# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 4 Discussion  
The composite formed in Figure 8 is a combination of RBR_VH, RBR_VV, RBD_VH and RBD_VV. The results of the experiment are shown in Figure 7, by taking samples in the burnt and unburnt areas the showed that the RBD variable was better at distinguishing between burnt and non-burnt objects, than the RBR variable.  
Figure 9 shows the comparison of the Sentinel-2A satellite imagery, three classification results, and reference image. It can be seen that the model results from the three classifiers show the same pattern as the appearance of the burnt land, depicted in the Sentinel-2A image, as well as the reference image. The model generated by the random forest and the SVM algorithms has a high degree of similarity, compared to the model generated by the CART algorithm, where a lot of noise was found. In the CART classification, there are many pixel points that should not be part of the burnt area, so this noise causes the total burnt area to be larger than the random forest and SVM models.  
Figure 8: Composite RGB (RBR_VH, RBR_VV, and RBD_VV) with the AoI marked in the red rectangle, where a bright color indicates the burnt areas.  
The results of the accuracy test of the three classifiers used in this research are shown in Table 2. Where the overall accuracy to detect the burnt and unburnt areas are above 75%. The model generated by the SVM classifier have the highest accuracy of 88.26%, compared to the field data, while CART classification has a much lower accuracy at 78.40%. The examination on the confusion matrix shows that CART classifies the unburnt area as burnt with a significantly wider area, this means that the classifier is more prone to mis-classification on the edges of the burnt areas. Based on these results, the SVM yields the best accuracy for classifying the forest area as burnt.  
As our research aims for a quick mapping method, the second parameter to test is the processing time for each method. The burnt area mapping, using the SVM and RF classification methods each require the processing time of 1 h, while CART takes 2 h. This processing time represents the temporal resolution of the map produced or, in other words,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classification Methods**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**CART**} & \multirow{2}{*}{**Field Data**} \\ \cline{2-2} \cline{5-6}  & **Unburnt** & **Burnt** & **Unburnt** & **Burnt** & **Unburnt** & **Burnt** \\ \hline Unburnt & 435,189 & 52,199 & 431,690 & 55,698 & 384,044 & 103,344 & 487,388 \\ Burnt & 7643 & 14,498 & 7360 & 14,781 & 6695 & 15,446 & 22,141 \\ \hline Overall Accuracy & \multicolumn{2}{c}{88.26\%} & \multicolumn{2}{c}{87.62\%} & \multicolumn{2}{c}{78.40\%} & \multicolumn{2}{c}{} \\ \hline Process Time (Hours) & \multicolumn{2}{c}{1} & \multicolumn{2}{c}{1} & \multicolumn{2}{c}{2} & \multicolumn{2}{c}{12} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall accuracy (OA) and processing time of the classifiers, compared to the field data.  
Figure 9: Sentinel-2 image (**A**) before and (**B**) after the fire and the results of the classification (**C**) SVM, (**D**) random forest, (**E**) CART; and (**F**) the distribution of the burnt area from the reference image.  
the period of updating the map. When compared to the minimum updating period of the forest fire mapping process that is currently valid and which is 12 h [33], the quick mapping method can speed up the process so that this information can more quickly reach the fire mitigation team in the field.