Anthony Vodacek

Digital Imaging and Remote Sensing

Laboratory (DIRS)

Center for Imaging Science

Rochester Institute of Technology

October 25, 2007

Assimilating remote sensing with dynamic

data-driven environmental models

Acknowledgments

Collaborators

Jan Mandel - UC-Denver - Data assimilation

Janice Coen - NCAR - Fire propagation modeling

Craig Douglas - UKY - DDDAS

Al Garrett - DOE/SRNL - Hydrodynamic modeling

Bob Kremens - RIT - Fire science and ground measurements

Students

Yan Li - Data assimilation and hydrodynamic modeling

Alvin Spivey - Hydrologic modeling/LCLU

Zhen Wang - Fire propagation modeling

Yushan Zhu - Aquatic optics

Staff

Scott Brown - DIRSIG Project Leader

Niek Sanders - DIRSIG developer

Jason Faulring - Airborne remote sensing

Funding

NSF - CNS-0324989 and CNS-0719626

NOAA - NA06OAR4600217

Dynamic Data-Driven Applications

Systems (DDDAS)

“DDDAS entails the ability to dynamically incorporate additional

data into an executing application, and in reverse, the ability of

an application to dynamically steer the measurement process”

www.dddas.org

Dynamic System

• System model

– Describes how the state of the system evolves over time

• Measurement model

– Describes how measurements are related to state variables - “synthetic data”

11 !! +=tttwAxx

state variable

(TSS concentration

or fire position)system model

(hydrodynamic model

or fire propagation model)

uncertainty in the system

tttvHx +=!

Measurement

(visible reflectance

or thermal radiance)

measurement model

(Hydrolight

or DIRSIG)

uncertainty in the measurement

MODEL

Synthetic

data or image

Real data

or images

output

data

assimilation

The synthetic data generation is an important aspect

and may or may not be complicated to produce

Ensemble

Kalman Filter

(EnKF)

update

Image Data: MODIS

Image Data: Landsat

Image Data: MISI

suspended sediment lots of algae

Lake Ontario

colored dissolved organic matter

very clear water

Water Quality Motivation:

Public Beach Closures

1. Swimming season 2003: June 21 ~ Sep. 1

2. Beach open 48 days (66%)

3. Closed 25 days (34%)

4. Genesee River

• Sediment dominated plume

• Wind and lake currents can push

polluted plume water onto the

beach

• Poor water clarity

• Prior day bacterial counts

• Local rainfall

• Excessive algae

2003 Ontario beach monitoring report. Monroe County Health Department, Bureau of Environmental Quality. September, 2003.

Water Quality Objective

1. Develop an integrated water quality modeling system to simulate thehydrodynamic and optical features in Lake Ontario and its coastalareas

2. Assimilate intermittent satellite or airborne data into a hydrodynamicmodel to obtain a better estimate of model state via an EnsembleKalman Filter

3. Evaluate the EnKF result at single point and spatially

4. Use the results to help forecast beach closures (funded to do this)

5. Use the knowledge of system uncertainty to prioritize or directmanagement efforts to the greatest effect and efficiency

Kalman Filter Notations

+post update

-prior to update

Covariance between

ensemble model states

Ptsystem uncertainty

EstimatedQmodel noise

Derived from MODISNtmeasurement noise

Remote-sensing reflectancemeasured observation

HydrolightHnon-linear measurement

model

ALGEAnon-linear system model

TSS concentrationXtstate variable

t!

+

!

!=

1ˆˆttxAx

11 !

+

!

!+= t

T

tt QAAPP

!

ˆ x t

+ = ˆ x t

" + Gt#

t"H( ˆ x

t

")[ ]

!

Pt

+= (I "G

tH( ˆ x

t

"))P

t

"

!

Gt

= Pt

"H

T( ˆ x

t

") H( ˆ x

t

")P

t

"H

T( ˆ x

t

") + N

t[ ]"1

Time Update (Prediction)

(1) Predict the state variable

(2) Predict the system uncertainty

Measurement Update (Correction)

(1) Compute the Kalman gain

(2) Update estimate with measurement

(3) Update the system uncertainty

Initial estimates for x and PWelch, G. and Bishop, G. (2006) An introduction to the Kalman Filter. Department of

Computer Science, University of North Carolina at Chapel Hill.

Kalman Filter Operation Cycle

Kalman Gain

• Kalman gain is an optimal weighting factor that minimizes

the a posteriori error covariance

• As the measurement noise N approaches zero,

• As the system uncertainty P approaches zero,

NHHP

HPG

T

T

+=

!

!

!

N"0limG =

P#H

T

HP#H

T=1

H

!

P"0limG = 0

large value

Weather variables

• Air/Dew point

temperature

• Wind speed/direction

• Cloud cover/height

• Precipitable water

• Pressure

BASINS

HSPF

3D TSS & CDOM

concentration

profiles

Modeled Rrs

EnKF

MODIS 250 m reflectance data

Updated TSS

concentration profiles

ALGE Hydrolight

• IOPs (a, b, bb)

• CHL concentration profiles

TSS & CDOM

concentration

and discharge

Modeling System Design Overview

Hydrodynamic model: ALGE

low

high

TSS

concentration(g/m3)

Synthetic image: Hydrolight

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0 50 100 150 200 250 300

TSS Concentration (g/m3)

Ca

lcu

late

d R

rs (s

r-1)

0

0.05

0.1

0.15

0.2

0.25

0.3

300 400 500 600 700 800

Wavelength (nm)

Sp

ec

ific

ab

so

rpti

on

co

eff

icie

nt

(m2/m

g)

3D TSS concentration

profiles

Modeled Rrs

Hydrolight

• IOPs (a, b, bb)

• CHL and CDOM concentration

profiles0

0.02

0.04

0.06

0.08

375 475 575 675 775

Wavelength (nm)

Sp

ec

ific

ab

so

rpti

on

co

eff

icie

nt

(m2/m

g)

MODIS 250 m Surface Reflectance Data (645 nm)

(Aug. 18, 2003)

August 18, 2003 – beach closed

due to westward flowing plume

Observation Error Distribution for MODIS

• Error distribution is approximately Gaussian

0

2

4

6

8

10

12

-0

.00

16

-0

.00

14

-0

.00

12

-0

.00

10

-0

.00

08

-0

.00

06

-0

.00

04

-0

.00

02

0.0

00

0

0.0

00

2

0.0

00

4

0.0

00

6

0.0

00

8

0.0

01

0

0.0

01

2

0.0

01

4MODIS Reflectance Observation Error

Freq

uen

cy (

%)

Experimental Data

• TSS-dominated plume dissipation was modeled by ALGE

– Study area: Rochester Embayment

– Study period: July 26 ~ Aug. 18, 2003 (24 days)

– Nudged from whole lake simulation

– Bathymetry

Hydrodynamic simulation

grid spacing:

135 m x 135 m x 3 mGenesee River

depth m

0

2

4

6

8

10

7/25 7/29 8/2 8/6 8/10 8/14 8/18

Date

Win

d s

pe

ed

(m

/s)

0

50

100

150

200

250

300

350

7/25 7/29 8/2 8/6 8/10 8/14 8/18

Date

Dis

ch

arg

e (

m/s

3)

0

50

100

150

200

250

7/25 7/29 8/2 8/6 8/10 8/14 8/18

Date

Riv

er

TS

S c

on

ce

ntr

ati

on

(g

/m3)

-90

0

90

180

270

360

7/25 7/29 8/2 8/6 8/10 8/14 8/18

Date

Win

d d

irecti

on

(d

eg

ree)

Varying Forcing Variables

7/29

west ( ), 7.5mph8/13

southeast ( ), 3 mph

8/8

northwest ( ), 9 mph

7/30

southwest ( ), 4 mph

8/18

East ( ), 10 mph

8/16

Northwest ( ), 12 mph

8/15

Southwest ( ), 8 mph8/14

West ( ), 9 mph

False-color image (R: 858 nm G: 645 nm B: 645 nm)

Vegetation appears red, water appears dark, and plume appears blue

MODIS Reflectance Images showing river plume,

with beach closings and wind direction indicatedOpen

Closed

Closed

ClosedClosed

Closed

Open

Open

Single point EnKF estimate at river mouth

0

40

80

120

160

200

7/25 7/29 8/2 8/6 8/10 8/14 8/18

Date

TS

S c

on

cen

trati

on

(g

/m3)

truth

EnKF estimate

ensemble

model states

0

4

8

12

16

20

7/25 7/29 8/2 8/6 8/10 8/14 8/18

Date

Err

or

of

TS

S c

on

ce

ntr

ati

on

(g

/m3)

Single point EnKF estimate error

Red rectangles indicate a day

MODIS data is available for

updating

• Error of the EnKF estimates decreases with a measurement update

•The EnKF estimates deteriorate until new MODIS data become available

8/14 8/15 8/16 8/18

MODIS

Comparison of spatial estimate of plume

8/14 8/15 8/16 8/18

MODIS

Open-loop

Comparison of spatial estimate of plume

Comparison of spatial estimate of plume

8/14 8/15 8/16 8/18

EnKF estimate

Open-loop

MODIS

90

116

143

170

TSS

concentration (g/m3)

0.0035EnKF estimate

0.0083Ensemble mean

RMSE for all water pixels

(reflectance units)

Spatial EnKF error test for August 18, 2003

Maximum MODIS reflectance value = 0.045 reflectance units

Maximum EnKF reflectance value = 0.044 reflectance units

Future Water Quality Work

• Better weather data (lake breeze)

• Include the variability of model parameters and adjust the parameters based onthe assimilation results

– Particle density and diameter

– Some physical parameters

• Assimilate other satellite/airborne data and field observations into thehydrodynamic model as well

– MODIS Thermal (day/night)

– Landsat series

– WASP

• Increase the number of ensemble members to improve the estimation accuracy

• Batch filtering?

ITR/NGS: Collaborative Research: DDDAS: Data Dynamic Simulation

for Disaster Management

Movie by Janice Coen, NCAR

Fire images captured with the RIT WASP Lite sensor of a

prescribed burn in Kentucky in April 2007

panchromatic

thermal infrared

image segmentation with

Multivariate Gaussian Hidden

Markov Random Field

automated extraction

of fire line parameters

smoke

fire

burn

scar

AVIRIS

MODIS

airborne

simulator

Image processing methods for fire features

atmosphere-fire model

EnKF

ground sensors and

airborne imagerydata transmission

and visualization

synthetic senor and

image dataoutput

update

Fire Propagation Modeling System

Estimating the infrared scene radiance requires 3 phenomena to be modeled:

1. Radiation from the hot ground under the fire front and the cooling of the

ground after the fire front passes.

2. The direct radiation to the sensor from the 3D flame.

3. The radiation from the 3D flame that is reflected from the nearby ground.

Generating the synthetic image

16 minutes after ignition 74 minutes after ignition

2D temperature maps with cooling

Flame tilt

Side projections of 3D flame structure

estimated from model output heat flux

t=0s t=12s t=24s t=36s

DIRSIG: The Digital Imaging and Remote Sensing Image Generation ModelA physics-based ray tracing model for generating synthetic remote sensing scenes

near infrared simulation of a section of the RIT campus

0 0.108 W cm-2 sr-1 µm-1

Final rendering: synthetic shortwave infrared scene of a grassfire at night

0 0.459 W cm-2 sr-1 µm-1

Final rendering: synthetic midwave infrared scene of a grassfire at night

0 0.117 W cm-2 sr-1 µm-1

Final rendering: synthetic longwave infrared scene of a grassfire at night

Short wave infrared

Mid wave infrared

Wildfire Airborne Sensor Project images

of a wildfire in Montana