Comparison of parametric and nonparametric methods for modeling height-diameter relationships

Abstract

This paper focuses on the problem of regionalization of the height-diameter model at the stand level. To this purpose, we selected two different modeling techniques. As a parametric method, we chose a linear mixed effects model (LME) with calibrated conditional prediction, whose calibration was carried out on randomly selected trees either close to mean diameter or within three diameter intervals throughout the diameter range. As a nonparametric method, the technique of classification and regression trees (CART) was chosen. These two methods were also compared with the local model created by ordinary least squares regression. The results show that LME with calibrated conditional prediction based on measurements of height at three diameter intervals provided results very close to the local model, especially when six to nine trees are measured. We recommend this technique for the regionalization of the global model. The CART method provided worse results than LME, with the exception of parameters of the residual distribution. Nevertheless, the latter approach is very user-friendly, as the regression tree creation and especially its interpretation are relatively simple, and could be recommended when larger deviations are allowed.

Evidence of climate effects on the height-diameter relationships of tree species

“…It turned out that this contribution was greater than that of the residual error for trees larger than 20 cm in most cases (see SM5 in the Supplementary Material). The mixedmodel approach has been widely used in the context of HD relationships (e.g., Castedo Feldpausch et al 2011;Lu and Zhang 2013;Adamec and Drápela 2016;Kearsley et al 2017), and the importance of the plot random effects that we found in this study is in accordance with the results of previous studies. For example, Hulshof et al (2015) showed that R 2 conditional on the random effects predictors were much higher than marginal R 2 .…”

“…The existing literature provides a large array of plot metrics that have been tested in HD relationship models, including stem density, basal area, dominant height, or diameter, arithmetic or quadratic mean diameter, relative spacing indices, and age Garber et al 2009;Crecente Campo et al 2014;Mehtätalo et al 2015;Sharma and Breidenbach 2015;Adamec and Drápela 2016;Saud et al 2016). However, climate variables have been overlooked in most studies.…”

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

“…FERRAZ FILHO et al, 2011;PAULO et al, 2014;BONTEMPS;BOURIAUD, 2014;ADAMEC;DRÁPELA, 2016;MARCATTI et al 2017;SCOLFORO et al, 2017).…”

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

“…In forestry practice, the relationship between stem DBH and tree H is generally reported as H = f(DBH) and mathematically expressed using various models, including power, exponential, hyperbolic, and others [34]. For the purpose of AGB estimation from ALS, however, the reverse form of DBH = f(H) is required.…”