## 5 Conclusions
Incorporating a statistically sufficient number of field samples alongside monthly Sentinel-2 RS ecological indices would enhance the accuracy of large-scale assessments while significantly reducing costs compared to traditional field inventories. From an applied perspective, the integration of field and RS data into a phenology-based forest conservation assessment provides relevant knowledge for forest monitoring and management. Considering that the data used are free and the procedures are standardized, the proposal could also be implemented by managing authorities to monitor the progress of the management models implemented. We encourage further studies to analyze increasingly larger areas to test the proposed approach and, at the same time, provide homogeneous information for forests in other biogeographical regions. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17050748/s1](https://www.mdpi.com/article/10.3390/rs17050748/s1)]([https://www.mdpi.com/article/10.3390/rs17050748/s1](https://www.mdpi.com/article/10.3390/rs17050748/s1)), The supplementary material is organized into three main sections: S1, S2, and S3. Supplementary Material S1 includes Table S1.1, which details the Sentinel-2 imagery dataset, reporting the month, day, and hour of acquisition, the tiles, platform (Sentinel-2A or Sentinel-2B), and the cloud percentage. Table S1.2 provides a comprehensive list of all spectral indices derived from Sentinel-2 imagery, classified according to photosynthetic activity, water content, water stress, soil characteristics, chlorophyll content, and fire stress. Each index is described with its acronym, full name, required Sentinel-2 bands, and relevant bibliographic reference. Supplementary Material S2 includes Figure S2.1., which presents the Principal Component Analysis(PCA) of sites based on field-measured structural and compositional variables of forests in the plains landscape, used to define the Structural Alteration Index. Figure S2.2. reports the contribution of variables to Principal Components 1 and 2 for sites sampled in the plains landscape. Figure S2.3. displays the Principal Component Analysis (PCA) of sites based on field-measured structural and compositional variables of forests in the foothills landscape, while Figure S2.4. reports the contribution of variables to Principal Components 1 and 2 for sites sampled in the foothills landscape. Table S2.1. Structure and composition variables derived from field sampling. Each index is described with its full name, acronym, formula and unit. Supplementary Material S3 presents the fitted models describing the relationship between remote sensing ecological indices (BI\({}_{2}\), MCARI\({}_{\text{Sent}}\) and NDWI\({}_{\text{Gao}}\)) and the Structural Alteration Index across months in the plains and foothills landscapes. Figure S3.1. shows the fitted models and 95% confidence intervals for the monthly indices as a function of the Structural Alteration Index across forest types and landscapes. Table S3.1. reports the best-fitting monthly models for each spectral index and their relationship with the Structural Alteration Index (_SAI_) across forests in the plains and foothills landscapes. The table includes landscape type, the spectral index used as the response variable, adjusted R\({}^{2}\) (model fit), sigma (residual error), F-statistic and its \(p\)-value (model significance), log likelihood, and the Akaike Information Criterion (AIC) for model evaluation, as well as the AIC of the null model (baseline comparison). Additionally, the table provides the number of observations (_nobs_) and the \(p\)-value from the Shapiro-Wilk test assessing residual normality. Conceptualization, F.G.A., M.I., L.C., D.R.L. and M.L.C. ; methodology, F.G.A., M.I., L.C., D.R.L., F.M. and M.L.C. ; software, F.G.A., M.I. and R.R.-T.; validation, F.G.A., L.C. and D.R.L. ; formal analysis, F.G.A., M.I., L.C., D.R.L. and M.L.C. ; investigation, F.G.A., M.I., L.C., D.R.L., F.P., F.M., P.G., R.R.-T. and M.L.C. ; resources, P.G.