Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique

Abstract

The diameter distribution of trees in a stand provides the basis for determining the stand's ecological and economic value, its structure and stability and appropriate management practices. Scots pine (Pinus sylvestris L.) is one of the most common and important conifers in Turkey, so a well-planned management schedule is critical. Diameter distribution models to accurately describe the stand structure help improve management strategies, but developing reliable models requires a deep understanding of the growth, output and constraints of the forests. The most important information derived by diameter distribution models is primary data on horizontal stand structure for each diameter class of trees: basal area and volume per unit area. These predictions are required to estimate the range of products and predicted volume and yield from a forest stand. Here, to construct an accurate, reliable diameter distribution model for natural Scots pine stands in the Turkmen Mountain region, we used Johnson's S-B distribution to represent the empirical diameter distributions of the stands using ground-based measurements from 55 sample plots that included 1219 trees in natural distribution zones of the forests. As an alternative, nonparametric approach, which does not require any predefined function, an artificial intelligence model was constructed based on support vector machine methodology. An error index was calculated to evaluate the results. Overall, both Johnson's S-B probability density function with a three-parameter recovery approach and the support vector regression methodology provided reliable estimates of the diameter distribution of these stands.