Estimating Biomass and Carbon Storage by Georgia Forest Types and Species Groups Using the FIA Data Diameters, Basal Areas, Site Indices, and Total Heights

We present here an example of research into methodology of an estimation of carbon and biomass pools in forests using the USDA Forest Service, Forest Inventory and Analysis (FIA), data of the 1989 and 1998 surveys for Georgia forests, as relevant for comparison with other extremely highly-cited estimates of similar, but different, methodologies. Based on the derived estimates, we produce an example map of the biomass density and pools at a sub-county level resolution, which is based on spatially explicit simulations of the potential cover-type polygons implied by the FIA data with approximate plot locations. Our results include estimates of the biomass pools in the belowground biomass in roots, aboveground woody biomass in trees, and the biomass of foliage. We estimated the biomass densities and pools at a tree level using diameters and heights and previously published models, then propagated these results to the plot level using tree expansion factors, and then transformed these estimates to plot-dependent polygons using plot expansion factors. The plot-dependent polygons were spatially simulated using a simplified assumption of homogeneity of conditions surrounding each plot to the extent of the area defined by this plot’s expansion factors. The derived map provides a visual representation of the distribution of forest biomass densities and pools in the state of Georgia with distinctive patterns observed in various areas of urban development, federally owned forests, primary commercial forestland, and other land use areas. Coniferous forests with the highest total biomass density are located mostly in three regions: northern Georgia (Appalachian Highlands), the southern part of Piedmont, and the eastern part of Coastal Plain. Deciduous and mixed forests with the highest biomass density are concentrated mostly in the northern part of the state—especially in the Blue Ridge physiographic province, and in the western part of the East Gulf Coastal Plain. Counties with the highest biomass density were located primarily in the northern part of the state, while counties with the lowest density tended to be located in the Coastal Georgia area

Empirical equations for estimating aboveground biomass of Betula pendula growing on former farmland in central Poland

We determined empirical models for estimating total aboveground as well as stem, branches, and foliage dry biomass of young (age up to 16 years) silver birch (Roth.) growing on the post-agricultural lands. Two sets of allometric models for trees with a height below or above 1.3 m (small and large trees respectively) were developed. Simplified models were elaborated based exclusively on appropriate tree diameter (diameter at ground level for small trees, diameter at breast height for large trees), while expanded models also included tree height. Total aboveground biomass was estimated as the sum of biomass of all tree components. To assure additivity of the developed equations, the seemingly unrelated regression approach for the final model fitting was used. Expanded models in both tree groups were characterized by a better fit to the data (Rfor total aboveground biomass for small and large trees equaled 0.8768 and 0.9752, respectively). Diameter at breast height appeared to be a better predictor than diameter at ground level â simplified models had better fit for large trees (R for total aboveground biomass equals 0.9611) than for small ones (Râ=â0.7516). The developed equations provide biomass predictions consistent with available Latvian, Estonian, Finnish, Swedish, and Norwegian models for silver birch.Betula pendula 2 2