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

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