Presentation on theme: "SAR detection and model tracking of oil slicks in the Gulf of Mexico"— Presentation transcript:
1 SAR detection and model tracking of oil slicks in the Gulf of Mexico Xiaofeng LiNOAA/NESDISContributors: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, DenmarkPeng Liu, George Mason University
2 Outline Oil Spill Detection in SAR image Tracking of oil spill movement in the Gulf of MexicoDeepwater Horizon Event –NESDIS Effort to Map Surface Oil with Satellite SAR
3 1. Oil Slicks Detection with SAR Oil detection with image data and complex data:1.1 Oil detection with single-pol SAR image1.2. A Multi-Pol SAR processing chain to observe oil fieldsJanuary, 2009
4 1.1 Oil Slicks Detection with single-polSAR image Mechanism:Oil slick damp the ocean surface capillary waves – making the surface smootherThe 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 imageSeparate the look-alikes from the oil slick
5 Neural Network Algorithm 1.1 Oil Slicks Detection with single-polSAR image- AlgorithmsNeural Network AlgorithmCanadian Journal of Remote Sensing, Vol 25, No
11 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 1-2ENVISATALOS
12 TCNNA GUI: Display of a a pre-processed output. This Window of the GUI shows wind conditions prevailingon the data from CMOD5 model.The TCNNA Output is exported with itsGeo-referenced tagged information.Ready for Arcmap.A scaled image is rotated and shownto adjust contrast along incidence angles
13 TCNNA output handled and converted to Shapefile in ArcMap or Kml for Google Earth
14 1.1 Single-Pol SAR oil detection summary Statistical-based SAR oil detection algorithms are developedThese algorithm are tuned for RADARSTA-1, ENVISAT, ALOS, ERS in various beam modeInteractive oil spill analysis software have been developed to aid oil spill analysis at NOAA
15 The combination of polarimetric features extraction 1.2. A Multi-Polarimetric SAR Processing Chain to Observe Oil Fields in the Gulf of MexicoThe combination of polarimetric features extractionTotal power span imageCo-polar correlation coefficientTarget Decompositionentropy (H)mean scattering angle (α)anisotropy AThe combined feature F
17 Example with: NASA UAVSAR polarimetric L-band SAR, with range resolution of 2 m and a range swath greater than 16 km, June 23, :42 (UTC)A sub scene of UAVSAR imageThe image recorded by a video cameraconfirmed the oil spill.
18 Extracted polarimetric features from the UAVSAR data
19 The combined polarimetric features and the result of OTSU segmentation
20 Case 2: RADARSAT-2 Oil slick observation 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 dBVVHHR2 fine quad-pol SAR image of oil slicks in the GOM acquired at 12:01 UTC May 8, 2010
21 Oil slick-covered area Case 2: RADARSAT-2 Oil slick observationClean sea surfaceOil slick-covered areaUnder moderate radar incidence anglesand wind speedsCapillary and small gravity waves were dampedSurface Bragg scatteringNon-Bragg scattering
22 R2 quad-pol observations Case 2: RADARSAT-2 Oil slick observationR2 quad-pol observationsscattering matrixentropyalpharepresent and characterize scattering mechanism
23 Entropy represents randomness of scattering mechanism Case 2: RADARSAT-2 Oil slick observationEntropy represents randomness of scattering mechanismEntropy lowEntropy highsignificantpolarimetric informationbackscatter becomesdepolarizedSurface Bragg scatteringNon-Bragg scattering
25 CP for quad-polarization: Case 2: RADARSAT-2 Oil slick observationCP for quad-polarization:For ocean surface Bragg scatteringFor non-Bragg scatteringis smallandhave low correlationandhighly correlatedphase difference is close tophase difference is close to
27 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: /2011GL
28 1.2. A Multi-Polarimetric SAR Processing Chain to Observe Oil 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.
29 2. Tracking of oil spill movement in the Gulf of Mexico Introduction to NOAA GNOME Oil drifting modelGNOME SimulationSimulation results – case studyConclusionsMain impacts are: - harm to life, property and commerce - environmental degradation
30 2. Tracking of oil spill movement in the Gulf of Mexico Oil Slicks drifting simulation with GNOME modelGNOME (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 oilspilled 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.
31 GNOME input:- Location file, specific for each region (tide, bathymetry ,etc.)User fileCurrents: ocean model outputsWinds: model or buoy windOil information: Oil locations from SAR image
32 Spill Trajectory Types Model OutputSpill Trajectory TypesBest 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.
39 Oil pipeline leak in July 2009 Surface Currents:Navy Coastal Ocean Model (NCOM) outputsspatial resolution of NCOM is 1/8ºtemporal resolution is 3 hours
40 Oil pipeline leak in July 2009 Winds:NDBC hourly wind vector
41 Oil pipeline leak in July 2009 Initial Oil distribution information: denoted by blue dots.Model run: 7/26/ :00 UTC7/29/ :00 UTC
42 Simulation Results:GNOME simulated best guess trajectory of oil spill denoted by blue circles:16:30 UTC on July 27, 2009At the ending of the simulation,04:00 UTC on July 29, 2009.
43 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.
44 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
45 Operational Response Requires: 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 tertiaryNeed multiple looks per day received within 1-2 hoursMany sources of data are requiredWell-trained staff of analysts (10-12) to cover multiple shifts per dayAutomated mapping would be useful for complicated spill patternsArray 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?