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Repeatable, documented forest calculation.

Multispectral and radar remote sensing, LiDAR data and ground inventory, integrated into a calculation method based on ISO 14064-2, with a positive validation opinion from an independent verification body. Every estimate comes with its own uncertainty.

Positive validation opinions are issued to the project's client and concern that client's project against ISO 14064-2. From the text of an opinion (original, in Italian: “Sulla base dell'esame delle evidenze ottenute, non è stato riscontrato alcun elemento che faccia ritenere che le ipotesi e le assunzioni adottate […] non costituiscano una base ragionevole per la previsione delle emissioni evitate.” Unofficial translation: “Based on the examination of the evidence obtained, no element was found suggesting that the hypotheses and assumptions adopted […] do not constitute a reasonable basis for the forecast of avoided emissions. It is important to stress that actual results are likely to differ from forecasts, because events often do not occur as expected, and the variation may be significant.”). The method is applied in forest projects with companies, operators and associations in the sector.

CALCULATION METHOD BASED ON ISO 14064-2 · POSITIVE VALIDATION OPINION FROM AN INDEPENDENT VERIFICATION BODY · ISSUED TO THE CLIENT · 2026

From satellite analysis to measurement in sample plots.

Illustrative figure. Orthophoto: AGEA 2012, National Geoportal. Cadastral parcels: Agenzia delle Entrate, CC BY 4.0. Data layers and sample tree: illustrative processing.

Seven steps, one thread. At the centre, the method.

  1. 01

    Satellite.

    The forest from above, at regular intervals. Public and commercial satellite imagery, acquired at regular intervals, tracks the state of forest cover across the whole area.

  2. 02

    Zoom.

    From scene to cadastral parcel. From the image of the whole valley down to a single land parcel, or a group of parcels: the area of interest on which we measure, plan and report.

  3. 03

    LiDAR.

    The structure of the forest in three dimensions. Laser scanning returns millions of points: canopy height, density and vertical structure, including the parts beneath the canopy that the signal reaches.

  4. 04

    Sample plots.

    On the ground, the measurements we use to calibrate the predictive models in development. Surveys on sample plots record species, diameters and heights, and calibrate the estimates from satellite and LiDAR.

  5. 05

    Method.

    The margin of uncertainty is part of the number. Every estimate follows repeatable, ISO-based steps and comes with its uncertainty stated.

  6. 06

    Report.

    The same area, followed over time. Measurements are repeated at regular intervals and feed into a report that can be compared year on year.

  7. 07

    Decision.

    What to do in the forest, and where. From data to intervention priorities: where to thin, where to protect, where to wait.

ISO 14064-2 sets out the principles for quantification, monitoring and reporting at project level. The method translates them into an operational forest protocol: baseline definition, sampling design, estimation models, uncertainty assessment, documentation. The calculation method has a positive validation opinion; validation of the reporting method is planned. Each project requires a new verification.

The data serves the reporting of those who use it; Syntropica's job is measurement.

What is not measured, we do not claim.

  1. 01

    Baseline.

    Stratification of the area from multispectral and LiDAR remote sensing, ground inventory on permanent sample plots, estimate of the initial stock: the reference against which every change is measured.

  2. 02

    Aggregation.

    Grouping parcels and holdings up to the scale at which a forest management plan and long-term monitoring become viable.

  3. 03

    Monitoring.

    Multispectral time series for vegetation condition and forest health stress; change detection for felling, windthrow and land-use change; synthetic aperture radar for slope deformation.

  4. 04

    Quantification.

    Estimation models applied to field data, for above-ground biomass and carbon stock, with stated uncertainty intervals. LiDAR canopy metrics, already acquired, will enter the estimate with the models in development.

  5. 05

    Reporting.

    A structured, reviewable report, with traceability of data, versions and calculation steps.

The same data supports forest management and planning, with plans signed by the forestry technician in charge.

What we quantify, and what we do not.

We quantify aboveground carbon stock against an explicit baseline, with a confidence interval. From the estimated value we deduct the prudential limit of uncertainty and then a reserve margin for adverse events.

Stock increment during the project period is held back as a reserve margin, separate from the initial estimate. It is allocated only after direct measurement, at the end of the period or through progressive release (roll-out) as expiry approaches, on the basis of repeated inventories on the sample plots.

Reference period: 10 years for each plot, with interventions every year on new plots, each with its own 10 years. Forest tenure is bound for 30 years by regional legislation (in Piedmont, Regional Government Resolution no. 24-4638 of 6 February 2017). The uncertainty on estimated values is ±10%; the safety buffer for adverse events is 25%.

Regional references: we operate in compliance with the Piedmont Regional Forest Plan 2017-2027 (D.G.R. no. 8-4585 of 23 January 2017) and take as our reference for calculation, baseline and duration of commitments the guidelines of D.G.R. no. 24-4638 of 6 February 2017, drawn up for the voluntary forest carbon credit market. We use its measurement criteria, not its mechanism for issuing and marketing credits.

We do not issue credits; the estimate covers the contractual period. The company's statements remain the company's.

Biodiversity: for now it is an additional benefit (additionality), not a quantity we declare nor a condition of the method. We monitor how a scenario develops in which biodiversity measures are finally recognised.

Platform and predictive models.

We are developing a platform that brings satellite data, LiDAR and the method together in a single environment, alongside machine learning predictive models trained on the sample plots and validated through temporal cross-validation. The models serve for preliminary assessment of areas and as support to reporting, with consistency checks against measured data; they complement field measurement. The aim is to move from monitoring what has happened to forecasting how the stand will develop, through to recurring reporting on the same area over time.

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