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

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.

A New Perspective on Estimation of Gas Flaring Volume From Space: OLI/TIRS, VIIRS, and TROPOMI

“…The DMSP Nighttime Lights, Night-Fire algorithm, RST (Robust Satellite Techniques)-FLARE algorithm, MOVET (MODIS flare volume estimation technique), SWIR-radiance FRP method are produced based on the Infrared bands of different sensors such as DMSP, VIIRS, MODIS, SLSTR, ATSR Elvidge et al, 2007Elvidge et al, , 2009Elvidge et al, , 2013Elvidge et al, , 2015Faruolo et al, 2014Faruolo et al, , 2018Faruolo et al, , 2020. On the other hand, the capability of SWIR bands for thermal anomaly detection caused by the flame of flares was confirmed by different algorithms/indices such as the NHI (Normalized Hotspot Index), DAFI (Daytime Approach for gas Flaring Investigation), TAI (tri-spectral thermal anomaly index) at the global scale and RXD (ReedXiaoli Detector) at the local scale (Asadi-Fard et al, 2023;Faruolo et al, 2022aFaruolo et al, , 2022bLiu et al, 2021;Wu et al, 2022) because the SWIR is the primary IR region for GF detection at night (Faruolo et al, 2021) and day time according to Wien's Law (Faruolo et al, 2022a(Faruolo et al, , 2022b.…”

“…Figure 2 shows the research flowchart in detail. The data set is access in figshare (Asadi-Fard et al, 2023).…”

Quantification of Gas Flaring from Satellite Imagery: A Comparison of Two Methods for SLSTR and BIROS Imagery

“…In the last years, several methods for the detection of flaring have been published. Initially, the visible range of the spectrum was used to detect the presence of a flare [48][49][50][51][52][53], but researchers later took advantage of the strong signal a high-temperature subpixel phenomenon leaves in the short and midwave infrared (SWIR and MWIR, respectively) [54][55][56], making a more selective and automated detection of gas flares possible [38,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. Techniques on the detection of gas flares were recently reviewed by Anejionu [71] and Faruolo et al [35].…”