estimating michigan’s trophic state trends with satellite ... · monitor surface water flow &...
TRANSCRIPT
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Estimating Michigan’s Trophic State Trends with Satellite Imagery
USGS Recent Inland Lake StudiesLori Fuller – USGS Michigan-Ohio Water Science Center
2016 Michigan Inland Lakes ConventionApril 29, 2016
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USGS MI-OHWater Science Center
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USGS MI-OH Water Science Center
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Estimation of a Trophic State Index for Selected Inland
Lakes in Michigan, 1999–2013
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Inland Lakes – Remote Sensing• Regress Secchi-disk transparency (SDT)
measurements to Landsat satellite imagery to produce estimated Trophic State Index (TSI)
• Seven date sets from 1999-2013• 1999-2000 (3,265 lakes)• 2002 (3,278 lakes)• 2003-05 (3,121 lakes) • 2007-08 (3,024 lakes) • 2009-10 (2,591 lakes)• 2011 (3,071 lakes)• 2013 (3,171 lakes)
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Minnesota• Olmanson, L.G., Kloiber, S.M., Bauer, M.E., Brezonik, P.L. (2001). Image
Processing Protocol for Regional Assessments of Lake Water Quality. Water Resources Center and Remote Sensing
Laboratory, University of Minnesota.
Wisconsin• Chipman, J. W., T. M. Lillesand, J. E. Schmaltz, J. E. Leale, and M. J.
Nordheim. (2004). “Mapping Lake Water Clarity with Landsat Images in Wisconsin, USA.” Invited paper, Canadian Journal of Remote Sensing, Special Issue on Remote Sensing and Resource Management in Nearshore & Inland Waters, 30(1):1-7.
Michigan• Nelson, S.A.C., Soranno, P.A., Cheruvelil, K.S., Batzli, S.A., Skole, D.L.
(2002). Assessing regional lake water clarity using Landsat and the role of inter-lake variability. http://foliage.geo.msu.edu/mdeq/docs/Nelson_RS-Secchi_text_2002__09.pdf
• Wiangwang, N. (2002). Water Clarity/Trophic condition monitoring by using satellite remote sensing data. Masters paper, Department of Geography Graduate Program, Michigan State University, MI.
Background
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• The State of Michigan has ~ 4,000 inland lakes greater than 20 acres.
• Measurements– Cooperative Lakes Monitoring Program (MDEQ & ML&SA)
• Each year ~250 inland lakes are sampled by the volunteer program • Weekly sampling
– Wisconsin Department of Natural Resources Surface Water Integrated Monitoring System Database
• Assist in western U.P. of Michigan
• Satellite Imagery– Landsat 5 (1984-2013)– Landsat 7 (1999 – Present (2016))– Landsat 8 (2013 – Present (2016))
Program potential for Michigan
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Methods• Landsat satellite imagery
(5, 7, and 8)– 30-meter cells– 16 day repeat cycle
• Choose/process Imagery– Late summer
• July-September
– Select lakes 20+ acres– Mask out non-water
• Clouds/shadows/haze• Shallow/shoreline• Dense vegetation• Number of 30-meter cells
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Landsat Satellite Scene Locations
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Methods• Field-measurements of SDT
– within +/- 10 days of the Landsat satellite imagery acquisition date, though +/- 3 days is preferable
– 1 measurement per lake in the deepest basin– produce a regression model specific to each path and date of
Landsat satellite imagery
• End results are estimated Trophic State Index (eTSI) values for open-water areas of inland lakes larger than 20 acres
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• Traditionally, water quality is indicated by its Trophic State Index (TSI) value
• TSI can be calculated based on measures of:– Total Phosphorus (TP)– Chlorophyll-a (Chl-a)– Secchi Disk Transparency (SDT)
Trophic State Index
Lake trophic condition TSI value SDT (ft) Chy-a (µg/l) TP (µg/L)
Oligotrophic < 38 > 15 < 2.2 < 10
Mesotrophic 38-48 7.5 - 15 2.2 - 6 10 - 20
Eutrophic 49-61 3 - 7.4 6.1 - 22 20.1 - 50
Hypereutrophic > 61 < 3 > 22 > 50
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Secchi Disk Transparency
Minnesota Pollution Control Agency http://earthobservatory.nasa.gov/Study/WaterQuality/water_quality2.html
Low SDT High TSI Eutrophic/
Hypereutrophic
Mid SDT Mid TSI
Mesotrophic
High SDT Low TSI
Oligotrophic
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CLMP Newsletter
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Regressing SDT to Satellite Imagery
LakeName Band 1 Band 2 Band 3 B1/B3 SDT ft SDT m ln(SDTm)Lansing 64.76923 41.38462 30.46154 2.12626 10.0 3.0480 1.1145
ln(SDTm) = a(Band1/3) + b(Band1) + c
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Regression Equations
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Table 3. Landsat-image and calibration-model data for Estimated Trophic State Index (eTSI), Michigan inland lakes, 1999-2013[SDT, secchi-disk transparency; m, meter; ft, feet, R2, coefficient of determination; SEE, Standard Error of Estimate; TM, Thematic Mapper; x, eTSI; B, Landsat satellite band number]
Image date
Path RowsNumber of
images used in Path
Satellite Days Prior
Days Past
Number of measurements
SDT range(m)
SDT range(ft) R2 SEE Equation:
1999-2000 Estimated TSI7/17/2000 20 30-31 2 Landsat TM 5 4 6 22 1.7 - 6.4 5.5 - 21.0 0.75 0.098x = B1/B3(2.4709) + B1(-0.0111) + -3.06077/30/1999 21 28-31 4 Landsat TM 5 7 7 54 0.9 - 10.0 3.0 - 33.0 0.69 0.234x = B1/B3(1.0250) + B1(-0.0040) + -2.61278/24/2000 22 28-31 4 Landsat TM 5 7 7 46 0.8 - 8.5 2.5 - 28.0 0.78 0.231x = B1/B3(1.0489) + B1(-0.0314) + -1.16728/22/2000 24 27-28 2 Landsat TM 5 7 7 41 0.9 - 8.0 3.0 - 26.0 0.82 0.229x = B1/B3(3.5069) + B1(0.1356) + -19.91728/29/2000 25 28 1 Landsat TM 5 7 7 90 0.6 - 6.9 2.0 - 22.5 0.81 0.239x = B1/B3(1.6309) + B1(0.0468) + -7.6868
2002 Estimated TSI9/1/2002 20 30-31 2 Landsat TM 5 7 5 27 1.8 - 5.0 6.0 - 16.5 0.71 0.146x = B1/B3(0.7484) + B1(-0.0264) + -0.32079/8/2002 21 28-31 4 Landsat TM 5 6 7 75 0.8 - 8.0 2.5 - 26.5 0.80 0.189x = B1/B3(1.6781) + B1(0.0301) + -6.7672
7/13/2002 22 28-31 4 Landsat TM 5 6 7 59 0.9 - 8.2 3.0 - 27.0 0.80 0.202x = B1/B3(1.2221) + B1(0.0236) + -4.89958/30/2002 22 29-31 3 Landsat TM 5 4 6 57 0.9 - 9.9 3.0 - 32.5 0.82 0.203x = B1/B3(1.5958) + B1(0.0535) + -7.85187/11/2002 24 27-28 2 Landsat TM 5 7 7 69 1.1 - 2.0 3.5 - 25.0 0.85 0.173x = B1/B3(1.5083) + B1(0.0492) + -7.73919/4/2002 25 28 1 Landsat TM 5 7 7 83 0.9 - 7.3 3 - 24.0 0.86 0.184x = B1/B3(1.4881) + B1(-0.0335) + -3.5791
2003-05 Estimated TSI9/22/2004 20 30-31 2 Landsat TM 5 6 4 22 1.5 - 7.3 5.0 - 24.0 0.73 0.238x = B1/B3(1.6413) + B1(-0.0196) + -4.86129/13/2004 21 28-31 4 Landsat TM 5 8 6 75 0.8 - 8.5 2.5 - 28 0.69 0.243x = B1/B3(1.3398) + B1(0.0104) + -4.48219/20/2004 22 28-31 4 Landsat TM 5 7 6 50 0.9 - 5.9 3 - 19.0 0.72 0.192x = B1/B3(0.8592) + B1(-0.0380) + -1.06629/21/2005 24 27 1 Landsat TM 5 0 5 10 2.0 - 5.3 6.5 - 17.5 0.73 0.194x = B1/B3(1.1798) + B1(0.1401) + -10.05807/19/2005 24 28 1 Landsat TM 5 0 6 15 2.1 - 6.7 7.0 - 22.0 0.73 0.214x = B1/B3(0.7688) + B1(0.1562) + -9.6935 8/22/2003 25 28 1 Landsat TM 5 10 6 12 6.0 - 20.5 1.8 - 6.3 0.65 0.256x = B1/B3(1.1529) + B1(0.0945) + -7.8513
R2 - statistic with information about the goodness of fit of a model. A measure of how well the regression line approximates the real data points.
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TSI
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Tukey’s Test
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Statistical Summary Table
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USGS Project Webpage
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Future Satellites• Landsat 8
– Additional bands to detect more gradations in light intensity, and added band to pick up on dark blues
https://www.nasa.gov/content/goddard/taking-nasa-usgs-s-landsat-8-to-the-beach/
• ESA Sentinel-2– “water quality parameters such as the surface concentration of
chlorophyll, detect harmful algal blooms, and measure turbidity (or water clarity)”
http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-2/Water_bodies
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Future SatellitesUSGS & NASA
European Space Agency (ESA)
Satellite Landsat Sentinel - 2Swath width 185 km 290 kmResolution 30 (60) m 10 (20, 60) m
Repeat cycle 16 10 days (1 sat), 5 days (2 sat)
Bands Landsat 5&7 Landsat 8 Sentinel - 2
Blue 0.45 - 0.52 0.45 - 0.51 0.46 - 0.52
Red 0.63 - 0.69 0.64 - 0.67 0.65-0.68
ESA Sentinel-2– Smaller lakes– Repeat cycle
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Spectral Band Comparison
http://landsat.gsfc.nasa.gov/?p=10643
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U.S. Department of the InteriorU.S. Geological Survey
Silver Lake Nutrient Loading Study, Oceana Co., MI
2012-2014Angela Brennan, Christopher Hoard,
and Joseph DurisUSGS-MI-OH Water Science Center
&GVSU-AWRI
In cooperation with the Silver Lake Improvement Board
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Project Problem In 2011, Progressive AE
published the “Silver Lake 2011 Water Quality Monitoring Report”
Study results indicated Silver Lake appeared to be undergoing more accelerated eutrophication and if the trend continued, that there would be more frequent and prolonged algal blooms, reduced transparency, and a decline in overall water quality.
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Project Objectives
Describe current water quality in the lake, groundwater, tributaries, and atmosphere
Quantify the water and nutrient budgets for Silver Lake and estimate the contribution of septic systems
Identify the nutrient(s) limiting algal growth in Silver Lake
Present model scenarios of future lake conditions in response to changes in nutrient loading inputs
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Silver Lake, Oceana County, MI
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Approach Monitor surface water flow & establish a
water budget
Water chemistry: Monitoring lake and stream chemistry 4 times per year for
2 years, plus 2-3 storm events (annually)
5 monitoring locations on lake - 3 second study yr Water temp, DO, Conductivity, pH Secchi disc transparency Chlorophyll-a, phytoplankton, N, P (surface & bottom)
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Approach Monitor groundwater influence 4 wells, measure GW levels & nutrient
chemistry for 2 years (North, South, East, & West)
Measure 10 private wells to supplement groundwater flow data
Observe groundwater flow by installing seepage meters
Measure drainage tiles for N & P (38 tiles) North end of lake, used to drain low-lying properties
adjacent to the lake
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Approach Precipitation Wet (rain and snow) & dry (several days
following no precip) samples to determine nutrient deposition (N and P)
Nutrient inputs from lawn runoff and waterfowl Estimated from previously published
literature values
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Approach Identify nutrient controlling
algal blooms (AWRI) Nutrient bioassay, algal ID
(cyanotoxins)
Internal nutrient loading estimates (AWRI), determine flux of P & N from sediments
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Project results – Trophic Status
Trophic status of Silver Lake, Oceana County, Michigan, based on Carlson’s Trophic State Index. (TSI, trophic stateindex; less than 40 represents oligotrophic conditions, 40-50 mesotrophic, greater than 50 represents eutrophic conditions).
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Project results
Concluded that internal loading is not a major source of P to Silver Lake
Algal growth appears to be co-limited by P and N
Cyanotoxin levels are not an issue to date
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Project Results – Nutrient Loading
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Nutrient adjustment scenarios of phosphorus and nitrogen to Silver Lake were processed using the BATHTUB model.
Predicting future lake conditions
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USGS Scientific Investigations Report 2015-5158
Prepared in cooperation with the Silver Lake Improvement Board
Angela K. Brennan, Christopher J. Hoard, Joseph W. Duris, Mary E. Ogdahl, and Alan D. Steinman
https://pubs.er.usgs.gov/ publication/sir20155158
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Thank you!
Contact Info:
Angela Brennan,HydrologistUSGSMI-OH Water Science Center, Lansing, MI
[email protected](517) 887- 8905