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Assessment of RXD Algorithm Capability for Gas Flaring Detection through OLI-SWIR Channels

Author

Listed:
  • Elmira Asadi-Fard

    (Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor 46417-76489, Iran)

  • Samereh Falahatkar

    (Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor 46417-76489, Iran)

  • Mahdi Tanha Ziyarati

    (Department of Health, Safety and Environment Engineering, Ferdous Rahjoyan Danesh Higher Education Institute, Borazjan 75617-86118, Iran
    Department of Health, Safety and Environment, Pars Special Economic Energy Zone, Asalouyeh 75119-46484, Iran)

  • Xiaodong Zhang

    (Division of Marine Science, School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USA)

  • Mariapia Faruolo

    (Institute of Methodologies for Environmental Analysis, National Research Council, 85050 Tito Scalo, Italy)

Abstract

The environment, the climate and human health are largely exposed to gas flaring (GF) effects, releasing significant dangerous gases into the atmosphere. In the last few decades, remote sensing technology has received great attention in gas flaring investigation. The Pars Special Economic Energy Zone (PSEEZ), located in the south of Iran, hosts many natural oil/gas processing plants and petrochemical industries, making this area one of the most air-polluted zones of Iran. The object of this research is to detect GF-related thermal anomalies in the PSEEZ by applying, for the first time, the Reed-Xiaoli Detector (RXD), distinguished as the benchmark algorithm for spectral anomaly detection. The RXD performances in this research field have been tested and verified using the shortwave infrared (SWIR) bands of OLI-Landsat 8 (L8), acquired in 2018 and 2019 on the study area. Preliminary results of this automatic unsupervised learning algorithm demonstrated an exciting potential of RXD for GF anomaly detection on a monthly scale (75% success rate), with peaks in the months of January and February 2018 (86%) and December 2019 (84%). The lowest detection was recorded in October 2019 (48%). Regarding the spatial distribution of GF anomalies, a qualitatively analysis demonstrated the RXD capability in mapping the areas affected by gas flaring, with some limitations (i.e., false positives) due to possible solar radiation contribution. Further analyses will be dedicated to recalibrate the algorithm to increase its reliability, also coupling L8 and Landsat 9, as well as exploring Sentinel 2 SWIR imagery, to overcome some of the observed RXD drawbacks.

Suggested Citation

  • Elmira Asadi-Fard & Samereh Falahatkar & Mahdi Tanha Ziyarati & Xiaodong Zhang & Mariapia Faruolo, 2023. "Assessment of RXD Algorithm Capability for Gas Flaring Detection through OLI-SWIR Channels," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5333-:d:1099971
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    References listed on IDEAS

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    1. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
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