SAR detection and model tracking of oil slicks in the Gulf of Mexico

Xiaofeng Li

NOAA/NESDIS

Xiaofeng.Li@noaa.gov

Contributors:William Pichel, NOAA, 5200 Auth Road, Room 102, Camp Springs, MD, 20746, USABiao Zhang and Will Perrie, Bedford Institute of Oceanography, Dartmouth, CANADAOscar Garcia, Florida State University, 117 N. Woodward Avenue, Tallahassee, FL, 32306, USAYongcun Cheng, Danish National Space Center, DTU, DK-2100, Copenhagen, Denmark Peng Liu, George Mason University

Outline

1. Oil Spill Detection in SAR image

2. Tracking of oil spill movement in the Gulf of Mexico

3. Deepwater Horizon Event – NESDIS Effort to Map Surface Oil with Satellite SAR

Oil detection with image data and complex data:

1.1 Oil detection with single-pol SAR image

1.2. A Multi-Pol SAR processing chain to observe oil fields

1. Oil Slicks Detection with SAR

January, 2009

Mechanism:• Oil slick damp the ocean surface capillary waves – making the

surface smoother• The smooth surface will reflect the radar pulse in the forward

direction -> Less backscatter. Radar image is dark.

Challenge:• There are a lot of look-alikes in the SAR image, i.e., low wind,

coastal upwelling, island shadow, rain cell, biogenic slicks, etc.

Solution:• Statistical method to extract oil slick from the SAR image• Separate the look-alikes from the oil slick

1.1 Oil Slicks Detection with single-polSAR image

Neural Network Algorithm

Canadian Journal of Remote Sensing, Vol 25, No. 5 2009

1.1 Oil Slicks Detection with single-polSAR image- Algorithms

Neural Network Algorithm demo

Slick

No-Slick

8bit pixel valueWind MagnitudWind Direction

Wind Magnitud (-3 h)Wind Direction (-3 h)Wind Magnitud (-6 h)Wind Direction (-6 h)Wind Magnitud (-9 h)Wind Direction (-9 h)

Beam Mode Incidence AngleSea Surface Height

Geostrophic Currents MagnitudGeostrophic Currents Direction

Neighboor Texture 1 (Brightness)Neighboor Texture 2 (Contrast)

Neighboor Texture 3 (Distribution)Neighboor Texture 4 (Entropy)

Neighboor Texture 5 (variability)Neighboor Texture 6 (Std Deviation)

1st Filter Reaction2nd Filter Reaction3rd Filter Reaction4th Filter Reaction5th Filter Reaction6th Filter Reaction7th Filter Reaction8th Filter Reaction9th Filter Reaction

1.1 Oil Slicks Detection with single-polSAR image- Algorithms

1.1 Oil Slicks Detection with single-polSAR image- Results

1.1 Oil Slicks Detection with single-polSAR image- Results

1.1 Oil Slicks Detection with single-polSAR image- Results in GIS

TCNNA now has been trained to process SAR data from:-RADARSAT 1-2- ENVISAT- ALOS

In this example, Monitoring BP oil spilla SAR image was collected by Envisat on June 9, 2010.Oil is detected close to Louisiana peninsula.

TCNNA GUI: Display of a a pre-processed output.This Window of the GUI shows wind conditions prevailing on the data from CMOD5 model.

A scaled image is rotated and shownto adjust contrast along incidence angles

The TCNNA Output is exported with itsGeo-referenced tagged information. Ready for Arcmap.

TCNNA output handled and converted to Shapefile in ArcMap or Kml for Google Earth

1.1 Single-Pol SAR oil detection summary

• Statistical-based SAR oil detection algorithms are developed• These algorithm are tuned for RADARSTA-1, ENVISAT, ALOS, ERS in various beam

mode• Interactive oil spill analysis software have been developed to aid oil spill analysis at

NOAA

• Total power span image

• Co-polar correlation coefficient

• Target Decomposition entropy (H) mean scattering angle (α) anisotropy A

• The combined feature F

2 2 2 2

hh hv vh vvspan S S S S

The combination of polarimetric features extraction

*

* *

hh vv

hh hh vv vv

S S

S S S S

3

3 31

1

3

1

1 2

1 2

log ( ) ii i i

ik

k

i ii

H p p p

p

A

F H A

1.2. A Multi-Polarimetric SAR Processing Chain to ObserveOil Fields in the Gulf of Mexico

PolSAR sea surface scattering

• Sea surface (Rough)• Bragg scattering• Low pol.entropy• High HH VV correlation

• Oil spill (Smooth)• Non Bragg scattering• High pol. entropy• Low HH VV correlation

Example with: NASA UAVSAR polarimetric L-band SAR, with range resolution of 2 m and a range swath

greater than 16 km, June 23, 2010 20:42 (UTC)

The image recorded by a video camera confirmed the oil spill.

A sub scene of UAVSAR image

Extracted polarimetric features from the UAVSAR data

The combined polarimetric features and the result of OTSU segmentation

VV HH

R2 fine quad-pol SAR image of oil slicks in the GOM acquired at 12:01 UTC May 8, 2010

Imaging mode: fine quad-pol SLCAzimuth pixel spacing: 4.95 mRange pixel spacing: 4.73 mNear range incidence: 41.9 degreeFar range incidence: 43.3 degreeNoise floor: ~ -36 dB

Case 2: RADARSAT-2 Oil slick observation

Clean sea surface Oil slick-covered area

Surface Bragg scattering Non-Bragg scattering

Capillary and small gravity waves were dampedUnder moderate radar incidence anglesand wind speeds

Case 2: RADARSAT-2 Oil slick observation

R2 quad-pol observations

scattering matrix

entropy alpha

represent and characterize scattering mechanism

Case 2: RADARSAT-2 Oil slick observation

Entropy represents randomness of scattering mechanism

Entropy low

significant polarimetric information

Entropy high

backscatter becomes depolarized

Surface Bragg scattering Non-Bragg scattering

Case 2: RADARSAT-2 Oil slick observation

Alpha angle characterizes scattering mechanism

o30 Surface Bragg scattering dominates

oo 5030~ Dipole scattering dominates

oo 9050~ Even-bounce scattering dominates

Non-Bragg scattering

Bragg scattering

Case 2: RADARSAT-2 Oil slick observation

For ocean surface Bragg scattering

HVS is small

HHS VVSand highly correlated

phase difference is close to o0

2* )Re( HVVVHH SSS

0

For non-Bragg scattering

HHS VVSand have low correlation

phase difference is close to o180

2* )Re( HVVVHH SSS

0

CP for quad-polarization:

Case 2: RADARSAT-2 Oil slick observation

Case 2: RADARSAT-2 Oil slick observation

Zhang, B., W. Perrie, X. Li, and W. G. Pichel (2011), Mapping sea surface oil slicks using RADARSAT-2 quad-polarizationSAR image, Geophys. Res. Lett., 38, L10602, doi:10.1029/2011GL047013.

Case 2: RADARSAT-2 Oil slick observation

0

Experimental results demonstrate the physically-based and computer-time efficiency of the two proposed approaches for both oil slicks and man-made metallic targets detection purposes, taking full advantage of full-polarimetric and full-resolution L-band ALOS PALSAR SAR data.

Moreover, the proposed approaches are operationally interesting since they can be blended in a simple and very effective processing chain which is able to both detect and distinguish oil slicks and manmade metallic targets in polarimetric SAR data.

1.2. A Multi-Polarimetric SAR Processing Chain to ObserveOil Fields in the Gulf of Mexico - Summary

• Introduction to NOAA GNOME Oil drifting model

• GNOME Simulation• Simulation results – case study• Conclusions

Main impacts are: - harm to life, property and commerce- environmental degradation

2. Tracking of oil spill movement in the Gulf of Mexico

Oil Slicks drifting simulation with GNOME model

GNOME (General NOAA Operational Modeling Environment) is the oil spill trajectory model used by NOAA’s Office of Response and Restoration (OR&R) Emergency Response Division (ERD) responders during an oil spill. ERD trajectory modelers use GNOME in Diagnostic Mode to set up custom scenarios quickly.

NOAA OR&R employs GNOME as a nowcast/forecast model primarily in pollution transport analyses.

GNOME can:• predict how wind, currents, and other processes might move and spread

oil spilled on the water. • learn how predicted oil trajectories are affected by inexactness

("uncertainty") in current and wind observations and forecasts. • see how spilled oil is predicted to change chemically and physically

("weather") during the time that it remains on the water surface.

2. Tracking of oil spill movement in the Gulf of Mexico

GNOME input:

- Location file, specific for each region (tide, bathymetry ,etc.)

- User file

Currents: ocean model outputs

Winds: model or buoy wind

Oil information: Oil locations from SAR image

Model Output

Spill Trajectory Types• Best Guess Trajectory (Black Splots)

Spill trajectory that assumes all environmental data and forecasts are correct. This is where we think the oil will go.

• Minimum Regret Trajectory (Red Splots) Summary of uncertainty in spill trajectories from possible errors in environmental data and forecasts. This is where else the oil could go.

Case study: Oil pipeline leak in July 2009

Oil Pipeline leaking in July 2009

Oil pipeline leak in July 2009

Surface Currents:

Navy Coastal Ocean Model (NCOM) outputsspatial resolution of NCOM is 1/8º

temporal resolution is 3 hours

Oil pipeline leak in July 2009

Winds:

NDBC hourly wind vector

Oil pipeline leak in July 2009

Initial Oil distribution information: denoted by blue dots.

Model run: 7/26/2009 15:00 UTC

7/29/2009 04:00 UTC

16:30 UTC on July 27, 2009

Simulation Results:GNOME simulated best guess trajectory of oil spill denoted by blue circles:

At the ending of the simulation,

04:00 UTC on July 29, 2009.

GNOME simulated locations of the oil spill at 04:00 UTC on July 29, 2009: (a) only use wind to force the model; (b) only use the currents to force the model.

Simulation Results:GNOME simulated best guess trajectory of oil spill denoted by blue circles:

• In this work, the GNOME model was used to simulate an oil spill accident in the Gulf of Mexico. The ocean current fields from NCOM and wind fields measured from NDBC buoy station were used to force the model. The oil spill observations from ENVISAT ASAR and ALOS SAR images were used to determine the initial oil spill information and verify the simulation results. The comparisons at different time show good agreements between model simulation and SAR observations.

Marine Pollution Bulletin, 2010

2. Tracking of oil spill movement in the Gulf of Mexico - Summary

Summary:

• SAR images from multiplatform spaceborne SAR satellite can be used for oil spill/seep detection in the Gulf of Mexico.

• Statistical-based oil spill detection algorithms have been developed for single-pol SAR image. These algorithms have been tuned for different satellites and different imaging mode.

• A Multi-Frequency Polarimetric SAR Processing Chain to Observe Oil Fields in the Gulf of Mexico are also developed to provide fast oil spill response at NOAA.

• The oil spill drifting can be simulated using the NOAA GNOME model with inputs from background current field, time series of wind measurement, and the initial oil spill location.

Operational Response Requires:• SAR is primary data, visible Sun glint secondary, others tertiary• Need multiple looks per day received within 1-2 hours• Many sources of data are required• Well-trained staff of analysts (10-12) to cover multiple shifts per day• Automated mapping would be useful for complicated spill patterns• Array of model, in situ, and complementary imagery and products help by providing an oceanographic context.

Wish for the Future:What if SAR data were available like this all the time at no per-image cost; i.e., just like most other satellite remote sensing data?