Volume is an important attribute used in many forest management decisions. Data from 83 fixed-area plots located in central New Brunswick, Canada, are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's (1967) volume equation. When density was included in the models (Volume = f (Diameter; Height; Density)) choice of diameter measure was more important than choice of height measure. When density was not included oVolume = f (Diameter; Height)), the opposite was true. For models with density included, moment-based estimators of stand diameter and height performed better than all other measures. For models without density, largest tree estimators of stand diameter and height performed better than other measures. The overall best equation used quadratic mean diameter, Lorey's height, and density (root mean square error = 5.26 m(3)center dot ha(-1); 1.9 % relative error). The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha (root mean square error = 32.08 m(3)center dot ha(-1); 11.8 % relative error). The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.

Computer graphics and visualizations are powerful tools to help understand forests. This chapter highlights useful tools developed and used over the author's career as a quantitative silviculturist to better understand and communicate the products available from forests and how those change in space and time. The tools range from statistical graphics, density management diagrams, stand and landscape visualization, LiDAR, and photography.

Silvopasture, the combination of trees, forage, and livestock, is a management practice that is gaining interest throughout the southeastern U.S. This research analyzed a hay-based alley cropping field trial that is transitioning into a silvopasture system. We planted four different tree spacings—2.4 × 2.4 m, 2.4 × 3.0 m, 3.0 × 3.0 m, and 1.8 × 3.0 m (8 × 8 ft, 8 × 10 ft, 10 × 10ft, and 6 × 10 ft)—of loblolly pine (Pinus taeda L.) and used secondary data for the possible planting of two different grass species—big bluestem (Andropogon gerardii Vitman) and switchgrass (Panicum virgatum L.). Tree inventories, forage samples, biometric modeling, and economic analysis of forage and timber monocultures and mixed systems were analyzed with discounted cash flow and capital budgeting analyses. Tree growth on the pasture site was exceptionally fast, generating high projected returns for timber monocultures, which exceeded returns for monoculture grass crops. Projected timber stand returns had the greatest Net Present Values (NPV) at the 4% discount rate, ranging between USD 3196 and USD 3552 per ha (USD 1294 and USD 1438 per ac) for a 2.4 × 3.0 m or 2.4 × 2.4 m tree spacing yield. Representative grass yields were obtained from secondary sources and had lower productivity, with switchgrass having the highest returns at USD 2581 per ha (USD 1045 per ac). Optimal NPVs for mixed silvopasture stands ranged between about USD 1500 per ha and USD 3500 per ha (USD 600/ac and USD 1400/ac), depending on the tree spacing within bands, the alley spacing, and the degree of competition between trees and grasses.

Growth and yield (G &Y) model systems aim at forecasting forest productivity. The lack of environmental variables to account for how water availability constrains eucalyptus production in Brazil, however, is argued to be a major drawback of these model systems. Thus, this study aimed to develop a stand-level G & Y model system that accounts for water availability (G & Y with SWD), highlighting its usefulness when applied for clonal eucalypt stands under drier climatic conditions. The dataset is composed of remeasurement information of sixteen research sites that span all climatic regions in Brazil. A total of eleven eucalypt clones were planted in single block plots at each site, and extra replications under the rainfall exclusion system were also installed for these eleven clones in fourteen sites. Linear algebra techniques were used to simultaneously fit a compatible set of prediction and projection basal area equations. A stand-level volume equation was also developed. These equations were validated through the use of an independent dataset composed of the rainfall exclusion plots. Finally, the accuracy and usefulness of a conventional G & Y model system applied to clonal eucalypt stands in Brazil was compared to the new proposed G & Y model system, which accounts for the impact of water availability in eucalyptus productivity. The prediction and projection basal area equations accounting for water availability displayed estimates in the order of 5% more accurate compared to the conventional basal area modeling. Stand-level volume estimates were 40% and 74% less biased through the use of the new G & Y model system. This result highlighted how useful and powerful the newly developed approach is, since the model system was capable to provide accurate estimates through the use of the rainfall exclusion plots. The new G & Y model system is a powerful alternative to estimate forest afforestation yield and is fully capable to accurately update forest inventories. The model system can also be used for projecting how forest growth may be impacted by short-term climate variation.

Understanding the interaction between the hydrological cycle and native forests is essential to improve watershed management and support ecological services. The objectives of this study were to analyse and evaluate rainfall interception models applied to an ecosystem from the Brazilian Atlantic Forest Biome (BAFB), classified as "Montane Semi-Deciduous Forest." The variables Gross Rainfall (P) (rainfall that reaches the canopy), Throughfall (Tf), Stemflow (Sf), and Interception Loss (I) were measured from September 2012 to March 2015. Weather variables were quantified above the canopy by a weather station and then used for canopy evaporation estimation. This estimation was based on two approaches: the slope of the linear regression of gross rainfall against interception, or the Gash procedure (E-v1), and the Penman-Monteith equation (E-v2). Two rainfall interception models (Liu and Gash) were employed, using both E-v1 and E-v2. Thirty-two fixed rain gauges were used for Tf measurements and 32 trees were selected across species and diameter at breast height ranges for Sf observations. The revised version of the analytical Gash model underestimated I by -17.5% and -11.1% for E-v1 and E-v2, respectively, resulting in less accurate estimates. Based on these relative errors, the performance of the Gash model was classified as "fair." The Liu model overestimated I by 5.3% and 11.3% for E-v1 and E-v2, resulting in assessments of "good" and "fair" respectively, and thus indicating improved performance compared to the Gash model. Therefore, the Liu model coupled with E-v1 is preferable for simulation of rainfall interception in semi-deciduous forests of the BAFB. However, slight overestimation bias was observed, and the model requires tweaking with respect to the estimation of forest storage capacity for this ecosystem. Interception loss modelling is a strategic tool for assessing the influence of different weather patterns and forest vegetative features on water balance in semi-deciduous forest ecosystems.

This report assesses how carbon stocks at regional scales and in individual national forests are affected by factors such as timber harvesting, natural disturbances, climate variability, increasing atmospheric carbon dioxide concentrations, and nitrogen deposition.Previous baseline assessments of carbon stocks (https://www.fs.fed.us/managing-land/sc/carbon)evaluated observed trends based on forest inventory data but were limited in ability to reveal detailed causes of these trends.The expanded assessments reported here are based on an extensive disturbance and climate history for each national forest, and two forest carbon models, to estimate the relative impacts of disturbance (e.g., fires, harvests, insect outbreaks, disease) and nondisturbance factors (climate, carbon dioxide concentration, nitrogen deposition).Results are summarized for each region of the National Forest System in the main document.A set of regional appendices to this report provides more detailed information about individual national forests within each region.Results are highly variable across the United States.Generally, carbon stocks are increasing in forests of the eastern United States as these forests continue to recover and grow older after higher historical harvesting rates and periods of nonforest land use.In contrast, carbon stocks in forests of the western United States may be either increasing or decreasing, depending on recent effects of natural disturbances and climate change.The information supports national forest units in assessing carbon stocks, quantifying carbon outcomes of broad forest management strategies and planning, and meeting carbon assessment requirements of the 2012 Planning Rule and directives.Results of these expanded assessments will provide context for project-level decisions, separated from the effects of factors that are beyond land managers' control.

Accurate characterization of Carbon (C) consequences of forest disturbances and management is critical for informed climate mitigation and adaptation strategies. While research into generalized properties of the forest C cycle informs policy and provides abstract guidance to managers, most management occurs at local scales and relies upon monitoring systems that can consistently provide C cycle assessments that explicitly apply to a defined time and place. We used an inventory-based forest monitoring and simulation tool to quantify C storage effects of actual fires, timber harvests, and forest regeneration conditions in the Greater Yellowstone Ecosystem (GYE). Results show that (1) the 1988 fires had a larger impact on GYE’s C storage than harvesting during 1985–2011; (2) continuation of relatively high harvest rates of the region’s National Forest land, which declined after 1990, would have shifted the disturbance agent primary importance on those lands from fire to harvest; and (3) accounting for local heterogeneity of post-disturbance regeneration patterns translates into large regional effects on total C storage. Large fires in 1988 released about 8.3 ± 0.3 Mg/ha of C across Yellowstone National Park (YNP, including both disturbed and undisturbed area), compared with total C storage reductions due to harvest of about 2.3 ± 0.3 Mg/ha and 2.6 ± 0.2 Mg/ha in adjacent Caribou-Targhee and Gallatin National Forests, respectively, from 1985–2011. If the high harvest rates observed in 1985–1989 had been maintained through 2011 in GYE National Forests, the C storage effect of harvesting would have quintupled to 10.5 ± 1.0 Mg/ha, exceeding the immediate losses associated with YNP’s historic fire but not the longer-term net loss of carbon (16.9 ± 0.8 Mg/ha). Following stand-replacing disturbance such as the 1988 fires, the actual regeneration rate was slower than the default regional average rate assumed by empirically calibrated forest growth models. If regeneration following the 1988 fire had reached regionally average rates, either through different natural circumstances or through more active management, YNP would have had approximately 4.1 Mg/ha more forest carbon by year 2020. This study highlights the relative effects of fire disturbances and management activities on regional C storage, and demonstrates a forest carbon monitoring system that can be both applied consistently across the US and tailored to questions of specific local management interest.

The goals of this study were to identify and group three eucalyptus clones, each under coppice and clear-cut management regimes, into two or more groups based on similar growth rates: and fit a site index equation as a function of rainfall variables for each group to evaluate how different groups were impacted by climatic variation. The database came from the Continuous Forest Inventory (CFI) and weather stations. The CFI was conducted between 1994 and 2012, with climatic data also being gathered for the same period. The study area was managed by clear-cut and coppice regimes, with 126 and 72 CFI plots, respectively. The relationship between clones, management regimes and stand age with annual dominant height growth was assessed by linear mixed effects modeling. Ridge regression was applied for fitting each group as a function of the rainfall variables. Finally, ordinary Kriging was applied for each of the rainfall variables in the study area. Then, site index equations were applied to the generated maps enabling the observation of their pattern throughout the study area as well as their evaluation under a pessimistic climatic scenario. Three groups were defined, since each clone exhibited similar growth behavior under either management regimes; however, the 3 clones differ among each other. A significant reduction in the annual dominant height growth over time was observed for all 3 clones. Ridge regressions afforded good accuracy and equations with sound biological behavior. Applying the fitted site index equations to the maps of precipitation and rainy days enabled the definition of the most appropriate clone to be planted throughout the area. Site quality as a function of rainfall variables could be an important tool to better enable silvicultural planning, since it provides estimates of the site index and also enables the incorporation of short-term climate change. (C) 2017 Elsevier B.V. All rights reserved.

Management of forest carbon stocks on public lands is critical to maintaining or enhancing carbon dioxide removal from the atmosphere. Acknowledging this, an array of federal regulations and policies have emerged that requires US National Forests to report baseline carbon stocks and changes due to disturbance and management and assess how management activities and forest plans affect carbon stocks. To address these requirements with the best-available science, we compiled empirical and remotely sensed data covering the National Forests (one fifth of the area of US forest land) and analyzed this information using a carbon modeling framework. We demonstrate how integration of various data and models provides a comprehensive evaluation of key drivers of observed carbon trends, for individual National Forests. The models in this framework complement each other with different strengths: the Carbon Calculation Tool uses inventory data to report baseline carbon stocks; the Forest Carbon Management Framework integrates inventory data, disturbance histories, and growth and yield trajectories to report relative effects of disturbances on carbon stocks; and the Integrated Terrestrial Ecosystem Carbon Model incorporates disturbance, climate, and atmospheric data to determine their relative impacts on forest carbon accumulation and loss. We report results for several National Forests across the USA and compare their carbon dynamics. Results show that recent disturbances are causing some forests to transition from carbon sinks to sources, particularly in the West. Meanwhile, elevated atmospheric carbon dioxide and nitrogen deposition are consistently increasing carbon stocks, partially offsetting declines due to disturbances and aging. Climate variability introduces concomitant interannual variability in net carbon uptake or release. Targeting forest disturbance and post-disturbance regrowth is critical to management objectives that involve maintaining or enhancing future carbon sequestration.

This study analyzes the trade-off between net present value (NPV) of timber resources, and carbon sequestration and storage for a working forest, the Hofmann Forest in North Carolina, USA. Multi-objective optimization is used to determine the production possibility curves showing the relationship between NPV and carbon. We then perform a sensitivity analysis to explore alternative management strategies. For carbon yields we used aboveground pools: branches, leaves, tops and bole as estimated by the Forest Vegetation Simulator (FVS) and LOBDSS using the California Carbon Market Protocols, including product carbon. Timber yields of sawtimber, chip-n-saw and pulpwood were estimated by LOBDSS for planted stands less than 49 years of age, and FVS was used for all natural stands and planted stands 49 years and over. Our results reveal that NPV opportunity costs associated with increasing carbon sequestration at Hofmann Forest are less than the current California carbon market price.

Forested lands, representing the largest terrestrial carbon sink in the United States, offset 16% of total U.S. carbon dioxide emissions through carbon sequestration. Meanwhile, this carbon sink is threatened by deforestation, climate change and natural disturbances. As a result, U.S. Forest Service policies require that National Forests assess baseline carbon stocks and influences of disturbance and management activities on carbon stocks and trends, with the goal of incorporating carbon stewardship into management activities. To accomplish these objectives, we utilize Forest Inventory and Analysis datasets and remote sensing-based disturbance histories within a carbon modeling framework to estimate past and present carbon stocks and trends for each national forest. We integrate three forest carbon models: 1) Carbon Calculation Tool, 2) Forest Carbon Management Framework, and 3) Integrated Terrestrial Ecosystem Carbon model, to calculate baseline carbon stocks and the relative impacts of disturbance and non-disturbance factors on forest carbon stocks and flux. Results of the assessments ultimately help forest managers quantify carbon consequences of broad forest management strategies and project-level decisions. A case study from Flathead National Forest shows that disturbances, primarily fire and disease, have had the largest effect on forest carbon stocks.

Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density.

Life-cycle analyses, energy analyses, and a range of utilization efficiencies were developed to determine the carbon dioxide (CO2) and fossil fuel (FF) saved by various solid wood products, wood energy, and unharvested forests. Some products proved very efficient in CO2 and FF savings, while others did not. Not considering forest regrowth after harvest or burning if not harvested, efficient products save much more CO2 than the standing forest; but wood used only for energy generally saves slightly less. Avoided emissions (using wood in place of steel and concrete) contributes the most to CO2 and FF savings compared to the product and wood energy contributions. Burning parts of the harvested logs that are not used for products creates an additional CO2 and FF savings. Using wood substitutes could save 14 to 31% of global CO2 emissions and 12 to 19% of global FF consumption by using 34 to 100% of the world's sustainable wood growth. Maximizing forest CO2 sequestration may not be compatible with biodiversity. More CO2 can be sequestered synergistically in the products or wood energy and landscape together than in the unharvested landscape. Harvesting sustainably at an optimum stand age will sequester more carbon in the combined products, wood energy, and forest than harvesting sustainably at other ages.

We conducted a meta-analysis on 64 econometric models from 47 studies predicting forestland conversion to agriculture (F2A), forestland to development (F2D), forestland to non-forested (F2NF) and undeveloped (including forestland) to developed (U2D) land. Over 250 independent econometric variables were identified from 21 F2A models, 21 F2D models, 12 F2NF models, and 10 U2D models. These variables were organized into a hierarchy of 119 independent variable groups, 15 categories, and 4 econometric drivers suitable for conducting simple vote count statistics. Vote counts were summarized at the independent variable group level and formed into ratios estimating the predictive success of each variable group. Two ratios estimates were developed based on (1) proportion of times the independent variables had statistical significance and (2) proportion of times independent variables met the original study authors' expectations. In F2D models, we confirmed the success of popular independent variables such as population, income, and urban proximity estimates but found timber rents and site productivity variables less successful. In F2A models, we confirmed success of popular explanatory variables such as forest and agricultural rents and costs, governmental programs, and site quality, but we found population, income, and urban proximity estimates less successful. In U2D models, successful independent variables found were urban rents and costs, zoning issues concerning forestland loss, site quality, urban proximity, population, and income. In F2NF models, we found poor success using timber rents but high success using agricultural rents, site quality, population, and income. Success ratios and discussion of new or less popular, but promising, variables was also included. This meta-analysis provided insight into the general success of econometric independent variables for future forest-use or -cover change research.

Natural resources are neither uniformly nor randomly distributed across the Earth. Rather, they are commonly grouped within geomorphologic and climatic boundaries. These groups—"Ecological Zones"—are generally large and cross political and socioeconomic boundaries. It is cumbersome to coordinate effective management for many values across these large areas. Consequently, we subdivide ecological zones into smaller areas and then further subdivide these, creating a hierarchy of sizes for management (Fig. 3.1; Oliver 2003). The term "ecosystem" refers to an ecological grouping of biotic and abiotic factors at any scale (Chapin et al. 2002; Kimmins 2003).

In 2009, five unique methods were used to inspect vegetation-related conditions along Bonneville Power Administration (BPA) rights-of-way (ROW). Some methods were trials that BPA committed to execute as part of a settlement with its regional regulatory organization, the Western Electric Coordination Council (WECC), for violations of reliability standards from vegetation grow-in related outages. A combination of simple, stratified, and 100