Skip to Main Content
Predicting forest attributes from climate data using a recursive partitioning and regression tree algorithmAuthor(s): Greg C. Liknes; Christopher W. Woodall; Charles H. Perry
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. 7 p.
Publication Series: Proceedings (P)
Station: Rocky Mountain Research Station
PDF: Download Publication (136.44 KB)
DescriptionClimate 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.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationLiknes, 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.
KeywordsDaymet, climate, forest inventory, data mining
- Partitioning the Uncertainty in Estimates of Mean Basal Area Obtained from 10-year Diameter Growth Model Predictions
- Technique for ranking potential predictor layers for use in remote sensing analysis
- Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information
XML: View XML