Estimating Michigan s Trophic State Trends with Satellite Imagery USGS Recent Inland Lake Studies

Transcription

1 Estimating Michigan s Trophic State Trends with Satellite Imagery USGS Recent Inland Lake Studies Lori Fuller USGS Michigan-Ohio Water Science Center 2016 Michigan Inland Lakes Convention April 29, 2016

2 USGS MI-OH Water Science Center

3 USGS MI-OH Water Science Center

4 Estimation of a Trophic State Index for Selected Inland Lakes in Michigan,

5 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 (3,265 lakes) (3,278 lakes) (3,121 lakes) (3,024 lakes) (2,591 lakes) (3,071 lakes) (3,171 lakes)

6 Background 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 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.

7 Program potential for Michigan 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 ( ) Landsat 7 (1999 Present (2016)) Landsat 8 (2013 Present (2016))

8 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 Methods

9 Landsat Satellite Scene Locations

10 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

11 Trophic State Index 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) Lake trophic condition TSI value SDT (ft) Chy-a (µg/l) TP (µg/l) Oligotrophic < 38 > 15 < 2.2 < 10 Mesotrophic Eutrophic Hypereutrophic > 61 < 3 > 22 > 50

12 Secchi Disk Transparency High SDT Low TSI Oligotrophic Mid SDT Mid TSI Mesotrophic Low SDT High TSI Eutrophic/ Hypereutrophic Minnesota Pollution Control Agency

13 CLMP Newsletter

14 Regressing SDT to Satellite Imagery ln(sdtm) = a(band1/3) + b(band1) + c LakeName Band 1 Band 2 Band 3 B1/B3 SDT ft SDT m ln(sdtm) Lansing

15 Regression Equations

16 Table 3. Landsat-image and calibration-model data for Estimated Trophic State Index (etsi), Michigan inland lakes, [SDT, secchi-disk transparency; m, meter; ft, feet, R 2, coefficient of determination; SEE, Standard Error of Estimate; TM, Thematic Mapper; x, etsi; B, Landsat satellite band number] Image date Path Rows Number of images used in Path Satellite Days Prior Days Past Number of measurements SDT range(m) SDT range(ft) R 2 SEE Equation: Estimated TSI 7/17/ Landsat TM x = B1/B3(2.4709) + B1( ) /30/ Landsat TM x = B1/B3(1.0250) + B1( ) /24/ Landsat TM x = B1/B3(1.0489) + B1( ) /22/ Landsat TM x = B1/B3(3.5069) + B1(0.1356) /29/ Landsat TM x = B1/B3(1.6309) + B1(0.0468) Estimated TSI 9/1/ Landsat TM x = B1/B3(0.7484) + B1( ) /8/ Landsat TM x = B1/B3(1.6781) + B1(0.0301) /13/ Landsat TM x = B1/B3(1.2221) + B1(0.0236) /30/ Landsat TM x = B1/B3(1.5958) + B1(0.0535) /11/ Landsat TM x = B1/B3(1.5083) + B1(0.0492) /4/ Landsat TM x = B1/B3(1.4881) + B1( ) Estimated TSI 9/22/ Landsat TM x = B1/B3(1.6413) + B1( ) /13/ Landsat TM x = B1/B3(1.3398) + B1(0.0104) /20/ Landsat TM x = B1/B3(0.8592) + B1( ) /21/ Landsat TM x = B1/B3(1.1798) + B1(0.1401) /19/ Landsat TM x = B1/B3(0.7688) + B1(0.1562) /22/ Landsat TM x = B1/B3(1.1529) + B1(0.0945) R 2 - statistic with information about the goodness of fit of a model. A measure of how well the regression line approximates the real data points.

17

18 TSI

19

20 Tukey s Test

21 Statistical Summary Table

22 USGS Project Webpage

23

24 Landsat 8 Future Satellites Additional bands to detect more gradations in light intensity, and added band to pick up on dark blues ESA Sentinel-2 water quality parameters such as the surface concentration of chlorophyll, detect harmful algal blooms, and measure turbidity (or water clarity)

25 Future Satellites USGS & NASA European Space Agency (ESA) Satellite Landsat Sentinel - 2 Swath width 185 km 290 km Resolution 30 (60) m 10 (20, 60) m Repeat cycle days (1 sat), 5 days (2 sat) ESA Sentinel-2 Smaller lakes Repeat cycle Bands Landsat 5&7 Landsat 8 Sentinel - 2 Blue Red

26 Spectral Band Comparison

27 Silver Lake Nutrient Loading Study, Oceana Co., MI Angela Brennan, Christopher Hoard, and Joseph Duris USGS-MI-OH Water Science Center & GVSU-AWRI In cooperation with the Silver Lake Improvement Board U.S. Department of the Interior U.S. Geological Survey

28 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.

29 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

30 Silver Lake, Oceana County, MI

31 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)

32 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

33 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

34 Approach Identify nutrient controlling algal blooms (AWRI) Nutrient bioassay, algal ID (cyanotoxins) Internal nutrient loading estimates (AWRI), determine flux of P & N from sediments

35 Project results Trophic Status Trophic status of Silver Lake, Oceana County, Michigan, based on Carlson s Trophic State Index. (TSI, trophic state index; less than 40 represents oligotrophic conditions, mesotrophic, greater than 50 represents eutrophic conditions).

36 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

37 Project Results Nutrient Loading

38 Predicting future lake conditions Nutrient adjustment scenarios of phosphorus and nitrogen to Silver Lake were processed using the BATHTUB model.

39 USGS Scientific Investigations Report 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 publication/sir

40 Contact Info: Angela Brennan, Hydrologist USGS MI-OH Water Science Center, Lansing, MI Thank you! (517)