Classification of oil spill by thicknesses using multiple remote sensors

Oscar Garcia-Pineda, Gordon Staples, Cathleen E. Jones, Chuanmin Hu, Benjamin Holt, Villy Kourafalou, George Graettinger, Lisa DiPinto, Ellen Ramirez, Davida Streett, Jay Cho, Gregg A. Swayze, Shaojie Sun, Diana Garcia, Francisco Haces-Garcia

Research output: Contribution to journalArticle

Abstract

Satellite Synthetic Aperture Radar (SAR) is an operational tool for monitoring and assessment of oil spills. Satellite SAR has primarily been used to detect the presence/absence of oil, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents methods for the rapid classification of oil types and estimated thicknesses, from which information about thick oil and oil emulsions (i.e., “actionable” oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions, a single polarization satellite SAR image can record a signal variance between thick stable emulsions and non-emulsified oil. During a series of field campaigns in the Gulf of Mexico with in situ measurements of oil thickness, multiple satellite data were obtained including fully polarimetric C-band SAR imagery from RADARSAT-2 and multispectral imagery from ASTER and WorldView-2. One campaign included the airborne polarimetric UAVSAR L-band sensor. An oil/emulsion thickness classification product was generated based on RADARSAT-2 polarimetric imagery using entropy and the damping ratio derivations. Herein, we present the classification methods to generate oil thickness products from SAR, validated by sea-truth observations, the multispectral imagery, and the UAVSAR data. We tested the ability to deliver these products with minimum latency to responding vessels via NOAA. During field operations in the Gulf of Mexico, a satellite SAR-based product of oil delineation by relative thickness was delivered to a responding vessel 42 min after the RADARSAT-2 data acquisition. This proof-of-concept test using satellite SAR and multispectral imagery to detect emulsions and deliver a derived information product to a vessel in near-real-time points directly to methods for satellite-based assets to be used in the near future for oil spill tactical response operations.

Original languageEnglish (US)
Article number111421
JournalRemote Sensing of Environment
Volume236
DOIs
StatePublished - Jan 2020

Fingerprint

synthetic aperture radar
Ostwald ripening
Radar imaging
Satellite imagery
Polarimeters
Emulsification
oil spills
Oil spills
emulsifying
emulsion
Synthetic aperture radar
oil spill
data acquisition
satellite imagery
Emulsions
sensors (equipment)
emulsions
remote sensing
Remote sensing
Data acquisition

Keywords

  • Oil emulsions
  • Oil spills
  • Oil thickness
  • Remote sensing imagery
  • SAR

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Garcia-Pineda, O., Staples, G., Jones, C. E., Hu, C., Holt, B., Kourafalou, V., ... Haces-Garcia, F. (2020). Classification of oil spill by thicknesses using multiple remote sensors. Remote Sensing of Environment, 236, [111421]. https://doi.org/10.1016/j.rse.2019.111421

Classification of oil spill by thicknesses using multiple remote sensors. / Garcia-Pineda, Oscar; Staples, Gordon; Jones, Cathleen E.; Hu, Chuanmin; Holt, Benjamin; Kourafalou, Villy; Graettinger, George; DiPinto, Lisa; Ramirez, Ellen; Streett, Davida; Cho, Jay; Swayze, Gregg A.; Sun, Shaojie; Garcia, Diana; Haces-Garcia, Francisco.

In: Remote Sensing of Environment, Vol. 236, 111421, 01.2020.

Research output: Contribution to journalArticle

Garcia-Pineda, O, Staples, G, Jones, CE, Hu, C, Holt, B, Kourafalou, V, Graettinger, G, DiPinto, L, Ramirez, E, Streett, D, Cho, J, Swayze, GA, Sun, S, Garcia, D & Haces-Garcia, F 2020, 'Classification of oil spill by thicknesses using multiple remote sensors', Remote Sensing of Environment, vol. 236, 111421. https://doi.org/10.1016/j.rse.2019.111421
Garcia-Pineda, Oscar ; Staples, Gordon ; Jones, Cathleen E. ; Hu, Chuanmin ; Holt, Benjamin ; Kourafalou, Villy ; Graettinger, George ; DiPinto, Lisa ; Ramirez, Ellen ; Streett, Davida ; Cho, Jay ; Swayze, Gregg A. ; Sun, Shaojie ; Garcia, Diana ; Haces-Garcia, Francisco. / Classification of oil spill by thicknesses using multiple remote sensors. In: Remote Sensing of Environment. 2020 ; Vol. 236.
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