Publication Details

Predicting forest attributes from climate data using a recursive partitioning and regression tree algorithm

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Year Published

2009

Publication

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. 7 p.

Abstract

Climate information frequently is included in geospatial modeling efforts to improve the predictive capability of other data sources. The selection of an appropriate climate data source requires consideration given the number of choices available. With regard to climate data, there are a variety of parameters (e.g., temperature, humidity, precipitation), time intervals (e.g., 30- year normal, seasonal average), and summary statistics (e.g., mean, minimum) which can be selected. In this study, we propose a technique for evaluating the combination of climate parameters that are most closely related to ground observations of forest attributes. Using data from the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service as response variables, recursive partitioning and regression tree analysis was applied using a suite of climate variables from the Daymet database as predictor data. Although model improvement scores for climate variables were modest, the technique provides opportunities for deciding among a wide array of possible climate predictors.

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Citation

Liknes, Greg C.; Woodall, Christopher W.; Perry, Charles H. 2009. Predicting forest attributes from climate data using a recursive partitioning and regression tree algorithm. 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. 7 p.

Last updated on: July 20, 2009