Local and General Above-Ground Biomass Functions for Pinus palustris Trees

Abstract

Abstract:There is an increasing interest in estimating biomass for longleaf pine (Pinus palustris Mill.), an important tree species in the southeastern U.S. Most of the individual-tree allometric models available for the species are local, relying on stem diameter outside bark at breast height (DBH) and total tree height (HT), but seldom include stand-level variables such as stand age, basal area or stand density. Using the biomass dataset of 296 longleaf pine trees sampled in the southeastern U.S. by different forestry research institutions, we developed a set of local and general systems of tree biomass equations to predict total tree total above-stump biomass, bole biomass outside bark, live branch biomass and live foliage biomass. The local systems were based on DBH or DBH and HT, and the general systems included in addition to DBH and HT, stand-level variables such as age, basal area and stand density. This paper reports the first set of general allometric equations reported for longleaf pine trees. These systems of biomass equations provide tools to support managers in making management decisions for the species in a variety of ecological, silvicultural and economics applications. The systems can be applied to trees growing over a large geographical area and having a wide range of ages and stand characteristics.

Individual Plant Allometric Equations for Estimating Aboveground Biomass and Its Components for a Common Bamboo Species (Bambusa procera A. Chev. and A. Camus) in Tropical Forests

“…whereσ 2 is the estimated error sum of squares; ν i is the weighting variable (D, D 2 H in this study) associated with the i th sampled culm; and δ is the variance function coefficient to be estimated. When the models of each component and the AGB are fit independently the total biomass calculated from the component models is different from the estimate obtained from the independently developed AGB model [37][38][39]. Seemingly unrelated regression (SUR) can solve that limitation by allowing simultaneous estimation of the component biomass as well as AGB.…”

“…Seemingly unrelated regression (SUR) can solve that limitation by allowing simultaneous estimation of the component biomass as well as AGB. Additionally, the SUR takes into account the cross-equation correlation (i.e., correlation among error terms of the biomass equations) among the equations and ensures the additivity among components and AGB predictions [38][39][40][41]. The weighted nonlinear SUR was implemented by using SAS procedure Proc Model with the generalized least squares (GLS) method [21,42].…”

“…Development of genus-specific bamboo models should be considered to reduce the volume of developing species-specific models for estimating the bamboo biomass. In addition, SUR provided the additivity that ensures the aboveground biomass was equal to the sum of the biomass of the bamboo components [39]. Therefore, this approach should be promoted to develop and estimate simultaneously biomass of components and total [37].…”

Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States

“…The dataset used in this study covered different locations in the southeastern United States, and the models reported here can be applied to a wide variety of ages and stands. However, since there was no information on thinning practices for the stands, the models should be used in conditions of no strong thinning influence, as recommended by Gonzalez-Benecke et al (2018).…”

LiDAR Voxel-Size Optimization for Canopy Gap Estimation

“…Canopy structure is an important ecosystem trait that governs the spatial and temporal distribution of light transmittance, impacting the composition, distribution, and productivity of the mid-and understory plant community. Accurately measuring and quantifying three-dimensional (3D) structural arrangement is indeed a long-standing and ever advancing subject that is foundational to many scientific investigations-especially relevant to dendrometry, allometry, and biomass estimation [1][2][3][4], light transmittance estimation [5][6][7][8], and wildland fire science [9][10][11][12]. In practice, canopy structure is typically measured indirectly using both passive and active remote sensing because direct sampling methods can be laborious, time consuming, and destructive.…”