Increasing interest in utilization of forest biomass for bioenergy has prompted extensive contemporary research regarding costs, supply and technology for efficiently producing electricity and other forms of renewable energy. One challenge facing both researchers and users is obtaining precise estimates of available forest biomass within plausible supply areas for individual power plants. Due to the wide distribution of power plants poised to co-fire with forest biomass, assessing its availability requires methods that can yield precise and low-bias estimates of aboveground forest biomass and other key attributes at varying spatial scales. Small area estimation (SAE) methods have high potential to accomplish this due to the availability of national forest inventory data, combined with satellite imagery and other forms of remotely-sensed auxiliary information. The study assessed several indirect, direct and composite estimators of four forest attributes: aboveground tree biomass, biomass of small-diameter trees, biomass of tops and limbs, and volume at the county-level and within the estimated supply areas around power plants across 20 states in the contiguous Northern U.S. Composite estimators using both k-nearest neighbors imputation and multiple linear regression provided superior estimates of indicators of forest biomass availability based on both precision and bias at the county-level at sampling intensities as low as 10–20%, compared to the other SAE methods examined. The composite estimator using k-nearest neighbors imputation was subsequently shown to produce precise estimates of forest biomass availability for selected power plant supply areas.

Forests cover 42 percent of the Northern United States, and collectively they store 13 billion tons of carbon in live trees (29 percent), roots (6 percent), forest floor (9 percent), dead trees (6 percent), and soils (50 percent). About half the biomass of a live tree (dry weight basis) is sequestered carbon (Woodall et al. 2011) - not the largest but the most dynamic source of sequestered forest carbon over time. Through photosynthesis, live trees emit oxygen in exchange for the carbon dioxide that they pull from the atmosphere, storing the carbon in wood above ground and roots below ground as they grow. Dead trees and down logs are also reservoirs of stored carbon, which is released back into the atmosphere slowly through decomposition or rapidly through combustion (McKinley 2011, Woodall et al. 2011).

In recent years, the state of Missouri has been converting to biomass weight rather than volume as the standard measurement of wood for buying and selling sawtimber. Therefore, there is a need to identify accurate and precise methods of estimating whole tree biomass and merchantable biomass of harvested trees as well as total standing biomass of live timber for resource assessments and silvicultural planning. In this study, we compared the traditional whole tree diameter-based biomass model currently used with alternative model forms fitted to tree data collected from four southeast Missouri species. Additionally, we reassessed each nonlinear model with total tree height and crown ratio included as covariates. Finally, we assessed the best model identified from the aforementioned analyses for estimation of merchantable biomass. Results of the analysis yielded several nonlinear models for estimating aboveground tree biomass with relatively high precision and low bias. The optimal model was chosen based upon precision and bias of estimation for all four species and was shown to produce precise estimates of merchantable biomass as well as total aboveground biomass for each species.

The Northern Forest Futures Project aims to reveal how today's trends and choices are likely to change the future forest landscape in the northeastern and midwestern United States. The research is focused on the 20-state quadrant bounded by Maine, Maryland, Missouri, and Minnesota. This area, which encompasses most of the Central Hardwood Forest region, is the most heavily forested and most densely populated region in the Nation (Shifley et al. 2012). The Northern Forest Futures Project adds detail and context (e.g., Bowker and Askew 2013, Cordell et al. 2012, U.S. Forest Service 2014) to national projections of forest conditions conducted as part of the recent Resources Planning Act assessment (U.S. Forest Service 2012). Analyses for northern forests explore projected conditions from 2010 to 2060 for seven scenarios with differing assumptions about changes in population, land use, harvest removals, and climate.

Substantial knowledge has been generated in the U.S. about the resource base for forest-and other residue-derived biomass for bioenergy including co-firing in power plants. However, a lack of understanding regarding power plant-level operations and manager perceptions of drivers of biomass co-firing remains. This study gathered information from U.S. power plant managers to identify drivers behind co-firing, determine key conditions influencing past and current use, and explore future prospects for biomass in co-firing. Most of the biomass used in co-firing was woody biomass procured within 100 km of a power plant. Results show that the most influential co-firing drivers included: adequate biomass supply, competitive cost of biomass compared to fossil fuels, and costs of biomass transport. Environmental regulations were generally considered second-most influential in decisions to test or co-fire with biomass, but were of high importance to managers of plants that are currently not co-firing but may in the future. (C) 2013 Elsevier Ltd. All rights reserved.

Past studies have established measures of co-firing potential at varying spatial scales to assess opportunities for renewable energy generation from woody biomass. This study estimated physical availability, within ecological and public policy constraints, and associated harvesting and delivery costs of woody biomass for co-firing in selected power plants of the Northern U.S. Procurement regimes were assessed for direct sources of woody biomass from timberland including logging residues (slash, by-products), small-diameter trees, and integrated harvest (logging residues and small-diameter trees). Concentric woody biomass procurement areas were estimated for each power plant using county-level estimates and varying procurement radii. Delivered fuel cost estimates were calculated for each power plant and procurement regime based on incremental maximum transport distances. Procurement regimes focused on small-diameter trees can potentially produce the most electric power, but are constrained by lower economical transport distances than logging residues. These estimates enabled us to assess which power plants in the Northern U.S. had the highest electricity generation potential. For most procurement regimes, an average power plant co-firing had the potential to replace greater than 30% of coal electricity generation if there was no competition for the feedstock. However, woody biomass resource competition from adjacent co-firing plants could reduce this generation potential to less than 10%.

Large-scale and long-term habitat management plans are needed to maintain the diversity of habitat classes required by wildlife species. Planning efforts would benefit from assessments of potential climate and land-use change effects on habitats. We assessed climate and land-use driven changes in areas of closed- and open-canopy forest across the Northeast and Midwest by 2060. Our assessments were made using projections based on A1B and A2 future scenarios developed by the Intergovernmental Panel on Climate Change. Presently, forest land covers 70.2 million ha and is evenly divided between closed- and open-canopy habitats. Projections indicated that total forest land would decrease by 3.8 or 4.5 million ha for A2 and A1B, respectively. Within persisting forest land, the balance between closed- and open-canopy habitats depended on assumed harvest rates of woody biomass. Standard harvest rates led to closed-canopy habitat attaining a slight majority of total forest land area. Intensive harvest rates resulted in the majority of forest land being in open-canopy habitat for A1B or maintained the even split between closed- and open-canopy habitats for A2. Ultimately, managers need to identify benchmark habitat conditions informed by historical conditions and wildlife population dynamics and plan to meet these benchmarks in dynamic forest landscapes.

The residential sector consumes about 23% of the energy derived from wood (wood energy) in the U.S. An estimated error correction model with data from 1967 to 2009 suggests that residential wood energy consumption has declined by an average 3% per year in response to technological progress, urbanization, accessibility of non-wood energy, and other factors associated with a time trend such as increasing income per capita and number of houses. But the rising price of non-wood energy has had a positive effect on the consumption and offset the downward trend effect in the last decade. Residential wood energy consumption has also been sensitive to changes in wage rate in both long-run and short-run, but the total estimated wage rate effect since 1967 is negligible. Wood energy is expected to continue to account for a small share of residential energy consumption unless public policies improve wood energy cost competitiveness relative to non-wood energy.

About 23% of energy derived from woody sources in the U.S. was consumed by households, of which 70% was used by households in rural areas in 2005. We investigated factors affecting household-level wood energy consumption in the four continental U.S. regions using data from the U.S. Residential Energy Consumption Survey. To account for a large number of zero observations (i.e., households that do not burn wood), left-censored Tobit models were estimated. Urban/rural location is a key determinant of level of household wood energy consumption. Wood energy consumption elasticity with respect to non-wood energy price changes was 1.55 at the U.S. level, and a much higher 2.30 among rural households. While household wood energy consumption was affected primarily by non-wood energy price in rural areas, it was influenced mainly by household size and level of income in urban areas. Elasticity of wood energy consumption with respect to income can be positive or negative depending on household urban/rural location, region and income level. Newer houses were found to use less wood energy than older ones, and greater urbanization was found to have negative effect on wood energy use. Our findings suggest that policies reducing relative wood energy cost or increasing non-wood energy prices in the residential sector will result in greater wood energy consumption in the U.S. The effect of policies may vary by region and are likely to be more effective in U.S. rural areas and in the U.S. Midwest in particular.

Woody biomass is a renewable energy feedstock with the potential to reduce current use of nonrenewable fossil fuels. We estimated the physical availability of woody biomass for cocombustion at coal-fired electricity plants in the 20-state US northern region. First, we estimated the total amount of woody biomass needed to replace total annual coal-based electricity consumption at the state level to provide a representation of the potential energy footprints associated with using woody biomass for electric energy. If all woody biomass available were used for electric generation it could replace no more than 19% of coal-based electric generation or 11% of total electric energy generation. Second, we examined annual woody biomass increment at the state level in a series of concentric circles around existing coal-fired electricity plants to examine some of the opportunities and limitations associated with using woody biomass for cofiring at those plants to coincide with state-level renewable portfolio standards. On average, an individual coal-fired power electricity plant could theoretically replace 10% of annual coal use if it obtained 30% of the net annual woody biomass increment within a 34-km radius of the plant. In reality, the irregular spatial distribution of coal-fired power plants means potential biomass supply zones overlap and would greatly diminish opportunities for cofiring with biomass, numerous other regulatory, economic, and social considerations notwithstanding. Given that woody biomass use for electricity will be limited to selected locations, use of woody biomass for energy should be complementary with other forest conservation goals.

Small-area estimation (SAE) is a concept that has considerable potential for precise estimation of forest ecosystem attributes in partitioned forest populations. In this study, several estimators were compared as SAE techniques for 12 counties in the northern Oregon Coast range. The estimators that were compared consisted of three indirect estimators, multiple linear regression (MLR), gradient nearest neighbor imputation (GNN), and most similar neighbor imputation (MSN), and five composite estimators based on MLR, MSN, and GNN with county-level direct estimates. Forest attributes of interest were density (trees/ha), basal area (m2/ha), cubic volume (m3/ha), quadratic mean diameter (cm), and average height of 100 largest trees per ha. The sample consisted of 680 annual Forest Inventory Analysis plots, a spatially balanced sample across all conditions and ownerships. The auxiliary data consisted of 16 Landsat variables, a land cover classification, tree cover, and elevation. Overall, the composite estimators were superior when both precision and bias of estimation were considered.

One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m2/ha), top height (m), and cubic stem volume (m3/ha). The precision and bias of the estimation methods (synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay–Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.

Massive fires in Indonesian peatlands in the 1990s and in eastern Russian peatlands in 2002 and last year have highlighted the need to manage forests overlying peat deposits, as well as converted peat forests and open peatlands. Peat or peaty soils, contain 65

Forests represent a major global C sink, and forest management strategies that maximize carbon storage offer one avenue for mitigating increases in atmospheric carbon dioxide concentrations. Our understanding of relationships between forest management, productivity, carbon storage, and stand age, however, is limited. We established research plots in a chronosequence of thinned and unmanaged red pine stands in northern Minnesota to study patterns of carbon storage, and the major fluxes that influence carbon sequestration. We completed an inventory of all major C pools across a chronosequence of 57 red pine stands ages 9‐306 years on the Chippewa National Forest in the fall of 2009. Results indicate total ecosystem C pools increase as red pine stands age for at least 150 years, and on-site C storage in thinned stands appears similar to unmanaged stands of comparable ages, despite different age-related trends in the live tree and forest floor pools. Thinned stands may have the potential to store more C than unmanaged stands in old age when C removed during harvesting is added into the total ecosystem C pool.

Three sets of linear models were developed to predict several forest attributes, using stand-level and single-tree remote sensing (STRS) light detection and ranging (LiDAR) metrics as predictor variables. The first used only area-level metrics (ALM) associated with first-return height distribution, percentage of cover, and canopy transparency. The second alternative included metrics of first-return LiDAR intensity. The third alternative used area-level variables derived from STRS LiDAR metrics. The ALM model for Lorey’s height did not change with inclusion of intensity and yielded the best results in terms of both model fit (adjusted R 2 0.93) and cross-validated relative root mean squared error (RRMSE 8.1%). The ALM model for density (stems per hectare) had the poorest precision initially (RRMSE 39.3%), but it improved dramatically (RRMSE 27.2%) when intensity metrics were included. The resulting RRMSE values of the ALM models excluding intensity for basal area, quadratic mean diameter, cubic stem volume, and average crown width were 20.7, 19.9, 30.7, and 17.1%, respectively. The STRS model for Lorey’s height showed a 3% improvement in RRMSE over the ALM models. The STRS basal area and density models significantly underperformed compared with the ALM models, with RRMSE values of 31.6 and 47.2%, respectively. The performance of STRS models for crown width, volume, and quadratic mean diameter was comparable to that of the ALM models.

There is increasing interest in producing woody biomass on marginal lands in Iowa, but there is little information about its economic feasibility. To address this issue, a study was initiated in 1995 to analyze growth of certain fast-growing tree species, clones of hybrids, and selected clones, which will be referred to as entries, on marginal lands. Three entries, including the "Crandon" clone (Populus alba x Populus grandidentata), the 'Eugenii" clone (Populus X canadensis), and silver maple (Acer succharinum), were established in test plantings on three land types - bottomland, steep slopes, and upland agricultural land-across the state. Trees generally were measured annually. Two types of yield models were developed to predict biomass per hectare over time for the three aforementioned entries. Crandon had the highest rate of biomass production on all land types. Economic analyses were conducted on the three entries, and Crandon produced the highest economic return on all land types.

Forest measurement and biometrics (FMB) programs have been at the heart of forestry education in North America since its beginnings at the Biltmore Forest School more than 100 years ago. Over the intervening period, the field of forestry has changed in critical ways. There are manyforest management and policy issues that, at first glance, do not appear to involve FMB but which, on further examination, are found to be closely linked. In this regard, FMB has both an “inside„ and “outside.„ The outside part faces interactions with its clients andfront-line sciences (e.g., forest ecology, silviculture, etc.) which bring new data-analytic ideas to FMB. The clients and professionals in these allied sciences need solutions to pressing quantitative questions. The inside face relates to the need to extend the structure of statistical inference,integrate emerging technologies, and adapt mathematical and statistical precepts to FMB needs. In this essay, we provide a brief overview of the current diversity of FMB applications using examples from business, policy analysis, and ecosystem and landscape analysis; offer our views on themost critical challenges facing FMB researchers and practitioners in the 21st Century; and outline ways how FMB professionals and academic forestry programs might cooperate to meet these challenges. We assert that FMB needs to be responsive to contemporary resource management challenges andaddress the many land management challenges in the Pacific Northwest and around the world.