Unpolarized infrared emissivity of oil films on sea surfaces

Pinel, Nicolas; Bourlier, Christophe

doi:10.1109/igarss.2009.5418007

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Thermal infrared detection is also an area of active research due to the absorption of solar radiation and emission as thermal energy at longer wavelengths (800 to 1400 nm). Increased thickness of the oil spill results in greater thermal infrared emission, which may be identified and classified [315].…”

Section: Water Contamination From Mining and Mine Spill Detection; Contaminants Affecting Wetland Functionmentioning

confidence: 99%

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

et al. 2020

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The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.

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Section: Water Contamination From Mining and Mine Spill Detection; Contaminants Affecting Wetland Functionmentioning

confidence: 99%

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

et al. 2020

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“…The infrared camera can detect thin film temperature rather than fibrillation on the surface, because the contrast may appear cooler than the surrounding water both day and night, as oil substances have lower emissivity than water. The thicknesses at which these transitions occur are poorly understood, but evidence indicates that the transition between a hot and cold layer lies between 50 and 150 m m and the minimum detectable layer is between 10 and 70 mm (Pinel and Bourlier, 2010). Thinner films, as a rule, create a solar heat trap and, thus, the film top is warmer than the water during the day in sunny weather.…”

Section: Requirements For Remotely Piloted Aircraft Payloadmentioning

confidence: 99%

Oil spill remote monitoring by using remotely piloted aircraft

Urbahs

Žavtkēvičs

2019

AEAT

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Purpose This paper aims to analyze the application of remotely piloted aircraft (RPA) for remote oil spill sensing. Design/methodology/approach This paper is an analysis of RPA strong points. Findings To increase the accuracy and eliminate potentially false contamination detection, which can be caused by external factors, an oil thickness measurement algorithm is used with the help of the multispectral imaging that provides high accuracy and is versatile for any areas of water and various meteorological and atmospheric conditions. Research limitations/implications SWOT analysis of implementation of RPA for remote sensing of oil spills. Practical implications The use of RPA will improve the remote sensing of oil spills. Social implications The concept of oil spills monitoring needs to be developed for quality data collection, oil pollution control and emergency response. Originality/value The research covers the development of a method and design of a device intended for taking samples and determining the presence of oil contamination in an aquatorium area; the procedure includes taking a sample from the water surface, preparing it for transportation and delivering the sample to a designated location by using the RPA. The objective is to carry out the analysis of remote oil spill sensing using RPA. The RPA provides a reliable sensing of oil pollution with significant advantages over other existing methods. The objective is to analyze the use of RPA employing all of their strong points. In this paper, technical aspects of sensors are analyzed, as well as their advantages and limitations.

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“…A thin film interference theory-based model is used to describe the thickness-dependent contrast between the background water and the sea surface covered by crude oil [18,19]. The thermal infrared emissivity of fuels was measured and analyzed using statistical methods [20][21][22]. At the same time, the different emission spectra of the fuels were detected, and a relationship between the fuel emissivity and the American Petroleum Institute (API) density was obtained.At present, thermal infrared technology is widely used in vegetation and soil detection.…”

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confidence: 99%

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Guo

Liu

2020

JMSE

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Remote sensing is an important method for monitoring marine oil-spill accidents. However, methods for measuring oil-film thickness remain insufficient. Due to the stable differences in the surface emissivity and temperature of oil and water, the oil film can be detected using thermal infrared. This study measured emissivity of seven different oil-film thicknesses and seven different American Petroleum Institute (API) densities, and analyzed the spectral characteristics. Results show an optimal wavelength position for oil-film thickness and fuel API density monitoring is 12.55 µm. Principal component analysis and continuum removal methods were used for data processing. Stepwise multiple linear regression was used to establish relationships between emissivity and oil slick thicknesses and API densities. Oil-film thickness and fuel API density data were analyzed by principal component analysis and continuum removal before regression analysis. The spectral emissivity data was convolved into Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) thermal bands to determine potential of the sensor in oil-film detection. The result shows that neither could be used to estimate thickness. The AVHRR-4 band and band 12 and 13 of the ASTER could be used to separate oils from water and have potential to distinguish different oil types. deficiencies in measuring fuel thickness [12]. Thermal infrared analysis distinguishes oil on the sea surface by thermal comparison. Because the emissivity of water and oil are different, thermal contrast is generated, and the emissivity depends on the thickness of the oil slick [15]. During the daytime, thick oil slicks become warmer than the surrounding water while thinner, detectable slicks appear cooler. This reverses at nighttime [16,17]. A thin film interference theory-based model is used to describe the thickness-dependent contrast between the background water and the sea surface covered by crude oil [18,19]. The thermal infrared emissivity of fuels was measured and analyzed using statistical methods [20][21][22]. At the same time, the different emission spectra of the fuels were detected, and a relationship between the fuel emissivity and the American Petroleum Institute (API) density was obtained.At present, thermal infrared technology is widely used in vegetation and soil detection. Among these [23], the relationship between the emissivity spectrum of the thermal infrared region and the leaf area index is obtained. The thermal infrared region is used to study soil moisture [24], and it has been proven that the long-wave infrared band is more accurate than visible-near infrared and shortwave infrared for the prediction of soil properties [25]. Some studies have applied thermal infrared technology to fuel-oil measurements, using regression analyses, to test the degree of soil oil pollution [26], and through experiments have identified midday as the optimal detection time for oil slicks [27]. The relationship between the...

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Unpolarized infrared emissivity of oil films on sea surfaces

Cited by 7 publications

References 14 publications

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

Oil spill remote monitoring by using remotely piloted aircraft

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

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