Evaluation of thermal infrared hyperspectral imagery for the detection of onshore methane plumes: Significance for hydrocarbon exploration and monitoring

Scafutto, Rebecca Del’Papa Moreira; Filho, Carlos Roberto de Souza; Riley, Dean N.; Oliveira, Wilson Jose ́ de

doi:10.1016/j.jag.2017.07.002

Cited by 22 publications

(12 citation statements)

References 26 publications

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“…Assuming that these components are neglectable, three terms remain to be calculated in Equation (2): path thermal (R ↑ T : 5-18%), surface emitted (ε S : 56-65%) and reflected downwelling solar irradiance (R ↓ S : 22-30%), whose respective contributions are indicated in the parentheses.…”

Section: Radiative Transfer Model-mwirmentioning

confidence: 99%

“…In August 2010 a controlled field experiment was performed at the former Rocky Mountain Oilfield Testing Center (RMOTC), located north of Casper, Wyoming (EUA) [2,16]. The experiment aimed to simulate possible scenarios of seepage and leakage of CH 4 , from natural and anthropogenic sources (i.e., pipelines and refineries).…”

Section: Field Experimentsmentioning

confidence: 99%

“…The detection of CH 4 plumes originated either from natural seepages or leakages can assist the oil and gas industry in the discovery of new petroleum plays and environmental monitoring of refineries and pipelines. Hyperspectral remote sensing data and methods have played an increasingly important role in such applications [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Bradley, et al [5], Kastek, et al [6], Roberts, et al [7], Thorpe, et al [3] and Thorpe, et al [8] successfully mapped CH 4 plumes using shortwave infrared (SWIR: 1.1-3 µm) hyperspectral imagery. Tratt, et al [9], Hall, et al [10], Hulley, et al [1], and Scafutto, et al [2] proved that a more robust and sensitive detection of CH 4 is possible in the longwave infrared (LWIR: 7-14 µm). An analogous investigation using airborne midwave infrared (MWIR: 3-6 µm) sensors is lacking due to the limited data available in this spectral range.…”

Section: Introductionmentioning

confidence: 99%

“…For comparison purposes, the same image processing techniques applied to the MWIR were also used to process SEBASS LWIR data acquired for the same experiment [2], intending to evaluate the efficacy in the detection of CH 4 plumes in both spectral ranges.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Scafutto

Filho

2018

Remote Sensing

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Methane (CH 4 ) display spectral features in several regions of the infrared range (0.75-14 µm), which can be used for the remote mapping of emission sources through the detection of CH 4 plumes from natural seeps and leaks. Applications of hyperspectral remote sensing techniques for the detection of CH 4 in the near and shortwave infrared (NIR-SWIR: 0.75-3 µm) and longwave infrared (LWIR: 7-14 µm) have been demonstrated in the literature with multiple sensors and scenarios. However, the acquisition and processing of hyperspectral data in the midwave infrared (MWIR: 3-5 µm) for this application is rather scarce. Here, a controlled field experiment was used to evaluate the potential for CH 4 plume detection in the MWIR based on hyperspectral data acquired with the SEBASS airborne sensor. For comparison purposes, LWIR data were also acquired simultaneously with the same instrument. The experiment included surface and undersurface emission sources (ground stations), with flow rates ranging between 0.6-40 m 3 /h. The data collected in both ranges were sequentially processed using the same methodology. The CH 4 plume was detected, variably, in both datasets. The gas plume was detected in all LWIR images acquired over nine gas leakage stations. In the MWIR range, the plume was detected in only four stations, wherein 18 m 3 /h was the lowest flux sensed. We demonstrate that the interference of target reflectance, the low contrast between plume and background and a low signal of the CH 4 feature in the MWIR at ambient conditions possibly explain the inferior results observed for this range when compared to LWIR. Furthermore, we show that the acquisition time and weather conditions, including specific limits of temperature, humidity, and wind speed, proved critical for plume detection using daytime MWIR hyperspectral data.

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Section: Radiative Transfer Model-mwirmentioning

confidence: 99%

Section: Field Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Scafutto

Filho

2018

Remote Sensing

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Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region

Hu,

Qin,

et al. 2024

Int J Coal Sci Technol

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A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach.

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Hyperspectral remote sensing applications in soil: a review

Kong

Wang

et al. 2020

Hyperspectral Remote Sensing

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Evaluation of thermal infrared hyperspectral imagery for the detection of onshore methane plumes: Significance for hydrocarbon exploration and monitoring

Cited by 22 publications

References 26 publications

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region

Hyperspectral remote sensing applications in soil: a review

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