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 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.

Iowa’s prairies are nearly gone, but restored prairies may cool local climates through evaporative cooling. Deep-rooted, structurally diverse plants increase water transfer to the atmosphere, reducing surface and air temperatures. Using drones, LiDAR, and flux towers, the researcher quantifies prairie cooling as a climate-mitigation tool.