STOCHASTIC-MODELS FOR CONIFER TREE CROWN PROFILES

We perform a feasibility study of using stochastic models to describe the profile of tree crowns and to capture the stochastic nature of the tree crown form for five conifer species of the Sierra Nevada. In 70% of the cases investigated we found that we could model tree crown pro files as a quadratic or cubic trend in conjunction with a simple autor egressive moving average model (ARMA). In the remaining cases we used a quadratic or cubic trend in conjunction with white noise. These stoc hastic ARMA models are visually and statistically an improvement over using Euclidean geometric crown profile models. Competing models were judged by using Akaike's Information Criterion (AIC) to achieve a pars imonious model. It was found that first-order moving average MA(1) mod els or first-order autoregressive AR(1) models were adequate for model ing the majority of the cases studied and that these models were quali tatively similar. MA(1) models were preferred over the AR(1) models be cause less information is required to simulate them.