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    Extra_Li_XF_2011_IGARSS_OilSpill.pptx Extra_Li_XF_2011_IGARSS_OilSpill.pptx Presentation Transcript

    • 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, USA
      Biao Zhang and Will Perrie, Bedford Institute of Oceanography, Dartmouth, CANADA
      Oscar Garcia, Florida State University, 117 N. Woodward Avenue, Tallahassee, FL, 32306, USA
      Yongcun Cheng, Danish National Space Center, DTU, DK-2100, Copenhagen, Denmark
      PengLiu, George Mason University
    • Outline
      Oil Spill Detection in SAR image
      Tracking of oil spill movement in the Gulf of Mexico
      Deepwater Horizon Event –
      NESDIS Effort to Map Surface Oil with Satellite SAR
    • 1. OilSlicks Detection with 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
      January, 2009
    • 1.1 Oil Slicks Detection with single-polSAR image
      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- Algorithms
      Neural Network Algorithm
      Canadian Journal of Remote Sensing, Vol 25, No. 5 2009
    • 8bit pixel value
      Wind Magnitud
      Wind 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 Angle
      Sea Surface Height
      Geostrophic Currents Magnitud
      Geostrophic 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 Reaction
      2nd Filter Reaction
      3rd Filter Reaction
      4th Filter Reaction
      5th Filter Reaction
      6th Filter Reaction
      7th Filter Reaction
      8th Filter Reaction
      9th Filter Reaction
      1.1 Oil Slicks Detection with single-polSAR image- Algorithms
      Slick
      No-Slick
      Neural Network Algorithm demo
    • 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
    • In this example, Monitoring BP oil spill
      a SAR image was collected by Envisat on June 9, 2010.
      Oil is detected close to Louisiana peninsula.
      TCNNA now has been trained to process SAR data from:
      -RADARSAT 1-2
      • ENVISAT
      • ALOS
    • 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 shown
      to adjust contrast along incidence angles
      The TCNNA Output is exported with its
      Geo-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
    • 1.2. A Multi-Polarimetric SAR Processing Chain to ObserveOil Fields in the Gulf of Mexico
      The combination of polarimetric features extraction
      Total power span image
      Co-polar correlation coefficient
      Target Decomposition
      entropy (H)
      mean scattering angle (α)
      anisotropy A
      The combined feature F
    • 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 UAVSARpolarimetric L-band SAR, with range resolution of 2 m and a range swath greater than 16 km, June 23, 201020:42 (UTC)
      A sub scene of UAVSAR image
      The image recorded by a video camera
      confirmed the oil spill.
    • Extracted polarimetric features from the UAVSAR data
    • The combined polarimetric features and the result of OTSU segmentation
    • Case 2: RADARSAT-2 Oil slick observation
      Imaging mode: fine quad-pol SLC
      Azimuth pixel spacing: 4.95 m
      Range pixel spacing: 4.73 m
      Near range incidence: 41.9 degree
      Far range incidence: 43.3 degree
      Noise floor: ~ -36 dB
      HH
      VV
      R2 fine quad-pol SAR image of oil slicks in the GOM acquired at 12:01 UTC May 8, 2010
    • Case 2: RADARSAT-2 Oil slick observation
      Clean sea surface
      Oil slick-covered area
      Under moderate radar incidence angles
      and wind speeds
      Capillary and small gravity waves were damped
      Surface Bragg scattering
      Non-Bragg scattering
    • Case 2: RADARSAT-2 Oil slick observation
      R2 quad-pol observations
      scattering matrix
      alpha
      entropy
      represent and characterize scattering mechanism
    • Case 2: RADARSAT-2 Oil slick observation
      Entropy represents randomness of scattering mechanism
      Entropy low
      Entropy high
      significant
      polarimetric information
      backscatter becomes
      depolarized
      Surface Bragg scattering
      Non-Bragg scattering
    • Case 2: RADARSAT-2 Oil slick observation
      Alpha angle characterizes scattering mechanism
      Surface Bragg scattering dominates
      Dipole scattering dominates
      Even-bounce scattering dominates
      Non-Bragg scattering
      Bragg scattering
    • Case 2: RADARSAT-2 Oil slick observation
      CP for quad-polarization:
      For ocean surface Bragg scattering
      For non-Bragg scattering
      and
      is small
      have low correlation
      and
      highly correlated
      phase difference is close to
      phase difference is close to
    • 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-polarization
      SAR image, Geophys. Res. Lett., 38, L10602, doi:10.1029/2011GL047013.
    • 1.2. A Multi-Polarimetric SAR Processing Chain to ObserveOil Fields in the Gulf of Mexico - Summary
      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.
    • 2. Tracking of oil spill movement in the Gulf of Mexico
      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.
    • 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) outputs
      spatial 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
    • 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.
      16:30 UTC on July 27, 2009
    • Simulation Results:
      GNOME simulated best guess trajectory of oil spill denoted by blue circles:
      GNOME simulated locations of the oil spill at 04:00 UTC on July 29, 2009:
      only use wind to force the model;
      only use the currents to force the model.
    • 2. Tracking of oil spill movement in the Gulf of Mexico - Summary
      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
    • 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?