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Title: Redrawing the baseline: a method for adjusting biased historical forest estimates using a spatial and temporally representative plot network

Author: Goeking, Sara A.; Patterson, Paul L.;

Date: 2015

Source: In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 228-232.

Publication Series: General Technical Report (GTR)

   Note: This article is part of a larger document. View the larger document

Description: Users of Forest Inventory and Analysis (FIA) data sometimes compare historic and current forest inventory estimates, despite warnings that such comparisons may be tenuous. The purpose of this study was to demonstrate a method for obtaining a more accurate and representative reference dataset using data collected at co-located plots (i.e., plots that were measured during both periodic and annual inventories). The approach described here uses co-located plot-level data to build linear regression models that relate annual inventory measurements to periodic inventory measurements. Separate models were constructed within each state, and wherever possible, for domains defined by factors that may affect forest attributes over time and that also affected the intensity of the periodic inventories (i.e., timber versus woodland forest types). We used these regressions to simulate periodic-era, plot-level response variables, on a per-acre basis, for annual plot locations that were not sampled during the periodic inventories. Because the extent of the resulting dataset coincides with the annual plot grid, the post-stratification procedures used to produce broad-scale annual inventory estimates can be applied to the simulated periodic dataset to produce periodic-era estimates of forest attributes. Construction of this simulated periodic-era dataset allows investigation of broad-scale trends in forest attributes, particularly as they vary across ownership group, reserved status, and forest type group due to disturbance and land management history.

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Goeking, Sara A.; Patterson, Paul L. 2015. Redrawing the baseline: a method for adjusting biased historical forest estimates using a spatial and temporally representative plot network. In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 228-232.

 


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