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

USGS MI-OHWater Science Center

USGS MI-OH Water Science Center

Estimation of a Trophic State Index for Selected Inland

Lakes in Michigan, 1999–2013

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)

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

• 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

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

Landsat Satellite Scene Locations

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

• 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

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

CLMP Newsletter

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

Regression Equations

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.

TSI

Tukey’s Test

Statistical Summary Table

USGS Project Webpage

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

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

Spectral Band Comparison

http://landsat.gsfc.nasa.gov/?p=10643

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

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.

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

Silver Lake, Oceana County, MI

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)

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

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

Approach Identify nutrient controlling

algal blooms (AWRI) Nutrient bioassay, algal ID

(cyanotoxins)

Internal nutrient loading estimates (AWRI), determine flux of P & N from sediments

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).

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

Project Results – Nutrient Loading

Nutrient adjustment scenarios of phosphorus and nitrogen to Silver Lake were processed using the BATHTUB model.

Predicting future lake conditions

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

Thank you!

Contact Info:

Angela Brennan,HydrologistUSGSMI-OH Water Science Center, Lansing, MI

akbrennan@usgs.gov(517) 887- 8905