Each of the U.S. Forest Service’s Forest Inventory and Analysis (FIA) regions has an occasional need to intensify the national sampling grid. A variety of methodologies exist within the various FIA regions and National Forest Systems regions for constructing plot intensifications, and there is no consensus on a national procedure The primary objectives of this paper were to identify various intensification methods being implemented by FIA or used by other groups, examine their advantages and disadvantages for establishing intensified plots within the existing FIA framework, and recommend a potential national FIA plot intensification procedure that might adequately address the needs for all FIA regions.

The Forest Inventory and Analysis (FIA) program of the U.S. Forest Service (USFS) measures a spatially distributed base grid of forest inventory plots across the United States. The sampling intensity of plots may be increased in some regions when warranted by specific inventory objectives. Several intensification methods have been developed within FIA and USFS National Forest Systems regions; however, a national standard routine for plot intensification does not exist. This report examines eight candidate intensification methods by a developed set of evaluation criteria, and based on those results proposes that FIA adopt the GRID method as its national plot intensification procedure. It is also suggested that the current version of the GRID method be updated by converting it to an ESRI ArcGIS add-in tool.

BACKGROUND:Although significant amounts of carbon may be stored in harvested wood products, the extraction of that carbon from the forest generally entails combustion of fossil fuels. The transport of timber from the forest to primary milling facilities may in particular create emissions that reduce the net sequestration value of product carbon storage. However, attempts to quantify the effects of transport on the net effects of forest management typically use relatively sparse survey data to determine transportation emission factors. We developed an approach for systematically determining transport emissions using: 1) -remotely sensed maps to estimate the spatial distribution of harvests, and 2) - industry data to determine landscape-level harvest volumes as well as the location and processing totals of individual mills. These data support spatial network analysis that can produce estimates of fossil carbon released in timber transport.RESULTS:Transport-related emissions, evaluated as a fraction of transported wood carbon at 4 points in time on a landscape in western Montana (USA), rose from 0.5% in 1988 to 1.7% in 2004 as local mills closed and spatial patterns of harvest shifted due to decreased logging on federal lands.CONCLUSION:The apparent sensitivity of transport emissions to harvest and infrastructure patterns suggests that timber haul is a dynamic component of forest carbon management that bears further study both across regions and over time. The monitoring approach used here, which draws only from widely available monitoring data, could readily be adapted to provide current and historical estimates of transport emissions in a consistent way across large areas.

Background: Coarse and fine woody debris are substantial forest ecosystem carbon stocks; however, there is a lack of understanding how these detrital carbon stocks vary across forested landscapes. Because forest woody detritus production and decay rates may partially depend on climatic conditions, the accumulation of coarse and fine woody debris carbon stocks in forests may be correlated with climate. This study used a nationwide inventory of coarse and fine woody debris in the United States to examine how these carbon stocks vary by climatic regions and variables. Results: Mean coarse and fine woody debris forest carbon stocks vary by Köppen's climatic regions across the United States. The highest carbon stocks were found in regions with cool summers while the lowest carbon stocks were found in arid desert/steppes or temperate humid regions. Coarse and fine woody debris carbon stocks were found to be positively correlated with available moisture and negatively correlated with maximum temperature. Conclusion: It was concluded with only medium confidence that coarse and fine woody debris carbon stocks may be at risk of becoming net emitter of carbon under a global climate warming scenario as increases in coarse or fine woody debris production (sinks) may be more than offset by increases in forest woody detritus decay rates (emission). Given the preliminary results of this study and the rather tenuous status of coarse and fine woody debris carbon stocks as either a source or sink of CO2, further research is suggested in the areas of forest detritus decay and production. Background Estimation of carbon sequestration using large-scale forest inventory data has become important due to the link between possible climate change and the accumulation of greenhouse gases in the atmosphere [1,2]. In 1992, 150 countries including the U.S. signed the United Nations Framework Convention on Climate Change that resulted in the development of annual reports of greenhouse gas inventories including carbon in forests. Forest carbon pools are often delineated as standing live trees, standing dead trees, down and dead woody materials, forest floor, understory, and soils. The down and dead woody materials pool (detritus) essentially consists of coarse woody debris, fine woody debris, and stumps. Coarse woody debris is defined by the Forest Inventory and Analysis (FIA) program of the USDA Forest Service as down and dead woody material at least 7.62 cm in diameter [3]. Fine woody debris is defined by FIA as dead and down woody material with a diameter between 0.01 and 7.61 cm [3]. In the U.S., it has been estimated that 35 % of the total forest Published: 9 June 2008 Carbon Balance and Management 2008, 3:5 doi:10.1186/1750-0680-3-5 Received: 28 March 2008 Accepted: 9 June 2008 This article is available from: http://www.cbmjournal.com/content/3/1/5 © 2008 Woodall and Liknes; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's© See5 and Cubist (for binary and continuous responses, respectively) are the tools of choice in many of these applications. These tools are widely used in large remote sensing applications, but are not easily interpretable, do not have ties with survey estimation methods, and use proprietary unpublished algorithms. Consequently, three alternative modelling techniques were compared for mapping presence and basal area of 13 species located in the mountain ranges of Utah, USA. The modelling techniques compared included the widely used See5/Cubist, generalized additive models (GAMs), and stochastic gradient boosting (SGB). Model performance was evaluated using independent test data sets. Evaluation criteria for mapping species presence included specificity, sensitivity, Kappa, and area under the curve (AUC). Evaluation criteria for the continuous basal area variables included correlation and relative mean squared error. For predicting species presence (setting thresholds to maximize Kappa), SGB had higher values for the majority of the species for specificity and Kappa, while GAMs had higher values for the majority of the species for sensitivity. In evaluating resultant AUC values, GAM and/or SGB models had significantly better results than the See5 models where significant differences could be detected between models. For nine out of 13 species, basal area prediction results for all modelling techniques were poor (correlations less than 0.5 and relative mean squared errors greater than 0.8), but SGB provided the most stable predictions in these instances. SGB and Cubist performed equally well for modelling basal area for three species with moderate prediction success, while all three modelling tools produced comparably good predictions (correlation of 0.68 and relative mean squared error of 0.56) for one species.

This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis.

Abstract Equations are presented to estimate total or partial stem content in cubic feet and pounds (green or dry) for loblolly pine (Pinus taeda L.) and slash pine (Pinus elliotti Engelm.) trees planted on non-old-fields in East Texas. Equations are included to estimate the content of the completetree (stem and branches). In addition, a set of compatible stem taper functions are described. South. J. Appl. For. 11(3):147-151.

Abstract Equations to estimate site index (index age 25 years) for plantations of loblolly pine (Pinus taeda L.) and slash pine (Pinus elliottii Engelm.) on non-old-fields in East Texas have been developed. The height-prediction curves were based on the Richards' growth function and track well within the range of the data (1-17 years). South. J. Appl. For. 10:109-112, May 1986.

Equati ons are presented to estimate the cubi c f oot content of the noocJ and bark in the stem end branches of i ndiYidual loblolly pine trees planted on site-prepared lend in E6st Tex6s. Taper funcUons are also developed.

Equi'.'ltions to predi ct site index (base age 25 years) for lotilolly pine (P1:1Jt1s toeo'o L.) and slE1sh pine (Pim1s elliatiii Engelrn.) pl6ntotions on non-old-fields in Eest Texfls are presented. Productivity is estimated using plantation age and avernge height of Urn ten tallest trees in the plflntotion. 1 Greduate Assistant, School of Forestry, Stephen F. Austin Stete Universi ly, Nocogdoches, TX 75962.

--Permanent plots have been established in 178 loblolly and 78 slash pine plantations throughout East Texas to study the development of stand structure over time. Analysis of the data will provide methods of estimating growth and yield, mortality, and site productivity to assist managers of these plantations. In August, 1984, after three years of work, the School of Forestry at Stephen F. Austin State University and participating East Texas forest industries finished installing an array of 178 permanent growth and yield plots in loblolly pine plantations and 78 permanent growth and yield plots in slash pine plantations throughout East Texas. The need for this comprehensive sample arose because about 15 years ago, many forest landowners started converting mixed pine-hardwood stands to planted pine stands. By 1984, approximately 2 million acres of loblolly and slash pine plantations were established on these non-oldf ield stands (usually site-prepared) in East Texas. Systems and procedures are needed to predict the future stand structures of these plantations. In 1981, several participating industrial forest landowners in East Texas and the School of Forestry started comprehensive long-term study of growth and yield The East Texas Pine Plantation Research Project. Paper presented at Southern Silvicultural Research Conference, Atlanta, Georgia, November 7-8, Professor, Associate Professor and Graduate Assistant, respectively, School of Forestry, Stephen F. Austin State University, Nacogdoches Texas, 75962.