TY - JOUR
T1 - Classification of oil spill by thicknesses using multiple remote sensors
AU - Garcia-Pineda, Oscar
AU - Staples, Gordon
AU - Jones, Cathleen E.
AU - Hu, Chuanmin
AU - Holt, Benjamin
AU - Kourafalou, Villy
AU - Graettinger, George
AU - DiPinto, Lisa
AU - Ramirez, Ellen
AU - Streett, Davida
AU - Cho, Jay
AU - Swayze, Gregg A.
AU - Sun, Shaojie
AU - Garcia, Diana
AU - Haces-Garcia, Francisco
N1 - Funding Information:
This study was made possible in part by a grant from The Gulf of Mexico Research Initiative (GOMRI, award GOMA 23160700 ) and in part by grants from the U.S. Bureau of Safety and Environmental Enforcement (BSEE) and the National Oceanic and Atmospheric Administration (NOAA) . UAVSAR data were provided through a grant from the National Aeronautics and Space Administration (NASA) . Data acquired during the GOMRI research cruises are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org ( https://doi.org/10.7266/N7M9072W ). This work was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. UAVSAR data are courtesy NASA/JPL; RADARSAT-2 data are courtesy of MDA Corporation. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of NOAA, BSEE, or the Department of Commerce.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
KW - Oil emulsions
KW - Oil spills
KW - Oil thickness
KW - Remote sensing imagery
KW - SAR
UR - http://www.scopus.com/inward/record.url?scp=85074604636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074604636&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2019.111421
DO - 10.1016/j.rse.2019.111421
M3 - Article
AN - SCOPUS:85074604636
VL - 236
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
M1 - 111421
ER -