Browsing by Author "Yang, Sheng-I"
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- Comparison of Data Grouping Strategies on Prediction Accuracy of Tree-Stem Taper for Six Common Species in the Southeastern USYang, Sheng-I; Green, P. Corey (MDPI, 2022-01-20)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.
- Efficient Sampling Methods for Forest Inventories and Growth ProjectionsYang, Sheng-I (Virginia Tech, 2019-06-24)For operational forest management, a forest inventory is commonly conducted to determine the timber stocking and the value of standing trees in a stand. With time and costs constraints, appropriate sampling designs and models are required to perform the inventory efficiently, as well as to obtain reliable estimates for the variables needed to make projections. In this dissertation research, a simulation study was conducted to extensively explore four important topics in forest inventories: selection of measurement trees in point samples, projection from plot- and stand-level aggregations, subsampling height for volume estimation, and updating stand projections using periodic inventories. A series of simulated loblolly pine plantations with varying degrees of spatial heterogeneity were generated at different stages in stand development. Repeated sampling was used to examine various sampling schemes and growth projection methods. Highlights for the four topics follow: 1. Stand total volume can be reliably estimated using measurement trees tallied by Big BAF, point-double sampling, or random selection of a specified number of trees. However, number of trees per unit area in small-size classes were overestimated across the three tree-selection methods when sample data were aggregated into diameter classes. 2. Plot-level and stand-level projections produced similar estimates for dominant height, basal area, and stems per unit area. As spatial heterogeneity increased, stand-level projections indicated a significant bias of predicted total volume compared with the plot-level projections. 3. Sampling intensity, stand age and spatial heterogeneity have greater influence on the reliability for total volume estimation compared to subsampling intensity and measurement error for height measurements. 4.The variability of total volume estimates increases with increasing projection length (i.e., longer time intervals between inventory entry points). However, the estimates of stand total volume can be greatly improved by updating the models with information obtained in periodic forest inventories, especially when the original models are not well calibrated. The results of this study provide useful guidance and insights for forest practitioners to design forest inventories and improve growth projection systems in operational forest management.
- Estimation and Determination of Carrying Capacity in Loblolly PineYang, Sheng-I (Virginia Tech, 2016-05-27)Stand carrying capacity is the maximum size of population for a species under given environmental conditions. Site resources limit the maximum volume or biomass that can be sustained in forest stands. This study was aimed at estimating and determining the carrying capacity in loblolly pine. Maximum stand basal area (BA) that can be sustained over a long period of time can be regarded as a measure of carrying capacity. To quantify and project stand BA carrying capacity, one approach is to use the estimate from a fitted cumulative BA-age equation; another approach is to obtain BA estimates implied by maximum size-density relationships (MSDRs), denoted implied maximum stand BA. The efficacy of three diameter-based MSDR measures: Reineke's self-thinning rule, competition-density rule and Nilson's sparsity index, were evaluated. Estimates from three MSDR measures were compared with estimates from the Chapman-Richards (C-R) equation fitted to the maximum stand BA observed on plots from spacing trials. The spacing trials, established in the two physiographic regions (Piedmont and Coastal Plain), and at two different scales (operational and miniature) were examined and compared, which provides a sound empirical basis for evaluating potential carrying capacity. Results showed that the stands with high initial planting density approached the stand BA carrying capacity sooner than the stands with lower initial planting density. The maximum stand BA associated with planting density developed similarly at the two scales. The potential carrying capacity in the two physiographic regions was significantly different. The value of implied maximum stand BA converted from three diameter-based MSDR measures was similar to the maximum stand BA curve obtained from the C-R equation. Nilson's sparsity index was the most stable and reliable estimate of stand BA carrying capacity. The flexibility of Nilson's sparsity index can illustrate the effect of physiographic regions on stand BA carrying capacity. Because some uncontrollable factors on long-term operational experiments can make estimates of stand BA carrying capacity unreliable for loblolly pine, it is suggested that the stand BA carrying capacity could be estimated from high initial planting density stands in a relatively short period of time so that the risk of damages and the costs of experiments could be reduced. For estimating carrying capacity, another attractive option is to choose a miniature scale trial (microcosm) because it shortens the experiment time and reduces costs greatly.
- Predicting bark thickness with one- and two-stage regression models for three hardwood species in the southeastern USYang, Sheng-I; Radtke, Philip J. (Elsevier, 2022-01-01)Tree bark, as the outermost protective layer of tree stems, is an important indicator to evaluate the fire resistance properties of trees and to assess the tree mortality induced by fire. Despite its importance, many existing bark thickness models were not primarily developed for predicting bark thickness directly, i.e. with bark thickness as a response variable, and most past studies were focused on modeling bark thickness in conifers. Thus, the objective of this study was to compare the efficacy of various bark thickness models/methods for three common hardwood species in the southeastern US. A total number of 47,281 measurements from 2,070 trees were used in analysis. Results showed that bark thickness at breast height (1.37 m or 4.5 ft above ground) varies by tree size and species, which can be predicted by a species-specific linear regression model with DBH as a single predictor. To predict bark thickness profile, a combination of stem taper function and bark thickness model, a two-stage method, is suggested, which generally performs better than a single bark thickness function (one-stage method) in terms of bias and precision. For a given model form, the two-stage method produced more reliable prediction of bark thickness at upper and lower portions of tree stem than the one-stage method. With the three species examined, the segmented stem taper functions provided more accurate predictions than the variable-exponent function. The results of this study can provide guidance for ecologists and forest managers when selecting appropriate approaches to predict bark thickness.