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

Ioan Dutcă,Any Mary Petrițan,Ion Cătălin Petrițan,Cosmin Ion Bragă,Jorge Curiel Yuste,Gheorghe Ștefan,Dimitris Zianis

doi:10.3390/f11111136

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.

Highlights

Forests play an important role in mitigating the effects of climate change [1,2,3], contributing significantly to the uptake of atmospheric carbon dioxide [4]
It is well documented that allometric biomass models are species- and site-specific [10,11,12,13,14], and, specific model parameters should be used for each species and each site
The simple regression models (i.e., Log-Transformed Data (LM); Table 5) fitted well to the data, and, despite of relatively limited sample size, the coefficients of determination (R2 ) for models based on single predictor were 0.9972 for European beech and 0.9862 for silver fir, whereas for models based on D and H, the R2 increased to 0.9986 for European beech and to 0.9921 for silver fir

Summary

Introduction

Forests play an important role in mitigating the effects of climate change [1,2,3], contributing significantly to the uptake of atmospheric carbon dioxide [4]. The large uncertainties usually associated with the estimation of forest biomass stock and stock change are an important limitation for the successful implementation of forest-based mitigation programs [2]. Developing species-specific allometric models, based on sample trees from multiple sites, requires the models to be applied in those same sites. This is required because the mean of site-effects would tend to zero and, the mean biomass per unit area is unbiased [10]. Applying a model calibrated for different sites to one single site, could yield biased biomass estimates [13,15]

Methods

Results

Discussion

Conclusion