Modeling change in forest biomass across the eastern US

Abstract

Predictions of above-ground biomass and the change in above-ground biomass require attachment of uncertainty due the range of reported predictions for forests. Because above-ground biomass is seldom measured, there have been no opportunities to obtain such uncertainty estimates. Standard methods involve applying an allometric equation to each individual tree on sample plots and summing the individual values. There is uncertainty in the allometry which leads to uncertainty in biomass at the tree level. Due to interdependence between competing trees, the uncertainty at the plot level that results from aggregating individual tree biomass in this way is expected to overestimate variability. That is, the variance at the plot level should be less than the sum of the individual variances. We offer a modeling strategy to learn about change in biomass at the plot level and model cumulative uncertainty to accommodate this dependence among neighboring trees. The plot-level variance is modeled using a parametric density-dependent asymptotic function. Plot-by-time covariate information is introduced to explain the change in biomass. These features are incorporated into a hierarchical model and inference is obtain within a Bayesian framework. We analyze data for the eastern United States from the Forest Inventory and Analysis (FIA) Program of the US Forest Service. This region contains roughly 25,000 FIA monitored plots from which there are measurements of approximately 1 million trees spanning more than 200 tree species. Due to the high species richness in the FIA data, we combine species into plant functional types. We present predictions of biomass and change in biomass for two plant functional types.