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Open AccessArticle

Stem Taper Approximation by Artificial Neural Network and a Regression Set Models

1
Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Al. 29 Listopada, 31-425 Krakow, Poland
2
Space Informatics Lab, University of Cincinnati, Cincinnati, OH 45221, USA
*
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 79; https://doi.org/10.3390/f11010079
Received: 30 October 2019 / Revised: 30 December 2019 / Accepted: 31 December 2019 / Published: 9 January 2020
(This article belongs to the Section Forest Ecology and Management)
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. View Full-Text
Keywords: stem form; stem profile; tree volume; stem taper modeling, stem diameter at any height stem form; stem profile; tree volume; stem taper modeling, stem diameter at any height
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MDPI and ACS Style

Socha, J.; Netzel, P.; Cywicka, D. Stem Taper Approximation by Artificial Neural Network and a Regression Set Models. Forests 2020, 11, 79.

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