This study modeled wild edible mushroom yields in Mediterranean forests using Planet satellite imagery, LiDAR, climate data, and field measurements. Results show that seasonal NDVI differences, precipitation, and forest structure are key predictors. Integrating high-resolution intra-annual remote sensing significantly improves yield prediction and ecological understanding.

This project addresses the gap between national and local forest data by integrating Spanish National Forest Inventories, forest maps, and municipal boundaries into interactive dashboards. Using Palencia as a case study, it tracks long-term evolution of pine and oak forests, supporting local decision-making through accessible visualization of forest stocks, carbon storage, and ecosystem services.

This study examines how early competition influences growth and structure in young mixed forests. Results show that competition strongly affects height, biomass allocation, and species interactions. Managing competition early is crucial for maintaining diversity, reducing dominance, and building climate-resilient forests, making early interventions more effective and cost-efficient.

This study reviews mangroves of the Americas and their vulnerability to climate change. Mangroves are vital carbon sinks, biodiversity hotspots, and coastal protectors, but face threats from deforestation, pollution, and urban expansion. Effective conservation requires ecosystem-based restoration, improved management, and reduced human pressures to ensure long-term resilience.

This study analyzed long-term changes in forest composition in the Spanish Iberian Range using National Forest Inventory data and Landsat imagery. Results show a significant shift from monospecific to mixed forests, with mixed stands nearly doubling over three decades. Satellite-derived vegetation indices successfully detected these temporal dynamics.

This study evaluated a PointNet++ deep learning model for binary classification of Pinus sylvestris and Quercus pyrenaica using only LiDAR 3D point clouds. A balanced dataset of 160 trees achieved 91% accuracy, showing that geometric features alone can effectively discriminate species, highlighting the potential of lightweight AI models for forest inventories.

This study reviews how climate change affects European beech distribution and its alignment with the Natura 2000 network. Findings show declines in warm, dry margins, upward shifts in mountains, and limited northern expansion. Water deficit is the main driver. Effective conservation requires connectivity, adaptive management, and climate-informed protected-area planning.

This study evaluated oak regeneration along a forest–mine gradient over 14 years, highlighting the key facilitating role of nurse shrubs. Results show that while acorn supply drives recruitment near forests, shrub cover significantly improves seedling survival and growth in harsh reclaimed mine conditions, supporting shrubs as effective nature-based restoration tools.

This project developed a strategic plan for urban green infrastructure in a small Spanish municipality. It created a detailed inventory, assessed condition and functionality, applied indicators, and classified areas using a traffic-light system. The study proposed improvement actions, ecological corridors, and a five-year implementation plan.

This study evaluated multispectral and hyperspectral vegetation indices to estimate wildfire severity in the 2022 Sierra de la Culebra fire. Field Composite Burn Index data were correlated with satellite-derived indices. Results showed hyperspectral imagery provided more accurate severity estimates, particularly using Cellulose Absorption Index and Red Edge indices.