Forest Inventory And Analysis Data Research Articles

Characterization of Chinese Tallow Invasion in the Southern United States

Chinese tallow is a non-native invasive tree expanding in range and abundance throughout the southern United States. Several biogeographical studies mapping tallow distribution and examining key underlying environmental factors relied on the U.S. Forest Service Forest Inventory and Analysis (FIA) data, representing forestlands at scales of ~2400 ha. However, given that most invasive trees, like tallow, are cosmopolitan and dynamic in nature, FIA data fails to capture the extent and severity of the invasion especially outside areas classified as forestlands. To develop tallow maps that more adequately depict its distribution at finer spatial scales and to capture observations in non-forestlands, we combined verified citizen science observations with FIA data. Further, we described spatiotemporal patterns and compared citizen science to FIA and other previously published distribution maps. From our work, although tallow is prevalent in the south, Louisiana, Texas, and Mississippi were the most invaded states. Tallow was associated with flatwoods and prairie grasslands of the Gulf Coast. Annual extreme minimum temperatures of less than −12.2 °C (10 °F) represented the northern limit of naturalized tallow populations. Tallow’s northward and inland expansion was captured in citizen science and FIA data, indicating a tallow spread rate ranging from 5 to 20 km annually over the last decade. Systematic sampling, such as FIA, and citizen science data both have their own unique pitfalls. However, the use of citizen science data can complement invasive plant distribution mapping, especially when combined with data from established systematic monitoring networks. This approach provides for a more complete understanding of invasive tree extent and spatiotemporal dynamics across large landscapes.

Site Index Models for Loblolly Pine Forests in the Southern United States Developed with Forest Inventory and Analysis Data

Abstract Accurate productivity estimates are essential to assess the overall sustainability of forest resources. Site index (SI) models for loblolly pine (Pinus taeda L.) in plantation and natural forests of the southeastern United States were developed using the Forest Inventory and Analysis (FIA) database. We extracted short (~20 years), unbalanced panel data from the FIA database. Ten different nonlinear models derived from the base models using the algebraic difference approach (ADA) or the generalized algebraic difference approach (GADA) were fitted to the extracted data. The performance of the models was ranked based on a variety of fit and evaluation statistics. The results showed that all top three models were derived using the GADA approach. The best model for loblolly pine plantation and natural forest stands was derived from the Hossfeld model and the Chapman–Richards model, respectively. The best-fitted models for planted forests were also compared with previously developed models. This study demonstrated that base-age invariant and polymorphic SI models could be developed using short panel data extracted from FIA data. The SI models presented here can be used as a height growth model component in forest growth and yield model systems. Study Implications: Improved site index equations for assessing the site quality of loblolly pine plantation and natural stands are now available to stakeholders at the policy, management, and operational levels. Activities such as forest management, restoration, and wildlife management, which require site quality data, will benefit from the new models. Furthermore, the approach of deriving panel data based on Forest Inventory and Analysis data offers information on developing and updating models for other species. Finally, the approach of this study, to use permanent plot measurement data in developing growth and yield models, is cost-effective and time-efficient.

Where are the large trees? A census of Sierra Nevada large trees to determine their frequency and spatial distribution across three large landscapes

Large trees (≥76.2 cm/≥30″ DBH) and especially very large trees (≥101.6 cm/≥40″ DBH) are key structures of Sierra Nevada forests for their ecological function, habitat, and carbon storage. Many of these trees have been lost to historic harvest and more recently to drought and wildfires. Understanding the current frequency and distribution of these large trees is essential to understanding their ecological contribution and management needs. We used airborne lidar to census large trees across three Sierra Nevada landscapes (cumulatively 396 K ha) in lower (dominated by ponderosa pine and mixed conifer) and upper (dominated by red fir) montane forest zones. We used data from a network of Forest Inventory and Analysis (FIA) plots to interpret our lidar-based results for large tree frequency, species, and ages. The lidar data identified > 8 M large and > 2.7 M very large trees, and their mean densities were similar to those from FIA data. Large portions of our study areas had either no or low densities (<20) of large trees per hectare. We found that large and very large tree concentrations were spatially aggregated with most in denser patches containing 20 to 50 + large trees per hectare. Depending on the study area, these often sizable (>1000 ha) patches of dense large trees can cover 20% to 40% of the landscape. (Patches of denser very large trees cover less of the landscape, typically 5% to 10%). However, these large patches are rarely simple blocks. Instead, they typically form complex amorphous matrices interspersed with patches of forests containing shorter trees or non-forest cover. Crucially, almost all large trees were in stands with high canopy cover, suggesting horizontal fuel continuity and low resilience to future wildfires. For lower montane large trees, canopy cover versus large tree density showed almost a unimodal response with canopy cover of 60% to 80% for locations with > 20 large trees per ha. For upper montane large trees, canopy cover versus large tree density showed a more linear relationship for all three study areas. High levels of canopy cover, especially for lower montane forests, suggest settings in which infilling following decades of fire suppression have created overly dense stands with lower resilience to drought and wildfire. Other studies have documented substantial recent losses of these large trees to both factors. The high canopy cover within which almost all large trees exist emphasizes the need for treatment almost everywhere that large trees are present for lower montane forests. This likely will require treatments both within the stands that contain large trees and across the landscapes in which they are found.

Temporal change in abundance of potential nesting cavities for wood ducks (Aix sponsa) in Northern Minnesota, United States

Wood duck females often nest in tree cavities located in large-diameter deciduous trees. Temporal changes in forest age and composition during recent decades may have influenced the abundance of stems with suitable nesting cavities in the forested portion of Northern MN, United States. Thus, we ascertained whether temporal changes in the abundance of such stems occurred in this area during six Forest Inventory and Analysis (FIA) evaluation cycles (i.e., 1977 to 2015–2019). We used FIA data and independently estimated proportions of stems with suitable cavities in different tree-species, health-status, and diameter classes to estimate change in the abundance of such stems at three spatial scales. These spatial scales increased in areal extent from a study area to three ecological subsections to the Laurentian Mixed Forest ecological province. We used a Bayesian analytical approach to accommodate changes in FIA protocols among cycles. Both the abundance of stems with suitable cavities and an attribute associated with cavity occurrence changed during the analysis period, but findings were not entirely consistent among spatial scales. The estimated abundance of both suitable stems overall and those of late-successional tree species generally increased at all scales during the analysis period. Since 1990, increases in the abundance of health-impacted stems that were suitable for nesting occurred at all spatial scales. Our estimated densities of suitable stems during the 2005–2009 (1.20 [85% CrI: 1.04–1.39] suitable stems / ha) and 2015–2019 cycles (1.41 [85% CrI: 1.21–1.63] suitable stems / ha) were substantially greater than the &amp;lt;0.25 and 0.25–0.49 suitable stems / ha another study predicted would occur in Northern Minnesota during 2008 and 2018, respectively. Our results can inform forest management decisions and research directions for follow-up studies of nesting wood ducks.

Evaluating Statewide NAIP Photogrammetric Point Clouds for Operational Improvement of National Forest Inventory Estimates in Mixed Hardwood Forests of the Southeastern U.S.

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.

Diversity-productivity relationships in forests of the southeastern United States: Leveraging national inventory data and tree functional traits

Numerous studies have evaluated the relationship between tree species diversity and productivity, with general trends suggesting a positive relationship. While most studies only use species richness, this study also analyzed how productivity changes with functional diversity and Shannon-Wiener's diversity index, to compare the impact of these different indicators of tree species diversity. This study used data from the USDA Forest Service Forest Inventory and Analysis (FIA) database collected in Alabama, Louisiana, and Mississippi to estimate plot-level productivity and diversity. Structural equation modeling was used to determine the strength of the relationship between different measures of tree species diversity and forest productivity directly and indirectly. This study found that species richness had the greatest influence on forest productivity, but it was largely an indirect effect mediated by stand density. Functional diversity did not have a substantial relationship with productivity while Shannon- Wiener's diversity index was negatively related to productivity when modeled with the other diversity indicators and mediated through stand density. The methodology of this research provides a framework for a similar analysis expanded spatially to other regions of the US to include more forest types for comparison of trends with the use of FIA data.

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.

Forest inventory and analysis data in action: Examples from eastern national forests

While many forestry practitioners and ecologists have a general understanding of the USDA Forest Service, 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 four case studies from National Forests in the eastern United States, to highlight a variety of applications of FIA data, though similar case studies could also be identified on private lands. These include informing a model to help managers decide where to invest in oak management, quantifying habitat characteristics as part of the Endangered Species Act listing process, developing focal species for forest monitoring, and assessing the health of the black cherry population. In three of the cases, collaboration between scientists and managers was the key to unlocking the power of the FIA database to address management questions without the expense of collecting additional field data. These case studies illustrate the utility of FIA data to meet managers’ information needs and the importance of the linkages between research and management and serve as examples of potential applications of data collected by regional or nationwide forest inventory programs.

United States Forest Service Use of Forest Inventory Data: Examples and Needs for Small Area Estimation

Forest Inventory and Analysis (FIA) data provides robust information for the United States Forest Service’s (USFS) mid-to-broad-scale planning and assessments, but ecological challenges (i.e., climate change, wildfire) necessitate increasingly strategic information without significantly increasing field sampling. Small area estimation (SAE) techniques could provide more precision supported by a rapidly growing suite of landscape-scale datasets. We present three Regional case studies demonstrating current FIA uses, how SAE techniques could enhance existing uses, and steps FIA could take to enable SAE applications that are user-friendly, comprehensive, and statistically appropriate. The Northern Region uses FIA data for planning and assessments, but SAE techniques could provide more specificity to guide vegetation management activities. State and transition simulation models (STSM) are run with FIA data in the Southwestern Region to predict effects of treatments and disturbances, but SAE could support model validation and more precision to identify treatable areas. The Southern Region used FIA to identify existing longleaf pine stands and evaluate condition, but SAE techniques within FIA tools would streamline analyses. Each case study demonstrates a desire to have FIA data on non-forested conditions and non-tree variables. Additional tools to measure statistical confidence would help maximize utility. FIA’s SAE techniques could add value to a widely used data set, if FIA can support key supplements to basic data and functionality.

Spatiotemporal dynamics and risk factors of oak decline and Mortality in the Missouri Ozarks of the United States based on repeatedly measured FIA data

During recent decades there has been widespread oak decline and mortality in the Missouri Ozarks, USA. We extracted data of 18,403 oak trees measured during 1999–2019 from the US Forest Service’s Forest Inventory and Analysis (FIA) database to examine the spatiotemporal patterns of oak decline and mortality and associated risk factors. The Missouri Ozarks were classified into three spatial clusters with low (<1.0%), moderate (1.0–1.5%) and high (>1.5%) annual oak mortality rates using kernel smoothing and the Jenks natural breaks method. Oaks within each spatial cluster were further divided into four, five and three risk groups respectively with differentiated annual mortalities (0.35%–3.05%) using a classification and regression tree model. The Kaplan-Meier survival analyses showed that oak trees from individual spatial clusters and risk groups responded differently to droughts, a major regional or sub-regional inciting factor of oak decline. Droughts could have a three to nine year lagged impact on tree mortality in the high risk groups, whereas had little effect on oak mortality in the low risk groups. Tree species, crown class, and stand age were the regionwide predisposing factors of oak decline. However, localized disturbances and geographic/vegetation conditions (e.g., drought, fire, insects, aspect, elevation, ecoregions, historical forest types) could either incite or alleviate the decline process and the fate of declining trees depending on the differentiated spatial clusters or risk groups. Resource managers can base associated risk factors and spatial clusters to rank risk levels to prioritize and plan management activities to mitigate oak decline and mortality.

Comparative species assessments of five-needle pines throughout the western United States

Five-needle white pine species provide important ecosystem services throughout the western U.S., and many of these species have become susceptible to stressors including warmer temperatures, insect epidemics, nonnative disease, and altered disturbance regimes. The objective of this study was to characterize recent broad-scale demographic patterns, including species abundance (i.e., numbers of individuals, tree density, size-class distributions, recruitment, growth rates, mortality rates, and causes of mortality, for the six species of five-needle pine that occur in the western US. We used the U.S. Forest Service’s Forest Inventory and Analysis (FIA) dataset, spanning greater than 10 years, to quantify demographic status and trends for each species. FIA data were compiled from a probabilistic sample design and consistent analysis framework that included not only the dominant community types of five-needle pines, but also all other forest community types, which have previously been demonstrated to encompass abundant regeneration of five-needle pine species. Our analysis revealed similar trends for whitebark and limber pines: both species exhibited increased levels of mortality that are occurring faster than growth of surviving trees, as well as abundant regeneration in forest types that are not dominated by five-needle pines. Although limber pine has experienced lower mortality rates than whitebark pine, it nonetheless showed signs of decline that are comparable to broad-scale indicators exhibited by whitebark pine 10 years prior. In contrast to whitebark and limber pines, Great Basin bristlecone and foxtail pine mortality rates were relatively low, and their populations exhibited a flat diameter distribution except for restricted recruitment from seedling to sapling size-classes. Our findings suggest that five-needle white pine species would benefit not only from increased seedling recruitment, but also from enhanced recruitment among older and larger age and size classes. Thus, it may be important to apply a variety of management strategies – including artificial regeneration with disease-resistant seedlings, direct seeding, and early intervention to decrease competition that may allow natural regeneration to achieve recruitment inter large size-classes – and also apply silviculture techniques to forest types that may be dominated by other species. The consistent monitoring conducted by FIA can allow future assessment of the demographic trajectory of each five-needle pine species, and thus can help prioritize management and restoration priorities, both within communities dominated by five-needle pines and in other community types that may be important targets for silvicultural interventions or restoration treatments.

Estimating Biomass and Carbon Storage by Georgia Forest Types and Species Groups Using the FIA Data Diameters, Basal Areas, Site Indices, and Total Heights

We present here an example of research into methodology of an estimation of carbon and biomass pools in forests using the USDA Forest Service, Forest Inventory and Analysis (FIA), data of the 1989 and 1998 surveys for Georgia forests, as relevant for comparison with other extremely highly-cited estimates of similar, but different, methodologies. Based on the derived estimates, we produce an example map of the biomass density and pools at a sub-county level resolution, which is based on spatially explicit simulations of the potential cover-type polygons implied by the FIA data with approximate plot locations. Our results include estimates of the biomass pools in the belowground biomass in roots, aboveground woody biomass in trees, and the biomass of foliage. We estimated the biomass densities and pools at a tree level using diameters and heights and previously published models, then propagated these results to the plot level using tree expansion factors, and then transformed these estimates to plot-dependent polygons using plot expansion factors. The plot-dependent polygons were spatially simulated using a simplified assumption of homogeneity of conditions surrounding each plot to the extent of the area defined by this plot’s expansion factors. The derived map provides a visual representation of the distribution of forest biomass densities and pools in the state of Georgia with distinctive patterns observed in various areas of urban development, federally owned forests, primary commercial forestland, and other land use areas. Coniferous forests with the highest total biomass density are located mostly in three regions: northern Georgia (Appalachian Highlands), the southern part of Piedmont, and the eastern part of Coastal Plain. Deciduous and mixed forests with the highest biomass density are concentrated mostly in the northern part of the state—especially in the Blue Ridge physiographic province, and in the western part of the East Gulf Coastal Plain. Counties with the highest biomass density were located primarily in the northern part of the state, while counties with the lowest density tended to be located in the Coastal Georgia area.

Insights into the restoration and sustainable management of Emory oak: A southwestern cultural keystone species

Emory oak acorns are a critically important commodity for Western Apache Tribal Nations, both as a food source and also for cultural and ceremonial uses. The Apache people historically inhabited much of Emory oak's range, but these lands are now divided among many entities, with the majority of trees occurring off-reservation in public domains. Based on observations of diminished acorn production and seedling recruitment, Apache Elders called for restoration and improved management of Emory oaks on public lands to ensure the long-term availability of acorns for Western Apache Tribes. Ecological information is critical to effectively design restoration efforts and successfully manage Emory oak, but little is known about Emory oak’s current ecological status on the landscape. Here, we use Forest Inventory and Analysis (FIA) data to examine factors that influence Emory oak distribution, abundance, and demography, and relate findings to observations made by Tribal Elders. We found that Emory oak distribution was constrained by metrics related to water availability. Drought and fire occurred frequently in Emory oak habitats, highlighting the likely importance of these disturbance events in determining oak population dynamics. Emory oak growth was predicted by environmental variables related to water availability, like physiographic class and elevation, and population characteristics, like stand age and size, but not by land ownership classifications. Interestingly, no environmental variables explained patterns of seedling recruitment and mortality perhaps owing to the stochastic nature of these events and the coarse temporal scale of FIA data in AZ. Drier, warmer conditions projected by climate models was shown to reduce future suitable Emory oak habitat, particularly in central AZ. Restoration efforts should focus on increasing water availability for Emory oaks and reducing the likelihood of high intensity fires. This could be accomplished, in part, by thinning competing vegetation at Emory oaks stands and restoring historical fire regimes. Given projections that suggest drier regional climatic patterns, strategic seed augmentation aimed at enhancing population resilience through selection of drought-adapted genotypes may also be warranted. Data were insufficient to confirm Apache Elders’ observations of diminished acorn production and seedling recruitment, highlighting the need for comprehensive demographic analyses of Emory oaks in the Southwest.

Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset

The forest volumes are essential as they are directly related to the economic and environmental values of the forests. Satellite-based forest volume estimation was first developed in the 1990s, and the accuracy of the estimation has been improved over time. One of the satellite-based forest volume estimation issues is that it tends to overestimate the large volume class and underestimate the small volume class. Free availability of the major satellite imagery and the development of cloud-based computational platforms facilitate an immense amount of satellite imagery in the estimation. In this paper, we set three objectives: (1) to examine whether the long Landsat time series contributes to the improvement of the estimation accuracy, (2) to explore the effectiveness of forest disturbance record and land cover data as ancillary spatial data on the accuracy of the estimation, and (3) to apply the bias correction method to reduce the bias of the estimation. We computed three Tasseled-cap components from the Landsat data for preparation of short (2014–2016) and long (1984–2016) time series. Each data entity was analyzed with harmonic regressions resulting in the coefficients and the fitted values recorded as pixel values in a multilayer raster database. Data included Forest Inventory and Analysis (FIA) unit field inventory measurements provided by the United States Department of Agriculture Forest Service and the National Land Cover Database and disturbance history data added as ancillary information. The totality of the available data was organized into seven distinct Random Forest (RF) models with different variables compared against each other to identify the ones with the most satisfactory performance. A bias correction method was then applied to all the RF models to examine the effectiveness of the method. Among the seven models, the worst one used the coefficients and fitted values of the short Landsat time series only, and the best one used coefficients and fitted values of both short and long Landsat time series. Using the Out-of-bag (OOB) score, the best model was found to be 34.4% better than the worst one. The model that used only the long time series data had almost the same OOB score as the best model. The results indicate that the use of the long Landsat time series improves model performance. Contrary to the previous research employing forest disturbance data as a feature variable had almost no effect on OOB. The bias correction method reduced the relative size of the bias in the estimates of the best model from 3.79% to −1.47%, the bottom 10% bias by 12.5 points, and the top 10% bias by 9.9 points. Depending on the types of forest, important feature variables were differed, reflecting the relationship between the time series remote sensing data we computed for this research and the forests’ phenological characteristics. The availability of Light Detection And Ranging (LiDAR) data and accessibility of the precise locations of the FIA data are likely to improve the model estimates further.

Using Invasional Meltdown Theory to Understand Patterns of Invasive Richness and Abundance in Forests of the Northeastern USA

Invasional meltdown hypothesizes that invasive species facilitate the establishment of subsequent invaders, with cascading consequences for ecosystem structure and function, including the extirpation of native species. However, meltdown has rarely been tested empirically with large regional data sets, leading to somewhat equivocal support. Recently, the USDA Forest Service initiated sampling of nonnative plants within a subset of their Forest Inventory and Analysis (FIA) plots, presenting an ideal dataset to evaluate meltdown and its consequences across large geographies. We used FIA data from 963 plots across 11 northeastern states to test the predictions of invasional meltdown and to examine the mechanism through which invasion impacts native tree seedling richness. Remeasured plots showed a significant increase in the number and cover of invasive species over time. We provide support for invasional meltdown, finding a pattern of accelerating invasive species richness over 4–5 y in plots with higher initial invasive species richness. Also, we found that plots with higher levels of invasive abundance corresponded to decreased native tree species richness at one point in time and that the magnitude of the relationship appeared to be stronger after 4–5 y. Our results suggest that evidence of invasional meltdown is more clearly evident when examining invasive plant species richness over time; further, invader abundance (measured as cover) is a better predictor of impacts than invader richness, which supports growing evidence that invasive abundance is an important driver of ecological impact.

Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data

The United Nations Framework Convention on Climate Change requires annual estimates for forestry and ecological indicators to monitor the change in forest resources, the sustainability of forest management, and the emission and sink of forest carbon. It is particularly important to update estimates of forestland area in a timely fashion and at flexible geographical scales, not only for its value in monitoring biological diversity at the ecosystem scale, but also because of its close association with other indicators such as forest biomass and carbon. However, in the US, the Forest Survey Handbook advises that the sampling error should not exceed 3% per 404686 ha (one million acres) of forestland area, a demanding standard barely met by pooling the Forest Inventory and Analysis (FIA) panel data measured in an inventory cycle of 5–10 years. Consequently, this study aims to propose and illustrate an updating procedure using data assimilation that integrates a design-based estimator with a model-based mixed estimator for updating annual estimates at two population levels, the state- and county-levels. The three states in the USA, Minnesota (MN), Georgia (GA) and California (CA), representing the Northern, the Southern and the Pacific Northwest FIA programs, constitute the study areas. FIA data collected were based on a 5-year inventory cycle for MN (2006–2010) and GA (2005–2009), and a 10-year cycle for CA (2001–2010). The total number of sample plots was 17764 for MN, 6323 for GA, and 16740 for CA. Distinguishing features attribute to this procedure include: (1) unbiasedness: the integration of design-based estimates into the mixed estimator introduces a favorable property – unbiasedness, which could be the property national forest inventories concern the most; (2) efficiency: considerable improvements in estimation precision greater than 55%, achieving sampling errors as small as those relying on using 5–10 years pooled FIA data; (3) time: compared with the temporal trends reflected by design-based estimates, the updated trends were of much smoother trend lines and narrower confidence intervals that would better depict temporal changes for a population at flexible spatial scales; (4) space: this procedure is scale-invariant, meaning its efficiency is not affected by an inventory employing either a large- or small-area estimation, which was demonstrated at the two population levels; and (5) generalizability: this procedure is unbiased and efficient, 100% compatible with the FIA database which is readily available to the public, and thus suitable for various official reporting instruments.

Community Classification of Piñon-Juniper Vegetation in the Four Corners Region, USA

Abstract Piñon-juniper is one of the most common vegetation types in the Four Corners states of the western United States (Arizona, Colorado, New Mexico, and Utah). Because of its high degree of community heterogeneity across the landscape, development of a more detailed and statistically supported classification system for piñon-juniper has been requested by regional land managers. We used a USDA Forest Service Forest Inventory and Analysis (FIA) data set from the Four Corners states to develop a statistics-based classification system for piñon-juniper vegetation. Cluster analysis was used to group piñon-juniper FIA data into community classes. Classification and regression tree analysis was then used to develop a model for predicting piñon-juniper community types. To determine which variables contributed most to classifying piñon-juniper FIA data, a random forest analysis was conducted. Results from these analyses support a six-class piñon-juniper community-type model within the Four Corners states. Using the classification tree, membership of FIA piñon-juniper communities can be accurately predicted (r2 = 0.81) using only relative overstory species abundance. Our dominance-based classification system was useful in classifying piñon-juniper community types and could be used in the field to identify broad community types and complement more refined tools available for stand-scale decisionmaking. Study Implications: Piñon-juniper vegetation communities commonly occur in the Four Corners region of the United States. We used a regional data set to develop a statistically based classification system for piñon-juniper communities. We found support for a dominance-based approach supporting initial classification into six community classes. Classes were based on different overstory species dominance patterns, stand structural characteristics (stand density index, basal area [square meters per hectare], trees per hectare, and stand age), and precipitation patterns (mean annual precipitation and monsoonal index) (Table S2). Community type can be predicted using relative overstory abundance to help managers prioritize regional areas (~6,000 acres [2,428 hectares]) for management and predict responses based on precipitation patterns, current understory tree regeneration, and plant community abundance. This system could lead to better planning documents and management decisions on a regional scale to complement more refined tools available for stand-scale management such as plant associations and detailed soil maps.

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.

Supporting National Forest System Planning with Forest Inventory and Analysis Data

AbstractIn 2012, the US Forest Service promulgated new regulations for land-management planning that emphasize the importance of scientifically credible assessment and monitoring strategies for adaptive forest planning and the maintenance or restoration of ecological integrity. However, in an era of declining budgets, the implementation of robust assessment and monitoring strategies represents a significant challenge for fulfilling the intent of the new planning rule. In this article, we explore opportunities for using data and products produced by the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program to support the implementation of the 2012 Planning Rule. FIA maintains a nationally consistent statistical sample of field plots that covers most national forests with hundreds of plots. We suggest that leveraging FIA data and products can generate efficiencies for assessment, planning, and monitoring requirements detailed in the 2012 Planning Rule, and help fulfill the adaptive intent of the new planning rule. However, strong national leadership and investment in regional-level analytical capacity, FIA liaisons, and decision-support tools are essential for systematically realizing the benefits of FIA data for forest planning across the National Forest System.

Mapping black ash dominated stands using geospatial and forest inventory data in northern Minnesota, USA

Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marsh.). In preparation, forest managers need reliable and complete maps of black ash dominated stands. Traditionally, forest survey data from the United States Forest Inventory and Analysis (FIA) Program have provided rigorous measures of tree species at large spatial extents but are limited when providing estimates for smaller management units (e.g., stands). Fortunately, geospatial data can extend forest survey information by generating predictions of forest attributes at scales finer than those of the FIA sampling grid. In this study, geospatial data were integrated with FIA data in a randomForest model to estimate and map black ash dominated stands in northern Minnesota in the United States. The model produced low error rates (overall error = 14.5%; area under the curve (AUC) = 0.92) and was strongly informed by predictors from soil saturation and phenology. These results improve upon FIA-based spatial estimates at national extents by providing forest managers with accurate, fine-scale maps (30 m spatial resolution) of black ash stand dominance that could ultimately support landscape-level EAB risk and vulnerability assessments.