Quantitative characterization of crude oils and fuels in mineral substrates using reflectance spectroscopy: Implications for remote sensing

Scafutto, Rebecca Del’Papa Moreira; Filho, Carlos Roberto de Souza

doi:10.1016/j.jag.2016.03.017

Cited by 26 publications

(24 citation statements)

References 31 publications

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“…Different methods utilizing both linear and nonlinear models have been demonstrated in the literature for the analysis of different hydrocarbon types. In a work by the authors of [13,15], Principal Component Analysis (PCA) and PLS regression are used. The authors used PCA to differentiate the types and density of HCs in soils while they used PLS to predict the concentration of oils and fuels in soil samples.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the rich information content in hyperspectral imagery, it is well suited to a range of applications such as crop/vegetation classification, disaster monitoring, oil spill detection, etc. There are several uses of imaging spectroscopy for oil spills, such as the enforcement of ship discharge laws, surveillance and general slick detection, mapping of spills, and direction of spills [11], due to its high spectral and spatial capabilities [12].More specifically, Near-and Shortwave Infrared (NIR-SWIR) spectroscopes have been popular methods for detecting, mapping, quantifying, and characterizing HCs in contaminated soils with reasonable accuracy [6,13,14]. Moreover, NIR-SWIR spectra provide good information on soils organic and inorganic material content [13].…”

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confidence: 99%

“…There are several uses of imaging spectroscopy for oil spills, such as the enforcement of ship discharge laws, surveillance and general slick detection, mapping of spills, and direction of spills [11], due to its high spectral and spatial capabilities [12].More specifically, Near-and Shortwave Infrared (NIR-SWIR) spectroscopes have been popular methods for detecting, mapping, quantifying, and characterizing HCs in contaminated soils with reasonable accuracy [6,13,14]. Moreover, NIR-SWIR spectra provide good information on soils organic and inorganic material content [13]. HCs demonstrate good absorption in spectral bands 1200 nm, 1725 nm, and 2310 nm [5,8,15].…”

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confidence: 99%

“…More specifically, Near-and Shortwave Infrared (NIR-SWIR) spectroscopes have been popular methods for detecting, mapping, quantifying, and characterizing HCs in contaminated soils with reasonable accuracy [6,13,14]. Moreover, NIR-SWIR spectra provide good information on soils organic and inorganic material content [13].…”

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confidence: 99%

“…HCs demonstrate good absorption in spectral bands 1200 nm, 1725 nm, and 2310 nm [5,8,15]. Therefore, spectral information obtained in the NIR-SWIR range is excellent for both the quantitative and qualitative analysis of HCs in soils [13]. Recent works have also successfully demonstrated the use of Longwave Infrared (LWIR) for Petroleum HC detection [16].…”

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confidence: 99%

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Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

et al. 2019

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Terrestrial hydrocarbon spills have the potential to cause significant soil degradation across large areas. Identification and remedial measures taken at an early stage are therefore important. Reflectance spectroscopy is a rapid remote sensing method that has proven capable of characterizing hydrocarbon-contaminated soils. In this paper, we develop a deep learning approach to estimate the amount of Hydrocarbon (HC) mixed with different soil samples using a three-term backpropagation algorithm with dropout. The dropout was used to avoid overfitting and reduce computational complexity. A Hyspex SWIR 384 m camera measured the reflectance of the samples obtained by mixing and homogenizing four different soil types with four different HC substances, respectively. The datasets were fed into the proposed deep learning neural network to quantify the amount of HCs in each dataset. Individual validation of all the dataset shows excellent prediction estimation of the HC content with an average mean square error of ~ 2 . 2 × 10 - 4 . The results with remote sensed data captured by an airborne system validate the approach. This demonstrates that a deep learning approach coupled with hyperspectral imaging techniques can be used for rapid identification and estimation of HCs in soils, which could be useful in estimating the quantity of HC spills at an early stage.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

et al. 2019

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Imaging Spectroscopy for the Detection, Assessment and Monitoring of Natural and Anthropogenic Hazards

et al. 2019

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Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long term future, together with the increasing availability, quality and moderate cost of off the shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS derived measurement can be integrated into operational monitoring services.

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Spectral remote sensing for onshore seepage characterization: A critical overview

Asadzadeh

Filho

2017

Earth-Science Reviews

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In this article, we overview the application of spectral remote sensing data collected by multi-, and hyperspectral instruments in the visible-near infrared (VNIR), short-wave infrared (SWIR), and longwave infrared (LWIR) wavelengths for characterization of seepage systems as an exploration indicator of subsurface hydrocarbon (HC) accumulations. Two seepage systems namely macro-, and microseepage are recognized. A macroseepage is defined as visible indications of oil and gas on the surface and in the air detectable directly by a remote sensing approach. A microseepage is defined as invisible traces of light HCs in soils and sediments that are detectable by its secondary footprints in the strata, hence an indirect remote sensing target. Based on these broad categories, firstly, a comprehensive set of well-described and reliable remote sensing case studies available in the literature are thoroughly reviewed and then systematically assessed as regards the methodological shortcomings and scantiness in data gathering, processing, and interpretation. The work subsequently attempts to go through seminal papers published on microseepage concept and interrelated geochemical and geophysical techniques, exhumed HC reservoirs, lab-based spectroscopic analysis of petroleum and other related disciplines from a remote sensing standpoint. The aim is to enrich the discussion and highlight the still unexplored capabilities of this technique in accomplishing exploration objectives using the concept of seepage system. Aspects of seepage phenomenon in environmental pollution and uncertainties associated with their role in global warming are also underlined.This work benefits from illustrative products generated over two study areas located in the Ventura Basin, State of California, USA and the Tucano Basin, State of Bahia, Brazil known to host distinctive macro-, and microseepage systems, respectively. In conclusion, we recommend further research over a diverse range of seepage systems and advocate for a mature conceptual model for microseepage phenomenon. ), whereas microseeps, which are argued to occur in a near vertical fashion over an accumulation, are employed as a targeting tool for petroleum exploration.Recent investigations have also revealed that seeps are a potent source of methane (with ethane and propane) greenhouse gasses to the atmosphere. It has been estimated that in the natural methane budget, seeps are the second most important source of emissions after wetlands. The estimates also reveal that onshore seepages are a more significant emitter of CH 4 than their offshore counterparts (Etiope, 2015;Etiope and Ciccioli, 2009;Etiope and Klusman, 2010;Etiope et al., 2008).Over the years, a diverse range of techniques, including remote sensing, has been employed for seepage detection. The remote sensing approach holds a great promise for this aim because it is a fast and costeffective tool applicable to different operational scales for both direct and indirect seepage mapping. In the marine environment, this technology alre...

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Quantitative characterization of crude oils and fuels in mineral substrates using reflectance spectroscopy: Implications for remote sensing

Cited by 26 publications

References 31 publications

Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

Imaging Spectroscopy for the Detection, Assessment and Monitoring of Natural and Anthropogenic Hazards

Spectral remote sensing for onshore seepage characterization: A critical overview

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