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 research develops new height–diameter models for key Spanish tree species to improve forest management planning. While initial models fit data visually, statistical performance remains weaker than current equations. Future work will incorporate stand-level variables such as basal area and dominant height to enhance accuracy and reduce estimation errors.