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Effects of plot size on forest-type algorithm accuracyAuthor(s): James A. Westfall
Source: In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 15 p.
Publication Series: Proceedings (P)
Station: Rocky Mountain Research Station
PDF: View PDF (275 B)
DescriptionThe Forest Inventory and Analysis (FIA) program utilizes an algorithm to consistently determine the forest type for forested conditions on sample plots. Forest type is determined from tree size and species information. Thus, the accuracy of results is often dependent on the number of trees present, which is highly correlated with plot area. This research examines the sensitivity of a forest-type algorithm to changes in amounts and types of input data that result from altering the sample plot area. Logistic regression was used to determine which plot metrics have the most influence on algorithm output. Relationships between plot area and key variables such as number of species, number of trees, and total basal area were established and applied to the regression models. The results allow for assessment of algorithm accuracy over a range of plot sizes. The algorithm was generally robust to changes in area for loblolly/shortleaf, oak/hickory, and oak/gum/cypress type groups. Algorithm accuracy was mediocre for other type groups, with oak/pine having the poorest performance. A comparison between field observed forest type and algorithm output showed average agreement rates of near 90 percent when computed types were conifer. However, agreement rates were lower for hardwood groups, especially when the computed type was aspen/birch. Better alignment between the field- and algorithm-based determinations may be achieved by providing real-time algorithm output to field crews.
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CitationWestfall, James A. 2009. Effects of plot size on forest-type algorithm accuracy. In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 15 p.
Keywordsforest inventory, logistic regression, species diversity, classification
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