## 5 Conclusions  
Our findings highlight the significant potential of spectral indices derived from Sentinel-2 imagery for assessing forest characteristics and monitoring forest degradation in semi-arid landscapes such as the Arid Chaco. The Brightness Index (BI\({}_{2}\)), Modified Chlorophyll Absorption in Reflectance Index (MCARISent), and Normalized Difference Water Index (NDWI\({}_{\text{Gao}}\)) proved effective in capturing differences in forest conservation status and structural alterations associated with varying management regimes and silvopastoral use. BI\({}_{2}\) demonstrates robust year-round applicability, distinguishing open formations with high levels of bare soil from dense forests with greater woody cover. It is particularly effective in identifying sites with high structural alteration (e.g., intensive livestock use) due to its sensitivity to bare soil exposure. On the other hand, MCARISent excels during wet periods, reflecting seasonal variations in chlorophyll content and canopy density. It effectively differentiates sites with varying levels of structural alteration and highlights phenological differences among forest types, particularly in areas dominated by herbaceous species. NDWI\({}_{\text{Gao}}\) was especially useful in distinguishing forest types during dry periods, providing reliable insights into vegetation water content and differentiating forest types based on water-use strategies. Its sensitivity to leaf water content makes it particularly valuable for identifying well-conserved forest areas and vegetation adapted to drought conditions. The seasonal variability of MCARISent and NDWIGao complements the year-round applicability of BI2, allowing for a nuanced understanding of vegetation structure, water stress adaptations, and conservation status. Together, these indices effectively differentiate sites with varying levels of forest degradation, reflecting differences in vegetation structure, water-use strategies, and management practices. The integration of remote sensing (RS) indices with field-based measurements such as the Structural Alteration Index (_SAI_) further underscores the value of a hybrid approach for ecosystem monitoring. The strong correlations between RS indices and _SAI_ values validate their utility as complementary tools for detecting and monitoring forest structural alteration and conservation status. Monitoring vegetation changes is critical in all ecosystems, but particularly in arid and semi-arid regions, where vegetation cover is sparse and highly sensitive to environmental changes. Early detection of structural changes and assessment of their extent and severity at local and regional scales are therefore essential. In summary, the combined use of Sentinel-2 RS ecological variables (BI2, MCARISent, and NDWIGao) enhances our ability to monitor forest degradation, assess conservation efforts, and understand the ecological dynamics of semi-arid forests, making them valuable tools for sustainable land management and biodiversity conservation. Despite the demonstrated effectiveness of these spectral indices, further efforts are needed to improve this phenology-based assessment. Incorporating Synthetic Aperture Radar (SAR) data or implementing spectral unmixing algorithms could enhance the accuracy and applicability of these analyses. Nevertheless, our results reveal distinct spectral differences between Arid Chaco forests under different management strategies, reinforcing the potential of RS-based approaches for ecosystem monitoring. Our findings support the hypothesis that in highly seasonal subtropical forests, the phenological patterns of remote sensing ecological indices are influenced by ecosystem characteristics and the extent of alteration. This reinforces the crucial role of RS as a valuable proxy for modeling forest attributes.