Measuring, Reporting, and Verification of Forest Restoration
3.2 SUPERB Database Structure
The SUPERB MRV database centralizes all monitoring outputs—vegetation surveys, soil chemistry, eDNA OTUs, bioacoustic detections, LiDAR metrics and derived ecological indices—into a relational framework with standardized R‑script processing for reproducible, site‑ to region‑scale analyses. All data are shared under FAIR principles (Darwin‑Core/INSPIRE metadata, persistent identifiers) via open repositories (e.g., Zenodo, GBIF) and visualized through tools like the Ecological Recovery Wheel to support transparent, adaptive restoration management.
Effective MRV relies on consolidating diverse monitoring data into a unified, queryable database. The SUPERB database integrates results from all monitoring indicators—vegetation surveys, soil chemistry analyses, environmental DNA assessments, bioacoustic recordings, and remote sensing data—into a cohesive framework suitable for both site-level management decisions and broader, regional-scale analyses. The structured database comprises relational tables detailing sample metadata (e.g., site identifiers, stand characteristics, sampling dates), vegetation metrics (covering mature trees, saplings, seedlings, and understory vegetation types), indicator-specific outputs (such as acoustic detections, soil carbon levels, canopy heights, and classified eDNA operational taxonomic units), reference condition ranges for counterfactual and reference models, and derived ecological indices (e.g., biodiversity metrics, NDVI trends, ecosystem service scores). Data processing is standardised through reproducible R scripts, ensuring validity, consistency, and long-term comparability across SUPERB’s restoration sites.
3.3.1 Data Sharing
Data sharing in the SUPERB project adheres strictly to FAIR principles—Findable, Accessible, Interoperable, and Reusable—to enhance broader ecological research and management goals. All datasets receive persistent identifiers and comprehensive metadata descriptions aligned with Darwin-Core and INSPIRE vocabularies, facilitating easy discovery and integration into global ecological data repositories such as Zenodo and GBIF. Visual tools, including the Ecological Recovery Wheel, are employed to communicate restoration progress clearly and intuitively to diverse stakeholders, thus promoting informed decision-making and adaptive management. Shared data resources enhance reproducibility, reduce future monitoring expenses, and support efficient and effective ecosystem restoration practices across Europe.