Modeling Height–Diameter Relationships for Mixed-Species Plantations of Fraxinus mandshurica Rupr. and Larix olgensis Henry in Northeastern China

Abstract

The mixture of tree species has gradually become the focus of forest research, especially native species mixing. Mixed-species plantations of Manchurian ash (Fraxinus mandshurica Rupr.) and Changbai larch (Larix olgensis Henry) have successfully been cultivated in Northeast China. Height–diameter (H–D) models were found to be effective in designing the silvicultural planning for mixed-species plantations. Thus, this study aimed to develop a new system of H–D models for juvenile ash and larch mixed-species plantations, based on competition information and tree and stand attributes. The leave-one-out cross-validation was utilized for model validation. The result showed that the H–D relationship was affected not only by the tree attributes (i.e., tree size and competition information) but also by stand characteristics, such as site quality and species proportion of basal area. The best model explained more than 80% and 85% variation of the tree height of ash and larch, respectively. Moreover, model validation also confirmed the high accuracy of the newly developed model’s predictions. We also found that, in terms of total tree height, ash in middle rows were higher than those in side rows, while larch in the middle rows were higher in the early growth period but then became lower than those in the side rows, as the diameter increased. The newly established H–D models would be useful for forestry inventory practice and have the potential to aid decisions in mixed-species plantations of ash and larch.

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

“…Using an optimal number of sample trees in mixed-effects model calibration will deliver a relatively high prediction accuracy and appears to be a more efficient strategy for forest management [37,85]. Both of the sampling strategies (based on subplots and individual trees) behaved logically in relation to the amount of calibration information (Figure 4), as corroborated by previous studies on forest modeling [63,65,66]. In the field measurements, plot delineation often required extra labor compared to random tree sampling.…”

“…First, each plot data was fitted using the selected base model (Equation (1)) to obtain the corresponding parameter estimation values [31,64,65]. The relationships between model coefficients and the extracted LiDAR metrics (see Section 2.3.4) and their logarithmic transformations were then scrutinized by graphical and correlation analyses [64,66]. As with many studies of LiDAR-derived DBH modeling, crown diameter (CD) was introduced as a predictor to explain the DBH size variation under the same tree height.…”

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

“…Several nonlinear single predictor height-diameter functions have been used to describe tree height and diameter relationships in both even-aged and uneven-aged stands (Mehtätalo et al 2015;Corral-Rivas et al 2019;Bronisz and Mehtätalo 2020;Ciceu et al 2020;Ercanli 2020a;Xie et al 2020), and in complex natural forests (Feldpausch et al 2011;Temesgen et al 2014;Kearsley et al 2017;Ogana 2019;Chenge 2021). To select the base model for the complex tropical forests, 18 single predictor h-d models were initially evaluated.…”

Construction of Reducible Stochastic Differential Equation Systems for Tree Height–Diameter Connections

“…Tree height and diameter are traditionally formalized by using their regression relationship [1], the artificial neural network (ANN) [2,3], or stochastic differential equations [4,5]. Height-diameter regression equations have been developed for the local (stand) level and the generalized (ecoregional) level, by introducing additional stand variables, as well as random parameters [6,7]. The application of stand-level models to a wider region would probably lead to bias in predictions, as the deterministic height-diameter equations are related to the growth conditions and stand characteristics.…”

“…The inclusion of stand variables improves the predictive capacity of the selected height-diameter equation, but this technique requires additional sampling effort and reduces its practical application. The second technique that is used to introduce stand attributes to the height-diameter equation uses stand-specific effects defined for each stand by a normally distributed random variable, named a random effect [7], which allows models to adapt to diverse growth environments.…”