Large-area forest inventory regression modeling: spatial scale considerations

This article is part of a larger document. View the larger document here.

  • Authors: Westfall, James A.
  • Publication Year: 2015
  • Publication Series: General Technical Report (GTR)
  • 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. p. 266.

Abstract

In many forest inventories, statistical models are employed to predict values for attributes that are difficult and/or time-consuming to measure. In some applications, models are applied across a large geographic area, which assumes the relationship between the response variable and predictors is ubiquitously invariable within the area. The extent to which this assumption holds for a tree height prediction model was evaluated at regional, ecoprovince, and ecosection spatial scales in the Northeastern United States. Two nonlinear regression models were tested, a spatially-ambiguous model that utilized tree and stand-level predictors, and a spatially-explicit model that incorporated latitude, longitude, and elevation as predictors. Regional-scale models evaluated at the state level showed considerable bias for some states, which suggests the statistical significance of spatial predictor variables does not translate into effective accounting for spatial variability. Similar results were obtained when fitting the models to an ecoprovince and evaluating bias within ecosections. Finally, fitting the models to ecosections within the ecoprovince provided a moderate level of local robustness as assessed by Moran’s I statistic; however there are cases where local biases may still exist. The results suggest that models should be developed and applied at small spatial scales to reduce local biases when model predictions are aggregated to larger geographic domains. However, small spatial scales often equate to relatively small sample sizes that can present problems in model fitting and result in increased model uncertainty. Therefore, modelers need to carefully balance the minimizing of spatial extent and obtaining acceptable sample size.

  • Citation: Westfall, James A. 2015. Large-area forest inventory regression modeling: spatial scale considerations. 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. p. 266.
  • Posted Date: February 16, 2016
  • Modified Date: September 20, 2016
  • Print Publications Are No Longer Available

    In an ongoing effort to be fiscally responsible, the Southern Research Station (SRS) will no longer produce and distribute hard copies of our publications. Many SRS publications are available at cost via the Government Printing Office (GPO). Electronic versions of publications may be downloaded, printed, and distributed.

    Publication Notes

    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
    • To view this article, download the latest version of Adobe Acrobat Reader.