USGS Earth Explorer Landsat 8 images ESRI Image Classification tool Created polygons at sampling sites Calculated mean reflectance at selected pixel In situ Secchi Depth (SD) readings

8 Landsat Bands Bands Wavelength (nm) Resolution (m) Band 1 - Coastal aerosol Band 2 - Blue Band 3 - Green Band 4 - Red Band 5 - Near Infrared (NIR) Band 6 - SWIR Band 7 - SWIR Band 8 - Panchromatic

9 Water Quality Sampling: 2015 & Seasons Spring, Summer, Fall Capture water quality spatial and temporal variability 2. Sample dates Temporally coincident with satellite overpass Sampling begins just prior to satellite overpass and continues for a short period after 3. Alternative +/- 2 days individual satellite overpasses (acceptable) Assumes no rainfall/runoff event

10 Grand Lake Sampling Sites Horse Sail Grand Elk Tree Shang Honey Duck Wood Dream P Dam Drip Drown

11 Field Sampling GPS Enhanced Bathymetry Sample Bottles & Ice Chest Water Sampling Hose YSI Multi-parameter Sampler Secchi Disc Van Dorn Sampler

12 Statistical Analysis Regression Response: Secchi depth (m) Continuous predictor: spectral bands (nm) Categorical predictor: sampling dates Evaluation Scatter plots Intercept and slope significance R-square Root mean square error (RMSE) Residual plots

13 Secchi Depth vs Spectral Band Scatterplot of Secchi Depth vs Band 2 (nm), Band 3 (nm),... Band 2 (nm) Band 3 (nm) Band 4 (nm) Bands 2, 3, 4 High surface reflectance 1.0 Secchi Depth (m) Band 5 (nm) Band 6 (nm) Band 7 (nm) Bands 5, 6, 7 Long wavelength, low surface reflectance

14 Regression equations for SD, single bands, and dates Equation SD = b 0 + b 1 Band 2 + b 2 Aug14 + b 3 Jul13 + b 4 Jul29 + b 5 Sept15 SD = b 0 + b 1 Band 3 + b 2 Aug14 + b 3 Jul13 + b 4 Jul29 + b 5 Sept15 SD = b 0 + b 1 Band 4 + b 2 Aug14 + b 3 Jul13 + b 4 Jul29 + b 5 Sept15 b 0 b 1 b 2 b 3 b 4 b Hypothesis: Temporal variability not significant in predictive model

15 Regression Analysis Secchi Depth vs Spectral Band Regression Equation Band 2 (Blue) Band 3 (Green) Band 4 (Red) R 2 RMSE p-value (m) Equation Date Slope Intercept < < < <0.001 < < <0.001 <0.001 Conclusion: Must account for temporal variability in at least 41% of data

16 Residual plots of single bands vs SD Distribution of residuals look normal They are not constant Transformation

17 Secchi Depth vs Spectral Band Ratio Scatterplot of Secchi Depth vs Band2/Band3, Band2/Band4,... Band2/Band3 Band2/Band4 Band3/Band Expected trend is seen in the bottom 3 graphs Secchi Depth (m) Band3/Band2 Band4/Band2 Band4/Band

18 Regression equations for SD, band ratios, and dates Equation SD = b 0 + b 1 Band3/Band2 + b 2 Aug14 + b 3 Jul13 + b 4 Jul29 + b 5 Sept15 SD = b 0 + b 1 Band4/Band2 + b 2 Aug14 + b 3 Jul13 + b 4 Jul29 + b 5 Sept15 SD = b 0 + b 1 Band 4/Band3 + b 2 Aug14 + b 3 Jul13 + b 5 Jul29 + b 4 Sept15 b 0 b 1 b 2 b 3 b 4 b Hypothesis: Temporal variability not significant in predictive model

19 Regression Analysis Secchi Depth vs Spectral Band Ratio Regression Equation R 2 RMSE p-value Equation Date Slope Intercept Band3/Band <0.001 <0.001 <0.001 <0.001 Band4/Band < <0.001 <0.001 Band4/Band < <0.001 Conclusion: Temporal accountability increased to 56% of data

20 Residual plots of band ratios vs SD Distribution of residuals look normal Band3/Band2 shows a more constant trend Few outliers

21 Discussion/Conclusions We need to account for temporal variation in model: Band3/Band2 Model: At least 56% of data requires temporal accountability Accounting for temporal variation: More temporally coincident data Factor in spatial variability Consider environmental factors: cloud cover, temperature, humidity, sun angle, etc.

22 Thank You!

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