A Regional Lake Clarity A Regional Lake Clarity Assessment Using Assessment Using

LandsatLandsatSteve Kloiber

Randy Anhorn

OutlineOutline

• Describe the remote sensing Describe the remote sensing method method

• Describe the application to TCMA Describe the application to TCMA lakeslakes

• Present regional lake clarity status Present regional lake clarity status and trendsand trends

Generalized Spectral-Radiometric Generalized Spectral-Radiometric ResponseResponse

450 500 550 600 650 700 750 800 850 900

Wavelength (nm)

Ref

lect

ance

or

Bri

gh

tnes

s Low Clarity Lake

High Clarity Lake

TM1 TM2 TM3 TM4

MSS1 MSS2 MSS3

Red Spectral Region

Remote Sensing Method StepsRemote Sensing Method Steps

• Data AcquisitionData Acquisition

• PreprocessingPreprocessing

• Data ExtractionData Extraction

• Regression ModelingRegression Modeling

• Model ApplicationModel Application

Landsat Data AcquisitionLandsat Data Acquisition

• Cloud-free (<10%)Cloud-free (<10%)

• Peak productivity Peak productivity – mid-July through mid-July through

early Septemberearly September

• About $500 per About $500 per imageimage

• One image covers One image covers TCMATCMA

Ground Observation DataGround Observation Data

• Sampled within 3 days Sampled within 3 days of overpass of overpass

• Water clarity measured Water clarity measured by Secchi diskby Secchi disk

• Sources include Met Sources include Met Council and MPCACouncil and MPCA

• Paired with average Paired with average lake brightness values lake brightness values from satellite imagesfrom satellite images

Data ExtractionData Extraction

• Unsupervised cluster classification used Unsupervised cluster classification used to mask off terrestrial portions of sceneto mask off terrestrial portions of scene

• Shoreline, littoral, and macrophytes Shoreline, littoral, and macrophytes avoided using unsupervised avoided using unsupervised classificationclassification

• Automated signature extraction using Automated signature extraction using vector layer of lakesvector layer of lakes

• Histogram trim leaving darkest 50%Histogram trim leaving darkest 50%

Correlation of Landsat TM and Correlation of Landsat TM and SecchiSecchi

y = -15.583x + 4.6742

R2 = 0.8431

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0.15 0.2 0.25 0.3 0.35 0.4 0.45

TM3:TM1

ln(S

DT

)

Regression ModelingRegression Modeling

cTMbTMTMaSDT 13

1)log(

• r-squared ranges from 0.70 to 0.80• SE ranges from 0.30 to 0.40

Translating Synoptic DataTranslating Synoptic Data

• Images are from late summer, but not Images are from late summer, but not same day each yearsame day each year

• To compare data from year to year, results To compare data from year to year, results should be tranlated to a common scale: should be tranlated to a common scale: Growing Season Mean SDTGrowing Season Mean SDT

• Common methods of estimating GSM Common methods of estimating GSM assume the data are not serially correlatedassume the data are not serially correlated

• Normalized ground observations were fit to Normalized ground observations were fit to a season model based on a sine functiona season model based on a sine function

Seasonal Clarity ModelSeasonal Clarity Model

-0.8

-0.4

0

0.4

0.8

130 155 180 205 230 255

Day of Year

Per

cen

t D

iffe

ren

ce

fro

m L

ake-

Yea

r M

ean

SDT SDT trend

1991 Ground 1991 Satellite

SDTrel = a [sin(2π (j - 90)/182.5)] + b

The sine model is a strong predictor of GSM SDT (R^2 = 0.75)

Map of 2005 ResultsMap of 2005 Results

YEAR

2005

2004

2003

1998

1996

1995

1993

1991

1988

1986

1983

1975

1973

6

4

2

0

Gro

win

g S

eao

n M

ean

SD

T (

m)• Typical range Typical range

– 0.6 – 3.4 m0.6 – 3.4 m

• Regional Regional medianmedian– 1.4 m1.4 m

• Above normalAbove normal– 1975, 1993, 1975, 1993,

19961996

• Below normalBelow normal– 1973, 1988, 1973, 1988,

20032003

Regional Growing Season Mean SDTRegional Growing Season Mean SDT

Lake Clarity TrendsLake Clarity Trends1973 - 20051973 - 2005

• 517 lakes evaluated517 lakes evaluated

• 61 lakes with increasing trends*61 lakes with increasing trends*

• 32 lakes with decreasing trends*32 lakes with decreasing trends** Kendall Tau (P < 0.05)

0

5

10

15

20

25

-12 to -16

-8 to -12

-4 to -8

0 to -4

0 to 44 to 8

8 to 12

12 to 16

Change in SDT (cm/yr)

Nu

mb

er

of

La

kes

Trend ResultsTrend Results

• More lakes had increasing clarity More lakes had increasing clarity (61) than decreasing (32)(61) than decreasing (32)

• Some improvements are related to Some improvements are related to point source removal or septic point source removal or septic system controls system controls – Minnetonka, Tanager, CoonMinnetonka, Tanager, Coon

• Some improvements are due to Some improvements are due to stormwater treatmentstormwater treatment– Josephine, StiegerJosephine, Stieger

Trends for Coon LakeTrends for Coon Lake

0

1

2

3

4

5

1970 1975 1980 1985 1990 1995 2000 2005

Year

Gro

win

g S

easo

n M

ean

SD

T (

m)

Satellite-estimated SDT

Ground-based SDT

Trends for Fish LakeTrends for Fish Lake

Connection of trunkstorm sewer

0

1

2

3

4

5

6

1970 1975 1980 1985 1990 1995 2000 2005

Year

Gro

win

g S

easo

n M

ean

SD

T (

m)

Satellite-estimated SDT

Ground-based SDT

Trends for Lake JosephineTrends for Lake Josephine

Stormwater diversion to wetland treatment

0

1

2

3

4

1970 1975 1980 1985 1990 1995 2000 2005

Year

Gro

win

g S

easo

n M

ean

SD

T (

m)

Satellite-estimated SDT

Ground-based SDT

ConclusionsConclusions

• Valuable tool for comparative limnologyValuable tool for comparative limnology

• BenefitsBenefits– complete spatial coveragecomplete spatial coverage– consistent method from lake to lakeconsistent method from lake to lake– cost-effectivecost-effective

• UsesUses– mapping regional lake clarity mapping regional lake clarity

(hotspots)(hotspots)– identifying trendsidentifying trends

AcknowledgementsAcknowledgements• Water Resources Center, UM (Water Resources Center, UM (Pat Pat

Brezonik)Brezonik)

• Remote Sensing Lab, UM (Remote Sensing Lab, UM (Marv Bauer, Marv Bauer, Leif Olmanson)Leif Olmanson)

• MPCA (Bruce Wilson)MPCA (Bruce Wilson)

• Metropolitan CouncilMetropolitan Council

Other EffortsOther Efforts

• Statewide water clarity assessment - Statewide water clarity assessment - LandsatLandsat

• Imperviousness assessment – Imperviousness assessment – LandsatLandsat

• River water quality assessment – River water quality assessment – hyperspectral (AISA)hyperspectral (AISA)

More InfoMore Info

http://es.metc.state.mn.us/eims

http://water.umn.edu