Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers are frequently challenged by deciding where to prioritize restoration actions, which usually includes dealing with multiple and complex needs under an always limited budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. In order to help decision-makers take informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social, and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing, and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called “RePlant alpha”, which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis with at least six indicators for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems.

This study evaluates the financial profitability and carbon sequestration in mixed native forests of the Roble-Raulí-Coigüe and evergreen types in the southern macrozone of Chile, integrating both ecosystem services into forest management decision-making. The Faustmann model and dynamic programming were applied to determine the optimal rotation periods and Land Expectation Value (LEV) under two scenarios: exclusive timber production and combined timber and carbon production. The results indicate that mixed forests consistently outperform monocultures in terms of profitability, especially in 25%–75% mix configurations and moderate densities (2000 trees/ha). The observed range of 25%–75% across different tree species is determined by the interplay of two critical factors: the average annual growth rate (AAGR) of biomass and the opportunity cost of the forest rotation. In fast-growing species, the upper limit (75%) reflects an optimisation towards early carbon sequestration, whilst in slow-growing species, the ratio shifts towards the lower limit (25%) to compensate for longer rotation periods and associated biotic risks. This range acts as an efficiency frontier that balances biological productivity with the stability of the accumulated carbon stock. The inclusion of the economic value of carbon increased the LEV and extended the optimal rotation periods, confirming the relevance of integrating ecosystem services into forest planning. These findings suggest that mixed native forests represent a competitive and sustainable alternative to monocultures, contributing to climate change mitigation and income diversification for forest owners.

This study evaluates the financial profitability and carbon sequestration in mixed native forests of the Roble–Raulí–Coigüe and evergreen types in the southern macrozone of Chile, integrating both ecosystem services into forest management decision-making. The Faustmann model and dynamic programming were applied to determine the optimal rotation periods and Land Expectation Value (LEV) under two scenarios: exclusive timber production and combined timber and carbon production. The results indicate that mixed forests consistently outperform monocultures in terms of profitability, especially in 25%-75% mix configurations and moderate densities (2000 trees/ha). The inclusion of the economic value of carbon increased the LEV and extended the optimal rotation periods, confirming the relevance of integrating ecosystem services into forest planning. These findings suggest that mixed native forests represent a competitive and sustainable alternative to monocultures, contributing to climate change mitigation and income diversification for forest owners.

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We calculated the Normalized Difference Vegetation Index (NDVI), the Topographic Position Index (TPI), and Diurnal Anisotropic Heat (DAH) to assess vegetation dynamics across different topographic and thermal gradients. Generalized Additive Models (GAM) revealed that tree species exhibited more stable, regular seasonal NDVI trajectories, while shrubs showed moderate fluctuations, and herbaceous species displayed high interannual variability, likely reflecting sensitivity to climatic events. Spatial analysis indicated that trees predominated on steep slopes and higher elevations, herbs were concentrated in low-lying, moisture-retaining areas, and shrubs were more common in areas with higher thermal load. These findings highlight the significant role of terrain and temperature in shaping plant phenology and distribution, underscoring the utility of remote sensing and topographic indices for monitoring ecological processes in complex mountainous environments.