Forest Inventory And Analysis Research Articles

A subcontinental view of forest plant invasions

Over the last few decades, considerable attention has focused on small-scale studies of invasive plants and invaded systems. Unfortunately, small scale studies rarely provide comprehensive insight into the complexities of biological invasions at macroscales. Systematic and repeated monitoring of biological invasions at broad scales are rare. In this report, we highlight a unique invasive plant database from the national Forest Inventory and Analysis (FIA) program of the United States Forest Service. We demonstrate the importance and capability of this subcontinental-wide database by showcasing several critical macroscale invasion patterns that have emerged from its initial analysis: (1) large portion of the forests systems (39%) in the United States are impacted by invasive plants, (2) forests in the eastern United States harbor more invasive species than the western regions, (3) human land-use legacies at regional to national scales may drive large-scale invasion patterns. This accumulated dataset, which continues to grow in temporal richness with repeated measurements, will allow the understanding of invasion patterns and processes at multi-spatial and temporal scales. Such insights are not possible from smaller-scale studies, illustrating the benefit that can be gained by investing in the development of regional to continental-wide invasion monitoring programs elsewhere.

A subcontinental view of forest plant invasions

Over the last few decades, considerable attention has focused on small-scale studies of invasive plants and invaded systems. Unfortunately, small scale studies rarely provide comprehensive insight into the complexities of biological invasions at macroscales. Systematic and repeated monitoring of biological invasions at broad scales are rare. In this report, we highlight a unique invasive plant database from the national Forest Inventory and Analysis (FIA) program of the United States Forest Service. We demonstrate the importance and capability of this subcontinental-wide database by showcasing several critical macroscale invasion patterns that have emerged from its initial analysis: (1) large portion of the forests systems (39%) in the United States are impacted by invasive plants, (2) forests in the eastern United States harbor more invasive species than the western regions, (3) human land-use legacies at regional to national scales may drive large-scale invasion patterns. This accumulated dataset, which continues to grow in temporal richness with repeated measurements, will allow the understanding of invasion patterns and processes at multi-spatial and temporal scales. Such insights are not possible from smaller-scale studies, illustrating the benefit that can be gained by investing in the development of regional to continental-wide invasion monitoring programs elsewhere.

Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements.

Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements.

Population dynamics of redbay (Persea borbonia) after laurel wilt disease: an assessment based on forest inventory and analysis data

Laurel wilt disease (LWD) is a lethal fungal infection of woody plants in the Lauraceae, including redbay (Persea borbonia). We used forest inventory and analysis (FIA) data from 2003 to 2011 to estimate redbay populations and see if any decline could be observed since the introduction of LWD. Throughout its entire range, the population estimate of redbay was about 862.2 ± 89.8 million in 2003, increasing to 951.8 ± 87.1 million in 2011. However, population estimates from 2003 to 2011 displayed a significant negative curvature, suggesting that the population might start to decline. The population in Georgia significantly decreased from 241.1 ± 11.9 million stems in 2003 to 150.3 ± 7.9 million in 2011. Results at the range-wide scale should be taken with caution, since the FIA estimates are based on a 5-year moving average. However, we show that the FIA data can also be used on a smaller scale. Redbay densities decreased significantly in plots surveyed before and after the reported infection by an average of 89.6 live redbay stems/ha. Density in plots without reported LWD were not statistically different between survey years. We developed a logistic regression model to predict the probability of redbay mortality due to LWD. Number of years since LWD infection was the most significant variable, with every increase in 1 year resulting in a 153.7 % increase in odds of death. Diameter was also a significant predictor, with an increase of 1 cm DBH resulting in a 5.0 % increase in odds of death.

Improving estimates of forest disturbance by combining observations from Landsat time series with U.S. Forest Service Forest Inventory and Analysis data

With earth's surface temperature and human population both on the rise a new emphasis has been placed on monitoring changes to forested ecosystems the world over. In the United States the U.S. Forest Service Forest Inventory and Analysis (FIA) program monitors the forested land base with field data collected over a permanent network of sample plots. Although these plots are visited repeatedly through time there are large temporal gaps (e.g. 5–10years) between remeasurements such that many forest canopy disturbances go undetected. In this paper we demonstrate how Landsat time series (LTS) can help improve FIA's capacity to estimate disturbance by 1.) incorporating a new, downward looking response variable which is more sensitive to picking up change and 2.) providing historical disturbance maps which can reduce the variance of design-based estimates via post-stratification. To develop the LTS response variable a trained analyst was used to manually interpret 449 forested FIA plots located in the Uinta Mountains of northern Utah, USA. This involved recording cause and timing of disturbances based on evidence gathered from a 26-year annual stack of Landsat images and an 18-year, periodically spaced set of high resolution (~1m) aerial photographs (e.g. National Aerial Image Program, NAIP and Google Earth). In general, the Landsat data captured major disturbances (e.g. harvests, fires) while the air photos allowed more detailed estimates of the number of trees impacted by recent insect outbreaks. Comparing the LTS and FIA field observations, we found that overall agreement was 73%, although when only disturbed plots were considered agreement dropped to 40%. Using the non-parametric Mann–Whitney test, we compared distributions of live and disturbed tree size (height and DBH) and found that when LTS and FIA both found non-stand clearing disturbance the median disturbed tree size was significantly larger than undisturbed trees, whereas no significant difference was found on plots where only FIA detected disturbance. This suggests that LTS interpretation and FIA field crews both detect upper canopy disturbances while FIA crews alone add disturbances occurring at or below canopy level. The analysis also showed that plots with only LTS disturbance had a significantly greater median number of years since last FIA measurement (6years) than plots with both FIA and LTS disturbances (2.5years), indicating that LTS improved detection on plots which had not been field sampled for several years. Next, to gauge the impact of incorporating LTS disturbances into the FIA estimation process we calculated design-based estimates of disturbance (for the period 1995–2011) using three response populations 1.) LTS observations, 2.) FIA field observations, and 3.) Combination of FIA and LTS observations. The results showed that combining the FIA and LTS observations led to the largest and most precise (i.e. smallest percent standard error) estimates of disturbance. In fact, the estimate based on the combined observations (486,458ha, +/−47,101) was approximately 65% more than the estimate derived solely with FIA data (294,295ha, +/−44,242). Lastly, a Landsat forest disturbance map was developed and tested for its ability to post-stratify the design-based estimates. Based on relative efficiency (RE), we found that stratification mostly improved the estimates derived with the LTS response data. Aside from insects (RE=1.26), the estimates of area affected by individual agents saw minimal gain, whereas the LTS and combined FIA+LTS estimates of total disturbance saw modest improvement, with REs of 1.43 and 1.50 respectively. Overall, our results successfully demonstrate two ways LTS can improve the completeness and precision of disturbance estimates derived from FIA inventory data.

Confronting terrestrial biosphere models with forest inventory data.

Efforts to test and improve terrestrial biosphere models (TBMs) using a variety of data sources have become increasingly common. Yet, geographically extensive forest inventories have been under-exploited in previous model-data fusion efforts. Inventory observations of forest growth, mortality, and biomass integrate processes across a range of timescales, including slow timescale processes such as species turnover, that are likely to have important effects on ecosystem responses to environmental variation. However, the large number (thousands) of inventory plots precludes detailed measurements at each location, so that uncertainty in climate, soil properties, and other environmental drivers may be large. Errors in driver variables, if ignored, introduce bias into model-data fusion. We estimated errors in climate and soil drivers at U.S. Forest Inventory and Analysis (FIA) plots, and we explored the effects of these errors on model-data fusion with the Geophysical Fluid Dynamics Laboratory LM3V dynamic global vegetation model. When driver errors were ignored or assumed small at FIA plots, responses of biomass production in LM3V to precipitation and soil available water capacity appeared steeper than the corresponding responses estimated from FIA data. These differences became nonsignificant if driver errors at FIA plots were assumed to be large. Ignoring driver errors when optimizing LM3V parameter values yielded estimates for fine-root allocation that were larger than biometric estimates, which is consistent with the expected direction of bias. To explore whether complications posed by driver errors could be circumvented by relying on intensive study sites where driver errors are small, we performed a power analysis. To accurately quantify the response of biomass production to spatial variation in mean annual precipitation within the eastern United States would require at least 40 intensive study sites, which is larger than the number of sites typically available for individual biomes in existing plot networks. Driver errors may be accommodated by several existing model-data fusion approaches, including hierarchical Bayesian methods and ensemble filtering methods; however, these methods are computationally expensive. We propose a new approach, in which the TBM functional response is fit directly to the driver-error-corrected functional response estimated from data, rather than to the raw observations.

A Comparison of Carbon Stock Estimates and Projections for the Northeastern United States

We conducted a comparison of carbon stock estimates produced by three different methods using regional data from the USDA Forest Service Forest Inventory and Analysis (FIA). Two methods incorporated by the Forest Vegetation Simulator (FVS) were compared to each other and to the current FIA component ratio method. We also examined the uncalibrated performance of FVS growth simulations for predicting net carbon accumulation in live trees. In general, the three carbon stock estimation approaches do not produce estimates that are either equivalent or are simply convertible. A strong spatial pattern of relationships between estimates was associated with regional variation in stand top height. Uncalibrated growth projections gave downwardly biased results that were also poorly correlated with observed carbon accumulation rates, yielding little improvement in root mean square error over the use of a simple regional average. These results reinforce the need for managers and scientists to be careful in choosing methods and reporting carbon stock estimates and to use appropriate model calibration methods in projecting future carbon accumulation.

Estimation of Aboveground Forest Carbon Flux in Oregon: Adding Components of Change to Stock-Difference Assessments

A substantial portion of the carbon (C) emitted by human activity is apparently being stored in forest ecosystems in the Northern Hemisphere, but the magnitude and cause are not precisely understood. Current official estimates of forest C flux are based on a combination of field measurements and other methods. The goal of this study was to improve on existing methods by directly tracking components of change in tree C across a large region using field measurements. We used repeated Forest Inventory and Analysis (FIA) measurements on permanent plots to quantify aboveground live tree C flux over an 11-year period due to land-use change, disturbance, and harvest, on 1,073 plots across 5.96 million ha of nonfederal forestland in Oregon. Land-use change resulted in a 110,000 ha (1.9%) net increase of forestland between 1986 and 1997. However, there was a net loss of 3.4 Tg of live tree C due to land-use change because the forestland lost was more productive than that gained. Live woody C decreased significantly in eastern Oregon (−14.4 Tg), with mortality and harvest exceeding growth, primarily due to severe defoliation by western spruce budworm. However, C stores increased significantly in western Oregon (19.2 Tg) due primarily to large accumulations from growth on nonfederal public lands. We demonstrate that C accounting that uses remeasured probabilistic field sample data can produce detailed estimates of C flux that identify causes and components of change and produce more consistent estimates than alternative approaches.

Strategies to compensate for the effects of nonresponse on forest carbon baseline estimates from the national forest inventory of the United States

Forest ecosystem carbon (C) stocks and stock change in the United States (US) have been documented using Intergovernmental Panel on Climate Change (IPCC) procedures and guidance with 1990 as a baseline reference for all United Nations Framework Convention on Climate Change reports. In the US, estimates of forest C stocks and stock change are obtained from data collected and maintained by the Forest Inventory and Analysis (FIA) program of the US Forest Service. Over the course of the IPCC monitoring period, the FIA program made a transition from state-by-state multiyear periodic inventories selected on a rotating basis – with reporting standards largely tailored to regional requirements – to nationally consistent, annual inventories (where a proportion of plots is measured in each state each year) designed for large-scale strategic requirements. Lack of measurements on all forest land during the periodic inventories, along with plot access difficulties and misidentification of forest plots as nonforest due to poor aerial imagery, have resulted in missing data (i.e., nonresponse) throughout the FIA database. Nonresponse, which in some US states is greater than 20%, may lead to differences in estimates of forest C stock change due to the procedural transition from periodic to annual inventories. As an initial step towards rectifying the differences in estimates, we examined several strategies to compensate for missing observations using the most recent annual inventory data from the Lake States region of the US. Results varied by state in the study but given the annual reporting cycle and requirements to compile national estimates of forest C, it was deemed that techniques, where non-observed samples are removed from estimation procedures, provided the optimal combination of statistical performance and efficiency. While the initial analysis focused on the Lake States region, several compensation strategies described may be useful in bridging the gap between national C flux estimates from periodic and annual forest inventories.

Potentials for Mutually Beneficial Collaboration Between FIA Specialists and IEG-40 Pathologists and Geneticists Working on Fusiform Rust

The purpose of this article is to encourage development of an enduring mutually beneficial collaboration between data and information analysts in the US Forest Service’s “Enhanced Forest Inventory and Analysis (FIA) Program” and forest pathologists and geneticists in the information exchange group (IEG) titled “Genetics and Breeding of Southern Forest Trees.” The goal of this collaborative partnership is to take full advantage of the Forest Health Monitoring capabilities within the Enhanced FIA Program to provide up-to-date information on the incidence of fusiform rust on loblolly and slash pine stands in the Southern United States and to periodically report the status of the rust epidemic in this region. Our initial analysis of 2000–2011 FIA data demonstrates that careful analysis and interpretation of results from continuing FIA observations can provide valuable guidance for optimizing the performance of forest tree improvement programs in this region.

Estimation of Merchantable Bole Volume and Biomass above Sawlog Top in the National Forest Inventory of the United States

Emerging markets for small-diameter roundwood along with a renewed interest in forest biomass for energy have created a need for estimates of merchantable biomass above the minimum sawlog top diameter for timber species in the national forest inventory of the United States. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service recently adopted the component ratio method for estimation of aboveground live tree component biomass, which incorporates regionally specific volume models by species and species group. The merchantable bole component is the biomass of sound wood from a 1.0-ft stump height to a minimum 4.0-in. top diameter. Minimum sawlog top diameters for softwoods and hardwoods in the FIA program are substantially larger than the merchantable bole top diameter, leaving additional bole biomass potentially available for utilization. This article describes a method for estimating merchantable bole biomass for the sawlog component and the component above the minimum sawlog top diameter for timber species in the FIA program.

A comparison of techniques for generating forest ownership spatial products

To fully understand forest resources, it is imperative to understand the social context in which the forests exist. A pivotal part of that context is the forest ownership. It is the owners, operating within biophysical and social constraints, who ultimately decide if the land will remain forested, how the resources will be used, and by whom. Forest ownership patterns vary substantially across the United States. These distributions are traditionally represented with tabular statistics that fail to capture the spatial patterns of ownership. Existing spatial products are not sufficient for many strategic-level planning needs because they are not electronically available for large areas (e.g., parcels maps) or do not provide detailed ownership categories (e.g., only depict private versus public ownership). Thiessen polygon, multinomial logit, and classification tree methods were tested for producing a forest ownership spatial dataset across four states with divergent ownership patterns: Alabama, Arizona, Michigan, and Oregon. Over 17,000 sample points with classified forest ownership, collected as part of the USDA Forest Service, Forest Inventory and Analysis (FIA) program, were divided into two datasets, one used as the dependent variable across all of the models and 10 percent of the points were retained for validation across the models. Additional model inputs included a polygon coverage of public lands from the Conservation Biology Institute’s Protected Areas Database (PAD) and data representing human population pressures, road densities, forest characteristics, land cover, and other attributes. The Thiessen polygon approach predicted ownership patterns based on proximity to the sample points in the model dataset and subsequent combining with the PAD ownership data layer. The multinomial logit and classification tree approaches predicted the ownership at the validation points based on the PAD ownership information and data representing human population, road, forest, land cover, and other attributes. The percentage of validation points across the four states correctly predicted ranged from 76.3 to 78.9 among the methods with corresponding weighted kappa values ranging from 0.73 to 0.76. Different methods performed slightly, but statistically significantly, better in different states Overall, the Thiessen polygon method was deemed preferable because: it has a lower bias towards dominant ownership categories; requires fewer inputs; and is simpler to implement.

Using Publicly Available Forest Inventory Data in Climate-Based Models of Tree Species Distribution: Examining Effects of True Versus Altered Location Coordinates

Species distribution models (SDMs) were built with US Forest Inventory and Analysis (FIA) publicly available plot coordinates, which are altered for plot security purposes, and compared with SDMs built with true plot coordinates. Six species endemic to the western US, including four junipers (Juniperus deppeana var. deppeana, J. monosperma, J. occidentalis, J. osteosperma) and two pinons (Pinus edulis, P. monophylla), were analyzed. The presence–absence models based on current climatic variables were generated over a series of species-specific modeling extents using Random Forests and applied to forecast climatic conditions. The distributions of predictor variables sampled with public coordinates were compared to those sampled with true coordinates using t tests with a Bonferroni adjustment for multiple comparisons. Public- and true-based models were compared using metrics of classification accuracy. The modeled current and forecast distributions were compared in terms of their overall areal agreement and their geographic mean centroids. Comparison of the underlying distributions of predictor variables sampled with true versus public coordinates did not indicate a significant difference for any species at any extent. Both the public- and true-based models had comparable classification accuracies across extent for each species, with the exception of one species, J. occidentalis. True-based models produced geographic distributions with smaller areas under current and future scenarios. The greatest areal difference occurred in the species with the lowest modeled accuracies (J. occidentalis), and had a forecast distribution which diverged severely. The other species had forecast distributions with similar magnitudes of modeled distribution shifts.

A large‐scale forest landscape model incorporating multi‐scale processes and utilizing forest inventory data

Two challenges confronting forest landscape models (FLMs) are how to simulate fine, stand‐scale processes while making large‐scale (i.e., >107ha) simulation possible, and how to take advantage of extensive forest inventory data such as U.S. Forest Inventory and Analysis (FIA) data to initialize and constrain model parameters. We present the LANDIS PRO model that addresses these needs. LANDIS PRO adds density and size mechanisms of resource competition. This is achieved through incorporating number of trees and DBH by species age cohort within each raster cell. Forest change is determined by the interactions of species‐, stand‐, and landscape‐scale processes. Species‐scale processes include tree growth, establishment, and mortality. Stand‐scale processes include density and size‐related resource competition that regulates self‐thinning and seedling establishment. Landscape‐scale processes include seed dispersal, as well as natural and anthropogenic disturbances. LANDIS PRO is designed to be straightforwardly comparable with forest inventory data, and thus the extensive FIA data can be directly utilized to initialize and constrain model parameters before predicting future forest change. We initialized a large landscape (∼107ha) from historical FIA data (1978) and the predicted forest structure and composition following 30 years of simulation were statistically calibrated against a prior time‐series of sequential FIA data (1978 to 2008). The results showed that the initialized conditions realistically represented the historical forest composition and structure at 1978, and the constrained model parameters predicted reasonable outcomes at both landscape and land type scales. The subsequent evaluation of model predictions showed that the predicted forest composition and structure were comparable with old‐growth oak forests; predicted forest successional trajectories were consistent with the expected successional patterns in oak‐dominated forests in the study region; and the predicted stand development patterns were in agreement with the established theories of forest stand development. This study demonstrated a framework for forest landscape modeling including model initialization, calibration, and evaluation of predictions.

Changing eastern broadleaf, southern mixed, and northern mixed forest ecosystems of the eastern United States

Recognition of changes in forests provides greater perspective about current trajectories of forests. I compared the newest USDA Forest Service Forest Inventory and Analysis (FIA) surveys to the oldest available surveys in the eastern United States to detect increasing and decreasing species and groups over large spatial extents. Species appeared to be increasing at the expense of decreasing species within each of the major forest ecosystems of the eastern United States. In eastern broadleaf forests, oaks and Virginia pine are decreasing and maples and eastern redcedar are increasing. In southern mixed forests, planted loblolly and slash pine are replacing shortleaf and longleaf pines, and longleaf pine wetland associates of swamp tupelo, pond cypress, and pond pine. In northern mixed forests of the Great Lake states, quaking aspen and paper birch are decreasing whereas red pine, later successional black spruce and northern white-cedar, and maples of eastern broadleaf forests are increasing. Indeed, red maple now is more frequent than aspen in northern mixed forests. The historical legacy of fire continues to affect forest composition. Without fire, fire-tolerant oaks and pines (that are not planted) still are decreasing, whereas fire-sensitive tree species are increasing. Forestry practices benefit planted pines but are not as effective at supporting quaking aspen against competition from red maple and species of the eastern broadleaf forest as in the past.

Evaluating the performance and mapping of three fuel classification systems using Forest Inventory and Analysis surface fuel measurements

Fuel Loading Models (FLMs) and Fuel Characteristic Classification System (FCCSs) fuelbeds are used throughout wildland fire science and management to simplify fuel inputs into fire behavior and effects models, but they have yet to be thoroughly evaluated with field data. In this study, we used a large dataset of Forest Inventory and Analysis (FIA) surface fuel estimates (n=13,138) to create a new fuel classification called Fuel Type Groups (FTGs) from FIA forest type groups, and then keyed an FLM, FCCS, and FTG class to each FIA plot based on fuel loadings and stand conditions. We then compared FIA sampled loadings to the keyed class loading values for four surface fuel components (duff, litter, fine woody debris, coarse woody debris) and to mapped FLM, FCCS, and FTG class loading values from spatial fuel products. We found poor performances (R2<0.30) for most fuel component loadings in all three classifications that, in turn, contributed to poor mapping accuracies. The main reason for the poor performances is the high variability of the four fuel component loadings within classification categories and the inherent scale of this variability does not seem to match the FIA measurement scale or LANDFIRE mapping scale.

Effects of climate on emerald ash borer mortality and the potential for ash survival in North America

Non-native invasive insects such as the emerald ash borer (Agrilus planipennis Fairmaire; EAB) cause billions of dollars’ worth of economic damage and unquantifiable but substantial ecological damage in North America each year. There are methods to mitigate, contain, control, or even eradicate some non-native invasive insects, but so far the spread of EAB across eastern North America appears to be unimpeded. Similar to the effect of chestnut blight (Cryphonectria parasitica (Murrill) Barr) on American chestnut (Castanea dentata (Marsh.) Borkh.) nearly 100 years ago, it is estimated that EAB will eventually decimate nearly all ash (Fraxinus spp.) in North America. Although previous literature suggests no impediment to the spread of EAB, we propose the possibility that obstacles to EAB population expansion into the northern ranges of ash could be formidable. We combined USDA Forest Service Forest Inventory and Analysis (FIA) 2010 ash data, historical climate data, beneath-snow and beneath-tree bark temperature modeling, and our current understanding of EAB physiology. We found that between 1945 and 2012, while some Canadian locations experienced temperatures potentially cold enough to kill all EAB, very few locations in the United States experienced such temperatures. However, more than 7% and 42% of weather stations located in the ranges of ash in the United States and Canada, respectively, experienced temperatures potentially cold enough to kill the majority of the EAB population. By killing the majority of the EAB population, EAB spread may be slower and EAB population may be held to densities to which ash trees can tolerate infestation. As in its native range in Asia, lower EAB densities may not cause ash mortality. This information should be helpful for the future sustainable management of ash.

Extending a Model System to Predict Biomass in Mixed-Species Southern Appalachian Hardwood Forests

Functions for estimating foliage, branches and nonmerchantable stem tops, merchantable stem, and total aboveground biomass for Appalachian hardwood trees were assembled and incorporated into an existing growth-and-yield simulator for mixed-species Appalachian hardwood forests. With these functions and user-defined stand table and stand characteristics, current estimates of biomass in the different aboveground tree components can be obtained by species and diameter class for a thinned or unthinned stand. In addition, future biomass estimates for a thinned stand can be obtained for a projection period of 1 to 10 years. These estimates can be used to assess forest ecosystem services of carbon sequestration and provide information that can be used to evaluate effects of different harvesting strategies on forest carbon and other nutrient pools. A comparison of these estimates to those obtained by the forest inventory and analysis (FIA) procedure, using data from eight FIA sample plots in the southern Appalachian region, indicated that estimating biomass directly from locally developed biomass equations may give biomass estimates for the tree bole and for tops and branches, that are different from those obtained by the FIA procedure.

A cover-based method to assess forest characteristics using inventory data and GIS

In most forest inventory data, it is not feasible to estimate the canopy coverage of trees having certain characteristics due to the lack of information on crown size. In this study, data from the Forest Inventory and Analysis (FIA) program were used to assign crown sizes to individual trees using published crown width models. This process effectively links trees to crown area such that estimates of canopy cover and changes therein can be made using domains that include tree-level attributes (e.g., dbh, total height, etc.). Advantages of implementing this approach are (1) estimation can proceed as with any other estimate of area derived from forest inventory data, and (2) canopy cover estimates provide different information than classical indicators such as number of trees. A disadvantage is the need to dissolve overlapping crowns after the tree-level domain is selected. Examples related to forest health, wildlife habitat, and old growth attributes are provided to illustrate applications of the method.