Forest Inventory And Analysis Program Research Articles

Simplifying Small Area Estimation With rFIA: A Demonstration of Tools and Techniques

The United States (US) Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) program operates the national forest inventory of the US. Traditionally, the FIA program has relied on sample-based approaches—permanent plot networks and associated design-based estimators—to estimate forest variables across large geographic areas and long periods of time. These approaches generally offer unbiased inference on large domains but fail to provide reliable estimates for small domains due to low sample sizes. Rising demand for small domain estimates will thus require the FIA program to adopt non-traditional estimation approaches that are capable of delivering defensible estimates of forest variables at increased spatial and temporal resolution, without the expense of collecting additional field data. In light of this challenge, the development of small area estimation (SAE) methods—estimation techniques that support inference on small domains—for FIA data has become an active and highly productive area of research. Yet, SAE methods remain difficult to apply to FIA data, due in part to the complex data structures and survey design used by the FIA program. Herein, we present the potential of rFIA, an open-source R package designed to increase the accessibility of FIA data, to simplify the application of a broad suite of SAE methods to FIA data. We demonstrate this potential via two case studies: (1) estimation of contemporary county-level forest carbon stocks across the conterminous US using a spatial Fay-Herriot model; and (2) temporally-explicit estimation of multi-decadal trends in merchantable wood volume in Washington County, Maine using a Bayesian multi-level model. In both cases, we show the application of SAE techniques offers considerable improvements in precision over FIA's traditional, post-stratified estimators. Finally, we offer a discussion of the potential role that rFIA and other open-source tools might play in accelerating the adoption of SAE techniques among users of FIA data.

Hierarchical Bayesian Small Area Estimation Using Weakly Informative Priors in Ecologically Homogeneous Areas of the Interior Western Forests

The U.S. Forest Inventory and Analysis Program (FIA) collects inventory data on and computes estimates for many forest attributes to monitor the status and trends of the nation's forests. Increasingly, FIA needs to produce estimates in small geographic and temporal regions. In this application, we implement area level hierarchical Bayesian (HB) small area estimators of several forest attributes for ecosubsections in the Interior West of the US. We use a remotely-sensed auxiliary variable, percent tree canopy cover, to predict response variables derived from ground-collected data such as basal area, biomass, tree count, and volume. We implement four area level HB estimators that borrow strength across ecological provinces and sections and consider prior information on the between-area variation of the response variables. We compare the performance of these HB estimators to the area level empirical best linear unbiased prediction (EBLUP) estimator and to the industry-standard post-stratified (PS) direct estimator. Results suggest that when borrowing strength to areas which are believed to be homogeneous (such as the ecosection level) and a weakly informative prior distribution is placed on the between-area variation parameter, we can reduce variance substantially compared the analogous EBLUP estimator and the PS estimator. Explorations of bias introduced with the HB estimators through comparison with the PS estimator indicates little to no addition of bias. These results illustrate the applicability and benefit of performing small area estimation of forest attributes in a HB framework, as they allow for more precise inference at the ecosubsection level.

TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data

A 30 × 30m-resolution gridded dataset of forest plot identifiers was developed for the conterminous United States (CONUS) using a random forests machine-learning imputation approach. Forest plots from the US Forest Service Forest Inventory and Analysis program (FIA) were imputed to gridded c2014 landscape data provided by the LANDFIRE project using topographic, biophysical, and disturbance variables. The output consisted of a raster map of plot identifiers. From the plot identifiers, users of the dataset can link to a number of tree- and plot-level attributes stored in the accompanying tables and in the publicly available FIA DataMart, and then produce maps of any of these attributes, including number of trees per acre, tree species, and forest type. Of 67,141 FIA plots available, 62,758 of these (93.5%) were utilized at least once in imputation to 2,841,601,981 forested pixels in CONUS. Continuous high-resolution forest structure data at a national scale will be invaluable for analyzing carbon dynamics, habitat distributions, and fire effects.

RFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database

Forest Inventory and Analysis (FIA) is a US Department of Agriculture Forest Service program that aims to monitor changes in forests across the US. FIA hosts one of the largest ecological datasets in the world, though its complexity limits access for many potential users. rFIA is an R package designed to simplify the estimation of forest attributes using data collected by the FIA Program. Specifically, rFIA improves access to the spatio-temporal estimation capacity of the FIA Database via space–time indexed summaries of forest variables within user-defined population boundaries (e.g., geographic, temporal, biophysical). The package implements multiple design-based estimators, and has been validated against official estimates and sampling errors produced by the FIA Program. We demonstrate the utility of rFIA by assessing changes in abundance and mortality rates of ash (Fraxinus spp.) populations in the Lower Peninsula of Michigan following the establishment of emerald ash borer (Agrilus planipennis).

Using Forest Inventory and Analysis Data to Support National Forest Management: Regional Case Studies

Abstract Although many forestry practitioners have a general understanding of the Forest Inventory and Analysis (FIA) program and the type of data collected, most non-expert users of FIA reports and basic data are unlikely to be familiar with the breadth of information available and the many potential uses of the data. We present case studies from three USDA Forest Service regions to highlight a variety of applications of FIA data, from informing the forest plan revision process to supplying managers with timely information on important forest attributes at the stand and landscape scales. These examples illustrate the utility of FIA data in meeting managers’ information needs, the importance of the linkages between research and management throughout the agency, and the role that the FIA program can play in fostering those collaborations.

Monitoring pinyon-juniper cover and aboveground biomass across the Great Basin

Since the mid-1800s pinyon-juniper (PJ) woodlands have been encroaching into sagebrush-steppe shrublands and grasslands such that they now comprise 40% of the total forest and woodland area of the Intermountain West of the United States. More recently, PJ ecosystems in select areas have experienced dramatic reductions in area and biomass due to extreme drought, wildfire, and management. Due to the vast area of PJ ecosystems, tracking these changes in woodland tree cover is essential for understanding their consequences for carbon accounting efforts, as well as ecosystem structure and functioning. Here we present a carbon monitoring, reporting, and verification (MRV) system for characterizing total aboveground biomass stocks and flux of PJ ecosystems across the Great Basin. This is achieved through a two-stage remote sensing approach by first using spatial wavelet analysis to rapidly sample tree cover from very high-resolution imagery (1 m), and then training a Random Forest model which maps tree cover across the region from 2000 to 2016 using temporally-segmented Landsat spectral indices obtained from the LandTrendr algorithm in Google Earth Engine. Estimates of cover were validated against field data from the SageSTEP project (R2 = 0.67, RMSE = 10% cover). Biomass estimated from cover-based allometry was higher than estimates from the Forest Inventory and Analysis Program (FIA) at the plot-level (bias = 5 Mg ha−1 and RMSE = 15.5 Mg ha−1) due in part to differences in tree-level biomass allometrics. County-level aggregation of biomass closely matched estimates from the FIA (R2 = 0.97) after correcting for bias at the plot level. Even after many previous decades of encroachment, we find forest area (i.e. areas with ≥10% cover) increasing at a steady rate of 0.46% per year, but 80% of the 9.86 Tg increase in biomass is attributable to infilling of existing forest. This suggests that the known consequences of encroachment such as reduced water availability, impacts to biodiversity, and risk of severe wildfire may have been increasing across the region in recent years despite the actions of sagebrush steppe restoration initiatives.

Lichen species as element bioindicators for air pollution in the eastern United States of America

AbstractLichen element (N, S, metals) indicators of local air pollution load (a widely used technique) are recommended for five predefined regions covering central and southern parts of the eastern United States. The final recommendations integrate the advice of regional lichenologists, information from regional floras, and species abundance data from a United States Forest Service Forest Inventory and Analysis Program (FIA) lichen database for 11 of the 21 covered eastern states. Recommended species were frequent in their region, easy for nonspecialists to distinguish in the field after training, and easy to handle using clean protocols. Regression models of species abundance in FIA plots from five southeastern states vs. climate, air pollution (both from a regional lichen response model) and type of nearby landcover (from the National Land Cover Database) identified species’ environmental limitations. Punctelia rudecta is recommended for cooler forested uplands of all regions, with three Physcia species combined and Punctelia missouriensis for isolated woodlands or urban areas of three regions. Parmotrema hypotropum and P. hypoleucinum combined (weak environmental limitation) or P. perforatum. and P. subrigidum combined (limited in more polluted areas) are recommended for warmer Coastal Plains in two regions each. Additional species are recommended for single regions. Each species must be quantitatively evaluated in each region, to demonstrate indication reliability in practice and to calculate element data conversions between species for region-wide bioindication.

Source or Sink? A comparison of Landfire- and FIA-based estimates of change in aboveground live tree carbon in California’s forests

Forests play a central role in addressing climate change, and accurate estimates of forest carbon are critical for the development of actions that reduce emissions from forests and that maximize sequestration by forests. Methodological challenges persist regarding how best to estimate forest carbon stocks and flux at regulatory-relevant scales. Using California, USA as a case study, we compare two approaches to stock-difference forest carbon estimation for aboveground live trees: one based on ground inventories and one on land cover classification of remotely-sensed data. Previous work using ground inventory data from the Forest Inventory and Analysis Program (FIA) showed net aboveground carbon (AGC) sequestration by live trees in California forests, while estimates using land cover classification from the Landscape Fire and Resource Management Planning Tools (Landfire) showed net reductions in live tree AGC over a similar time period. We examined the discrepancy by re-analyzing the FIA inventory data through the lens of a category-change analysis based on Landfire. This analysis showed more than 50% of the live tree AGC in fewer than 4% of Landfire-equivalent categories and that the overwhelming majority (>80%) of forest area did not change height category between measurement periods. Despite the lack of categorical change, the majority of FIA plots increased in both 95th percentile tree height and in live tree AGC. These findings suggest that an approach based on observing categorical changes risks undercounting AGC sequestration resulting from growth and thus overstating the relative importance of AGC reductions that result from disturbances. This would bias AGC flux estimates downward, leading us to validate the conclusion that live trees in California were a net sink of aboveground carbon in the decade ending in 2016. Our findings suggest an inventory-based or hybrid approach is preferable to methods that depend on categorical bins for estimating AGC in disturbance-prone forest ecosystems.

Disequilibrium in Trait-Climate Relationships of Trees and Birds

Climate change is expected to drive changes in the structure of ecological assemblages, but these responses might show considerable time lags. If these lags are large, then the observed composition of assemblages will fall out of equilibrium with current climatic conditions. This disequilibrium has several expected signatures, including trends in the magnitude of climate relationships through time and stronger climate relationships across space than through time. Here, I describe spatial and temporal patterns of functional assemblage composition of birds and trees and ask whether there is evidence of disequilibrium between climate and functional assemblage structure. I used bird assemblage data from the Breeding Bird Survey (BBS) and tree data from the Forest Inventory and Analysis program (FIA), in both cases spanning the continental United States and covering the time period 1970-2016. To describe functional assemblage composition, I used the community weighted means of five functional traits for trees and six traits for birds, and fit linear models to explain the variation in composition through time and across space. For most traits, spatial relationships were much stronger than temporal relationships, consistent with lagged responses of assemblages to climate change. Several of the traits showed trends over the study period, in some cases associated with trends in mean annual temperature. Others showed dynamics in the spatial relationships between climate and trait values. For example, the mean clutch size of birds depended only weakly on temperature in the early study period, but the relationship became strongly negative by 2016. While precipitation was often an important predictor of spatial patterns of functional composition, it was typically weakly correlated with the temporal patterns. This likely reflects the fact that temperature experienced more consistent long-term trends over the study period, whereas large year-to-year fluctuations in precipitation prevent composition from ever coming into equilibrium with precipitation in a particular year. Overall, there was substantial evidence of disequilibrium between functional assemblage composition and climate.

Lichen indices assess local climate and air quality status in the Mid-Atlantic Region, U.S.A.

Lichen-based indices were developed for monitoring local climate and air quality impacts in the United States of America (U.S.A.) Mid-Atlantic states (MidA). The U.S.A. Forest Service Forest Inventory and Analysis Program (FIA) uses such biological indicators to monitor forest environments. Index development used a unique combination of recommended analysis techniques. The Climate Index (Axis 1 of nonmetric multidimensional scaling [NMS] ordination of 189 plots, 80 lichen species) accounted for 44.7% of data variation, and was correlated with latitude, temperature and humidity (r2 = 0.5 to 0.7). The Pollution Index was based on abundances of 10 tolerant and 27 sensitive lichen species selected from Indicator Species Analysis and other techniques (210 plots, 180 species). Strong quantitative support guided careful selection of pollution indicator species. The Pollution Index was strongly correlated with Al, Cu, Fe, N and S measured in lichen samples (51 plots; maximum r2=0.796), and with 51-plot NMS Axis 1 (r2=0.694; 41.7% of information) correlated with pollution. Indices were uncorrelated with each other or with nearby forest cover, another important factor. From within-site repeatability, Climate Index changes of 13–21% of full range and Pollution Index changes of 18–24% will be detectable. These indices fill a gap to complete FIA index coverage for much of eastern U.S.A. Both indices are suitable for application in other MidA studies. Comparisons with similar FIA studies supported guidelines for use of our indicator selection process and evaluation of environmental interactions to improve other studies.

Applications of the United States Forest Inventory and Analysis dataset: a review and future directions

The United States Forest Inventory and Analysis (FIA) program has been monitoring national forest resources in the United States for over 80 years; presented here is a synthesis of research applications for FIA data. A review of over 180 publications that directly utilize FIA data is broken down into broad categories of application and further organized by methodologies and niche research areas. The FIA program provides the most comprehensive forest database currently available, with permanent plots distributed across all forested lands and ownerships in the United States and plot histories dating back to the early 1930s. While the data can be incredibly powerful, users need to understand the spatial resolution of ground-based plots and the nature of the FIA plot coordinate system must be applied correctly. As the need for accurate assessments of national forest resources continues to be a global priority, particularly related to carbon dynamics and climate impacts, such national forest inventories will continue to be an important source of information on the status of and trends in these ecosystems. The advantages and limitations of FIA’s national forest inventory data are highlighted, and suggestions for further expansion of the FIA program are provided.

Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

BackgroundKnowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) plots.MethodsEach plot had three forest community type labels: (1) “FIA” types were assigned by the FIA program using a supervised method; (2) “USNVC” types were assigned via a key based on the U.S. National Vegetation Classification; (3) “empirical” types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables.ResultsThe classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The longleaf pine forest community type stood out as the exception: it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as loblolly pine types. Indicator species metrics were generally high for the USNVC, suggesting that USNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables.ConclusionsThe classifications have similarities and differences that reflect their differing approaches and objectives. They are most consistent for forest community types that occur in a relatively narrow range of environmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at a variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, suggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.

Using routinely collected regional forest inventory data to conclude that resting habitat for the fisher (Pekania pennanti) in California is stable over ∼20 years

Abstract The conservation and management of species-at-risk requires periodically collecting information about their distributions and abundances. A comprehensive monitoring plan should, in addition to monitoring the population itself, also assess the status of habitat elements that are key factors in species survival. Places where animals seek safe and secure places to rest are such key habitat elements. We used previously published models to predict resting habitat for fishers (Pekania pennanti) throughout much of their range in California. Unique to this work is that the two models (northwestern California and southern Sierra Nevada) were developed using, as predictors, variables directly from a national plot-based forest inventory program called Forest Inventory and Analysis (FIA). Using these models, relative resting habitat suitability can be estimated at each geographically relevant plot in the FIA system every time the plot is resampled. We applied these predictive models to data collected at 3 time periods over an approximately 20 year period to evaluate the trend in predicted fisher resting habitat. None of the 8 national forests, 4 in the northwestern California region and 4 in the southern Sierra Nevada region – nor either of these 2 regions as a whole – exhibited trends in predicted resting habitat suitability that were significantly increasing or decreasing. Predicted resting habitat suitability tended to be lower on private land than public land, in both regions. As expected, plots that were disturbed by fire exhibited a decrease in resting habitat suitability but, surprisingly, the few plots within harvest unit boundaries had indistinguishable values before and approximately 7 years after the harvest. Using FIA data for future assessments of habitat value will avoid the significant cost incurred when the data need to be collected repeatedly using different data and a field protocol that may vary. We anticipate that the FIA program will continue to be the preeminent plot-based vegetation survey in the United States, and the data to run the resting habitat models will be available every 10 years. Moreover, access to routinely updated plot-based data provides the only way we can envision sampling something as fine-scaled as resting habitat over thousands of square miles of potentially suitable habitat. We hope our example encourages others to parlay the FIA data into a predictive model of fine-scale habitat features that is relevant to other species. Demonstrating the utility of our models should also encourage managers to use the predictions to evaluate the status of fisher habitat in California.

Gridded Snow Water Equivalent Reconstruction for Utah Using Forest Inventory and Analysis Tree-Ring Data

Snowpack observations in the Intermountain West are sparse and short, making them difficult for use in depicting past variability and extremes. This study presents a reconstruction of April 1 snow water equivalent (SWE) for the period of 1850–1989 using increment cores collected by the U.S. Forest Service, Interior West Forest Inventory and Analysis program (FIA). In the state of Utah, SWE was reconstructed for 38 snow course locations using a combination of standardized tree-ring indices derived from both FIA increment cores and publicly available tree-ring chronologies. These individual reconstructions were then interpolated to a 4-km grid using an objective analysis with elevation correction to create an SWE product. The results showed a significant correlation with observed SWE as well as good correspondence to regional tree-ring-based drought reconstructions. Diagnostic analysis showed statewide coherent climate variability on inter-annual and inter-decadal time-scales, with added geographical details that would not be possible using courser pre-instrumental proxy datasets. This SWE reconstruction provides water resource managers and forecasters with better spatial resolution to examine past variability in snowpack, which will be important as future hydroclimatic variability is amplified by climate change.

Toward inventory-based estimates of soil organic carbon in forests of the United States.

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on Earth; this pool plays a critical role in ecosystem processes and climate change. Given the cost and time required to measure SOC, and particularly changes in SOC, many signatory nations to the United Nations Framework Convention on Climate Change report estimates of SOC stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the U.S. Department of Agriculture, Forest Service. The FIA program has been consistently measuring soil attributes as part of the NFI since 2001 and has amassed an extensive inventory of SOC in forest land in the conterminous United States and southeast and southcentral coastal Alaska. That said, the FIA program has been using country-specific predictions of SOC based, in part, upon a model using SOC estimates from the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation Service. Estimates obtained from the STATSGO database are averages over large map units and are not expected to provide accurate estimates for specific locations, e.g., NFI plots. To improve the accuracy of SOC estimates in U.S. forests, NFI SOC observations were used for the first time to predict SOC density to a depth of 100cm for all forested NFI plots. Incorporating soil-forming factors along with observations of SOC into a new estimation framework resulted in a 75% (48±0.78Mg/ha) increase in SOC densities nationally. This substantially increases the contribution of the SOC pool, from approximately 44% (17Pg) of the total forest ecosystem C stocks to 56% (28Pg), in the forest C budget of the United States.

Potential for regenerating major and minor ash species (Fraxinus spp.) following EAB infestation in the eastern United States

Incidentally introduced in 2002, the expansion of emerald ash borer (EAB) led to widespread ash mortality throughout the eastern United States. A great deal of effort has been invested in containing and controlling this forest invasive species, whereas the ability to regenerate ash from extant seedling and sapling populations following the initial EAB invasion has received less attention. Using recent data available from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis program (FIA), we quantified ash seedling and sapling regeneration counts across FIA forest type groups containing white, green, black, blue, pumpkin, and Carolina ash. In addition, all other seedling and sapling species in these stands were quantified to determine overall species composition of the regeneration and the potential for inter-specific competition. Ash seedlings and saplings represented the greatest proportion of regeneration across most forest type groups containing mature ash. Top competitors of ash tended to be far less economically and ecologically valuable. If retaining ash in current stands remains a management goal, and provided that an effective biological control for EAB is identified and established across infested areas, the success of extant ash seedlings and saplings into larger size classes will depend on silvicultural treatments designed for controlling inter-specific competition across stand cohorts.

Using Traditional Ecological Knowledge as a Basis for Targeted Forest Inventories: Paper Birch (Betula papyrifera) in the US Great Lakes Region

Traditional ecological knowledge (TEK) has been proposed as a basis for enhanced understanding of ecological systems and their management. TEK also can contribute to targeted inventories of resources not included in standard mensuration. We discuss the results of a cooperative effort between the Great Lakes Indian Fish and Wildlife Commission (GLIFWC) and USDA Forest Service’s Forest Inventory and Analysis Program (FIA). At the urging of member tribes, GLIFWC staff worked with tribal gatherers to document TEK regarding desired characteristics of birch bark for traditional uses and translated this into an inventory field guide. The guide was provided to FIA, which incorporated the methods into its field manual and trained inventory crews in implementation of the protocol. Birch bark data were collected during three field seasons from 2004 to 2006. Results show birch bark supply has declined. Lessons learned from this multiyear, multistage project provide a model for future targeted inventory efforts.

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.

Updated generalized biomass equations for North American tree species

Historically, tree biomass at large scales has been estimated by applying dimensional analysis techniques and field measurements such as diameter at breast height (dbh) in allometric regression equations. Equations often have been developed using differing methods and applied only to certain species or isolated areas. We previously had compiled and combined (in meta-analysis) available diameter-based allometric regression equations for estimating total aboveground and component dry-weight biomass for US trees. This had resulted in a set of 10 consistent, national-scale aboveground biomass regression equations for US species, as well as equations for predicting biomass of tree components as proportions of total aboveground biomass. In this update of our published equation database and refinement of our model, we developed equations based on allometric scaling theory, using taxonomic groupings and wood specific gravity as surrogates for scaling parameters that we could not estimate. The new approach resulted in 35 theoretically based generalized equations (13 conifer, 18 hardwood, 4 woodland), compared with the previous empirically grouped 10. For trees from USDA Forest Inventory and Analysis Program (FIA) plots, with forest types grouped into conifers and hardwoods, previous and updated equations produced nearly identical estimates that predicted ∼20 per cent higher biomass than FIA estimates. Differences were observed between previous and updated equation estimates when comparisons were made using individual FIA forest types.

Biomass and carbon data from blue oaks in a California oak savanna

Stem circumference at breast height (1.4 m above ground), tree height, distance from the ground to the base of the crown, and crown radii from the stem to the average drip line were measured for fourteen blue oak (Quercus douglasii) trees from a California site. The trees were felled, and we determined mass of stems, branches, twigs, and foliage; and analyzed carbon and nitrogen in branch and stem (0.44% N, 45.85% C) and leaf (2.13% N, 45.11% C) samples. Equations estimating biomass from diameter at breast height (dbh) or diameter at root collar (drc) were developed through regression. We applied our equations to blue oak data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis Program (FIA) database. Our biomass and carbon estimates were generally comparable to but exceeded FIA's predictions by about 15% and 7%, respectively.