Comparison of Data Grouping Strategies on Prediction Accuracy of Tree-Stem Taper for Six Common Species in the Southeastern US

Sheng-I Yang,P Corey Green

doi:10.3390/f13020156

Abstract

Clustering data into similar characteristic groups is a commonly-used strategy in model development. However, the impact of data grouping strategies on modeling stem taper has not been well quantified. The objective of this study was to compare the prediction accuracy of different data grouping strategies. Specifically, a population-level model was compared to the models fitted with grouped data based on taxonomic rank, tree form and size. A total of 3678 trees were used in the analyses, which included six common species in upland hardwood forests of the southeastern U.S. Results showed that overall predictions are more accurate when building stem taper models at the species, species group or division level rather than at the population level. The prediction accuracy was not considerably improved between species-specific functions and models fitted with species-related groups for the four hardwood species examined. Grouping data by taxonomic rank provided more reliable predictions than height-to-diameter ratio (H–D ratio) or diameter at breast height (DBH). The form/size-related grouping methods (i.e., data grouped by H–D ratio or DBH) generally did not improve the prediction precision compared to a population-level model. In this study, the effect of sample size in model fitting showed a minimal impact on prediction accuracy. The methodology presented in this study provides a modeling strategy for mixed-species data, which will be of practical importance when data grouping is needed for developing stem taper models.

Highlights

Publisher’s Note: MDPI stays neutralTree-stem taper, defined as the change in tree diameter with increasing tree height from ground level to total tree height, is a quantitative description of stem profile [1]
The effect of sample size in model fitting showed a minimal impact on prediction accuracy
Unlike using taxonomic rank in data grouping, the results showed that increasing the number of H–D ratio or diameter at breast height (DBH) groups in model fitting did not appreciably improve the prediction accuracy

Summary

Introduction

Publisher’s Note: MDPI stays neutralTree-stem taper, defined as the change in tree diameter with increasing tree height from ground level to total tree height, is a quantitative description of stem profile [1]. Stem taper functions are typically built by species, known as species-specific models, e.g., [2,3,4]. Since every species in a plant community may respond differently to environmental and management changes and conditions, developing stem taper models at the species level has been generally assumed to better capture variable tree forms compared to a single population or community-level model (i.e., a single taper model for the entire population) [5,6]. Building species-specific models usually requires relatively large samples due to complex model forms and large numbers of parameters [7]. Rather than grouping data at the species level, an alternative approach is to re-aggregate individuals into a smaller number of groups based on similar tree characteristics

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Discussion

Conclusion