Forest Inventory and Analysis

Forest inventories are required to quantify tree compositions and forest values, as well as a prerequisite of sustainable management. For vast areas with poor infrastructure, satellite remote sensing combined with field data and machine learning is a cost-effective choice for strategic level analyses and investment decisions. For operational planning, individual tree inventories using drone images are now a realistic and efficient option.

Benefits

  • Improved understanding of the asset’s value
  • Optimizing forest management with more accurate data leads to more effective schedule of operations
  • Accurate distribution and mapping of all species (including dead trees)
  • Up-to-date aerial images

Target audiences

  • Forest owners who need forest management plan
  • Investors who need the best available estimate of forest value
  • Forest management service providers

Determining forest attributes and value tree-by-tree

Mapping and Inventory

A successful and cost-efficient forest inventory must take into account the following aspects: 

  • General considerations (accuracy, management unit size, users of the data)
  • Existing databases (remote sensing data, old inventory data, models) 
  • Available resources (funding, equipment, infra)
  • Measurement methods (sample vs wall-to-wall inventory, stratification)
  • Processing methods for the collected data (image processing, calculation of the desired metrics)
  • Presentation of the results and integration to forest management information systems.

Our solutions for inventory and analysis

Large-scale forest inventory with satellite remote sensing

Investment decisions for acquiring large forest areas in remote regions is often challenging due to old or non-existing inventory data. For vast areas covering hundreds of thousands or even tens of millions of hectares, Simosol has developed cost-effective methods (achieved by using Simosol’s existing codebase and openly available satellite data) to estimate the forest volume per species or species groups to a square unit of 10 x 10 metres. Results can be aggregated to any geographical unit and used for further analyses including long-term wood supply simulations.

Drone inventory measuring every individual tree

When the highest possible forest data accuracy is needed, drone inventory has emerged as a cost-efficient alternative to airborne LiDAR. A tree-based inventory with drones measures every single individual tree, generating a database that can be used as an input to forest valuation or forest management planning. The tree data can be aggregated to management units of any size. In the conditions of nordic boreal forests, management unit-specific volume estimates (for units of 1-10 ha)  can reach accuracies which are 50% better when compared to traditional stand-by-stand inventories. Higher data accuracy allows more precise modelling and optimization, ultimately increasing the  forest’s value and its economic returns.

What software is used

Software

Drone based inventories

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Software

Large scale assessments

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How this solution was used

Reference

Metsäni.fi

Metsäni.fi forest management plan is based on data from specially equipped UAVs (drones), from which we can identify and measure each individual tree in your forest. Volume estimations are more accurate, and plans become more operationally-efficient.

Metsäni.fi

Reference

Large-scale inventory of forest resources

Large-scale inventory of forest resources in Russia using Sentinel satellite images and field reference data collection with TRESTIMA inventory method. The project was linked to the development of a wood sourcing strategy for a global forest industry company, and included simulation of the development of the forest resources in the target area (approx. 90 million ha).

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Reference

Plantation performance review

The purpose of the review in Laos was to provide an independent assessment of the plantations for the lenders of the company (connected to a planned plywood mill investment). The work included a field assessment of the resources and operations, wood flow modelling, and harvest potential analysis.

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