Allometric Biomass Models for European Beech and Silver Fir: Testing Approaches to Minimize the Demand for Site-Specific Biomass Observations

Abstract

In this paper, site-specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several approaches to minimize the demand for site-specific observations in allometric biomass model development were also investigated. Developing site-specific allometric biomass models requires new measurements of biomass for a sample of trees from that specific site. Yet, measuring biomass is laborious, time consuming, and requires extensive logistics, especially for very large trees. The allometric biomass models were developed for a wide range of diameters at breast height, D (6–86 cm for European beech and 6–93 cm for silver fir) using a logarithmic transformation approach. Two alternative approaches were applied, i.e., random intercept model (RIM) and a Bayesian model with strong informative priors, to enhance the information of the site-specific sample (of biomass observations) by supplementing with a generic biomass sample. The appropriateness of each model was evaluated based on the aboveground biomass prediction of a 1 ha sample plot in Șinca forest. The results showed that models based on both D and tree height (H) to predict tree aboveground biomass (AGB) were more accurate predictors of AGB and produced plot-level estimates with better precision, than models based on D only. Furthermore, both RIM and Bayesian approach performed similarly well when a small local sample (of seven smallest trees) was used to calibrate the allometric model. Therefore, the generic biomass observations may effectively be combined with a small local sample (of just a few small trees) to calibrate an allometric model to a certain site and to minimize the demand for site-specific biomass measurements. However, special attention should be given to the H-D ratio, since it can affect the allometry and the performance of the reduced local sample approach.

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

“…The originally selected sample size was decreased from 25 to 13 according to the guidelines of the local forest service, due to the potential damage from the fallen trees and the sampling procedures to the understory. In fact, previous studies that focussed on allometric equations for biomass components estimation reported similar sample sizes to ours [10,73,[88][89][90]. Subsequently, a sample size of 13 was considered suitable for our study as well.…”

“…In fact, these equations constitute regression models that can predict either the total biomass Remote Sens. 2023, 15, 2919 2 of 23 or the biomass of tree components as a function of morphometric measurements (e.g., DBH, tree height) [9,10].…”

Carbon Sequestration Dynamics in Peri-Urban Forests: Comparing Secondary Succession and Mature Stands under Varied Forest Management Practices

“…where "Biomass" indicates the combined above and belowground biomass of young trees, exp is the exponential function, d denotes the diameter at breast height for young trees a, b are the regression coefficients, and cf is a correction factor (cf = exp(SEE 2 /2), SEEstandard error of the estimate that accounts for the systematic bias introduced by back transformation ( [44,45] Table S2).…”

Above-Ground Forest Biomass Estimation using Multispectral LiDAR Data in a Multilayered Coniferous Forest

“…Forest biomass consists of various components (stems, leaves, needles, branches, bark, roots) and can be classified into above-ground biomass (AGB) and below-ground biomass, commonly expressed as the dry mass at the individual tree level (Kajimoto et al, 1999;Luo et al, 2017). Traditionally, field measurements are required to estimate AGB, which in most cases involve destructive sampling, diameter at breast height (DBH) measurements, and tree height (Zhang Z., Cao & She, 2017) -direct measurements that are considered laborious and time-consuming (Dutcă et al, 2020).…”