Summary
The VESTA (Vegetation Spatialization of Traits Algorithm) project proposes the development of a workflow to map global above and belowground plant traits for the present and future through the integration of a trait-based dynamic global vegetation model (DGVM) and vegetation EO (Earth observation) data. Trait-based DGVMs are process-based and provide a direct link between the environment, plant ecology and the emerging vegetation patterns. Observations from recent global trait databases will be used to initialize the model. Then, vegetation EO data will be used to optimize the model, using a calibration procedure which adjusts the trait relationship curves allowing the model to best reproduce satellite measurements of vegetation structure and productivity. Similar to previous process-based model/observational data integration methods of climate reanalysis, EO-constrained trait-based DGVMs can provide a multivariate, spatially complete and coherent record of global vegetation traits. The final output will be based on trait distributions, allowing the plotting of detailed aspects of plant functional diversity in each particular location, such as the mean, variance, skewness and kurtosis. In addition the maps will be a temporal series, allowing a deeper understanding of the current state of functional diversity and its shift in time. The final map dataset will be an invaluable EO product representing leaf, wood and root traits that can showcase the potential of Sentinel missions, the Earth Explorers and the ESA long-term data archives support the analysis of global biodiversity.
