1. A Myopic History of Great Lakes Remote Sensing
Dr. John R. Schott
Digital Imaging and Remote Sensing Laboratory (DIRS)
Center for Imaging Science
Rochester Institute of Technology

2. Lake Ontario Comparison of Temperature & Transmission

3. Ontario Mid-lake Temperature Sections
late April
mid May
early June
late June

4. May 25, 1978 ITOS

5. Skylab Photos: chlorophyll maps

6. AVHRR Lake Ontario Thermal Bar

7. HCMM Lake Ontario Thermal Bar

8. IFYGL Aerial Photos
Off Ginna May 22, 1978

9. Landsat Evolution 1972 4 80 m 1982 7 30 m 1999 7 15 m Year
Number of Bands
Spot Size
Rochester
false color infrared
true color

10. Landsat TM

11. Landsat TM
Ontario Thermal Bar

12. LANDSAT: April 23, 1991 Lakes Ontario & Erie
Cold center
Warm ring
True Color Composite
Thermal Channel

13. Landsat TM
April 23, 1991

14. LANDSAT: May 11, 1992 Lakes Ontario & Erie
Cold center
Warm ring
True Color Composite
Thermal Channel

16. Landsat June 12, 1992
True Color Composite
Thermal Channel

18. Landsat TM Braddock Bay to True Color Irondequoit Bay Thermal band
Composite
(Enhanced)
Thermal
band
warm
cold
June 23, 1996

20. Linking Hydrodynamic Models with Remotely Sensed Data

21. AGLE Simulation including Niagara Inflow
Example outputs of the ALGE 3-D hydrodynamic model with two validation images. The surface temperature map images show the formation and the two phase propagation of the thermal bar (water temperature of C) in Lake Ontario. (Top) Images are for the spring warming conditions after the Niagara inflow and St. Lawrence outflow were added. (Bottom right (2)) Images are east-west cross sections of the lake corresponding to the surface images directly above. (Bottom left (2)) Images are AVHRR derived temperature maps using a different color code and illustrate the need for the incorporation of the Niagara inflow.

22. Hyperspectral Imagery

23. MISI RIT’s Modular Imaging Spectrometer Instrument
Ginna Nuclear Power Plant

24. MISI RIT’s Modular Imaging Spectrometer Instrument
West Roch Embayment
Russell Power Plant
July 5, 2000
Altitude=4000ft
East Roch Embayment
Genesee River Plume
July 5, 2000
Altitude=4000ft
MISI thermal image of Russell Power Plant Effluent

25. Imaging Spectroradiometer
MODIS
Moderate Resolution
Imaging Spectroradiometer
Resolution Trades:
Temporal: Global Coverage
in 1- 2 days
Spatial: 1 km pixels (low)
Spectral: 36 bands um

26. MODIS
March 5, 2005

27. SeaWiFS
April 12, 1998

28. SeaWiFS
September 3, 1999

29. Hyperspectral Imagery: AVIRIS
solar glint
AVIRIS Flightlines
May 20, 1999
11:45 AM
Digital Imaging and Remote Sensing Laboratory

30. Hyperspectral Concentration Maps
AVIRIS Image Cube: Lake Ontario Shoreline
Provide user community with water quality maps derived from hyperspectral data to address environmental issues.
Dr. Rolando Raqueno

31. Spectral Bottom Type Mapping
Dr. Anthony Vodacek
AVIRIS
May 20, 1999

32. Spectral Bottom Type Mapping
Dr. Anthony Vodacek
RIT’s MISI
October 1, 2002

33. Comparison of EO-1 and Landsat 7

34. Airborne Hyperspectral Imagery Analysis
Assessing Near Shore Water Quality
Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality
MODTRAN
ALGE Model
Agriculture
Urban
bacteria
CDOM
phytoplankton
HydroLight…
macrophytes
Modeling Strategy
Solar Spectrum Model (MODTRAN)
Atmospheric Model (MODTRAN)
Air-Water Interface (DIRSIG/Hydrolight)
In-Water Model (HYDROMOD=
Hydrolight/OOPS + MODTRAN)
Bottom Features(HYDROMOD/DIRSIG)
particles & algae
Bottom Type A
Bottom Type B

35. Model of Land/Water Interface What the Future Holds
TopoBathymetry
required

36. Where are we going?
GIS with satellite derived temporal history of Landuse/Landcover
Hydrological models
precipitation
stream flow
materials transport
Environmental forcing functions
insolation
cloud cover
wind speed
air temperature
GL GIS

37. Where are we going? Lakewide Hydrodynamic models with local
and regional inputs
temperature and flow models
material transport models
bio-optical models
productivity models driven by temperature, flow, transport, and optical models
bio-optical models to predict remotely sensed observables
Use of thermal and reflective remote sensing and surface measurements in feedback loops to calibrate models
GL GIS
HydroMod

38. Future Remote Sensing Trends:
commercial satellites
more than just pretty pictures / actual
physical earth measurements
higher spatial resolution
increased spectral resolution/
hyperspectral imaging
RS links to models:
inputs to climate models
verification and validation of models
more products available to public
IKONOS
MODIS
AVIRIS
MISI

39. ENJOY!!!

40. Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality
Agriculture
Urban
CDOM
bacteria
phytoplankton
macrophytes
particles & algae
Bottom Type A
Bottom Type B

41. Remote Sensing Platforms: Airborne compared to Satellite
Advanced Very High Resolution Radiometer (1km)
Landsat 5 (120m) Landsat 7 (60m)
MISI (2-10ft)
LANDSAT
AVHRR
MISI

42. Coverage vs. Spatial, Spectral, Temporal Resolutions
AVHRR
~1km
1 day
Landsat7
30m (vis)
16 day

43. Chlorophyll Concentration
CZCS
Winter

44. Chlorophyll Concentration
CZCS
Spring

45. Chlorophyll Concentration
CZCS
Summer

46. Chlorophyll Concentration
CZCS
Fall

47. Global Biosphere
Ocean - CZCS
Land - AVHRR

48. Chernobyl, Russia
Landsat
April 29, 1986

49. Thermal Patterns in Reactor Cooling Pond
April 22, 1986
plant in
normal use,
pond is warm
May 8, 1986
pond in ambient,
no activity
April 29, 1986
pond cooling, little or no activity

50. Gulf Stream Composite Thermal Patterns
Great Lakes and Western Atlantic

51. Gulf Stream HCMM thermal Urban heat islands New York City Philadelphia
Baltimore
Washington

52. Great Lakes Hydrodynamics
A story based on only two graphs...
Understanding & Monitoring
water quality & flow
R.I.T 52
Digital Imaging and Remote Sensing Laboratory

53. Maximum Density of Water

54. Colors of Light
Solar Irradiance
Outside Earth’s
Atmosphere:
Transmission of the
: Earth’s Atmosphere
: Radiant Exitance of Earth
Humans can see in the visible region
These are mostly reflected photons from the Sun, Moon or lights.
Some animals can see in the near infrared (NIR) region
This gives them improved contrast of prey against vegetation.
Some sensors can “see” in the long-wave infrared (LWIR)
This allows them to measure temperatures without touching it.

55. Great Lakes of the World

56. Great Lakes Profile (Bathymetry & Flow)
Sea Level
229 m
282 m
244 m
406 m
Superior Michigan Huron Erie Ontario
modified from The Great Lakes Atlas, 1995

57. Laurentian Great Lakes
Hold 18% of the world’s fresh water
US coast line exceeds US Atlantic coast
About 10% of US and 32% of Canadian population (about 35 million people) live in the Laurentian Basin
Large fraction of the industrial northeast

58. Seasons of a Dimictic Lake

59. Thermal Stratification & Mixing in a Dimictic Lake
winter
stratification
spring
mixing
summer
stratification
fall
mixing

60. Summer Stratification
Thermal Bar Process
Lake cross-section
Thermal Bar
Density
Temperature (Celsius)
maximum density
Summer Stratification
Winter
Stratification

61. Thermal Bar Spring Progression Lake Ontario Cross-Sections
Late April
Mid May
Early June
Late June

62. Lake Ontario Comparison of Temperature & Transmission

64. Can Remote Sensing Help?
Can we ‘see’ :
Water quality
Hydrodynamic processes that impact water quality and materials transport
Impact of global / regional forcing functions

65. Questions When does the thermal bar occur? How long does it last?
What functions drive the start, progression
and end?
Can we predict these occurrences?
How does it effect water quality?

66. Hydrodynamic Model to Predict this Thermal Bar Phenomenon

67. Temperature Maps from Hydrodynamic Model
Thermal Bar at 4 Celsius N S N S
vertical
cross-section
Digital Imaging and Remote Sensing Laboratory

68. ALGE Simulation without Niagara inflow0C 4C
11C
22C
Example outputs of the ALGE 3-D hydrodynamic model with two validation images. The surface temperature map images show the formation and the two phase propagation of the thermal bar (water temperature of C) in Lake Ontario. (Top) Images are for the spring warming conditions before the Niagara inflow and St. Lawrence outflow were added. (Bottom right (2)) Images are east-west cross sections of the lake corresponding to the surface images directly above. (Bottom left (2)) Images are AVHRR derived temperature maps using a different color code and illustrate the need for the incorporation of the Niagara inflow.

105. Niagara River: localized plume study
6 hours
12 hours
18 hours
24 hours

106. ALGE simulation including variable inflow at Niagara (March-August 1998)

107. ALGE simulation including variable inflow at Niagara (March-August 1998)

108. ALGE simulation including variable inflow at Niagara (March-August 1998)

109. ALGE simulation including variable inflow at Niagara (March-August 1998)

110. ALGE simulation including variable inflow at Niagara (March-August 1998)

111. ALGE simulation including variable inflow at Niagara (March-August 1998)

112. ALGE simulation including variable inflow at Niagara (March-August 1998)

113. ALGE simulation including variable inflow at Niagara (March-August 1998)

114. 4D Hydrodynamic Modeling
Reference: Schott, de Alwis, Raqueno, Barsi. “Calibration of a Great Lake Hydrodynamic Model Using Remotely Sensed Imagery,” presented at the International Association for Great Lakes Research 43rd Conference on Great Lakes and St. Lawrence River Research, Cornwall, Ontario, May, 2000
Thesis: de Alwis. Simulation of the formation and propagation of the thermal bar on Lake Ontario. RIT, M.S. Thesis, 1999.

115. Landsat TM
April 7, 1991