Speaker
Description
Terrestrial biodiversity drives ecosystem functions that regulate land-atmosphere interactions. Biodiversity-Ecosystem Functioning (BEF) relationships are critical for ecosystem stability and resilience. While current BEF knowledge stems from relatively small-scale experiments, in-situ data, and theoretical work, Earth Observation (EO) data offer ample opportunities for global vegetation monitoring. However, the extent to which EO data accurately captures BEF relationships remains uncertain. The absence of an integrated dataset combining in-situ BEF measurements with EO products has hindered comprehensive global BEF studies. Here we present EO4BEF, a dataset integrating multi-modal EO data with in-situ BEF data for understanding BEF relationships at a global scale. This integration includes data from optical and thermal satellite imagery at different spatiotemporal scales (Sentinel-2, Landsat, and MODIS), Synthetic Aperture Radar (SAR; Sentinel-1), and climate data across sites spanning the FLUXNET and SAPFLUXNET networks. These networks provide crucial ecosystem function estimates from globally distributed stations that include observations of carbon, water, and energy fluxes and tree-level sap flow measurements. By combining these in-situ observations with multi-modal EO data, EO4BEF enables the upscaling of local BEF relationships to global scales. The dataset includes high-quality, harmonized spatiotemporal data that facilitate advanced Artificial Intelligence (AI) analyses. EO4BEF demonstrates the feasibility of merging diverse data sources and modalities to investigate global BEF dynamics. Furthermore, the utilized approach creates the opportunity to merge multi-modal EO data with additional global vegetation databases such as TRY, sPlot, and the European Vegetation Archive, providing a valuable resource for global BEF studies using AI techniques.
| Status Group | Postdoctoral Researcher |
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