Quantifying Gas Flaring CH4 Consumption Using VIIRS

Zhang, Xiaodong; Scheving, Beau; Shoghli, Bahareh; Zygarlicke, Chris J.; Wocken, Chad

doi:10.3390/rs70809529

Cited by 27 publications

(22 citation statements)

References 22 publications

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“…The emission factors have been measured in situ and reported in the literature [20][21][22]. The conversion factor has been calculated as fraction of reported BCM and the observed radiative energy in entire countries [19], at the regional scale or for individual flares [23,24].The identification of flaring in the night-time observations in the visual and near infra-red (Vis-NIR) spectral range [25,26] allowed for the first semi-automatic monitoring of flares from space [27][28][29]. With the advent of vegetation fire detection products based on mid and thermal infrared bands (MIR and TIR), flaring was highlighted as a main source of false alarms [30,31].…”

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Persistent Hot Spot Detection and Characterisation Using SLSTR

Caseiro

Rücker²,

Tiemann³

et al. 2018

Remote Sensing

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Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 μm is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

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Persistent Hot Spot Detection and Characterisation Using SLSTR

Caseiro

Rücker²,

Tiemann³

et al. 2018

Remote Sensing

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“…In the MWIR, the higher signals can be associated to clouds, earth surface features, and combustion sources. In the LWIR, some of the largest flares shows faintly detectable signals [13,16].…”

Section: Spectral Characterization Of Covamentioning

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The VIIRS-Based RST-FLARE Configuration: The Val d’Agri Oil Center Gas Flaring Investigation in Between 2015–2019

et al. 2020

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The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d'Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring.

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“…Remote sensing has been used to find flares since at least 1978 (Croft 1978). A variety of approaches have been published over the past decade using different raw satellite measurements and different processing methods (Elvidge et al 2009, Casadio et al 2012a, Anejionu et al 2014, Anejionu et al 2015, Zhang et al 2015, Caseiro et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of satellite-derived estimates of flaring volume for offshore oil and gas operations in nine countries

Brandt

2020

Environ. Res. Commun.

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Flaring of natural gas contributes to climate change and wastes a potentially valuable energy resource. Various groups have estimated flaring volumes via remote sensing by nighttime detection of flares using multi-spectral imaging. However, only limited efforts have been made to independently assess the accuracy of these estimation methods. I analyze the accuracy of the VIIRS Nightfire published flare detection results, comparing yearly estimated flaring rates to reported flaring data from governments in 9 countries (Brazil, Canada, Denmark, Mexico, Netherlands, Nigeria, Norway, USA, UK) and 7 years (2012-2018 inclusive). We analyze only flares occurring at offshore oil and gas production platforms and floating production units. A total of 1054 flare volume estimates were compared to volumes reported to government agencies. 80.8% of flare estimates lie within 0.5 orders of magnitude (OM) of reported volumes, which 93.7% fall within 1 OM of the reported volume. Little systematic bias is found except in the smallest size classes (<10 6 m 3 y −1 ). Relative error ratios are larger for smaller flares. No significant trend was observed across years, and variation by country is in line with that expected by size distribution of flares by country. Wide aggregate estimates for groups of flares will exhibit little bias and dispersion, with the sum of 1000 flares having an expected interquartile range of −6% to +3% of actual reported volumes. Social media blurb: Test of remote sensing for flare detection shows accuracy across 9 countries and 8 years.

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Quantifying Gas Flaring CH4 Consumption Using VIIRS

Cited by 27 publications

References 22 publications

Persistent Hot Spot Detection and Characterisation Using SLSTR

Persistent Hot Spot Detection and Characterisation Using SLSTR

The VIIRS-Based RST-FLARE Configuration: The Val d’Agri Oil Center Gas Flaring Investigation in Between 2015–2019

Accuracy of satellite-derived estimates of flaring volume for offshore oil and gas operations in nine countries

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