## 2 Materials and Methods
### Spatial Estimation of Vegetation Biomass Lising the Net Primary Production (NPP) Model  
Remote sensing provides spatially and temporally continuous data for quantifying and monitoring vegetation productivity [16]. Three approaches exist for estimating primary vegetation productivity. The most widely used technique using remote sensing data to estimate _NPP_ is the light use efficiency (_LUIE_) model [37; 38]. _LUIE_ models estimate vegetation primary productivity by multiplying the remotely sensed fraction of incident photosynthetically active radiation (_PAR_) absorbed by vegetation (_fAPAR_) by an energy-to-biomass conversion factor, typically referred to as the _LUIE_ coefficient. The _NDVI_, the _SR_ (simple ratio vegetation index), and _NPP_-based radiation use efficiency models [39; 40] were used to assess the living organic matter available in the landscape in 2022 [3]. The SR (simple ratio vegetation index) is calculated by the _NDVI_; it represents the richness of vegetation but is affected by the region and seasonality [13]. \[NDVI(x,t)=\frac{\left(\text{Band }8-\text{NIR}\right)-\left(\text{Band }4-\text{Red}\right)\left(x,t\right)}{\left(\text{Band }8-\text{NIR}\right)+\left(\text{Band }4-\text{Red}\right)\left(x,t\right)} \tag{3}\]  
\[SR(x,t)=\frac{1+NDVI(x,t)}{1-NDVI(x,t)} \tag{4}\]  
The CASA (Carnegie-Ames-Stanford approach) and SEBAL (surface energy balance algorithm for land) algorithms [41] were used for model evaluation. _NDVI_ and _SR_ were applied to estimate the fraction of the incident photosynthetically active radiation (_FPAR_) and absorbed incident photosynthetically active radiation (_APAR_). This allowed for the estimation of the _NPP_[42] as follows:  
\[NPP\left(x,\ t\right)=APAR\times\textit{LUIE} \tag{5}\]  
\[NPP\left(x,\ t\right)=FPAR(x,\ t)\times PAR\times\textit{LUIE} \tag{6}\]  
where _LUIE_ = light use efficiency factor, \(x\) = pixel, and \(t\) = time. FPAR has a linear relationship with _NDVI_ and _SR_[43; 44]. _FPAR_ can be calculated from the _NDVI_ and the _SR_ using the following equations:  
\[FPAR(x,t)=\frac{\left(SR(x,t)-SR_{i,\ min}\right)\times\left(FPAR_{max}-FPAR_{ min}\right)}{\left(SR_{i,\ max}-SR_{i,\ min}\right)}+FPAR_{min} \tag{7}\]  
\[FPAR(x,t)=\frac{\left(NDVI(x,t)-NDVI_{i,\ min}\right)\times\left(FPAR_{max}- FPAR_{min}\right)}{\left(NDVI_{i,\ max}-NDVI_{i,\ min}\right)}+FPAR_{min} \tag{8}\]  
(_FPAR\({}_{max}\)_ and _FPAR\({}_{min}\)_ are assumed to be 0.95 and 0.001, respectively). Estimates of _FPAR_ from the _NDVI_ are usually higher than the absolute values. This is in contrast to calculations based on _SR_, which tend to be lower than the absolute values [45]. \begin{table}
\begin{tabular}{|c|c|c|c|} \hline  & **Severity Level** & _dNBR Range (Scaled by 10\({}^{3}\))_ & _dNBR Range (Not Scaled)_ \\ \hline  & Enhanced regrowth, high (post-fire) & \(-\)500 to \(-\)251 & \(-\)0.500 to \(-\)0.251 \\ \hline  & Enhanced regrowth, low (post-fire) & \(-\)250 to \(-\)101 & \(-\)0.250 to \(-\)0.101 \\ \hline  & Unburned & \(-\)100 to \(+\)99 & \(-\)0.100 to \(+\)0.99 \\ \hline  & Low severity & \(+\)100 to \(+\)269 & \(+\)0.100 to \(+\)0.269 \\ \hline  & Moderate-low severity & \(+\)270 to \(+\)439 & \(+\)0.270 to \(+\)0.439 \\ \hline  & Moderate–high severity & \(+\)440 to \(+\)659 & \(+\)0.440 to \(+\)0.659 \\ \hline  & High severity & \(+\)660 to \(+\)1300 & \(+\)0.660 to \(+\)1.300 \\ \hline \end{tabular}
\end{table} 
Table 2: Burn severity levels obtained by calculating _dNBR_, proposed by USGS [35]. To reduce the error in estimating _FPAR_, averaging _NDVI_- and _SR_-derived results has been suggested [46; 47]. The following equation was used for this purpose:  
\[FPAR\ (x,t)=\alpha FAPAR_{NDVI}+(1-\alpha)FPAR_{SR} \tag{9}\]  
\(\alpha\) = 0.5 is the adjustment factor of _NDVI_ and _SR_[3; 13]. For clear-sky conditions and in tropical countries, _PAR_ is commonly set to 0.51 [3; 48].