# Mapping the Most Susceptible Regions to Fire in Portugal
## 2 Materials and Methods
### Applied Methodology
#### 2.2.2 Identification of Susceptible Areas  
Fire frequency (FF) was studied by calculating the number of times that pixels burned between 2001 and 2021. The number of months elapsed since the last fire occurrence (time within burns, TwB) was also studied, allowing the observation of the fuel accumulation in ecosystems after the fire events. In this context, vegetation activity was assessed taking into account the GPP and NPP evolution. Annual anomalies of GPP (GPP\({}_{\text{ANOM}}\)), at pixel-level, were computed through the sum of monthly anomalies that were determined in the first place, by removing the climatological median of the corresponding month. The accumulation of GPP\({}_{\text{ANOM}}\) was also determined for the period of 2001-2021 (EGFP\({}_{\text{ANOM}}\)). Moreover, linear trends of GPP and NPP (GPP\({}_{\text{TREND}}\) and NPP\({}_{\text{TREND}}\), respectively) were calculated at pixel-level, considering the 2001-2021 period. Relying on FWI, and with the aim of estimating the trend of favourable fire weather conditions in Portugal, we determined the probability of occurrence of moderate, high, very high, and extreme danger classes over the last four decades (1980-1990, 1990-2000, 2000-2010, and 2010-2020), considering fire seasons between June and October. The danger classes were previously calibrated for the Mediterranean Europe by [PERSON] et al. [49], being defined as: 'Moderate' (\(24.6<\text{FWI}<38.3\)), 'High' (\(38.3<\text{FWI}<50.1\)), 'Very High' (\(50.1<\text{FWI}<64\)), and 'Extreme' (\(\text{FWI}>64\)). Furthermore, in this context, we also performed an analysis of the probability of finding 'Very High' and 'Extreme' values of FWI, to infer the regions with more persistence of favourable fire weather conditions in Portugal. Some of the variables described above are likely to be significantly correlated, especially those with clear inter-dependencies, as the cases of FRM and FRE, or GPP and NPP. Assuming that the multivariable set characterizes each pixel exposure to fire, we performed a Principal Component Analysis (PCA) to obtain an optimal (i.e., orthogonal) and smaller number of variables, which still explains most of the original dataset's variability. This statistical method converts the potentially correlated variables into an uncorrelated set, efficiently displaying the main patterns among the multivariate data [50]. The method's \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Source** & **Spatial Resolution** & **Temporal Coverage** \\ \hline Gross Primary Production (GPP) & MODIS & 500 m & 2001–2021 \\ Net Primary Production (NPP) & MODIS & 500 m & 2001–2021 \\ Elevation & Copernicus GLO-90 & \(-\)90 m & - \\ Burned Areas & MODIS & 500 m & 2001–2022 \\ Fire Weather Index (FWI) & ERA5 & \(\sim\)25 km & 1979–2021 \\ Fire Released Energy (FRE) & LSA-SAF & 3–4 km & 2004–2020 \\ Fire Risk Map (FRM) & LSA-SAF & 3–4 km & 2017–2021 \\ Land Cover & DGT 2021 & \(\sim\)10 m & 2021 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables used in this study with the correspondent source, spatial resolution, as well as their temporal coverage. efficiency was also displayed by [PERSON] et al. [51], who performed a PCA to characterize the exposure, sensitivity, and adaptative capacity of crops to drought events in Portugal and, also, by [PERSON] et al. [52], who performed a PCA to evaluate the vulnerability of Iberian forests to droughts. On the other hand, PCA can also be a useful tool in the context of fire prevention, as shown by [PERSON] et al. [53], where the authors applied a PCA to a large set of variables, using Italy as case study. The PCA, performed on standardized variables, identifies a reduced number of variables capable of explaining most of the variability of our dataset and allowing the finding of the most susceptible areas to burn. All the computations were based on the Python Sklearn module.