Maximize Return on Investments using High Resolution Data in Forest Planning — Part 1

Using high resolution data in forest planning ensures more cost-effective planning with unparalleled accuracy at the operational, tactical and strategic levels. That is why Tesera, taking the holistic view of forest asset management, envisioned the development of an accurate, reliable high resolution inventory solution (HRIS) that enables seamless integration of strategy, planning, and operations.

This use of HRIS inventory data finally allows an inventory that matches a host of timber and non-timber attributes (e.g. land cover, species compositions, heights, volumes, basal area, crown closure, slope data, etc.) to the on-the-ground realities. It allows for forest companies to prove that they are sustainable through using science-based estimates and not relying on assumptions that occur in traditional forest planning. If we start with an accurate, objective, high resolution inventory, then any resulting forest management plans can be implemented in the field much easier since the values are more accurately mapped and modelled.

Generally, the initial step is to develop a stakeholder group to review previous higher level plans, to assess the current status of timber and non-timber resources on the management area, and to develop goals and objectives for timber and non-timber resources for the next management plan. Usually an updated inventory is used in this process. Having more detailed, high resolution inventory such as HRIS provides a critical piece to support this work.

“it is the first time that the management plan results from a forest estate model could be directly implemented without review and changes.”

The next step is to identify the area that can be harvested (e.g. Total Harvestable Land Base in BC, Net Land Base in AB), developing constraint datasets from the high resolution data. Some example datasets include:

  • Steep and unstable slopes where harvesting will not occur from the HRIS Digital Elevation Model (DEM)
Terrain Analysis indicating areas with gullies and slope failures. Image courtesy of UBC Alex Fraser Research Forest.
  • Riparian buffers using stream, wetland, lake and river datasets derived from high resulting imagery and the above DEM
Predicted Streams and Depth to Water Table Mapping for the UBC Alex Fraser Research Forest courtesy of the University of New Brunswick, Forest Watershed Research Centre.
  • Accurate volume/hectare or site index estimates to define areas of low site productivity that will not be harvested from the HRIS inventory dataset
  • Identify non-forested (roads, well sites, landings, shrubs, agricultural areas, etc.), minor, non-commercial species that will not be harvested from the HRIS inventory dataset
  • Viewshed analysis using the HRIS DEM to determine visual quality objectives using the HRIS DEM and HRIS inventory dataset if not already known
  • Refine any habitat areas using the HRIS inventory dataset if habitat is defined by timber characteristics that are measured in the inventory, or use extent of any reserve zones (e.g old growth management areas)
  • Administrative boundaries (e.g. parks, private land, defined forest area boundaries, etc.)
  • Past harvest and silviculture treatment data, though this is usually part of the inventory
  • Planned harvest and silvicultural treatments that may already be planned or finalized in the field.
Harvestable area map (grey) and areas that can not be harvested indicated by other colours and described in the legend. Image courtesy of UBC Alex Fraser Research Forest.

As mentioned in the opening paragraph, developing the above datasets based on accurate, high resolution data is instrumental in forest planning for a number of reasons:

  1. Reduces office and field time to plan/verify harvest boundaries and road access.
  2. Decreases permitting time and costs due to working with high resolution data.
  3. The use of volume, basal area, species compositions and density (number of stems/hectare) can be direct inputs into growth & yield models, to project the future growth of the forest in individual tree growth models as well as whole stand growth models.
  4. Allows forest management plans (developed from forest estate models, like Remsoft’s Spatial Planning Suite) to be easily implemented since the values on the landscape are accurately mapped and attributed with objective data that meet the rigour of science.
  5. Helps to ensure that forest management plans are as sustainable as possible by using the most accurate, precise data to make estimates now and into the future.
Areas and timing for when wildlife habitat with specific criteria will be available on the landscape. Image courtesy of UBC Alex Fraser Research Forest.

Tesera’s approach to incorporating high resolution inventory data into traditional forest management planning processes have been reviewed and accepted by government and industry. Ken Day, the forest manager for the UBC Alex Fraser Research Forest was very encouraged by the results stating that “it is the first time that the management plan results from a forest estate model could be directly implemented without review and changes”.


Read more about Tesera’s approach to high resolution forest inventory.

Dwight Crouse, RPF is a Senior Data Analyst at Tesera.com

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