Search Results (42)

National-scale forest inventories such as the Forest Inventory and Analysis (FIA) program in the United States are designed to provide data and estimates that meet target precision at the national and state levels. However, such design-based direct estimates are often not accurate at a smaller geographic scale due to the small sample size. Small area estimation (SAE) techniques provide precise estimates at small domains by borrowing strength from remotely sensed auxiliary information. This study combined the FIA direct estimates with gridded mean canopy heights derived from recently published Global Ecosystem Dynamics Investigation (GEDI) Level 3 data and Landsat data to improve county-level estimates of total and merchantable volume, aboveground biomass, and basal area in the states of Alabama and Mississippi, USA. Compared with the FIA direct estimates, the area-level SAE models reduced root mean square error for all variables of interest. The multi-state SAE models had a mean relative standard error of 0.67. In contrast, single-state models had relative standard errors of 0.54 and 0.59 for Alabama and Mississippi, respectively. Despite GEDI’s limited footprints, this study reveals its potential to reduce direct estimate errors at the sub-state level when combined with Landsat bands through the small area estimation technique. Full article

In this study, we present a methodology for predicting timber board foot volume using a forest landscape model, incorporating allometric equations and forest inventory data. The research focuses on the Ozark Plateau, a 48,000-square-mile region characterized by productive soils and varied precipitation. To simulate timber volume, we used the LANDIS PRO forest landscape model, initialized with forest composition data derived from the USDA Forest Service’s Forest Inventory and Analysis (FIA) plots. The model accounted for species-specific growth rates and was run from the year 2000 to 2100 at five-year intervals. Timber volume estimates were calculated using both quadratic mean diameter (QMD) and tree diameter in the Hahn and Hansen board foot volume equation. These estimates were compared across different forest types—deciduous, coniferous, and mixed stands—and verified against FIA plot data using a paired permutation test. Results showed high correlations between QMD and tree diameter methods, with a slightly lower volume estimate from the QMD approach. Projections indicate significant increases in board foot volume for key species groups such as red oak and white oak while showing declines toward the end of the model period in groups like shortleaf pine due to age-related mortality and regeneration challenges. The model’s estimates closely align with state-level FIA data, underscoring the effectiveness of the integrated approach. The study highlights the utility of integrating landscape models and forest inventory data to predict timber volume over time, offering valuable insights for forest management and policy planning. Full article

Forest disturbance is one of the main drivers of forest carbon flux change. How to accurately estimate the carbon flux caused by forest disturbance is an important research problem. In a previous study, the authors proposed a Grid-based Carbon Accounting (GCA) model that used remote sensing data to estimate forest carbon fluxes in North Carolina from 1986 to 2010. However, the original model was unable to track legacy emissions from previously harvested wood products and was unable to consider forest growth conditions before and after forest disturbance. This paper made some improvements to the original GCA model to enable it to track fluxes between all major aboveground live carbon pools, including pre-disturbance growth and growth of undisturbed forests, which were not included in the initial model. Based on existing timber product output (TPO) survey data and annual TPO records inversed from remote sensing data, we also worked to clarify the distribution ratios of removed C between slash and different wood product pools. Specifically, the average slash ratio for North Carolina was calculated from the difference between the C removed and the C flowing into the wood product as calculated from TPO survey data. County- and year-specific ratios were then calculated using the annual TPO records obtained from remote sensing and TPO survey data, dividing the removed remaining C into pools P1, P10, and P100, which were then applied to each 30 m pixel based on the county and year to which the pixel belonged. After compensating for these missing legacy emissions and adjusting forest growth rates from Forest Inventory and Analysis (FIA) data, we estimated a net carbon sink of 218.1 Tg of the flux associated with live aboveground biomass and harvested wood products from North Carolina woodlands over the 25-year study period (1986–2010). This estimate is close to the greenhouse gas emission and sink data provided by the U.S. Department of Agriculture for North Carolina and is comparable to estimates reported by several other studies. Full article

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. Full article

United States Army trucks and trailers use an estimated one million board feet (2381 cubic meters) of a critically endangered tropical hardwood, apitong (Dipterocarpus spp.), from southeast Asian rainforests, for wood decking annually. However, their purchasing specifications require the use of domestic hardwoods for decking, floorboards, and platforms. Several US hardwood species, including northern red oak (Quercus rubra), white oak (Quercus alba), hickory (Carya spp.), black locust (Robinia pseudoacacia), and sugar maple (Acer saccharum) could serve as viable substitutes. They have comparable strength properties to apitong, and there is an abundant and sustainable feedstock based on the United States Forest Service Forest Inventory Analysis (USFS FIA) database. The economic impact in New York State of manufacturing the decking panels in Onondaga County from three selected species: hickory, white oak, and black locust, was estimated using IMPLAN. The economic impact could be as high as $27 million, creating 128 full-time equivalent (FTE) jobs. Equally important to providing local and regional economic benefits, domestically sourced decking panels also contributes to the preservation of tropical rainforests, particularly when the entire decking market is considered (beyond the US Army), which includes wood decking consumption by other government agencies at various levels and the private sector. Full article

This study utilizes forest inventory and climate attributes as the basis for estimating models of wildfire risk and associated biomass loss (tree mortality) and then demonstrates how they can be applied in calculating CO₂ emissions related to the incidence of wildfires from U.S. forests. First, we use the full set of over 150,000 FIA plots of national forest inventory and climatic parameters to estimate models of the annual probability of wildfire occurrence and loss of live tree biomass. Then, maps of the spatial allocation of both the model-derived probability of wildfire occurrences and tree mortality are presented at the national level. The probability of wildfire occurrences and tree mortality were defined by a complex non-linear association of climatic conditions and forest ownerships, available aboveground biomass, and the age of the stand. Then, we provide an example of how these models can estimate potential CO₂ emissions from wildfires by using FIA inventory data. We estimated 6.10, 16.65, 22.75, and 31.01 million metric tons of annual CO₂ emissions with low, medium, high, and catastrophic combustion rates, respectively, from forests due to wildfire in the continental U.S. The wildfire risk and biomass loss due to tree mortality maps can be used by landowners, managers, public agencies, and other stakeholders in identifying high-risk wildfire zones and the potential CO₂ emissions. These equations can also help estimate fire risk and associated CO₂ emissions for future climate conditions to provide insight into climate change-related wildfire occurrences. Full article

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. Full article

Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and remote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r² = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of understory LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how forest change influences hydrologic processes. Full article

Accurately predicting the mortality of trees that initially survive a fire event is important for management, such as planning post-fire salvage, planting, and prescribed fires. Although crown scorch has been successfully used to predict post-fire mortality (greater than one-year post-fire), it remains unclear whether other first-order fire effect metrics (e.g., stem char) and information on growing conditions can improve such predictions. Droughts can also elevate mortality and may interact, synergistically, with fire effects to influence post-fire tree survival. We used logistic regression to test whether drought exposure, as indicated by summarized monthly Palmer Drought Severity Index (PDSI) over ten-years could improve predictions of delayed mortality (4–9 years post-fire) at the individual tree level in fire-affected forest inventory and analysis (FIA) plots in California (USA). We included crown scorch, bark thickness, stem char, soil char, slope, and aspect in the model as predictors. We selected the six most prevalent species to include in the model: canyon live oak, Douglas-fir, Jeffrey pine, incense-cedar, ponderosa pine, and white fir. Mean delayed mortality, based on tree count, across all FIA plots across all tree species and plots was 17%, and overall accuracy was good (AUC = 79%). Our model performed well, correctly predicting survivor trees (sensitivity of 0.98) but had difficulty correctly predicting the smaller number of mortality trees (specificity of 0.27) at the standard probability=0.5 mortality threshold. Crown scorch was the most influential predictor of tree mortality. Increasing crown scorch was associated with greater risk of delayed mortality for all six species, with trees exhibiting over 75% crown scorch having a probability of dying that exceeded 0.5. Increasing levels of stem char and soil char (first order indicators) were associated with increasing mortality risk but to less effect than crown scorch. We expected that greater drought exposure would increase delayed post-fire mortality, but we found that increasing drought exposure (median and minimum PDSI) was associated with a modest decrease in post-fire mortality. However, we did find that trees with high levels of crown scorch were less likely to survive with increasing drought exposure (median PDSI). Delayed mortality risk decreased as terrain slope increased. Taken together, our results suggest that trees with substantial crown damage may be more vulnerable to delayed mortality if exposed to drought and that crown scorch is an effective post-fire mortality predictor up to 10 years post-fire. Full article

Mass timber products are growing in popularity as a substitute for steel and concrete, reducing embodied carbon in the built environment. This trend has raised questions about the sustainability of the U.S. timber supply. Our research addresses concerns that rising demand for mass timber products may result in unsustainable levels of harvesting in coniferous forests in the United States. Using U.S. Department of Agriculture U.S. Forest Service Forest Inventory and Analysis (FIA) data, incremental U.S. softwood (coniferous) timber harvests were projected to supply a high-volume estimate of mass timber and dimensional lumber consumption in 2035. Growth in reserve forests and riparian zones was excluded, and low confidence intervals were used for timber growth estimates, compared with high confidence intervals for harvest and consumption estimates. Results were considered for the U.S. in total and by three geographic regions (North, South, and West). In total, forest inventory growth in America exceeds timber harvests including incremental mass timber volumes. Even the most optimistic projections of mass timber growth will not exceed the lowest expected annual increases in the nation’s harvestable coniferous timber inventory. Full article

Genetic diversity is essential because it provides a basis for adaptation and resilience to environmental stress and change. The fundamental importance of genetic variation is recognized by its inclusion in the Montréal Process sustainability criteria and indicators for temperate and boreal forests. The indicator that focuses on forest species at risk of losing genetic variation, however, has been difficult to address in a systematic fashion. We combined two broad-scale datasets to inform this indicator for the United States: (1) tree species occurrence data from the national Forest Inventory and Analysis (FIA) plot network and (2) climatically and edaphically defined provisional seed zones, which are proxies for among-population adaptive variation. Specifically, we calculated the estimated proportion of small trees (seedlings and saplings) relative to all trees for each species and within seed zone sub-populations, with the assumption that insufficient regeneration could lead to the loss of genetic variation. The threshold between sustainable and unsustainable proportions of small trees reflected the expectation of age–class balance at the landscape scale. We found that 46 of 280 U.S. forest tree species (16.4%) may be at risk of losing genetic variation. California and the Southeast encompassed the most at-risk species. Additionally, 39 species were potentially at risk within at least half of the seed zones in which they occurred. Seed zones in California and the Southwest had the highest proportions of tree species that may be at risk. The results could help focus conservation and management activities to prevent the loss of adaptive genetic variation within tree species. Full article

Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional, spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure for a selected North Carolina region, a 2.3 km² area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data, and two publicly available, open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD), are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative, widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large-scale assessments of woody biomass in the region. Full article

Federal land management agencies in the US are tasked with maintaining the ecological integrity of over 2 million km² of land for myriad public uses. Citizen science, operating at the nexus of science, education, and outreach, offers unique benefits to address socio-ecological questions and problems, and thus may offer novel opportunities to support the complex mission of public land managers. Here, we use a case study of an iNaturalist program, the Tribal Nations Botanical Research Collaborative (TNBRC), to examine the use of citizen science programs in public land management. The TNBRC collected 2030 observations of 34 plant species across the project area, while offering learning opportunities for participants. Using occurrence data, we examined observational trends through time and identified five species with 50 or fewer digital observations to investigate as species of possible conservation concern. We compared predictive outcomes of habitat suitability models built using citizen science data and Forest Inventory and Analysis (FIA) data. Models exhibited high agreement, identifying the same underlying predictors of species occurrence and, 95% of the time, identifying the same pixels as suitable habitat. Actions such as staff training on data use and interpretation could enhance integration of citizen science in Federal land management. Full article

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification. Full article

In temperate coniferous forests, biotic disturbances such as bark beetle outbreaks can result in widespread tree mortality. The characteristics of individual trees and stands, such as tree diameter and stand density, often influence the probability of tree mortality during a bark beetle outbreak. However, it is unclear if these relationships are mediated by climate. To test this, we assembled tree mortality data for over 3800 ponderosa pine trees from Forest Inventory and Analysis (FIA) plots measured before and after a mountain pine beetle outbreak in the Black Hills, South Dakota, USA. Logistic models were used to determine which tree, stand, and climate characteristics were associated with the probability of mortality. Interactions were tested between significant climate variables and significant tree/stand variables. Our analysis revealed that mortality rates were lower in trees with higher live crown ratios. Mortality rates rose in response to increasing tree diameter, stand basal area (both from ponderosa pine and non-ponderosa pine), and elevation. Below 1500 m, the mortality rate was ~1%, while above 1700 m, the rate increased to ~30%. However, the association between elevation and mortality risk was buffered by precipitation, such that relatively moist high-elevation stands experienced less mortality than relatively dry high-elevation stands. Tree diameter, crown ratio, and stand density affected tree mortality independent of precipitation. This study demonstrates that while stand characteristics affect tree susceptibility to bark beetles, these relationships may be mediated by climate. Thus, both site and stand level characteristics should be considered when implementing management treatments to reduce bark beetle susceptibility. Full article