气候变化领域集成服务门户(CCPortal): Classification of oil spill by thicknesses using multiple remote sensors

CCPortal

DOI	10.1016/j.rse.2019.111421
	Classification of oil spill by thicknesses using multiple remote sensors
	Garcia-Pineda O.; Staples G.; Jones C.E.; Hu C.; Holt B.; Kourafalou V.; Graettinger G.; DiPinto L.; Ramirez E.; Streett D.; Cho J.; Swayze G.A.; Sun S.; Garcia D.; Haces-Garcia F.
发表日期	2020
ISSN	00344257
卷号	236
英文摘要	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. © 2019 Elsevier Inc.
英文关键词	Oil emulsions; Oil spills; Oil thickness; Remote sensing imagery; SAR
语种	英语
scopus关键词	Classification (of information); Data acquisition; Emulsification; Emulsions; Oil spills; Ostwald ripening; Polarimeters; Radar imaging; Remote sensing; Satellite imagery; Synthetic aperture radar; Classification methods; Monitoring and assessment; Multi-spectral imagery; Oil emulsions; Oil thickness; Proof-of-concept tests; Remote sensing imagery; Satellite remote sensing; Space-based radar
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179587
作者单位	Water Mapping, LLC, Gulf Breeze, FL, United States; MDA, Vancouver, BC, Canada; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; College of Marine Science University of South Florida, St Petersburg, FL, United States; Department of Ocean Sciences, University of Miami/RSMAS, Miami, FL, United States; Office of Response and Restoration, NOAA's Ocean Service, Seattle, WA, United States; Satellite Analysis Branch, NESDIS, NOAA, Greenbelt, MD, United States; Bureau of Safety and Environmental Enforcement, Oil Spill Preparedness Division, SterlingVA, United States; U.S. Geological Survey, Geology, Geophysics and Geochemistry Science Center, Denver, CO, United States
推荐引用方式 GB/T 7714	Garcia-Pineda O.,Staples G.,Jones C.E.,et al. Classification of oil spill by thicknesses using multiple remote sensors[J],2020,236.
APA	Garcia-Pineda O..,Staples G..,Jones C.E..,Hu C..,Holt B..,...&Haces-Garcia F..(2020).Classification of oil spill by thicknesses using multiple remote sensors.Remote Sensing of Environment,236.
MLA	Garcia-Pineda O.,et al."Classification of oil spill by thicknesses using multiple remote sensors".Remote Sensing of Environment 236(2020).