## 4 Discussion
### RS Ecological Variables Relationship with Field Integrity Values
This variability likely reflects the rapid physiological response of herbaceous species to rainfall, which triggers chlorophyll renewal and photosynthetic activity during wet periods [44; 106]. In contrast, woody communities (CL 1-4) exhibited more stable MCARI\({}_{\text{Sent}}\) values due to their ability to access deeper water reserves through extensive root systems, decoupling their chlorophyll dynamics from surface rainfall patterns [91; 107]. These contrasting strategies highlight the ecological mechanisms underlying seasonal differences in chlorophyll dynamics, with herbaceous vegetation relying on short-term water availability [108; 109] and woody vegetation sustaining photosynthesis through long-term water reserves [106; 110]. As a result, altered sites showed greater variability in biomass and chlorophyll content (e.g., MCARI\({}_{\text{Sent}}\) delta) between wet and dry seasons, with a clear relationship between MCARI\({}_{\text{Sent}}\) and _SAI_ during wet periods (Figure 3C,D; Table 5). These results suggest that MCARI\({}_{\text{Sent}}\) is an effective indicator for monitoring ecosystems during wet periods, when vegetation reaches peak cover, productivity, and photosynthetic activity. The Normalized Difference Water Index (NDWI\({}_{\text{Gao}}\)), a proxy for vegetation water content, showed a significant inverse relationship with forest structural alteration (_SAI_) across sites. Beyond its ability to differentiate forests with varying alteration levels--an ability also demonstrated by _BI\({}_{2}\)_ and MCARI\({}_{\text{Sent}}\)--NDWI\({}_{\text{Gao}}\) enables more precise differentiation of sites with high woody cover, particularly where the \(SAI\) is less than 30% (Figure 3E,F). High NDWI\({}_{\text{Gao}}\) values in well-preserved forests (\(SAI<30\%\)) effectively indicate the amount of biomass and water stored in the canopy, which is significantly greater compared to altered sites (\(SAI>70\%\)) [68]. NDWI\({}_{\text{Gao}}\) proved particularly effective in distinguishing forest classes with varying levels of \(SAI\) during periods of greatest water deficit. This seasonal behavior may be attributed to the ability of woody species to access deep water layers, allowing them to maintain stable foliar water levels during dry periods and decouple their water stress from seasonal rainfall [40,111]. Additionally, during this period, perennial herbaceous vegetation is dry (or in seasonal dormancy), and there is no cover of annual species, enabling a focus on structural differences in woody vegetation. Previous studies have shown that species of the genus _Neltuma_ exhibit remarkable adaptations for accessing deep groundwater reserves in arid and semi-arid regions. They employ a stratified root system with vertical roots extending to depths of 17 m to tap into groundwater, stabilizing growth during low-rainfall periods and mitigating water stress by relying on stable aquifers. This strategy is particularly relevant in environments such as the Arid Chaco, where surface water is scarce [112,113]. The ability of NDWI\({}_{\text{Gao}}\) to differentiate various woody communities' likely stems from the fact that those with adult individuals capable of accessing the water table can decouple their growth from the rainy season, unlike shallow-rooted species that rely solely on surface water. These findings highlight NDWI\({}_{\text{Gao}}\) as a valuable indicator for identifying well-preserved forest areas (i.e., low \(SAI\)) and an essential tool for monitoring forest ecosystem degradation in semi-arid regions such as the Arid Chaco. Monitoring vegetation changes is particularly important in arid and semi-arid regions due to the sparse and sensitive nature of vegetation cover [114]. The correlation between remote sensing ecological indices (\(BI_{2}\), MCARISent, and NDWI\({}_{\text{Gao}}\)) and field-measured forest structural alteration (\(SAI\)) suggests a strong potential for using these indices complementarily in forest monitoring in semi-arid landscapes.