212-7 Regional Scale Uncertainty Estimates from Fine-Scale Forest Inventory: Stored and Accumulated Forest Carbon in the Eastern US.
See more from this Division: SSSA Division: Forest, Range and Wildland Soils
See more from this Session: Symposium--Quantifying Uncertainty in Forest Ecosystem Studies
Abstract:
We developed a Bayesian statistical model framework for predicting hectare-scale forest biomass and productivity, with uncertainty in both, using information on climate, soil, and topography. This model is novel in the way interactions between densities of individuals, sizes of individuals, and species composition are handled. The dependence on tree number and size structure provides a link to area, because the numbers and sizes of trees, rather than plot area per se, are the principle source of uncertainty that can vary across scales. We applied this model to predict 1.5 million pixels of forested land in the eastern United States and found 45.0 ± 0.4 billion trees greater than 12.7 cm in diameter contain an aboveground biomass of 13.7 ± 0.14 Pg. Sensitivity calculations of biomass productivity (t/ha/yr) from the model indicate widespread interactions between stand age, climate, soil, and topography. This method not only offers not only out-of-sample biomass and productivity estimates with meaningful uncertainty at the hectare scale, but also provides insight on the consequences of future climate change to in-situ forest biomass.
See more from this Division: SSSA Division: Forest, Range and Wildland Soils
See more from this Session: Symposium--Quantifying Uncertainty in Forest Ecosystem Studies