The United States has recently set ambitious national goals for greenhouse gas (GHG) reductions over the coming decades. A portion of these reductions are based on expected sequestration and storage contributions from land use, land use change, and forestry (LULUCF). Significant uncertainty exists in future forest markets and thus the potential LULUCF contribution to US GHG reduction goals. This study seeks to inform the discussion by modeling US forest GHG accounts per different simulated demand scenarios across a grid of over 130,000 USDA Forest Service Forest Inventory and Analysis (FIA) forestland plots over the conterminous United States. This spatially disaggregated future supply is based on empirical yield functions for log volume, biomass and carbon. Demand data is based on a spatial database of over 2300 forest product manufacturing facilities representing 11 intermediate and 13 final solid and pulpwood products. Transportation costs are derived from fuel prices and the locations of FIA plot from which a log is harvested and mill or port destination. Trade between mills in intermediate products such as sawmill residues or planer shavings is also captured within the model formulation. The resulting partial spatial equilibrium model of the US forest sector is solved annually for the period 2015-2035 with demand shifted by energy prices and macroeconomic indicators from the US EIA's Annual Energy Outlook for a Reference, Low Economic Growth, and High Economic Growth case. For each macroeconomic scenario simulated, figures showing historic and scenario-specific live tree carnon emissions and sequestration are generated. Maps of the spatial allocation of both forest harvesting and related carbon fluxes are presented at the National level and detail is given for both regions and ownerships.

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Cavity trees contribute to diverse forest structure and wildlife habitat. For a given stand, the size and density of cavity trees indicate its diversity, complexity, and suitability for wildlife habitat. Size and density of cavity trees vary with stand age, density, and structure. Using Forest Inventory and Analysis (FIA) data collected in western Oregon and western Washington, we applied correlation analysis and graphical approaches to examine relationships between cavity tree abundance and stand characteristics. Cavity tree abundance was negatively correlated with site index and percent composition of conifers, but positively correlated with stand density, quadratic mean diameter, and percent composition of hardwoods. Using FIA data, we examined the performance of Most Similar Neighbor (MSN), k nearest neighbor, and weighted MSN imputation with three variable transformations (regular, square root, and logarithmic) and Classification and Regression Tree with MSN imputation to estimate cavity tree abundance from stand attributes. There was a large reduction in mean root mean square error from 20% to 50% reference sets, but very little reduction in using the 80% reference sets, corresponding to the decreases in mean distances. The MSN imputation using square root transformation provided better estimates of cavity tree abundance for western Oregon and western Washington forests. We found that cavity trees were only 0.25 percent of live trees and 13.8 percent of dead trees in the forests of western Oregon and western Washington, thus rarer and more difficult to predict than many other forest attributes. Potential applications of MSN imputation include selecting and modeling wildlife habitat to support forest planning efforts, regional inventories, and evaluation of different management scenarios.

Regional estimation of potential forest productivity is important to diverse applications, including biofuels supply, carbon sequestration, and projections of forest growth. Using PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate and productivity data measured on a grid of 3356 Forest Inventory and Analysis plots in Oregon and Washington, we evaluated four possible imputation methods to estimate potential forest productivity: nearest neighbour, multiple linear regression, thin plate spline functions, and a spatial autoregressive model. Productivity, measured by potential mean annual increment at culmination, is explained by the interaction of annual temperature, precipitation, and climate moisture index. The data were randomly divided into 2237 reference plots and 1119 target plots 30 times. Each imputation method was evaluated by calculating the coefficient of determination, bias, and root mean square error of both the target and reference data set and was also tested for evidence of spatial autocorrelation. Potential forest productivity maps of culmination potential mean annual increment were produced for all Oregon and Washington timberland.