# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods
### Rst-Flare
#### 2.3.1 Flaring Sites Detection
In Figure 3 an example of the two \"extreme\" maps for 2005 (randomly chosen within the investigated period) are shown. A high number of potential flaring sites was identified over the scene when the 15% recurrence percentage threshold was used (blue dots in Figure 3). Most of these sites is localized in the northeast corner of the scene (an example shown in Figure 3), mainly due to the (periodic) occurrence of wildfires in Nigerian areas [41], suggesting that a recurrence value of 15% is not adequate to discriminate the flaring sites, resulting in a consistent amount of false detections. On the contrary, the highest percentage level (i.e., 35%) seems to be too restrictive in the gas flares detection: by means of a visual inspection using Google Earth, some missed detections were observed (an example shown in Figure 3). The threshold, which has shown to assure the best trade-off between reliability (i.e., low false positive rate) and sensitivity (i.e., low false negative rate), resulted equal to 30%. Therefore, within each year, a pixel was classified as flaring site when thermal anomalies (ALICE\({}_{\text{20-31}}\geq 1.5\)) are detected over there at least in 30% of the images of the annual dataset (i.e., all clear pixels with SatZA \(<\) 40\({}^{\circ}\) and SolZA \(<\) 101\({}^{\circ}\)). At the end of this step, the algorithm provides, for each year, a list of candidate flaring locations. However, the availability of a multi-temporal dataset can allow further improving the accuracy of such a list, which might have missed intermittent active flares. At each site, in fact, the flaring process can vary in time due to the decommissioning of some flares as well as to their reactivation during a specific year or to the variation in the amount of oil produced. So, it can happen that an active flare is prevented to be detected only because the detection frequency is slightly lower than the defined threshold. Figure 4 shows an example of this occurrence for two pixels, named P1 (lat 5\({}^{\circ}\)52\({}^{\prime}\)32.25\({}^{\prime\prime}\) N-lon 6\({}^{\circ}\)13\({}^{\prime}\)23.25\({}^{\prime\prime}\) E) and P2 (lat 4\({}^{\circ}\)20\({}^{\prime}\)38.63\({}^{\prime\prime}\) N-lon 5\({}^{\circ}\)55\({}^{\prime}\)26.62\({}^{\prime\prime}\) E), arbitrarily chosen within the ROI (Figure 1). Figure 3: Flaring sites detected by RST-FLARE in 2005 for a temporal persistence percentage of 15% (blue dots) and 35% (pink stars). On the right a detail of false positives (**top**) and missed detections (**bottom**) are shown. They were not flagged as flaring sites in some years (i.e., 2008, 2014, 2016 for P1 and 2004 and 2009 for P2) only because their occurrence frequency was 29% (in 2008), 29% (in 2014) and 27% (in 2016), for P1 and 29% (for both 2004 and 2009), for P2. Hence, such an under-sampling reduces the probability of detection and decreases the accuracy of flared gas estimates for flares with highly variable flaring activity. Owing to the long-term MODIS dataset analyzed, this issue can be faced mainly producing a multitemporal map of flaring locations. This map provides a global and general overview of areas affected by gas flaring activity in the whole 17-year period analysed. In fact, it considers as flaring sites all those pixels that, at least in one year (within the 17 investigated), have been detected as anomalous (i.e., ALICE\({}_{20-31}\geq 1.5\)) for 30% of the imagery available in the same year. Hence, by such a strategy, the RST-FLARE detection-devoted step allows for obtaining a twofold information about flaring locations: the annual-based map shows the flaring sites active \"for sure\" in that year while the multitemporal-based one, taking into account the activity variation of the site, highlights the areas affected by the gas flaring phenomenon in the whole 17-year period analyzed.