# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 5 Discussion  
This is the first study to evaluate the performance of near real-time FSR extraction from the Himawari-8 data. The FSR estimated from the CSIRO GFS model was used as a benchmark to compare and assess the performance of H8-FSR. The H8-FSR compared favorably to the CSIRO GFS model when it came to the extraction of the near real-time FSR in the Esperance wildfire and produced errors that were acceptable within the literature (\(\sim\)30% MAPE [28]). However, H8-FSR underestimated FSR in some instances. These underestimations may have been the result of several reasons. Firstly, the CSIRO GFS model may have overestimated the FSR because this model assumes a fire will not be constrained during its growth [70]. Additionally, the CSIRO GFS model does not take into account topography. However, weather, fuel and topography together are the key factors affecting fire behavior [71]. Nevertheless, Esperance, the site that was studied is very flat and hence, the effect of topography may be insignificant. Another potential reason for the underestimations may have arisen from the meteorological information that was used. The meteorological information was calculated from the average values obtained from three different meteorological stations. For example, the wind speed and relative humidity values reported by the three stations highly differed at certain time periods (13:20-13:30, 14:10-14:20, 14:40-14:50, and 15:00-15:10), coinciding with the time periods where the FSR estimated with H8-FSR and CSRO GFS model differed the most (Figure 5). Using values for the average wind speed and relative humidity for the data recorded at the three meteorological stations as inputs to the CSIRO GFS model may have led to the observed deviations. On the other hand, fuel moisture content (FMC), including live and deal fuels moisture content, has proven to be a key indicator for fire risk assessment and FSR prediction [72; 73; 74; 75]. However, the CSIRO GFS model only considers the dead FMC, which is derived from meteorological factors [27]. In recent years, several studies have propagated for the use of remote sensing data to retrieve FMC and indeed, this has achieved reasonable results [76; 77; 78]. Therefore, further work will focus on applying the method to the Himawari-8 data to retrieve near real-time live and dead FMC and using those estimates as input parameters for fire behavior and risk assessments. Moreover, different vegetation types and growth conditions may also affect the FSR. The NDVI of the vegetation near the Esperance bushfire that ignited (at AWST time 08:30) was also computed from the Himawari-8 data, and then the NDVI of the burned area which increased at different timesteps were eventually extracted. The relationship between the NDVI and the FSR is shown in Figure 6. The FSR is faster in the area with lower NDVI. In contrast, when the NDVI is higher, which means the vegetation is very lush and difficult to ignite [79], the FSR becomes smaller. In this study, the region that was selected for this research was dominated by grassland but also comprised of little crops and shrubs. Consequently, this may also affect the evaluation of H8-FSR since the benchmark model was constructed within the context of grassland and without accounting for other vegetation types. Figure 6: Scatterplot showing the FSR extracted from the Himawari-8 data (V\({}_{\rm H-8}\)) and the NDVI of different timesteps. The solid line indicates a linear regression. The H8-FSR also has certain limitations. Firstly, it was assumed that the centroid of the burned area represented the average location of the fire, thus, the burned area data formed the base of FSR extraction. The FSR extracted from the Himawari-8 data was small at the time periods 12:50-13:00, 13:20-13:30 and 15:00-15:10 (Figure 5). As a result, the rate of burned area growth was also small. Therefore, the inaccuracy of burned area data affects the precision of the H8-FSR.