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 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.