Today Xiongjie presented the second chapter of his DPhil thesis - Mapping functional traits and assessing functional diversity in Chilean forests with remote sensing.
Yadvinder and Jesús collected trait data, multispectral drone images, and handheld LiDAR data in Chile, and Xiongjie used these data to build relationships between remotely sensed data and other environmental variables using machine learning algorithms, and we got spatially continuous maps of functional traits and functional diversity. We noticed that the model performed better for predicting functional diversity, and hydrological stress and soil properties were found to be the main drivers of functional diversity in Chilean forest ecosystem.
