Forest Ecosystem Inventories

Metsähallitus awarded Arbonaut with a tender for deadwood mapping. Arbonaut delivered a detailed single-tree inventory of standing dead trees.

ZAFFICO needed accurate information on wood quantity and quality, including volume, growth, structure, and health, and a reliable way to keep that information up to date.

As forestry operations become increasingly data-driven, new data sources are changing how inventories are built, and decisions are made. One of the most promising is HPR data.

With slow and not precise enough field-based inventory methods, Tärna-Stensele & Sorsele Övre needed a new approach for efficient operations.

The aim of the case is to evaluate the suitability of high-resolution satellite stereo-imagery data for creating forest firefuel load maps in the boreal region.

Älvdalen and Särna-Idre Besparingsskogar required a robust, data‑driven understanding of forests, both as an economic resource and as an important landscape for biodiversity.

Instead of relying on traditional methods for inventory, Jokkmokk Allmänning continues to use the forest in a responsible way with comprehensive tree-level insights.

Orsa Besparingsskog has land holding of about 75,000 hectares, of which 60,000 hectares are productive forest land, which requires reliable forest information.

Arbonaut and Forestry France were selected as winners of the 7th IGNfab call for projects, “Give relief to your projects with LiDAR.”

To ensure long-term sustainability, the measurement accuracy of tree height is crucial for forest biomass and carbon stock monitoring, particularly in young forests.

Arbonaut was assigned to produce maps on forest understory biomass for pellets, forest monitoring, and fire risk reduction.

Arbonaut was commissioned by WWF Denmark to carry out remote-sensing–based baseline assessments for conservation areas in Uganda and Albertine Rift Landscape.

The National REDD+ Office partnered with Arbonaut and WWF Pakistan to develop the country’s first comprehensive FREL/FRL, NFMS, and MRV frameworks.