What kind of tree cover is on this land: primary forest, regrowth, plantations, tree crops, or none? The answer shapes what a reforestation project can do there. The analysis splits the property into six classes for 2020 and outlines the other land the model is most confident about, as candidates for a field visit.
Separate open land from forest, plantations and tree crops early, so ARR planning starts from land that may qualify.
Get a first map of where planting could go before a site visit, with every area traced to its dataset.
Describe a site’s starting point by kind of tree cover, not only as trees or no trees.
A property boundary as a polygon, and these settings, which you can change before each run.
| Setting | Default | Range |
|---|---|---|
| Minimum uncalibrated Other Land model probability for candidate screening. | 0.6 | 0.5–1 |
Run it at pre-feasibility, beside forest cover history. It narrows a property to the open land worth walking; the field visit, recent imagery and your standard’s eligibility rules make the call.
Forest cover analysis tells you how much tree cover there was in 2000 and where it was lost through 2024. Land structure tells you what kind of tree cover was there in 2020, so natural forest, plantations and tree crops are told apart. Carbon teams usually run both.
Not as it is. The outlines are places to check. Confirm them with recent imagery and a field visit, rule out land your standard excludes, and draw the project area from what holds up.
The dataset’s pixels are 10 m across, so field edges, strips along rivers and small woodlots show up that 30 m data would blur.
A property boundary as a polygon, drawn on the map or imported from a KML, shapefile or GeoJSON file. The result is saved on the property, beside its other records and maps.
Tell us about the properties you are looking at, and we will set the analyses up in your project.
Bring in the spreadsheets, field forms and maps you already use, and see every site in one place.