HABs Identified and Quantified Using Hyperspectral Imagery

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HABs Identified and Quantified Using Hyperspectral Imagery
Authors: Chelpon S.M.¹, Becker, R.², Cline M.²
1
CUNY Hunter College Department of Geography
NSF REU Program, University of Toledo
2 University of Toledo Department of Environmental Sciences
0.03
R² = 0.7683
20
10
0
0
10
20
Wynne Validation: Ground Reference
Data Derived CI
30
Figure 1. Simis’ method shows good correlation
(R²=0.7683) when used with ground reference data
R² = 0.7784
0.02
0.01
0
0
-0.01
Measured [Blue-Green Algae] (mcg/L)
50.00* radiance
(W/m^2/micrometer/sr)
Simis Validation: Ground Reference
Data Derived [PC]
30
Methods
5
10
15
20
25
30
Uncorrected Spectral Profile: Site
8M
3000
2000
1000
0
400
Measured [Blue-Green Algae] (mcg/L)
500
600
700
800
900
Wavelength (nm)
Figure 2. Wynne’s method shows good correlation
(R²=0.7784) when used with ground reference data
Atmospherically Corrected Spectral
Profile: Site 8M
0.15
Reflectance
• Ground and on water spectral reference data collected
using ASD Spectroradiometer
• Ground reference data used to atmospherically correct
airborne HSI data
• Corrected HSI used to correct HICO satellite imagery
• Algorithms by Lee, Randolph, and Wynne were used to
estimate phycocyanin concentrations [PC] and
cyanobacterial activity (CI)
• Algorithms utilize spectral signatures of chl-a and PC for
identification of HABs
• Statistical analysis used to determine validity of various
methods in comparison to the site measured
concentrations of blue-green algae
Atmospheric Correction
Cyanobacterial Index
• Lake Erie’s Western Basin faces water quality concerns
due to Harmful Algal Blooms (HABs)
• Remote sensing can be cheaper and more efficient than
in situ methods of HAB identification
• Analysis of different types of Hyperspectral Imagery
(HSI) has allowed us to identify and quantify HABs in
Lake Erie’s Western Basin
Results
Derived [PC] (mcg/L)
Introduction & Objectives
0.1
0.05
0
400
-0.05
500
600
700
800
900
Wavelength (nm)
Conclusion
•
Study Area
•
•
Wynne Validation: Remotely Sensed CI
Simis Validation: Remotely Sensed [PC]
HICO
0.03
HSI
R² = 0.4956
20
10
0
-10
0
10
20
30
Measured [Blue-Green Algae] (mcg/L)
Figure 2. Simis’ method shows a moderate correlation
(R²=0.4956) when used with remotely sensed imagery
Cyanobacterial Index
Derived [PC] (mcg/L)
30
Simis and Randolph’s method is only accurate at the
ground level and is adversely affected by errors in the
atmospheric correction technique
Wynne’s method provides consistent accuracy at all
levels of data and is unaffected by errors related to
atmospheric correction
Wynne’s method proves to be more useful in the
detection and quantification of HAB levels overall
HICO
HSI
Acknowledgements
R² = 0.7794
0.02
0.01
0
0
-0.01
5
10
15
20
25
30
Measured [Blue-Green Algae] (mcg/L)
Figure 4. Wynne’s method maintains a good correlation
(R²=0.7794) when used with remotely sensed imagery
This project was aided by National Science Foundation Grant #NSF
DBI-1461124 to the University of Toledo's Lake Erie Center,
“Undergraduate Research and Mentoring- Using the Lake Erie
Sensor Network to Study Land-Lake Ecological Linkages”. We thank
the principal investigators: Dr. Carol Stepien and Dr. Kevin
Czajkowski, the program manager Rachel Lohner, and teaching
assistants Lucas Groat and John Dilworth for help and logistic
support. HSI data provided by NASA GRC

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