A model of your forest that gets sharper with every measurement.

Built from satellite imagery and your own fieldwork, and updated each time new data comes in. Biomass on every hectare, so you can track growth, spot loss early and manage risk.

Trusted by

  • Foresight
  • Great Yellow
  • Nattergal
  • Grosvenor
  • Highlands Rewilding
  • Rebalance Earth
  • Octopus Investments
  • Rewilding Britain
  • Naturalis Biodiversity Center

R&D funding from

  • European Space Agency
  • UK Space Agency
  • UK Research and Innovation

What our biomass model tells you.

  • What is there.

    Biomass density for every hectare at 10 m resolution, each with its own uncertainty figure, so you can see where the volume sits and how much it varies across the site.

  • Year 1
    Year 5
    Year 9

    What changed.

    Nine years of biomass change, so you see growth, loss and disturbance over time rather than one snapshot. Add new data and the history is rebuilt with it.

  • Where to act.

    The model knows where it is least confident, so field campaigns go where they are worth the most. Spend less on fieldwork and get a better model each time.

Our differentiator

Your data, integrated into the model itself.

There are two ways to use field data. Calibration corrects a finished model after it has run, which only helps where the plots are. Step outside them and you are back on a global average. Integration puts your plots inside the model and rebuilds it.

We do the second. Your field and drone data move the estimate, the pattern across the whole site and the confidence, all at once.

Calibration is a patch on the output. Integration is your data in the engine.
The same site with field data in the model: most of it reads lower, the central compartments most of all, and a few western blocks higherWith your field data in the model
Mean biomass map of a whole project site from satellite data only, with its brightest, highest-biomass compartments in the centreSatellite only
Both at 10 m. What changes is not the detail, it is the estimate. Field data moves the estimate to where the ground says it is, up or down.
  • It improves backwards as well as forwards.

    Every time data is added the model updates, including the historic time series, so past estimates get better too.

  • Continuous learning.

    The longer the model runs and the more data you add, the better it performs.

The Treeconomy Biomass Engine.

  • Satellite.

    Runs anywhere from a site boundary alone. No field data needed.

  • Integrated.

    Your field data goes into the model, so the whole site sharpens and the confidence tightens.

Resolution
10 m
Resolution on both tiers
Record
Versioned
And auditable
Uncertainty
Per pixel
Uncertainty quantified

Solutions

Different goals, same engine.

  • Valuation · Reporting · Risk

    Asset managers

    Valuation, reporting, risk management and where to spend next. Put your inventory in the model and every holding gets a current number you can stand behind.

  • Screening · Evidence · Monitoring

    Project developers

    Build the model once, run the assessments a verifier needs, and improve it over time. Every campaign you add makes the next assessment stronger.

Already running on real portfolios and projects.

  • Looking straight down on a dense conifer forest canopy

    Asset manager

    Gresham House

    Uses it for

    Biomass analysis across part of a managed forestry portfolio, digitising institutional inventories.

  • Part of a Great Yellow project site on the platform: its boundary over satellite imagery

    Asset manager

    Great Yellow

    Uses it for

    The biomass analysis across a nature recovery portfolio.

  • Aerial view of a forested mountain peak with low cloud on its slopes

    Project developer

    Africa Carbon Partners

    Uses it for

    An ARR project across roughly 350,000 hectares in Gashaka Gumti National Park, Nigeria.

Forest decisions deserve more than a snapshot.

Screen recording of the Sherwood platform: a project's biomass map over satellite imagery, stepping through the years and zooming in and out.