Forest Inventory And Analysis Plots Research Articles

A comparison of methodologies for estimating delivered forest residue volume and cost to a wood-based biorefinery

Plant location can be a major factor in the financial success of a company when feedstock transport costs are high, such as for wood-based biorefineries. Biorefineries sited near large amounts of forest residue can better mitigate against the risk of reduced feedstock availability due to exogenous market constraints. Two methodologies for estimating the volume and cost of delivered forest residues to a biorefinery are presented. Both methodologies are based on data provided by the U.S. Forest Service Forest Inventory and Analysis (FIA) program. The first methodology is past-predictive in that it uses individual state Timber Product Output (TPO) datasets, while the second methodology is future-predictive in that it uses a spatially explicit economic optimization model of the U.S. forestry sector coupled with stand data at FIA plot locations to project near- and medium-term residue volumes. A Total Delivered Feedstock Cost Model is used with both biomass estimation methods to enable comparison of facility supply curves. A case study assesses four pulp mills, considered as candidate repurposed biorefinery locations, for their ability to procure sufficient biomass under average- and low-yield scenarios utilizing both methods. The facility that procures sufficient feedstock to meet annual biorefinery demand at the least cost under both yield scenarios theoretically provides the least risk to investors in terms of insufficient feedstock availability. The past-predictive methodology was found to be best-suited for refining a list of candidate facilities for further analysis. The future-predictive methodology is best-suited for a robust analysis of facilities using multiple economic and policy scenarios.

Invasibility of three major non-native invasive shrubs and associated factors in Upper Midwest U.S. forest lands

We used non-native invasive plant data from the US Forest Service’s Forest Inventory and Analysis (FIA) program, spatial statistical methods, and the space (cover class)-for-time approach to quantify the invasion potential and success (“invasibility”) of three major invasive shrubs (multiflora rose, non-native bush honeysuckles, and common buckthorn) in broadly classified forest-type groups in seven Upper Midwest states. Smoothed maps of presence and cover percent showed a strong clustering pattern for all three invasive shrubs despite their different ranges. The species are clustered around major cities or urban areas (e.g., Chicago, Illinois, and Des Moines, Iowa), indicating the potential role humans played in their invasion and spread on the landscape and throughout the Midwest. Conditional inference tree (ctree) models further quantified the significant factors contributing to the observed regional patterns: for distribution of multiflora rose, percentage of forest cover in the county (measuring human disturbance intensity) and stand density index; for distribution of common buckthorn, distance to major highways. Non-native bush honeysuckles were not associated with any disturbance and site/stand variables except for latitude and longitude. The infested FIA plots by cover class were positively associated in space, signifying a concentric-like spread trend from previously infested sites (hot spots) to surrounding areas. By forest-type groups or as a whole, the three species spread slowly at earlier stages, but recently have increased significantly in presence/expansion. Oak/hickory and elm/ash/cottonwood forests were more susceptible to the three invasive shrubs compared to other forests. We recommend that resource managers and planners prescribe control and mitigation treatments for non-native invasive plants by forest types and spatial locations close to highways and residences.

Average Stand Age from Forest Inventory Plots Does Not Describe Historical Fire Regimes in Ponderosa Pine and Mixed-Conifer Forests of Western North America.

Quantifying historical fire regimes provides important information for managing contemporary forests. Historical fire frequency and severity can be estimated using several methods; each method has strengths and weaknesses and presents challenges for interpretation and verification. Recent efforts to quantify the timing of historical high-severity fire events in forests of western North America have assumed that the “stand age” variable from the US Forest Service Forest Inventory and Analysis (FIA) program reflects the timing of historical high-severity (i.e. stand-replacing) fire in ponderosa pine and mixed-conifer forests. To test this assumption, we re-analyze the dataset used in a previous analysis, and compare information from fire history records with information from co-located FIA plots. We demonstrate that 1) the FIA stand age variable does not reflect the large range of individual tree ages in the FIA plots: older trees comprised more than 10% of pre-stand age basal area in 58% of plots analyzed and more than 30% of pre-stand age basal area in 32% of plots, and 2) recruitment events are not necessarily related to high-severity fire occurrence. Because the FIA stand age variable is estimated from a sample of tree ages within the tree size class containing a plurality of canopy trees in the plot, it does not necessarily include the oldest trees, especially in uneven-aged stands. Thus, the FIA stand age variable does not indicate whether the trees in the predominant size class established in response to severe fire, or established during the absence of fire. FIA stand age was not designed to measure the time since a stand-replacing disturbance. Quantification of historical “mixed-severity” fire regimes must be explicit about the spatial scale of high-severity fire effects, which is not possible using FIA stand age data.

Integrating LIDAR and forest inventories to fill the trees outside forests data gap.

Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all "nonforest" Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6%, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47% increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.

Mortality rates associated with crown health for eastern forest tree species.

The condition of tree crowns is an important indicator of tree and forest health. Crown conditions have been evaluated during inventories of the US Forest Service Forest Inventory and Analysis (FIA) program since 1999. In this study, remeasured data from 55,013 trees on 2616 FIA plots in the eastern USA were used to assess the probability of survival among various tree species using the suite of FIA crown condition variables. Logistic regression procedures were employed to develop models for predicting tree survival. Results of the regression analyses indicated that crown dieback was the most important crown condition variable for predicting tree survival for all species combined and for many of the 15 individual species in the study. The logistic models were generally successful in representing recent tree mortality responses to multiyear infestations of beech bark disease and hemlock woolly adelgid. Although our models are only applicable to trees growing in a forest setting, the utility of models that predict impending tree mortality goes beyond forest inventory or traditional forestry growth and yield models and includes any application where managers need to assess tree health or predict tree mortality including urban forest, recreation, wildlife, and pest management.

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.

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.

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.

A Density Management Diagram for Even-Aged Sierra Nevada Mixed-Conifer Stands

We have developed a density management diagram (DMD) for even-aged mixed-conifer stands in the Sierra Nevada Mountains using forest inventory and analysis (FIA) data. Analysis plots were drawn from FIA plots in California, southern Oregon, and western Nevada which included those conifer species associated with the mixed-conifer forest type. A total of 204 plots met the selection criteria for analysis, which were for even-agedness and species composition. Even-agedness was characterized by a ratio between two calculations of stand density index. Species composition included admixtures of the species characterizing the Sierra Nevada mixed-conifer type with up to 80% of stand basal area contributed by ponderosa and Jeffrey pines. The DMD is unbiased with respect to species composition and therefore should be broadly applicable to the mixed-conifer type. The DMD is intended for use in even-aged stands, but may be used for uneven-aged management where a large-group selection system is used. Examples of density management regimes are illustrated, and guidelines for use are provided.

The Incidence of Dwarf Mistletoe in Minnesota Black Spruce Stands Detected by Operational Inventories

We surveyed black spruce stands within 0.5 miles of US Forest Service Forest Inventory and Analysis (FIA) plots and compared dwarf mistletoe status with that of the FIA and Minnesota Department of Natural Resources (DNR) forest inventories. Our results differed from FIA results in 3 of 16 stands with FIA plots, with FIA most often not recording dwarf mistletoe in infested stands. The infestation status of 140 of 202 surrounding stands was the same as recorded for the nearby FIA plot. Minnesota DNR forest inventory identified dwarf mistletoe in only 26 of 112 infested stands. Using only the most recent FIA plot data, 8% of FIA plots were recorded as infested. Considering an FIA plot to be infested if it was infested at any time in its history raises the percentage of infested FIA plots to 11%. Of the stands we surveyed, 112 of 202 (55%) were infested, but spatial autocorrelation may bias our sample. For the infested stands, dwarf mistletoe is projected to infest 20% of the stand area and reduce timber volume by at least 14% in the current rotation.

Linking Forest Inventory and Analysis Ground Data to Maps: An Example for Nevada Pinyon-Juniper Forests

Using data from the USDA Forest Service's Forest Inventory and Analysis (FIA) program, we developed a mapping tool for displaying statistical forestland information. To develop this technique, we used data from Nevada's pinyon–juniper dryland forests, a major component of Nevada's forest cover important for food and wood sources, wildlife cover, and carbon storage. Land managers must grapple with issues, such as the health of these forests, the explanation of their encroachment into grasslands and shrublands, and causes of pinyon mortality associated with drought and bark-beetle activity. Our methodology can assist managers by offering the potential to map FIA statistics, such as carbon, volume, and stand structure to local conditions. FIA plots were classified according to the basal area occupied by dominant species groups. Employing this classification, we modeled the probability of pinyon–juniper occurrence at stand scale in Nevada from FIA data, using readily available remote sensing and geographic information system variables. Then, using the model with a Nevada-specific classification rule, we produced a cover map for the state. Finally, we linked our model classification rule to FIA summary data to create a detailed statistical map of Nevada forest carbon distribution—a visual representation of FIA data on the landscape.

Use of pixel- and plot-scale screening variables to validate MODIS GPP predictions with Forest Inventory and Analysis NPP measures across the eastern USA

Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of gross primary production (GPP) were validated using field-based estimates of net primary production from the Forest Inventory and Analysis (FIA) Program across the eastern USA. A total of 54 969 MODIS pixels and co-located FIA plots were analysed to validate MODIS GPP estimates. We used a data resolution of individual MODIS pixels and co-located FIA plots, and used detailed pixel- and plot-specific attributes by applying screening variables (SVs) to assess conditions under which MODIS GPP was most strongly validated. Eight SVs were used to test six hypotheses about the conditions under which MODIS GPP would be most strongly validated. The six hypotheses addressed were (1) MODIS pixel quality checks, (2) FIA plot quality checks, (3) land-cover classification comparability of co-located MODIS pixels and FIA plots, (4) FIA plot homogeneity, (5) FIA plot tree density and (6) MODIS seasonal variation. SVs were assessed in terms of trade-off between improved relations and reduced number of samples. MODIS seasonal variation and FIA plot tree density were the two most efficient SVs, followed by basic quality checks for each data set. Sequential application of SVs indicated that combined usage of five of the eight SVs provided an efficient data set of 17 090 co-located MODIS pixels and FIA plots, which raised the Pearson correlation coefficient from 0.01 for the Complete data set of 54 969 plots to 0.48 for this screened subset of 17 090 plots. The screened subset of plots exhibited good representation of the Complete data set in terms of species abundance, plot distribution and mean productivity. We conclude that the application of SVs provides a useful approach to ensure compatibility of two data sets for broad-scale forest carbon budget analysis and monitoring.

Factors influencing ponderosa pine regeneration in the southwestern USA

Improving predictions of ponderosa pine ( Pinus ponderosa var. scopulorum) regeneration is critical as landscape-scale restoration efforts are being implemented across the western United States. The establishment of new ponderosa pine cohorts following restoration treatments will have long-term impacts on the future development of forest stands. At a regional scale, we used data associated with 364 Forest Inventory and Analysis (FIA) plots within the ponderosa pine forest type on National Forests in Arizona and New Mexico. At a local scale, we sampled 28 stands on basalt and sedimentary parent materials on the Coconino National Forest in northern Arizona. Structural equation models were developed to determine the relative importance of direct and indirect factors affecting ponderosa pine seedling densities at both regional and local scales. The regional model only explained 13% of the variation in pine seedling density. The model indicated that ponderosa pine seedling density was highest where (1) mean annual precipitation was highest, (2) average May minimum temperature was highest, (3) overstory stand basal area was lowest and (4) Muhlenbergia spp. were dominant herbaceous plants. The local model explained 76% of the variation in pine seedling density. Densities were highest where (1) mean annual precipitation was highest, (2) soil clay content was lowest, (3) soil pH was lowest, (4) Muhlenbergia virescens frequency was highest and (5) seed tree frequency was highest. If additional factors such as soil texture, soil pH, screwleaf muhly frequency and seed tree frequency were measured on FIA plots, then perhaps more variation in pine seedling density could be explained at the regional scale.

Maintaining the confidentiality of plot locations by exploiting the low sensitivity of forest structure models to different spectral extraction kernels

The United States Forest Service Forest Inventory and Analysis (FIA) unit maintains a large national network of inventory plots. While the consistency and extent of this network make FIA data attractive for ecological modelling, the FIA is charged by statute not to publicly reveal inventory plot locations. However, use of FIA plot data by the remote sensing community often requires that plot measurements be matched with spatially correspondent values from spectral or geographic data layers. Extracting spatial data in a known way and associating it with plot information leaves open the possibility that a user might use extracted spatial characteristics and a moving window filter to directly infer the plot's location. Direct inference of plot location in this way would be impossible, however, if the original method of sampling the geographic data was unknown. Tests using five Landsat scenes covering a wide range of ecological types showed that varying the weights of pixels within approximately 50 m of the plot centre has little effect on the quality of subsequent models predicting basal area. This finding may support the development of automated extraction routines that vary (perhaps randomly) the geographic data extraction process and therefore increase the security of FIA plot locations.

Carbon sequestration potential of forest land: Management for products and bioenergy versus preservation

A 40 year projection of potential carbon sequestration is based on USDA Forest Service Forest Inventory and Analysis (FIA) data from the state of Georgia. The objective is to compare carbon sequestration under a sustainable management strategy versus a preservation strategy. FIA plots are projected ahead in time with hotdeck matching. This matches each subject plot with another plot from the database that represents the subject plot at a future time. The matched plot sequences are used to provide input data to a harvest scheduling program to generate a management strategy for the state. The sequestration from the management strategy is compared with a preservation strategy that involves no harvesting. Harvested wood is assumed to go into products with various half life decay rates. Carbon sequestration is increased as increasing proportions go into wood for energy, which is treated like a product with an infinite half life. Therefore, the harvested carbon does not return immediately to the atmosphere. Public land and land close to cities is assumed to be unavailable, and all other private land is assumed to be accessible. The results are presented as gigatonnes of CO 2 equivalent to make them directly comparable to US annual carbon emissions. The conclusion is that forest management will sequester more above-ground carbon than preservation over a 40 year period if the wood is used for products with an average half life greater than 5 years.

Forest carbon storage in the northeastern United States: Net effects of harvesting frequency, post-harvest retention, and wood products

Temperate forests are an important carbon sink, yet there is debate regarding the net effect of forest management practices on carbon storage. Few studies have investigated the effects of different silvicultural systems on forest carbon stocks, and the relative strength of in situ forest carbon versus wood products pools remains in question. Our research describes (1) the impact of harvesting frequency and proportion of post-harvest structural retention on carbon storage in northern hardwood-conifer forests, and (2) tests the significance of including harvested wood products in carbon accounting at the stand scale. We stratified Forest Inventory and Analysis (FIA) plots to control for environmental, forest structural and compositional variables, resulting in 32 FIA plots distributed throughout the northeastern U.S. We used the USDA Forest Service's Forest Vegetation Simulator to project stand development over a 160 year period under nine different forest management scenarios. Simulated treatments represented a gradient of increasing structural retention and decreasing harvesting frequencies, including a “no harvest” scenario. The simulations incorporated carbon flux between aboveground forest biomass (dead and live pools) and harvested wood products. Mean carbon storage over the simulation period was calculated for each silvicultural scenario. We investigated tradeoffs among scenarios using a factorial treatment design and two-way ANOVA. Mean carbon sequestration was significantly ( α = 0.05) greater for “no management” compared to any of the active management scenarios. Of the harvest treatments, those favoring high levels of structural retention and decreased harvesting frequency stored the greatest amounts of carbon. Classification and regression tree analysis showed that management scenario was the strongest predictor of total carbon storage, though site-specific variables were important secondary predictors. In order to isolate the effect of in situ forest carbon storage and harvested wood products, we did not include the emissions benefits associated with substituting wood fiber for other construction materials or energy sources. Modeling results from this study show that harvesting frequency and structural retention significantly affect mean carbon storage. Our results illustrate the importance of both post-harvest forest structure and harvesting frequency in carbon storage, and are valuable to land owners interested in managing forests for carbon sequestration.

Acid deposition effects on forest composition and growth on the Monongahela National Forest, West Virginia

The northern and central Appalachian forests are subject to high levels of atmospheric acid deposition (AD), which has been shown in some forests to negatively impact forest growth as well as predispose the forest system to damage from secondary stresses. The purpose of this study was to evaluate the possible contribution of AD to changes in composition and productivity of the Monongahela National Forest, and to evaluate soil-based indicators of acidification that might be useful for detecting AD-related forest changes. Soils adjacent to 30 Forest Inventory and Analysis (FIA) sites were sampled and analyzed for a suite of acidity indicators. These indicators were correlated with the periodic mean annual volume increment (PMAVI) of the forest stands on FIA plots for the 10-yr period 1989–2000. PMAVI ranged from −9.5 to 11.8 m 3 ha −1 yr −1, with lower-than-expected growth (<3 m 3 ha −1 yr −1) on two-thirds of the sites. In the surface horizon, effective base saturation, Ca 2+ concentration, base saturation, K + concentration, Ca/Al molar ratio, and Mg/Al molar ratio, were positively correlated with PMAVI and Fe concentration was negatively correlated with PMAVI ( p ≤ 0.1). In the subsurface horizon pH (w) and effective base saturation were positively correlated and Al 3− concentration and K + concentration were negatively correlated with PMAVI. We hypothesized that NO 3-N/NH 4-N ratio would also be correlated with PMAVI, but it was not. Correlations between soil chemical indicators and PMAVI suggest that AD may contribute, in part, to the lower-than-expected forest growth on the Monongahela National Forest.

Spatial distribution of forest landscape change in western New York from presettlement to the present

Changes in tree taxon composition and distribution in western New York over a 200 year time period ca. 1797–1993 were examined by comparing the presettlement land survey with the US Forest Inventory and Analysis (FIA) survey. To ensure data quality, biases in presettlement bearing tree selection and FIA plot location were assessed. A 6 mile × 6 mile grid of taxa abundance was then estimated using geostatistics. Overall, significant changes in taxon composition occurred, with the taxa most abundant in the presettlement land survey — beech (37.0%), sugar maple (21.0%), and eastern hemlock (8.3%) — being replaced by sugar maple (19.2%), ash (11.7%), and red maple (11.4%) in the FIA survey. Spatially resolved comparisons showed that the landscape changed from fairly spatially homogeneous to more heterogeneous; in the presettlement survey, the most abundant taxon in most of the 6 mile × 6 mile grid cells was beech, while in the present survey, the most abundant taxon in most of the cells was one of several early successional taxa that each displayed a distinctly clustered geographic pattern of dominance. The clusters of dominance of the different early successional taxa may correspond to environmental factors. This study demonstrates the insights available through spatially resolved analyses of changes in the forest landscape between presettlement and present.