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 Technologyschott@cis.rit.edu

Lake Ontario Comparison of Temperature & Transmission

Ontario Mid-lakeTemperature Sections

late Aprilmid May

early June

late June

May 25, 1978ITOS

Skylab Photos: chlorophyll maps

AVHRR Lake Ontario Thermal Bar

HCMM Lake Ontario Thermal Bar

IFYGL Aerial Photos

Off Ginna May 22, 1978

Landsat Evolution

Rochester

true color

1972 4 80 m1982 7 30 m1999 7 15 m

YearNumber of Bands

Spot Size

false color infrared

Landsat TM

Landsat TM

Ontario Thermal Bar

LANDSAT: April 23, 1991 Lakes Ontario & Erie

True Color Composite Thermal Channel

Cold center

Warm ring

Landsat TM April 23, 1991

Thermal ChannelTrue Color Composite

Cold center

Warm ring

LANDSAT: May 11, 1992 Lakes Ontario & Erie

Landsat June 12, 1992

True Color Composite Thermal Channel

Landsat TM

True Color Composite(Enhanced)

Thermal band

warmcold

Braddock Bay to Irondequoit Bay

June 23, 1996

Linking Hydrodynamic Models with Remotely Sensed Data

AGLE Simulation including Niagara Inflow

0C 4C 11C 22C

Hyperspectral ImageryHyperspectral Imagery

MISIRIT’s Modular Imaging Spectrometer Instrument

Ginna Nuclear Power Plant

MISIRIT’s Modular Imaging Spectrometer Instrument

West Roch EmbaymentRussell Power PlantJuly 5, 2000Altitude=4000ft

East Roch EmbaymentGenesee River PlumeJuly 5, 2000Altitude=4000ft

MISI thermal image of Russell Power Plant Effluent

MODISModerate Resolution

Imaging Spectroradiometer

Resolution Trades:Temporal: Global Coverage in 1- 2 daysSpatial: 1 km pixels (low)Spectral: 36 bands .4-14.4um

MODISMarch 5, 2005

SeaWiFS

April 12, 1998

SeaWiFSSeptember 3, 1999

AVIRIS FlightlinesMay 20, 1999

11:45 AM

Digital Imaging and Remote Sensing Laboratory

solar glint

Lake Ontario

Hyperspectral Imagery: AVIRIS

Hyperspectral Hyperspectral Concentration MapsConcentration Maps

• Provide user community with water quality maps derived from hyperspectral data to address environmental issues.

AVIRIS Image Cube: Lake Ontario Shoreline

Dr. Rolando Raqueno

AVIRIS May 20, 1999

Spectral Bottom Type Mapping

Dr. Anthony Vodacek

Spectral Bottom Type Mapping

RIT’s MISIOctober 1, 2002

Dr. Anthony Vodacek

Comparison ofEO-1 and Landsat 7

Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality

ALGE Model

Bottom Type A Bottom Type B

particles & algae

CDOM phytoplankton

MODTRAN

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)

HydroLight…

Agriculture Urban

macrophytes

bacteria

Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality

TopoBathymetryTopoBathymetry

requiredrequired

Model of Land/Water InterfaceWhat the Future Holds

•GIS with satellite derived temporal history of Landuse/Landcover

•Hydrological models

•precipitation

•stream flow

•materials transport

•Environmental forcing functions

•insolation

•cloud cover

•wind speed

•precipitation

•air temperature

Where are we going?

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

HydroMod

GL GIS

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

MODIS

IKONOS

AVIRIS MISI

ENJOY!!!

Bottom Type A Bottom Type B

particles & algae

CDOMphytoplankton

Agriculture Urban

macrophytes

bacteria

Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality

• Advanced Very High Resolution Radiometer (1km)• Landsat 5 (120m) Landsat 7 (60m)• MISI (2-10ft)

Remote Sensing Platforms: Remote Sensing Platforms: Airborne compared to SatelliteAirborne compared to Satellite

AVHRRLANDSAT

MISI

Coverage vs. Spatial, Spectral, Temporal Resolutions

AVHRR ~1km1 day

Landsat730m (vis)16 day

CZCSWinter

Chlorophyll ConcentrationChlorophyll Concentration

CZCSSpring

Chlorophyll ConcentrationChlorophyll Concentration

CZCSSummer

Chlorophyll ConcentrationChlorophyll Concentration

Chlorophyll Concentration

CZCSFall

Global Biosphere

Ocean - CZCSLand - AVHRR

LandsatApril 29, 1986

Chernobyl, Russia

April 22, 1986plant in

normal use, pond is warm

Thermal Patterns in Reactor Cooling Pond

April 29, 1986pond cooling,

little or no activity

May 8, 1986pond in

ambient, no activity

Gulf Stream Composite Thermal Patterns

Great Lakes and Western

Atlantic

Gulf Stream

New York CityPhiladelphiaBaltimoreWashington

HCMM thermal

Urban heat islands

Understanding & Monitoring water quality & flow

Great Lakes Hydrodynamics

52Digital Imaging and Remote Sensing LaboratoryRR..II..TT

RR..II..TT

Maximum Density of Water

Colors of Light

• 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.

: Radiant Exitance of EarthTransmission of the : Earth’s Atmosphere

Solar Irradiance Outside Earth’s Atmosphere:

Great Lakes of the World

Great Lakes Profile(Bathymetry & Flow)

Sea Level

282 m229 m

64 m

244 m

Superior Michigan Huron Erie Ontariomodified from The Great Lakes Atlas, 1995

406 m

183.2 m 176 m 176 m 173.5 m

74.2 m

Laurentian Great Lakes

• Hold 18% of the world’s fresh waterHold 18% of the world’s fresh water

• US coast line exceeds US Atlantic coastUS coast line exceeds US Atlantic coast

• About 10% of US and 32% of Canadian About 10% of US and 32% of Canadian population (about 35 million people) live in population (about 35 million people) live in the Laurentian Basinthe Laurentian Basin

• Large fraction of the industrial northeastLarge fraction of the industrial northeast

Seasons of a Dimictic Lake

Thermal Stratification & Mixing in a Dimictic Lake

winter stratification

summerstratification

springmixing

fallmixing

Thermal Bar Process

Summer Stratification

WinterStratification

Lake cross-section

Den

sity

Temperature (Celsius)0 2 4 6

max

imum

den

sity

Thermal Bar

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

Digital Imaging and Remote Sensing Laboratory

N

S

N S

Thermal Bar at 4 Celsius

Temperature Maps from Hydrodynamic Model

vertical cross-section

ALGE Simulation without Niagara inflow

0C 4C 11C 22C

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: 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 43 rd Conference on Great Lakes and St. Lawrence River Research, Cornwall, Ontario, May, 2000

Thesis: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