Exploratory Inventory

sampling the forest

This type of inventory is built on a preliminary analysis of the forest population. The information obtained is mainly used to inform early management choices. On a regional or overall area basis, the inventory data are condensed.

Developing a virtual forest model allows further quantitative and qualitative assessment of the forest stands and of forest management practices, including elements regarding natural capital, ecosystem services and biodiversity.

The virtual forest is containing

High-resolution digital terrain/canopy/elevation models,
Digital forest models including canopies and models of tree/undergrowth trees identified in the forest,
Spatial distribution/mapping and other relevant stand/terrain/landscape data gathered through LiDAR scanning.

machine learning

The vertical structure of the forest can be easily observed through satellite observations.

Information such as textures, granulation, spectral variations can be used as predictors in a machine learning algorithm to determine forest characteristics needed for planning and management.

Moreover, this activity can produce continuous data (raster data at pixel level constitution using VIRTSILV machine learning algorithm) using discontinuous data collected from the ground (plot data with ground-truth information processed by VIRTSILV sample plot module)

data extrapolation

Building the regression model based on terrestrial LIDAR scan in the forest

Extrapolating the model at the property level

digital twins

Diameter at breast height (cm)

Dominant height (m)

Standing volume (m3/ha)

Basal area (m2/ha)

A.I. Decision forest maps

Forest stands are used as timber inventory units, forestry data containers and operation units in timberland management. Stands can be made either on operational or on biological basis. Biological stands tend to be smaller and more detailed than stands made on operational basis. Typical stand criteria have been timber size, density and species, as well as site type.

A faster and more precise way of creating spatial decision-making maps for forest management is to combine digital twin data (basal area, volume, dbh, h) into a simplified, homogeneous stand map

carbon stock

In VIRTSILV workflow generalized allometric equations are used to determine biomass (dry weight). Equations regarding stem volume or biomass of tree components (stem, branches, leaves and total area) at dbh or dbh ar used based on international research papers and on the unique shape of the scanned trees.

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