Forest Modeling Sensitivity to FRI Data 120-401

Description: Forest Resources Inventory (FRI) data is the primary data source for all forest modeling on public lands in Ontario for forest management. The planning inventory is a specific forest resource inventory required for the preparation, implementation and monitoring of a forest management plan. Most forest managers have concerns about the quality of the FRI data they use to develop forest management plans or silvicultural prescriptions. Their concerns relate to the degree of variation between FRI attributes and actual on the ground conditions. These data are used in various strategic forest management models such as SFMM, PatchWorks, and FSOS. The use of these models and tools facilitate forest management decision making and influence decisions made regarding wildlife habitat supply, biodiversity, wood supply, and socio-economics. The concern is that erros in the FRI data will lead to varying degrees of uncertainty in our predictive models and tools. Understanding this uncertainty will allow forest managers to better interpret the results of forest analysis and should lead to improvements in the various analytical methodologies used in forest management.

The objectives of the study were to:

1. Obtain a measure of the precision and bias associated with important FRI data attributes through ground surveys.

2. Use the imprecision and bias of FRI attributes in sensitivity analyses of various data systems and analytical methodologies used in forest management planning.

3. Describe the methods that forest managers can use to determine the error in their FRI data.

4. Describe methods for addressing potential errors in decision-support analysis attributable to FRI errors.

5. For specific decision support models and analytical methodologies, identify the potential error attributable to FRI error and describe how this information can affect the maintenance of existing models and the development of new methodologies and/or models/tools.

The Project Team: Fred Pinto OMNR, Dan Rouillard, OMNR, Brian Naylor, OMNR, Dianne Othmer, OMNR, Tom Moore, Spatial Planning Systems. Chris Lyons, independent contractor.

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