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

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.

[1]  Daniel Fisher,et al.  Shortwave IR Adaption of the Mid-Infrared Radiance Method of Fire Radiative Power (FRP) Retrieval for Assessing Industrial Gas Flaring Output , 2018, Remote. Sens..

[2]  Mikhail Zhizhin,et al.  Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data , 2015 .

[3]  Bryan N. Duncan,et al.  A decade of changes in nitrogen oxides over regions of oil and natural gas activity in the United States , 2017 .

[4]  Johannes W. Kaiser,et al.  Persistent Hot Spot Detection and Characterisation Using SLSTR , 2018, Remote. Sens..

[5]  O. A. Oyewunmi,et al.  Managing Gas Flaring and Allied Issues in the Oil and Gas Industry: Reflections on Nigeria , 2016 .

[6]  Eman A. Emam,et al.  Environmental Pollution and Measurement of Gas Flaring , 2016 .

[7]  O. S. Ismail,et al.  Global Impact of Gas Flaring , 2012 .

[8]  Mikhail Zhizhin,et al.  A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data , 2009 .

[9]  T. Lacava,et al.  A satellite-based analysis of the Val d'Agri Oil Center (southern Italy) gas flaring emissions , 2014 .

[10]  O. Arino,et al.  Gas flaring monitoring from space using the ATSR instrument series , 2012 .

[11]  Christopher D. Elvidge,et al.  VIIRS Nightfire: Satellite Pyrometry at Night , 2013, Remote. Sens..

[12]  Xiaodong Zhang,et al.  Quantifying Gas Flaring CH4 Consumption Using VIIRS , 2015, Remote. Sens..

[13]  Lei Ma,et al.  Identifying industrial heat sources using time-series of the VIIRS Nightfire product with an object-oriented approach , 2018 .

[14]  Bernhard Vogel,et al.  Development of a new gas-flaring emission dataset for southern West Africa , 2016 .

[15]  Can Li,et al.  Satellite observation of pollutant emissions from gas flaring activities near the Arctic , 2016 .

[16]  Olivier Arino,et al.  Use of ATSR and SAR measurements for the monitoring and characterisation of night-time gas flaring from off-shore platforms: The North Sea test case , 2012 .

[17]  Ambrish Sharma,et al.  Intercomparison of MODIS and VIIRS Fire Products in Khanty-Mansiysk Russia: Implications for Characterizing Gas Flaring from Space , 2017 .

[18]  William A. Anderson,et al.  Air Quality Impacts of Petroleum Refining and Petrochemical Industries , 2017 .

[19]  Solomon O. Giwa,et al.  Gas flaring attendant impacts of criteria and particulate pollutants: A case of Niger Delta region of Nigeria , 2017, Journal of King Saud University - Engineering Sciences.

[20]  C. Elvidge,et al.  Night-time lights of the world: 1994–1995 , 2001 .

[21]  Teodosio Lacava,et al.  On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space , 2018, Sensors.

[22]  Mikhail Zhizhin,et al.  The potential role of natural gas flaring in meeting greenhouse gas mitigation targets , 2018 .

[23]  Shana Mattoo,et al.  Continuing the MODIS Dark Target Aerosol Time Series with VIIRS , 2020, Remote. Sens..

[24]  Johannes W. Kaiser,et al.  Gas flaring activity and black carbon emissions in 2017 derived from Sentinel-3A SLSTR , 2019 .

[25]  Yongxue Liu,et al.  Assessment of offshore oil/gas platform status in the northern Gulf of Mexico using multi-source satellite time-series images , 2018 .

[26]  Vincenzo Cuomo,et al.  A self-sufficient approach for GERB cloudy radiance detection , 2004 .

[27]  Ritesh Gautam,et al.  Satellite‐Observed Changes in Mexico's Offshore Gas Flaring Activity Linked to Oil/Gas Regulations , 2019, Geophysical Research Letters.

[28]  J. D. Whyatt,et al.  Satellite survey of gas flares: development and application of a Landsat-based technique in the Niger Delta , 2014 .

[29]  J. D. Whyatt,et al.  Detecting gas flares and estimating flaring volumes at individual flow stations using MODIS data , 2015 .

[30]  K. Emumejaye.  Effects of Gas Flaring On Surface and Ground Water in Irri Town and Environs, Niger-Delta, Nigeria , 2012 .

[31]  Kan Huang,et al.  A global gas flaring black carbon emission rate dataset from 1994 to 2012 , 2016, Scientific Data.

[32]  Yuhao Yang,et al.  Automatic extraction of offshore platforms using time-series Landsat-8 Operational Land Imager data , 2016 .

[33]  Christopher D. Elvidge,et al.  Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data , 2019, Remote. Sens..

[34]  Martin J. Wooster,et al.  Multi-decade global gas flaring change inventoried using the ATSR-1, ATSR-2, AATSR and SLSTR data records , 2019, Remote Sensing of Environment.

[35]  Eugenia Kalnay,et al.  A review of global gas flaring and venting and impact on the environment: Case study of Iran , 2016 .