## 3 Results
### Application of the Method
#### 3.1.2 Spectral Analysis Results
The primary reason for the results is the differences of the fuels involved in the three wildfire cases, including the vegetation differences between the long grass of Hot Pot Fire [64], the tall grass of Range 12 Fire [65], and the oak canopy of Rey Fire. Also, the varying space and time of wildfires can cause inconsistent performance of the spectral band [5]. In addition, the results also highlight the regional limitation of the single spectral band that is not normalized to the pre-fire spectral condition, as found by Reference [66]. Thus, the reflectance of a single band with the separating capacity of regional differences should not be used as the input feature vector of the BPNN. Despite this, the spectral bands are sensitive to the burned area (e.g., the reflectance of the NIR and SWIR spectral regions can be used to generate the spectral indices), which can further enhance the capacity to identify the burned area. Figure 6: (**a**) Reflectance curve of the five typical types extracted from Hot Pot Fire; (**b**) separability index M of the three comparisons: burned area vs. shadow, burned area vs. bare soil, burned area vs. vegetation. To investigate the capacity of the spectral indices listed in Table 4 for burned area discrimination, we calculated the separability index M value of each spectral index on the comparisons between the burned area and unburned types over the three wildfire cases. Figure 8a-c shows the M statistics of the Hot Pot Fire, Range 12 Fire and Rey Fire, respectively. The horizontal lines in the figures with an M value equal to 1.0 can be used to separate the spectral indices of high (>1.0) or poor (<1.0) distinguishing capacity. In general, a spectral index with M value above the horizontal line performs well in discriminating the burned area from other types. The results indicate that the MIRBI, NBR, bsVI, NsEv1, and NsTv1 have the best performance among all the spectral indices. We also selected CSI as the input feature vector of BPNN, considering that it has a relatively good separating performance despite the fact that its M value for the comparison of burned area vs. vegetation in the Rey Fire is slightly lower than 1.0. It can be found that the indices MIRBI, NBR, bsVI, and CSI, combining the reflectance of the NIR and SWIR spectral region, have a more robust capacity to discriminate burned area than the indices BAI, NDVI, EVI, GEMI, and SAVI which incorporate the reflectance of the visible spectral region. In addition, the high separability of NSEv1 and NSTv1 also proves that the LST and the emissivity of the thermal infrared (TIR) band are useful for burned area detection [22]. Figure 8: _Cont._  
Figure 7: Separability index M of reflectance at band 5 for two comparisons (burned area vs. bare soil, burned area vs. vegetation) in the Hot Pot Fire, Range 12 Fire and Rey Fire. Our separability analysis between the burned area and unburned types demonstrated that the spectral indices MIRBI, NBR, CSI, bsVI, NSEv1, and NSTv1 are applicable for burned area discrimination, and those indicators are used as the six feature vectors of the input layer of BPNN.