A spatially explicit height-diameter model for Scots pine in Estonia

作者：Schmidt Matthias^*; Kiviste Andres; von Gadow Klaus

来源：European Journal of Forest Research, 2011, 130(2): 303-315.

DOI：10.1007/s10342-010-0434-8

• Estonia
• Scots pine
• Height-diameter model
• Generalized additive model
• Varying coefficient model VCM
• Mixed model
• Structured additive regression model STAR

This contribution presents an approach to model individual tree height-diameter relationships for Scots pine (Pinus sylvestris) in multi-size and mixed-species stands in Estonia using the Estonian Permanent Forest Research Plot Network. The dataset includes 22,347 trees. The main focus of the study was to use an approach that is spatially explicit allowing for high accuracy prediction from a minimum set of predictor variables that can be easily derived. Consequently, the height-diameter relationship is modeled as a function of only the stand quadratic mean diameter (dg) and the plot geographical coordinates. A specific generalized additive model gam is employed that allows for the integration of a varying coefficient term and 2-dimensional surface estimators representing a spatial trend and a spatially varying coefficient term. The high flexibility of the model is needed due to the very few predictor variables that subsume a variety of potential influential factors. Subsequently, a linear mixed model is used that quantifies the random variation between plots and between measurement occasions within plots, respectively. Hence, our model is based on the theory of structured additive regression models (Fahrmeir et al. 2007) and separates a structured (correlated) spatial effect from an unstructured (uncorrelated) spatial effect. Additionally, the linear mixed model allows for calibration of the model using height measurements as pre-information. Model bias is small, despite the somewhat irregular distribution of experimental areas within the country. The overall model shows some similarity with earlier applications in Finland. However, there are important differences involving the model form, the predictors and the method of parameter estimation.

• 出版日期2011-3