Elia Vangi has a master's degree in forest systems sciences and technologies from the University of Florence. His interest includes but is not limited to forest modeling through remote sensing data, and spatialization of environmental variables, particularly in growing stock volume trends and the carbon cycle. He completed a Ph.D. in environmental ecology at Molise University, where his main focus was the development of a spatial approach for the high-resolution yearly prediction of forest growing stock volume and above-ground carbon pool. Other interests include the analysis of the new data from the Global Ecosystem Dynamics Investigation (GEDI), an high-resolution laser ranging of Earth's forests, and topography from the International Space Station (ISS). At the CNR Forest Modeling Lab., he is engaged in the assimilation of remotely sensed and inventory data for the development, implementation, and use of the 3D-CMCC-FEM forest model within the "Multi-scale observations to predict Forest response to pollution and climate change" (MULTIFOR) and the H2020 ForestNavigator projects. He is an R enthusiastic user.