# Mapping the Most Susceptible Regions to Fire in Portugal
## 3 Results
### Principal Component Analysis  
Nine different variables, whose influences on fires were described in previous sections, were then used to perform the statistical analysis; these are summarized in Table 2.  
Figure 3: Spatial patterns of the probability of occurrence of Moderate (first line of panels), High (second line of panels), Very High (third line of panels), and Extreme (fourth line of panels) danger classes of FWI in Portugal during the last four decades.  
The explained variance of each PC, as well as the associated eigenvalues, can be observed in Table S2. The first six PCs were retained for analysis since they are responsible for almost 84% of the variance. PC\({}_{1}\) explains more than 26%, whereas PC\({}_{2}\) and PC\({}_{3}\) explain each around 14% of the variance.  
The maps of the spatial distribution of each PC are illustrated in Figure 4. PC\({}_{1}\) shows a higher signal across the northern and central regions of the country and, more specifically, over points that have already burned. The loading factors (Figure 5) show that PC\({}_{1}\) has strong contributions mainly from FF and TwB. On the other hand, \(\Sigma\)GPP\({}_{\rm ANOM}\) also has a strong, but negative, contribution, which is consistent with the fact that the lowest anomalies are found in burned areas.  
\begin{table}
\begin{tabular}{c c c} \hline
**CATEGORY** & **VARIABLE** & **ACRONYM** \\ \hline Physical & Elevation & Elevation \\ \hline \multirow{4}{*}{Ecological} & GPP\({}_{\rm ANOM}\) accumulation & \multirow{2}{*}{\(\Sigma\)GPP\({}_{\rm ANOM}\)} \\  & along the period 2001–2021 & \\  & Linear Trend of GPP & GPP\({}_{\rm TREND}\) \\  & Linear Trend of NPP & NPP\({}_{\rm TREND}\) \\ \hline \multirow{4}{*}{Fire} & Fire Frequency & FF \\  & Number of months elapsed & \\  & since the last fire occurrence & \\ \cline{1-1}  & Fire Released Energy & FRE \\ \cline{1-1}  & Fire Risk Map & FRM \\ \cline{1-1}  & Probability of FWI \(>\) 50.1 & FWI\({}_{\rm P>50.1}\) \\ \hline \end{tabular}
\end{table}
Table 2: Variables considered in the PCA, aggregated into three different categories with the corresponding acronym used in the analysis.  
Figure 4: Spatial patterns of the first six PCs that explain about 84% of the total variance.  
The highest signal of PC\({}_{1}\) can be found in the centre of Portugal (area with higher FF), in the north, but also in the south. The lowest signal is found between the centre and the south of the country, which is characterized by low FF.  
PC\({}_{2}\) can be linked to elevation and vegetation productivity due to the strong positive signals, especially shown on the northeastern areas. In PC\({}_{2}\), the highest values are mainly located over the Iberian Peninsula Central System of mountains, whereas the weaker signal is located on the plains of the south. Additionally, in the southern region, a relevant negative contribution from GPP\({}_{\text{TREND}}\) and FWI is detected due to the contrasting patterns of these variables compared with the northern regions.  
Regarding the PC\({}_{3}\), the stronger signal is detected in central-southern regions and, according to the loading factors, it is likely associated with the positive contribution of FWI in this area. Once again, NPP\({}_{\text{TREND}}\) shows a negative contribution for PC\({}_{3}\), shown by the contrast of lower vegetation productivity in this region with the higher values of the signal of PC\({}_{3}\).  
The other lower-order PCs are essentially associated with FRE and FRM and, to a lesser extent, with FWI and anomalies of vegetation activity, and they capture the variance that is not accounted for by higher-order PCs.