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 schott@cis.rit.edu

Lake Ontario Comparison of Temperature & Transmission

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

May 25, 1978 ITOS

Skylab Photos: chlorophyll maps

AVHRR Lake Ontario Thermal Bar

HCMM Lake Ontario Thermal Bar

IFYGL Aerial Photos Off Ginna May 22, 1978

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

Landsat TM

Landsat TM Ontario Thermal Bar

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

Landsat TM April 23, 1991

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

Landsat June 12, 1992 True Color Composite Thermal Channel

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

Linking Hydrodynamic Models with Remotely Sensed Data

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 3.8 - 4.2C) 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.

Hyperspectral Imagery

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

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

Imaging Spectroradiometer MODIS Moderate Resolution Imaging Spectroradiometer Resolution Trades: Temporal: Global Coverage in 1- 2 days Spatial: 1 km pixels (low) Spectral: 36 bands .4-14.4um

MODIS March 5, 2005

SeaWiFS April 12, 1998

SeaWiFS September 3, 1999

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

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

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

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

Comparison of EO-1 and Landsat 7

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

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

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

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

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

ENJOY!!!

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

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

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

Chlorophyll Concentration CZCS Winter

Chlorophyll Concentration CZCS Spring

Chlorophyll Concentration CZCS Summer

Chlorophyll Concentration CZCS Fall

Global Biosphere Ocean - CZCS Land - AVHRR

Chernobyl, Russia Landsat April 29, 1986

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

Gulf Stream Composite Thermal Patterns Great Lakes and Western Atlantic

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

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

Maximum Density of Water

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.

Great Lakes of the World

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

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

Seasons of a Dimictic Lake

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

Summer Stratification Thermal Bar Process Lake cross-section Thermal Bar Density Temperature (Celsius) 0 2 4 6 maximum density Summer Stratification Winter Stratification

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

Lake Ontario Comparison of Temperature & Transmission

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

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?

Hydrodynamic Model to Predict this Thermal Bar Phenomenon

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

ALGE Simulation without Niagara inflow 0C 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 3.8 - 4.2C) 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.

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

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

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

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

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

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

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

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

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

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.

Landsat TM April 7, 1991