Abstract
Variation in tree stem form depends on species, age, site conditions, etc. Stem taper models that estimate stem diameter at any height and volume should comply with this complexity. In the paper, we propose new methods taking into account both unbiased estimates and stem variability: (i) an expert model based on an artificial neural network (ANN) and (ii) a statistical model built using a regression tree (REG). We used the variable-exponent taper equation (STE) as a reference for these two models. Input data contain information about 2856 trees representing eight dominant forest-forming tree species in Poland (birch, beech, oak, fir, larch, alder, pine, and spruce). The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built ANN and REG models and calculated both stem taper and tree volumes. The results show that ANN is a universal approach that offers the most precise estimation of stem diameter at a particular stem height for different tree species. The results for alder are an exception. In this case, the REG model performs slightly better than ANN. In terms of volume prediction, the ANN model provides the most accurate predictions for coniferous and beech. In general, flexibility and predictive performance of the ANN are better than REG and reference the STE equation.
Highlights
Models for estimating a stem taper enable one to estimate stem volume, being useful in both the assessment of the economic value of timber production and forest conservation management [1].According to the IPCC methodology, merchantable timber volume is used to convert the growing stock of forest stands into the amounts of biomass and carbon accumulated in trees [2], with the help of either biomass conversion and expansion factor (BCEF) or biomass expansion factor (BEF)
We propose two new solutions for modeling tree taper: (i) an expert model based on an artificial neural network model and (ii) a statistical model built using a regression tree
In terms of RMSE, our analyses indicate that artificial neural networks allowed for the most precise determination of a stem shape for all the species studied except for alder, for which the regression tree (REG) model was better
Summary
According to the IPCC methodology, merchantable timber volume is used to convert the growing stock of forest stands into the amounts of biomass and carbon accumulated in trees [2], with the help of either biomass conversion and expansion factor (BCEF) or biomass expansion factor (BEF). Such conversion requires accurate and unbiased systematic errors as well as methods for timber volume determination. Taper models, which allow the estimation of tree shape and wood assortment volume are one of the most important types of practical information used in the forest management and timber industry [3]. The linear models are less biased in tree diameter estimation, these models suffer from a serious disadvantage: they do Forests 2020, 11, 79; doi:10.3390/f11010079 www.mdpi.com/journal/forests
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