## 3 Results
### Burned Area Map
#### 3.2.2 Threshold Selection and Accuracy Evaluation  
The new burned area mapping method aims to derive a MODIS-based burned area map, which can be assessed by comparison to the OLI-based burned area map. In this method, the threshold T used in the BPNN output layer will determine the commission errors and omission errors of the burned area map. The threshold selection was investigated to validate its applicability compared to the OLI-based results for burned area detection. The threshold application was analyzed in the four wildfire cases (Hot Pot Fire, Range 12 Fire, Tohakum 2 Fire, and Cinder Butte Fire), but Powerline Fire was not investigated due to the lack of its corresponding cloud-free Landsat OLI data. The OLI L1 TP scene-pairs in the pre-fire and post-fire period of the four wildfire cases were obtained. The specific information of the OLI scenes used is shown in Table 7. The acquisition dates between the post-fire OLI and MODIS scenes in three wildfire cases were within 1 day except for Tohakum 2 Fire, which can reduce the differences of fire spreading between image date and burn date, and the error caused by the rapid change of ash and vegetation as much as possible. The variation in the accuracy of the burned area map along with the threshold T was analyzed by examining evaluation metrics including CE and OE [25], as shown in Figure 10. The range of T is [0, 0.9]. Figure 10 indicates that CE decreases and OE increases as T increases. This is because high values of T (i.e., conservative threshold) can avoid unburned pixels being misidentified as burned area pixels, but a few burned pixels cannot be identified. In contrast, a low T value contributes to the identification of all the burned areas, but some unburned pixels can be misidentified as burned. The threshold T in the BPNN output layer can be modified according to the user's demand. The change trends of the average errors in the four wildfire cases shows that the average CE intersects with the average OE around the T value of 0.8 with CE of 12.125% and OE of 12.458%. Accordingly, we adopted the threshold T = 0.8 for each wildfire case as a result of the balanced low errors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fire**} & \multicolumn{4}{c}{**Landsat Scenes**} & \multirow{2}{*}{**CE**} & \multirow{2}{*}{**OE**} \\ \cline{2-2} \cline{4-5}  & **Period** & & & **Path/Row** & **Acquisition Date** & \\ \hline \multirow{4}{*}{Hot Pot Fire} & pre-fire & 41/31 & \multirow{4}{*}{22 June 2016} & \multirow{4}{*}{9.88\%} & \multirow{4}{*}{10.31\%} \\  & & 41/32 & & & \\ \cline{2-2} \cline{4-5}  & post-fire & 41/31 & & & \\ \cline{2-2} \cline{4-5}  & pre-fire & 45/27 & \multirow{2}{*}{20 July 2016} & \multirow{4}{*}{16.15\%} & \multirow{4}{*}{14.29\%} \\  & & 45/28 & & & \\ \cline{2-2} \cline{4-5}  & post-fire & 45/27 & \multirow{2}{*}{5 August 2016} & \multirow{4}{*}{8.07\%} & \multirow{4}{*}{8.07\%} \\  & post-fire & 45/28 & & & \\ \cline{2-2} \cline{4-5}  & post-fire & 44/30 & 1 August 2017 & & \\ \cline{2-2} \cline{4-5}  & post-fire & 44/30 & 17 August 2017 & & \\ \hline \multirow{2}{*}{Tohakum 2 Fire} & pre-fire & 43/32 & 26 August 2017 & \multirow{2}{*}{14.40\%} & \multirow{2}{*}{17.16\%} \\  & post-fire & 43/32 & 27 September 2017 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Information of the used Landsat-8 OLI scenes and the CE and OE of the new method in the four wildfire cases. To validate the applicability of our method, a pixel-wise accuracy assessment has been applied to the new method in comparison to the OLI-based burned area map. The OE and CE in the application of Hot Pot Fire, Range 12 Fire, Cinder Butte Fire, and Tohakum 2 Fire are shown in Table 7. Comparing with the traditional empirical formula method, the proposed method greatly improves the accuracy of burned area detection. In the Hot Pot Fire, the CE and OE have been reduced by 10.28% and 9.69%, respectively. Also, in the Range 12 Fire, the CE and OE have been reduced by 50.58% and 6.03%, respectively.