Assessment and comparison of the capability of two algorithms, RXD and NHI for detection of thermal anomalies of gas flaring based on the short-wave infrared bands of Landsat 8 satellite.

Assessment and comparison of the capability of two algorithms, RXD and NHI for detection of thermal anomalies of gas flaring based on the short-wave infrared bands of Landsat 8 satellite
Flares are elevated metallic vertical structures that are used in industries to dispose of flammable gases. It is an essential source of production and emission of greenhouse gases into the air. Therefore, gas flaring detection and identification is very important. The Pars Special Economic Energy Zone (PSEEZ) is one of the industrial areas in the south of Iran. The object of this research is to compare and investigate the potential of two algorithms, RXD and NHI, for the detection of thermal anomalies due to flames of gas flaring in the industries of PSEEZ by Near and shortwave infrared bands of Landsat 8 in 2018 and 2019. The innovation of this research is the first-time use of the RXD algorithm for detection and the utilization of the NHI algorithm at the local scale. The findings represented both algorithms had a high capability for detecting the thermal anomalies of flare during the day. However, the NHI algorithm is more accurate than the RXD algorithm due to taking into consideration the near-infrared band in the detection of thermal anomalies process in the studied area. In the validation section, the RXD rate of the detection algorithm showed over 70% for most months of these two years (2018-2019). This rate was above 80% for the first-NHI index, and it was more than 50% for the second index. In conclusion, the near and shortwave inferred bands of the Landsat 8 have a good capability in detecting thermal anomalies due to the flames of flares, which are located in the study area.
Extended Abstract
Introduction
Gas flaring(GF) is the necessary way to dispose of the gas produced in industries that do not have enough facilities to dispose of these produced gases. The process has significant regional and global environmental effects; thus, routine monitoring, detection, and estimation of the volume of gas flared are very important. Remote sensing has enough potential to prepare useful information about this process. Some sensors can detect the thermal anomalies of the flame of fire and other hot spots. Accordingly, pixels containing flare have different spectral behavior than surrounding pixels. Flares are subpixel objects; it is necessary to use special sensors with appropriate spatial resolution, such as Landsat 8. All hot spots emit most of their thermal radiation in the infrared region of the electromagnetic spectrum. The emission peak for thermal sources such as flares (1450 K) is in the shortwave infrared range. In general, SWIR bands from various/different sensors like OLI and MSI/sentinel2 have a suitable spatial resolution for GF detection. Many researchers have been focusing on the detection of gas flaring all over the world from 2015 until now. They have used different algorithms such as NHI, SMACC, TAI, and DAFI by using daytime or nighttime images or products of different satellite sensors on a global scale. According to these studies, all detection algorithms were done at the global scale, and due to a lack of access to all GF sites, researchers had to use Google Earth for validation. Unfortunately, it turned out that many flares were missed in some sites. Also, the distinction between gas and oil refinery flares is not considered in most research. Therefore, the main purpose of this research is to detect thermal anomalies due to active flares which are located at the Pars Special Economic Energy Zone using RXD and NHI algorithms based on NIR/SWIR bands of the OLI sensor of L8 (2018-2019) and compare the ability of these algorithms together. To increase the rate of accuracy, only flares of gas refineries were investigated (at the local scale). We determined the geographic location of all flares during local visits; therefore, validation was done with high accuracy.
 
Methodology
The main goal of this research is to detect thermal anomalies due to the flames of flares using two methods, RXD and NHI algorithms, in the study area. RXD can find spectral differences between a test region and its surrounding pixels. This algorithm extracts targets that are spectrally distinct from the image background. First, all images of the OLI sensor were downloaded for two years. Then, the OLI sensor bands 6 and 7 were stacked with each other for all months. The RXD algorithm was applied for anomaly detection on bands 6-7 shortwave from January 2018 to December 2019 in PSEEZ. In the next step, the NHI algorithm was done based on the difference of the bands/divided by the bands' sum by considering the OLI NIR/SWIR bands from January 2018 to December 2019 in PSEEZ. Finally, we compared the results of these algorithms together. The finding of the RXD algorithm was shown monthly by anomaly pictures. Unfortunately, the NHI algorithms could not represent the results by visual outputs. For this section, exact points of active flares (64) were used. The last one was the percentage of detection rate calculated for all months. The point layer of flares was used to calculate the rate percentage of anomaly detection. Using Shapefile of flares location, the number of flares detected each month and the number of flares ignored were counted. Next, the detection rate of both algorithms was determined.
 
 Results and discussion
A gas flare signal peaks in the SWIR region; then, the RXD algorithm produced monthly images that showed anomaly pixels (with white clusters) for all images, and the NHI finding was shown. In total, the results of both algorithms represented that these detection algorithms are capable of anomaly detection of gas flaring in the study area. The OLI sensor's bands 6 and 7 displayed almost more accurate detecting gas flaring locations in RXD/algorithm. As mentioned above, the NHI has two indexes, but the index of SWIR based on SWIR bands/OLI did not have enough ability for detection, and the next one, the NIR index based on the NIR band, provided accurate detection due to the flame of the flare. It is important to mention that these algorithms and OLI bands did not have complete detection, and some flares were missed in both algorithms. Furthermore, the rate of thermal anomaly detection based on RXD and NHI was high. That shows the adequate ability of RXD/NHI to detect thermal anomalies of flare flames at the local scale. In the validation phase, the RXD algorithm achieved a detection rate of over 70% for the majority of months in the years 2018 and 2019. Specifically, the detection rate exceeded 80% for the first-NHI index; for the second index, it was above 50% for most months. In summary, the near and shortwave infrared bands of the Landsat 8 demonstrate strong effectiveness in detecting thermal anomalies caused by GF in the study area.
 
 Conclusion
In this study, the detection of flares in refineries and petrochemicals in the PSEEZ was applied using the RXD/NHI algorithm with NIR/SWIR bands of the OLI-Landsat-8 in different months in 2018 and 2019. Many flares in the PSEEZ are related to refineries and petrochemicals. Monthly pictures and tables showed the findings of each method; then, a comparison was made. As a result, these algorithms had enough capability to detect thermal anomalies due to the flare flame. Nevertheless, the NHI algorithm, which used a near-infrared band, represented more accuracy than the RXD algorithm. However, both detection processes were incomplete, and some flares were missed. The results of the validation part proved our findings. All in all, these algorithms with NIR/SWIR bands of OLI are recommended for detecting thermal anomalies due to flares at the global/local scales.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
Conflict of Interest
Authors declared no conflict of interest.