## 2 Materials and Methods
### Data Processing  
RS Spectral Indices  
For each month, we calculated a series of 22 spectral indices useful for remote sensing (RS)-based ecosystem descriptions (for the complete list of calculated indices (see S1, Table S1.2) within the 49 forest sample sites. The analyzed spectral indices serve as indicators of photosynthetic activity and biomass, canopy water content and moisture, water stress, soil characteristics, chlorophyll and carotenoid content, and fire-induced stress. Since many spectral indices exhibit high correlation with one another, we conducted a Variance Inflation Factor (VIF) analysis to eliminate redundant variables, ensuring that the retained indices were statistically independent. Accordingly, we selected three RS variables with VIF values lower than 5 for further analysis [66]. These selected indices (Table 3) include the following: Modified Chlorophyll Absorption in Reflectance Index (MCARI\({}_{\text{Sent}}\))--an indicator of leaf chlorophyll content [67]; Normalized Difference Water Index (NDWI\({}_{\text{Gao}}\))--an indicator of leaf water content [68]; Brightness Index 2 (BI\({}_{2}\))--an indicator of surface presence and soil moisture [69]. The MCARI\({}_{\text{Sent}}\) index combines red edge, red, and green bands to estimate the depth of chlorophyll absorption (Table 3). It is highly sensitive to leaf chlorophyll content and strongly correlates with the Leaf Area Index. Additionally, MCARI\({}_{\text{Sent}}\) values are not affected by illumination conditions, soil background reflectance, or other non-photosynthetic materials [67]. The NDWI\({}_{\text{Gao}}\) index estimates leaf water content through a normalized difference of NIR and SWIR bands (Table 3). It is sensitive to changes in canopy water content and useful for estimating water stress levels. The typical NDWI\({}_{\text{Gao}}\) range for green vegetation is \(-0.1\) to \(0.4\), with \(0.4\) indicating high leaf water content [68]. The BI\({}_{2}\) index is sensitive to soil brightness and quantifies bare surfaces by calculating the square root of brightness for each pixel (Table 3). It effectively differentiates bare soil from \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Field Sampling Scheme**} & \multicolumn{1}{c}{**Forest Variable**} \\ \hline \multirow{6}{*}{Central straight line (transect) 250 m} & -Woody sp. cover (total; for each species and for each vegetation \\  & layer: low \textless{} 2 m, medium 2-8 m, and high \textgreater{}8 m) and the derived \\  & horizontal heterogeneity index. \\  & -Bare soil cover (m). \\  & -Maximum height of the woody species (m; measurement carried \\  & out every 5 m, and the derived vertical heterogeneity index. \\ \hline \multicolumn{1}{l}{Plot woody species seedling density (2 m\({}^{2}\); 50 plots along} \\  & -Woody species seedling density (seedlings*ha\({}^{-1}\)). \\ \hline \multicolumn{1}{l}{Initial straight line (transect) 20 m} & -Composite litter sample (comprising 10 sub-samples taken every \\  & 2 m). \\ \hline \multicolumn{1}{l}{Plot 2500 m\({}^{2}\)} & -Tree density (trees*ha\({}^{-1}\)). \\  & -Basal area (m\({}^{2}\)*ha\({}^{-1}\)). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Field sampling scheme and forest variables measured (adapted from [29; 58; 63]). vegetation in arid environments. The BI\({}_{2}\) index ranges from 0 (indicating no bare surfaces) to higher positive values, which correspond to increasing percentages of bare surfaces [69]. We focused on monthly data, as they are particularly effective for tracking fine-scale ecological temporal variations, which are especially pronounced in subtropical semiarid systems such as the Arid Chaco, where vegetation undergoes strong seasonal fluctuations. The high temporal resolution of our analysis captures phenological differences across forest types and management regimes, offering a detailed view of forest condition dynamics.