Abstract #302194

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JSM 2003 Abstract #302194
Activity Number: 98
Type: Contributed
Date/Time: Monday, August 4, 2003 : 9:00 AM to 10:50 AM
Sponsor: Section on Statistics & the Environment
Abstract - #302194
Title: A Hierarchical Linear Model for Tree Height Prediction
Author(s): Vicente Monleon*+
Companies: Oregon State University
Address: Statistics Department, Corvallis, OR, 97331-4606,
Keywords: BLUP ; forestry
Abstract:

Measuring tree height is time-consuming. In most Forestry applications, tree diameter is measured and height estimated from a regression model. The trees used to develop these models are clustered into stands, but this structure is ignored and independence assumed. Also, the explanatory variable (diameter) and the cluster (stand) effect are confounded, since within-stand slope tends to be different than between-stand slope. In this study, hierarchical linear models that account explicitly for the clustered structure and the confounding effect are compared with model forms used in Forestry. The data consist of 1,433 Douglas-fir in 99 Oregon stands measured in 2001, and an evaluation dataset of similar size measured in 2002. Overall model performance improved substantially if the stand random effect could be predicted: RMSE decreased from 16.2 ft (current models) to 12.7 ft (hierarchical model). However, if the random effect could not be estimated, the improvement was small (RMSE 15.5 ft). The hierarchical model performed much better when the range of diameters in the stand was small. The implications of using regional models for prediction at the individual stand level are discussed.

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