The USDA Forest Service Forest Inventory and Analysis (FIA) program, as mandated by US Congressional legislation, provides statistically valid and unbiased estimates of forest characteristics across the United States and territories, which underlie a variety of analyses supporting ecological, economic, and policy needs across spatial scales. New allometric models have been implemented in the FIA program database based on a recent National Scale Volume and Biomass (NSVB) study for updated tree biomass and carbon estimates. Moreover, newer soil and litter carbon models with corresponding new estimates implemented in the FIA database, dependent on live tree biomass inputs. To understand effects of these updated models on forest carbon estimates, we compared estimates between previous and new (current) models. The most significant differences in total carbon (i.e., combined total carbon stock across various forest pools, including soil organic matter, litter, and live and dead tree biomass) were driven by larger estimates of soil organic carbon and smaller estimates of litter carbon. Overall, live tree merchantable volume, aboveground live biomass and carbon, and belowground live biomass estimates, were larger with the new models. Magnitude of differences varied by attribute, region, forest type, species, and tree sizes, as expected. Importantly, because previous models underestimated biomass in tops and limbs, there is now a higher estimated proportion of total tree biomass in these components and a lower proportion in the merchantable bole. We discuss some implications in the context of voluntary carbon accounting and forest carbon projects under the different registries and the potential influence of the new model system to timber market analyses and associated asset valuations.

Forest ecosystems play a crucial role in the global carbon cycle, acting as substantial carbon sinks and offering pathways for climate change mitigation and adaptation strategies, including greenhouse gas (GHG) emission offsetting and bioeconomic opportunities collectively referred to as Natural Climate Solutions (NCS). Over 100 forest carbon modeling experts, primarily from the US, were engaged through a Forest Carbon Modeling Group (FCMG) to identify and prioritize research needs, opportunities, and knowledge gaps for refining the application of NCS to meet a growing spectrum of GHG mitigation and adaptation strategies initially focused on US forests with possible applicability to other temperate/boreal systems. This engagement informed the development of a framework for forest carbon decision-making, which offers a scalable, hierarchical, and transdisciplinary approach that can address immediate research needs (e.g., regeneration modeling) while advancing critical, long-term scientific advances (e.g., lateral flux modeling) that aligns technology and model development with perspectives across users and sectors.

Sustainable forestry typically involves integration of several economic and ecological objectives which, at times, may not be compatible with one another. Multi-objective prioritization via harvest scheduling programs can be used to elucidate these relationships and explore solutions. One such program is a spatially explicit harvest scheduler that adopts the Metropolis-Hastings algorithm to iteratively find management solutions to achieve multiple objectives (Habplan). Although this program has been used to address forest management scheduling and simulation-based tasks, its utility is constrained by time-intensive data preparation and challenges with incorporating spatial configuration objectives. To address these shortcomings, we introduce an open-source software package, HabplanR, streamlines data preparation, sets parameters, visualizes results, and assesses spatial components of ecological objectives. We developed four example objectives to incorporate into a multi-objective management problem: habitat quality indices for three species “types” (open, closed, and intermediate-canopy-associated species), and harvested pine pulpwood (revenue). We demonstrate the utility of this package to find management schedules that can accommodate potentially conflicting habitat needs of species, while achieving economic targets. We produced 100 software runs and prioritized individual objectives to select four management schedules for further comparisons. We compared outcome differences of the four schedules, including a spatial comparison of two high performing schedules. The software package makes costs and benefits of different schedules explicit and allows for consideration of the spatial configuration of management outcomes in decision-making.

The Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture, Forest Service conducts the national forest inventory of the United States.Although FIA assembles a myriad of forest resource information, many analyses rely on the fundamental attributes of tree volume, biomass, and carbon content.Due to the chronological development of the FIA Program, numerous models and methods are currently used across the country, contingent upon the tree species and geographic location.Thus, an effort to develop nationally consistent methods for prediction of tree volume, biomass, and carbon content was undertaken.A key component of this study was amassing existing data in conjunction with collection of new data to fill information gaps related to tree size and species frequency and spatial distributions.These data were used in a modeling framework that provides compatible predictions of tree volume, biomass, and carbon content across the entire United States.National-scale comparisons to currently used methods show that only a small increase in volume occurs, but substantial increases in biomass and carbon are realized due to relatively large increases in predicted tree top/limbs biomass and carbon.Changes in tree carbon were also affected by use of newly developed species carbon fractions instead of the current constant conversion factor of 0.5.Examples of the calculations required to predict tree volume, biomass, and carbon content for commonly encountered tree conditions provide step-by-step implementation details.

A land use history of intensive agricultural practices, combined with highly erodible soils, have made erosion control and water quality protection essential for forest management in the Piedmont and Upper Coastal Plain of the southeastern U.S. Forestry Best Management Practices (BMPs), which include sustainable harvesting guidelines approved by state forestry agencies, are utilized in these regions to mitigate potential impacts to water quality. In this study, the relationship between three levels of BMP implementation ranging from < 80% (BMP-), 80-90% (BMP-standard) and > 90% (BMP+ ), estimated erosion (Mg ha-1 yr-1) based on the USLE-Forest empirical model, and sediment delivery ratios was explored to better understand impacts to forest water qual-ity. Erosion and sediment delivery estimates by BMP level were multiplied by estimates of area occupied by each operational feature (e.g., decks, harvest areas only, haul roads, skid trails, stream crossings) and annual clearcut timberland (ha-1 yr-1) provided by the U.S. Forest Service FIA Program. The overall goal was to estimate total annual masses of erosion and sediment (Mg yr-1) by BMP level for twelve states with timberland located in either the Piedmont or Upper Coastal Plain. Forty-four clearcut harvest sites were assessed in the region. Over 431,000 ha yr- 1 are clearcut in the Piedmont and Upper Coastal Plain, which represents approximately 2% of total forestland area in the regions. The BMP+ (0.5 Mg ha-1 yr-1) and BMP-standard (0.9 Mg ha-1 yr-1) levels of implementation provide considerably more water quality protection than BMP- (2.0 Mg ha-1 yr-1) based on average sedimentation rates for the overall harvest. Overall sediment removal efficiencies at the BMP-standard (54%) and BMP+ (74%) levels indicate that current BMPs are highly effective at mitigating potential sediment delivery from clearcut operations in the regions. The BMP+ level most efficiently limited potential sediment from skid trails (80%), harvest areas (75%), and haul roads (61%), which were also the primary contributors of sediment. Over 93% of clearcut harvests sampled were classified as either BMP-standard or BMP+. These findings indicate that current BMPs are implemented at high levels across the southeastern Piedmont and Upper Coastal Plain, and that BMP effectiveness has improved substantially over time in the regions.

Companies that produce and use wood for products and energy find it increasingly important to communicate the carbon balance and potential climate effects of these activities. Computing forest carbon stocks and stock changes, and emissions from operations, are often part of institutional reporting for environmental, social, and governance purposes. This article describes an example methodology to assess forest carbon changes associated with the harvesting of wood products and proposes metrics that could be used to allocate harvesting effects to individual organizations for their reporting purposes. We discuss boundaries (types of forests and carbon pools to include), spatially appropriate evaluations given uncertainty, temporal considerations, risk of reversals, and allocation of net sequestration to products sourced from the region. We also discuss the complex nature of the biogenic carbon cycle and warn about the appropriate interpretation of this methodology. Study Implications: Purchasers of wood products are increasingly interested in the carbon effects of the wood they purchase. For example, are the forests from which this wood was harvested continuing to sequester carbon or are they in decline? One means of communicating this information would be a carbon accounting factor that expresses the net forest carbon change per unit of wood consumed. We describe an approach to develop such a factor and report results for regions of the conterminous United States. However, any single metric is unlikely to fully capture the carbon dynamics of wood sourcing, as illustrated by the carbon stock declines in the Rocky Mountain regions that cannot be attributed to forest harvesting or the very high factors for the Great Plains due to low harvest levels. We discuss several other metrics that can shed additional light on land carbon resiliency and land-use efficiency and could be considered in conjunction with net carbon stock change.

COPYRIGHT © 2023 Morin, Healey, Prisley, Randolph, Westfall and Gray. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Monitoring and responding to global change to promote resilient and productive forests through innovative forest inventory

The drivers of individual landowners' adoption of conservation easements have been well-studied. However, the role and relative predictive power of drivers at the community, rather than individual, scale have not. This study employs diffusion of innovations theory to examine easement adoption in Virginia at the community scale, using geospatial analysis as well as surveys and interviews with easement practitioners. Geospatial modeling results suggest that community-level easement likelihood can be predicted well, but community-level predictors differ from typical individual-level predictors. The literature suggests that easement adopters are typically wealthier, more educated, and less land-dependent. The communities containing easements in our study were generally less wealthy, less educated, and more economically dependent on the land. Data collected from practitioners highlighted the importance of community-scale forces in predicting patterns of easement adoption, including community cohesion, aspects of local land-use planning, and the influence of change agents and opinion leaders.

National Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.

ABSTRACT Collecting spatially explicit locations of individual animals often is an important part of the study of habitat use. Obtaining accurate locations without disturbing an individual can be difficult for small species and may be limited for species of conservation concern, such as piping plover ( Charadrius melodus ), where a close approach is undesirable because of the potential for disturbance. To reduce disturbance while estimating an animal's location, an observer can collect their location using Global Positioning System (GPS) and offset that position using a distance and azimuth. Typically, error is not considered when these locations are determined, despite the potential effects of inaccuracy on habitat association results. Therefore, our objectives were to quantify potential error using the offset method and then evaluate how that error may manifest. During the plover breeding season in 2017 (Apr through Sep), we tested the error of Trimble GPS units compared with benchmark locations to assess the accuracy of locations derived from these units. We then assessed the error associated with offset locations of a small target using Trimble GPS units, laser rangefinders, and 2 compass types. Finally, we determined the potential consequence of unaccounted error in our system by comparing the difference between point locations and land‐cover data using our estimated error as a point buffer. Average error of the GPS units at benchmark locations was 0.95 m. The mean error of the offset locations increased with increasing observer distance from the decoy location (from = 2.9 m to 7.6 m at distances of 20 m and 100 m, respectively). Error also increased with increasing error in the distance and azimuth measurements, and was greater using a digital GPS compass as compared with a handheld magnetic compass corrected for magnetic declination. In addition, potential misclassification of land‐cover type increased with increasing potential location error. When modeling animal locations, the error of point locations using this method, especially for land‐cover at the point location, should be accounted for using appropriately sized position buffers. Using this location collection method, we can increase our knowledge and study of conspicuous species to ensure that we consider habitats used throughout the species' life stages. © 2020 The Wildlife Society.

In the last century, mobile pastoralists around the world have transitioned to more sedentary lifestyles. Traditionally mobile people can be both pushed to settle by environmental or political forces, and pulled by new economic activities. While researchers have examined the causes and consequences of growing sedentarization, few contemporary studies have focused on the spatial patterns of settlement. This study examines settlement site selection using GIS and remote sensing techniques to quantify patterns and correlates of settlement location in four Maasai communities in northern Tanzania. We identify landscape scale factors that shape settlement locations and test the competing hypotheses that settlement is associated with: (1) resource access; (2) environmental constraints; and (3) infrastructural amenities. Spatial models offer support for each hypothesis, with slight variations. However, a combined model offers the greatest predictive power suggesting significant heterogeneity in site selection and/or a transition in selection criteria over time. These findings characterize a poorly understood aspect of the settlement of mobile groups, and point to new questions regarding the spatial drivers and consequences of sedentarization.

Lower-value biomass (LVB) in forests constitutes non-commercial material traditionally left on site following harvesting. Emerging markets for energy and bioproducts have increased incentives to harvest and utilize this material in some cases. Removal of LVB can reduce forest health risks stemming from wildfire, diseases, and pests but has raised questions about effects on forest productivity and environmental sustainability. The status, trends, and quantities of forest biomass are most often estimated from forest inventories but socioeconomic and physical factors limit the quantity of residual biomass available for use. National forest inventory data suggest quantities of LVB are likely substantial, with annual timberland growth exceeding harvest removals in all states where data are available and unutilized dead biomass roughly half that of current harvest removals. Cost-effective transport distance and logistical factors limit the practical availability of LVB, with in-woods chipping as part of conventional harvesting operations generally providing the greatest economic returns. Harvesting LVB removes higher quantities of nutrients from forested sites with greater potential impact on soil physical properties, soil carbon, and forest productivity than traditional, stem-only harvesting. However, global assessments show inconsistent forest productivity and soil responses and impacts across contrasting sites and soil types. Lower residue retention and changes in forest structure resulting from LVB harvesting can also influence habitat for some wildlife species. Some field studies suggest removals of downed coarse woody debris negatively impact bird diversity and abundance while other assessments suggest minimal or only temporary effects with benefits to other species, particularly those associated with early seral habitat. While LVB harvesting and residue removal have the potential to increase soil disturbance and delivery of sediment and nutrients to streams compared to traditional harvesting, best management practices demonstrated effective in protecting water quality through decades of field research should be largely applicable to practices that include LVB harvesting. Although states and other entities have developed biomass harvesting guidelines and revised certification standards that restrict or modify intensive harvesting practices, the efficacy of such guidelines is uncertain due to highly variable site limitations and responses and a lack of site-specific response data. This paper provides an overview of environmental implications and practical considerations related to harvesting LVB in forests and how they differ from harvesting of traditional, commercial biomass sources.

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95±0.45Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17±0.06Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87±0.49Gt) overestimated the national ground-based estimate by 7.5%. The comparable log-linear model result (63.29±1.36Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log-linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon, −40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., >2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10±0.88t/ha, and the total biomass for the country, given a total forest area of 688,096km2, is 3.65±0.06Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06Gt) by 19%.

Approximately one-third of Wisconsin loggers left the industry in recent years, prompting concerns about logging capacity. In addition, research suggests seasonal barriers to timber harvesting in the state, which may result in seasonally low utilization of remaining capacity. A 1-year study of logging capacity utilization was conducted in Wisconsin beginning in late September 2014. Thirty participating loggers provided weekly reports describing number of loads delivered; hours worked; and number of loads lost due to weather, breakdowns, and other reasons. Logging capacity utilization was calculated by dividing loads delivered by the sum of loads delivered and loads lost. Logging efficiency was calculated using stochastic frontier analysis (SFA). For the SFA model, the output variable was delivered loads per week, input variables were man-hours worked and capital invested per week, and six environmental variables were included in the model. Logging capacity utilization averaged 72% when spring break-up shutdowns were excluded and 64% when these weeks were included. The primary causes of lost production were weather (11.5% reduction in delivered loads) and equipment breakdowns (3.8%). Logging efficiency averaged 64%, as measured by stochastic frontier analysis, with mechanized crews significantly more efficient than chainsaw crews (p < 0.01). Productivity was highest during winter (p < 0.01) and efficiency was higher during winter than summer (p < 0.01). This study suggests that adequate logging capacity exists to support current forest industry demand. Adequate capacity also exists to increase production outside the winter months; however, weather-related downtime and restrictions on timber sales make this difficult.

Nonindustrial private forest (NIPF) landowners own 62 percent of Virginia's forestland and determine the likelihood of its harvest and utilization. As many studies have found, NIPF landowners are diverse in management goals, and many factors can affect a landowner's willingness to harvest. Although many landowner surveys have been conducted, adequate information regarding the characteristics of NIPF landowners and their willingness to harvest in Virginia is lacking. Given new markets for wood in the state, there is considerable interest in examining fiber supply availability to determine the sustainability of an expanded forest and renewable energy industry. Landowners and their willingness to supply timber play a vital role in future resource availability. During 2014, a survey was mailed to 3,000 NIPF landowners who owned at least 10 acres of wooded land. Using a base question of willingness to harvest, groups across the state were compared to determine factors that affect their behavior. We found that the variables income, age, forested acres owned, and forest management were all significant and positively related to willingness to harvest. Knowledge of the characteristics of forest landowners and their attitudes toward harvesting can guide efforts to engage more landowners.

The ability to automatically delineate forest stands and determine their age is useful for natural resources professionals. Two common approaches to estimating forest area and age-class distributions are inventory -based methods, such as Forest Inventory and Analysis (FIA), and remote sensing based methods. Vegetation Change Tracker (VCT) is an algorithm that uses time series stacks of Landsat images to identify forest disturbances. However, additional computation is required to identify type of disturbance. This paper evaluates the usefulness of machine learning tools, such as support vector machine (SVM), for reclassifying VCT disturbances as stand -clearing disturbances or partial disturbances. Overall accuracy for a 2010 VCT disturbance map of the entire state of Virginia was determined to be 87 percent. 100 percent of 2010 Virginia clearcut harvests recorded in a reference dataset were classified as disturbances by VCT. Neighboring disturbed pixels, as classified by VCT, were clumped together and reclassified as stand -clearing disturbances or partial disturbances using SVM and variables for average disturbance magnitude and shape and size metrics of the clumped pixels, with an overall accuracy rate of 86 percent. The users and producers accuracy rates for stand -clearing disturbances were 88 percent and 95 percent respectively. In addition, an algorithm was developed in R for determining years since last stand -clearing disturbance for each pixel in a time series stack of reclassified VCT disturbance maps from 1984 to 2011. Neighboring pixels of the same age, in number of years since last stand -clearing disturbance, were clumped together and correspond, in general, to clearcut harvest boundaries.

Lyme disease is the United States' most significant vector-borne illness. Virginia, on the southern edge of the disease's currently expanding range, has experienced an increase in Lyme disease both spatially and temporally, with steadily increasing rates over the past decade and disease spread from the northern to the southwestern part of the state. This study used a Geographic Information System and a spatial Poisson regression model to examine correlations between demographic and land cover variables, and human Lyme disease from 2006 to 2010 in Virginia. Analysis indicated that herbaceous land cover is positively correlated with Lyme disease incidence rates. Areas with greater interspersion between herbaceous and forested land were also positively correlated with incidence rates. In addition, income and age were positively correlated with incidence rates. Levels of development, interspersion of herbaceous and developed land, and population density were negatively correlated with incidence rates. Abundance of forest fragments less than 2 hectares in area was not significantly correlated. Our results support some findings of previous studies on ecological variables and Lyme disease in endemic areas, but other results have not been found in previous studies, highlighting the potential contribution of new variables as Lyme disease continues to emerge southward.

Data from the Forest Inventory and Analysis program (including the Timber Products Output portion) are critical for assessing the sustainability of US timber production. Private sector users of this information rely on it for strategic planning, and their strong support of the FIA program has helped to ensure funding and program viability. Non-timber forest products harvested from US forests also play a critical economic and social role, yet much less is known about their abundance, spatial distribution, and trends. Recent research has demonstrated that FIA data can provide important insights into the status of NTFPs from trees measured in Phase II plots. However, there are several shortcomings that prevent the widespread use of FIA data for evaluation of many other NTFPs. These shortcomings include: (1) lack of data on non-tree (typically understory) species of importance, (2) traditional forest inventory measurements that are unrelated to non-timber products (roots, sap, seeds and cones, bark, boughs, etc.), (3) lack of data on harvest and trade of non-timber forest products. Efforts to overcome these challenges in order to enhance the value of information for NTFP assessment might include: (1) identifying minor alterations to data collection protocols (perhaps on phase III plots), (2) conduct research that relates production of NTFPs to tree/plant measurements (e.g., estimation of bark or nut yield based on tree or plot measurements), (3) collecting data on NTFP abundance and distribution that would support modeling of likely occurrence, (4) extending the TPO data collection to survey non-timber forest product markets. We suggest that considering the costs and benefits of these and other options is the first step in expanding the value of the FIA program for NTFP assessment.