## 4 Results
### Burn Severity  
Next, burn severity classification was derived from the different VIs. Figure 6 shows a boxplot of the \(\Delta\)VI values for the three burn severity classes determined in field. In general, \(\Delta\)NDVI was better correlated with burn severity, followed by \(\Delta\)GNDVI. For both indices, there was a significant difference between \(\Delta\)VI values of the different burn severity classes (\(p<0.05\) using ANOVA followed by Tukey HSD). There was no significant difference, though, between \(\Delta\)GCC values in low, medium, and high burn severity areas.  
Figure 5: Stand density retrieved from GCC using (**a**) Planet (3 m) and (**b**) Sentinel-2A (10 m) images; and woody aboveground biomass retrieved from Equation (1) and DBH derived using GCC from (**c**) Planet and (**d**) Sentinel-2A.  
Figure 6: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Domain**} & \multicolumn{2}{c}{**Stand Density (trees ha\({}^{-1}\))**} & \multicolumn{2}{c}{**AGB (ton ha\({}^{-1}\))**} \\ \cline{2-5}  & **Mean** & **std** & **Mean** & **std** \\ \hline UR & 414.85 & 160.69 & 20.43 & 27.64 \\ WLD & 222.42 & 165.27 & 5.30 & 13.47 \\ WLD + UR & 318.63 & 162.98 & 10.21 & 20.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tree stand density (trees ha\({}^{-1}\)) and AGB (ton ha\({}^{-1}\)) in the UR, WLD and total burnt area (UR + WLD).  
Hence, we used \(\Delta\)NDVI to derive a continuous burn severity map at the spatial resolution of the satellite images, providing low, medium, and high severity classification at the high spatial resolution of the satellite images (Figure 7). The classification provided in Figure 7 was further validated with the vector layer derived from a combined very-high-spatial resolution aerial photograph and in-field assessment. Table 6 summarizes the results of this validation. The overall accuracy (OA) of the satellite-derived burn severity map was 87%, with a kappa of 0.75.  
Figure 7 shows the spatial burn severity distribution at the UR and WLD domains with severely burned areas, particularly at the high stand density and AGB woodland areas of the city (see Figure 5). In the UR, 52% (52%) of the area was classified with low severity, while 26% (23%) and 22% (25%) were classified as medium and high burn severity, respectively, from the Planet (Sentinel-2A) \(\Delta\)NDVI map. In the WLD, where the stand density and AGB were substantially lower, 71% (73%) of the area was classified with low severity, while only 17% (20%) and 12% (7%) were classified as medium and high burn severity classes from Planet (Sentinel-2A) \(\Delta\)NDVI.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{3}{c}{**Reference Data**} \\ \hline
**Classifier Results** & **Low** & **Moderate** & **High** & **Producer Accuracy (Precision)** \\ \hline Low & **93.7** & 6.3 & 0.0 & 93.7 \\ Moderate & 20.1 & **70.5** & 9.4 & 70.5 \\ High & 0.0 & 20.4 & **79.5** & 79.5 \\ User Accuracy (Recall) & 93.8 & 66.3 & 86.2 & \\ \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for the three burn severity classes (all values are presented as percentages from total). In bold is the percent (%) hit for each burn class category.  
Figure 6: Boxplots of \(\Delta\)VI in areas classified in field as low, moderate and highly burned. \(\Delta\)GNVI, \(\Delta\)NDVI, and GCC were derived from Planet and Sentinel-2A images. Different letters signify statistically different means (\(p<0.05\)) using one-way ANOVA followed by Tuckey HSD separation procedure.