a regional lake clarity assessment using landsat steve kloiber randy anhorn
TRANSCRIPT
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A Regional Lake Clarity A Regional Lake Clarity Assessment Using Assessment Using
LandsatLandsatSteve Kloiber
Randy Anhorn
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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
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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
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Remote Sensing Method StepsRemote Sensing Method Steps
• Data AcquisitionData Acquisition
• PreprocessingPreprocessing
• Data ExtractionData Extraction
• Regression ModelingRegression Modeling
• Model ApplicationModel Application
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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
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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
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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%
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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
)
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Regression ModelingRegression Modeling
cTMbTMTMaSDT 13
1)log(
• r-squared ranges from 0.70 to 0.80• SE ranges from 0.30 to 0.40
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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
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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)
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Map of 2005 ResultsMap of 2005 Results
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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More InfoMore Info
http://es.metc.state.mn.us/eims
http://water.umn.edu