Measuring, Reporting, and Verification of Forest Restoration

Measurement, Reporting, and Verification (MRV) is a structured approach designed to track progress, ensure accountability, and assess the impacts of interventions, and the basis for reporting in the UN-REDD+ programme. More recently as governments and managers seek to address benefits of forest conservation beyond the carbon that can be stored within forests, the features relevant to MRV reporting have expanded to include multiple biodiversity and ecosystem services (BES) indicators. Within forest restoration, MRV frameworks help stakeholders determine whether planned activities are delivering measurable ecological benefits over time, evaluate outcomes against reference conditions, and identify necessary adjustments in management. Effective MRV begins with baseline data collection, performed before any restoration activity commences. Baseline surveys establish initial conditions of degraded, restoring, and fully restored reference ecosystems, providing critical reference points to assess ecological improvements attributable to restoration actions.

In the SUPERB project's MRV-BES system, the workflow proceeds systematically. Clear ecological objectives are agreed upon, ensuring they are explicit, measurable, achievable, and time-bound. Following this, a targeted set of powerful yet practical ecological indicators were selected, typically encompassing vegetation diversity, soil carbon and microbial communities, environmental DNA (eDNA), bioacoustic monitoring, and remote sensing methods like LiDAR and multispectral imaging. To accurately detect ecological changes resulting from restoration, the project established a monitoring framework at stands of different ages or stages of restoration to define the Reference model trajectory. Like baseline surveys, monitoring from reference stands helps optimise sampling efficiency for future restoration projects, ensuring sufficient statistical power to detect progress while avoiding excess costs. Data collection combines professional surveys with citizen science approaches, affordable sensors, and freely available satellite imagery (e.g., Copernicus), and open-source script-based tools are made available to quantify uncertainty and compare outcomes against established benchmarks (counterfactual and reference condition models). Data collected as part of the SUPERB project were subject to rigorous quality assurance and quality control procedures, and were stored in centralised database adhering to standards such as Darwin-Core and INSPIRE, with persistent identifiers and Creative Commons licensing to facilitate transparency and data reuse.