|Kate Marcille||TPO-Plus: A Western Value-added Perspective||Tuesday||3a||Sequoyah||Pusing timber producsts data in new directions||The western TPO-plus approach allows more powerful analyses of forest industry activity, timber harvest and the utilization of wood fiber to be conducted than the base TPO data alone. Collecting detailed information on the forest products industry in the west addresses a host of forest economic and policy questions, augments land management decision-making tools, helps National Forest System meet planning needs and enhances various collaborative forest-related research projects.||The FIAs Timber Product Output (TPO) program characterizes timber removals. For the western states, the University of Montanas Bureau of Business and Economic Research (BBER) conducts mill surveys, forest industry analyses and logging utilization studies. In addition to collecting the base data for the national FIA-TPO database, BBER maintains broader information on the forest products industry. This TPO-Plus approach adds value by providing a more complete accounting of harvested wood and enabling clients and data users to answer a variety of questions about timber flow and utilization. Western data are used by public land managers to guide strategic decision-making and meet National Forest System (NFS) planning needs. The TPO-Plus approach has: helped define economic impact areas; informed NFS transaction evidence appraisal systems; parameterized harvested wood products (HWP) carbon models; and linked mill infrastructure to forest restoration planning at various scales. TPO-Plus allows for applied research beyond tracking harvest volume and the product mix of utilized wood fiber, thus providing important information for forest-related policy and management decisions across the western United States.|
|Consuelo Brandeis||Pulpwood Production- An Analysis of Pulpmill Capacity and Feedstock Changes||Tuesday||3a||Sequoyah||Pusing timber producsts data in new directions||Indexed pulpwood production was used to assess shifts in consumption occurring along with pulpmill closures over the past decade. The index, based on 2006 volumes, shows an overall decline in mill residue use (close to 40 percent lower in 2016 compared to 2006) and an increase in roundwood use (close to 7 percent). Likewise, hardwood feedstocks declined while softwood volumes remained above 2006 levels. Most southern states followed similar trend patterns, but change magnitudes differed notably||Pulpwood is one of the primary uses for harvested volume across the U.S. However, changing market conditions have resulted in pulpmill closures and consequent fluctuations in volumes of pulpwood production. Annual pulpwood production data, gathered by the USDA Forest Service Forest Inventory and Analysis program, were used to analyze shifts in pulpwood consumption occurring along with pulpmill closures over the past decade. As preliminary analysis, southern feedstock production volumes were indexed using as reference volumes observed in 2006 (i.e. index=100 for 2006). Comparing index values across time revealed an overall decline in mill residue use (close to 40 percent lower in 2016 compared to 2006) and a slight increase in roundwood use (close to 7 percent increase). Likewise, hardwood feedstocks declined while softwood volumes remained above 2006 levels. Most southern states followed similar trend patterns, but change magnitudes differed notably. Research examining these trends and patterns at the state and county level is underway.|
|Nidia Panti||Mill Dynamics: Exploring mill entry and exit patterns in the Southern U.S.||Tuesday||3a||Sequoyah||Pusing timber producsts data in new directions||This research is being conducted as part of a Master of Forestry thesis and is funded by the US Forest Service under the Forest Inventory and Analysis (FIA) Timber Products Output (TPO) program. Entry and exit patterns of primary wood-using mills was studied using historical TPO mill information. A survival analysis (time to event analysis) was used to determine the survival of mills controlling for identified internal and external factors.||Timber is the most valuable commercial commodity taken from most forests. The US South produces approximately 60% of the Nations timber products, where the majority is obtained from private forests. U.S. mills were studied using information from the US Forest Service, Forest Inventory and Analysis (FIA) Timber Products Output (TPO) program survey of primary wood-using mills. Surveys were conducted biennially from 2005 to 2015 in 12 southern states and participants included all primary mills varying from sawmills, veneer mills, poles and post production mills. This historical TPO mill information was analyzed using a time to event analysis (survival analysis), controlling for size and other internal and external factors likely affecting its survival. Variables included plant size, plant structure (single-firm or multi-firm), mill consumption capacity, county demographics, etc. In terms of size and structure, studies have shown that larger plants are less likely to close while multi-firm plants are more likely to close. Competition is also another factor which has shown a positive influence on plant closure. We also studied how the changes in mill numbers and distribution affect wood procurement patterns. Studies indicate that plan|
|Brett Butler||Wood Supply Assessment Using FIA Plot and Landowner Survey Data||Tuesday||3a||Sequoyah||Pusing timber producsts data in new directions||Of the nearly 13 billion cubic feet of wood annually harvested across the U.S., 89% comes from private forests. This presentation will combine FIA plot and landowner survey data to explore the private ownerships from which this timber is originating in term of detailed ownership types, size of forest holdings, and other attributes.||Although timber supply is a common topic of many studies, there is a surprising lack of information on exactly where the timber is coming from. We know, based on FIA plot data, that there are nearly 13 billion cubic feet of wood annually harvested across the USA with 3% of this wood coming from National Forests, 8% from other public forests, and the remaining 89% coming from private forests. But we do not know much more about the private ownerships from where this wood is originating. By combining data from the FIA plot and landowner survey programs, we will provide new insights to the origins of timber supplied in the USA including detailed information on ownership types, size of forest holdings, and other topics. For example, preliminary results from this analysis show that 47% of the total timber supply comes from family forest lands. This work will also be able to quantify the timber being supplied from lands owned by timber investment management organizations (TIMOs) and real estate investment trusts (REITs). Information such as this is important to those who rely on this timber supply and those designing policies that influence it.|
|Esther Parish||Use of FIA datasets to analyze effects of wood-based bioenergy production||Tuesday||3a||Sequoyah||Pusing timber producsts data in new directions||FIA data from 1978-2017 were analyzed for changes in two forested landscapes supplying biomass to SE US bioenergy wood pellet mills, including total forest and timberland area, growth and removal ratios, proportion of planted vs. naturally regenerating area, proportion of softwood vs. hardwood area, number of snags, stand size distributions, and carbon storage at three spatial extents: supply areas delineated with 35-mile, 50-mile, and 75-mile procurement distances around each major pellet mill.||FIA datasets gathered from 1978-2017 were used to estimate changes in two forested landscapes supplying biomass to SE US pellet mills. These fuelsheds supply over half of US industrial wood pellet exports to an established bioenergy market. The Chesapeake fuelshed supplies the port of Norfolk, VA with biomass from VA and NC timberlands, and the Savannah fuelshed supplies the port of Savannah, GA with biomass from GA and SC timberlands. We analyzed trends in total forestland and timberland area, growth and removal ratios, proportion of planted versus naturally regenerating area, proportion of softwood versus hardwood area, number of snags per hectare, stand size distributions, and tons of carbon stored in three different biomass pools. Because TPO data indicate that roundwood procurement distances for pellet production have increased over time, the analyses were conducted for three spatial extents: fuelsheds delineated with 35-mile, 50-mile, and 75-mile procurement distances around each major pellet mill operating as of April 2018. Results of the analyses indicate little sensitivity of the indicators to fuelshed size and no significant impacts of export bioenergy wood pellet production on SE US timberland health as of 2017.|
|Siriluck Thammanu||Assessment of Forest Biodiversity in Providing Non-Timber Forest Products and Effects of Environmental Factors on Tree Species in Deciduous Forests for Community Forest Management in Northern Thailand||Tuesday||3a||Sequoyah||Pusing timber producsts data in new directions||This study aims to assess the forest biodiversity as well as investigate the relationship between tree species and environmental factors in the Ban Mae Chiang Rai Lum Community Forest, Northern Thailand. The inventory of the subject forest area yielded a total of 18,555 stems encompassing 197 species, 144 genera and 60 plant families. The Shannon diversity index was 2.486. The CCA analysis showed that environmental factors had significant effects on 129 tree species (p < 0.05).||This study aims to assess the potential of forest biodiversity in providing NTFPs as well as investigate the relationship between tree species and environmental factors in the Ban Mae Chiang Rai Lum Community Forest, Northern Thailand. 0.1 % of the total forest inventory was sampled using the stratified systematic sampling method. Twenty-five 40m x 40 m (0.16 ha) square plots were established in an area of 3,925 ha. Species IVI values and environmental factors were evaluated the ecological gradient of vegetation using a Canonical Correspondence Analysis (CCA). The inventory of the subject forest area yielded a total of 18,555 stems encompassing 197 species, 144 genera and 60 plant families. The Shannon diversity index was 2.486. As NTFPs, 160 of these species have been classified as having medicinal uses, 89 species are used as food, 37 species as extractives, 32 species as fuelwoods and 12 species as fibers. The CCA analysis clearly showed that environmental factors had significant effects on 129 tree species in the community forest (p < 0.05). Organic matter, soil moisture, elevation, distance to streams and distance to communities were the most important factors explaining the species composition and distribution.|
|John Coulston||Overview of timber products monitoring: recent changes and applications||Tuesday||3b||Sequoyah||Advances in timber products monitoring||The timber products monitoring component of the FIA program is shifting to an annual effort. Here we provide an overview of the statistical design and provide relevant examples of how annual information can enhance reporting efforts in the Western United States.||The Advances in timber products monitoring session aims to highlight key technical changes to how the estimates of roundwood consumption and production are constructed. The annual timber products design is based on an innovative stratified simple random sample approach that approximates probability proportional to size design. We will review this design to provide context for subsequent presentations in the session. Given the programmatic thrust to shift to an annual sample based design we will also provide examples of some assessment applications that can be enhanced with annual timber products estimates. Our examples will focus on application in the Western United States.|
|James Westfall||Estimating change in annual timber products output using a stratified sampling with certainty design||Tuesday||3b||Sequoyah||Advances in timber products monitoring||Estimation of change is a key output of the TPO monitoring program in the U.S. Approaches to estimating the covariance between successive samples were evaluated, where often only a single sample unit occurred in both samples within a stratum. While the covariance estimation methods performed poorly, treating the samples as being independent provided results that were consistent with the Monte Carlo simulation variance. This outcome was partially due to some strata being sampled with certainty.||The national timber products output (TPO) monitoring program in the U.S. is adopting a stratified sampling approach to be conducted annually. Estimation of change from year-to-year is necessary, but is complicated due to shifts in the population as well as changing strata over time. In this study, various approaches to estimating the covariance between successive samples were evaluated. A primary challenge was that often only a single sample unit occurred in both samples within a given stratum. The result that none of the covariance estimation approaches performed adequately was largely overshadowed by the outcome that treating the samples as being independent provided an overall variance estimate that was very consistent with the Monte Carlo variance obtained via simulation. It is proposed that this outcome was a derivative of the sampling design, which included some strata that were sampled with certainty. Due to the complexities introduced through changes in populations and strata over time, being able to treat the samples as independent is very beneficial because it avoids the need to introduce complex covariance calculations into the estimation process.|
|Christopher Edgar||Alternative measures of size and sample-with-certainty thresholds in monitoring of timber production in the Lake States||Tuesday||3b||Sequoyah||Advances in timber products monitoring||A new sample design for annual monitoring of timber production is currently being implemented in the Lake States. We examine two key areas of the new sample design: the selection of an effective measure of size used in constructing strata and the identification of a threshold value for allocating mills into sample-with-certainty strata. We discuss the efficiency of the new design, implementation considerations, and potential areas for further research.||The Forest Inventory and Analysis program is implementing a new sample design for annual monitoring of timber production in the Lake States and other regions of the United States. We examine two key areas of the new design: the selection of an effective measure of size used in constructing strata and the identification of a threshold value for allocating mills into sample-with-certainty strata. Precision of estimates can be increased by using measures of size that are more highly correlated with the variable of interest. We review the availability of mill profile information in Minnesota, Michigan, and Wisconsin and discuss the strength of relationships of candidate measures of size and timber production. When sampling skewed populations, a few large units may account for a large portion of the total. We examine different approaches to allocating mills to sample-with-certainty strata and the impacts on precision of the estimates. We conclude the presentation with general discussion of the efficiency of the new design, implementation considerations, and potential areas for further research.|
|Erik Berg||Western region TPO annual sampling- first year experience||Tuesday||3b||Sequoyah||Advances in timber products monitoring||To guide TPO annual sampling plans, University of Montana staff simulated sampling of active mills in 11 western states. The national TPO R program was used to select mills to sample; outputs were post-processed for nonresponse. Differences in FIA TPO censused (the true volumes) and sample-predicted timber volumes varied from 0.44 to 4.73 percent and standard errors varied from 0.10 to 4.77 percent by state. Accounting for nonresponse was the most critical component of this work.||Annual mill sampling has been added to the suite of FIA TPO services, such as the periodic censuses of facilities, to provide stakeholders timely estimates of received roundwood and mill residue volumes. To help guide future sampling plans University of Montana (UM) staff developed non-replicated simulations of sampling protocols for 11 western states. Our goal was to identify protocols which minimized differences in state-level received timber volumes between the most recent UM censuses (assumed to represent true volumes, including nonresponse) and annual sampling estimates, and which also produced standard errors of the mean of less than five percent. The national TPO R program was used to select active mills to sample; we tailored program sampling percent, certainty volumes and product type mixes for each state. Program outputs were post-processed to adjust for nonresponse. Differences in censused and program-predicted state-level roundwood volumes varied from 0.44 to 4.73 percent and standard errors varied from 0.10 to 4.77 percent. Accounting for anticipated nonresponse was the most critical component of this work. These efforts have helped prepare UM staff to conduct informative annual samples.|
|Marcus Taylor||Improving residential fuelwood estimates for TPO||Tuesday||3b||Sequoyah||Advances in timber products monitoring||TPO is implementing a new methodology to estimate residential fuelwood use. In the past the program simply reported data from the Energy Information Administration (EIA) as firewood is typically obtained through a path not captured by TPOs surveys of primary wood processors. However, EIA estimates are only available for the four Census Regions while TPO strives to provide county-level data. This new model estimates residential fuelwood by county using EIA, Census, climate, and fuel price data.||Timber Products Output (TPO) studies report estimates of forest removals by county. The majority of data for the TPO program are collected by surveying primary wood processors, such as sawmills. This survey methodology largely omits fuelwood for residential use as many users of residential fuelwood obtain the product themselves or through a path not captured by the industrial surveys. Previously, data from the U.S. Energy Information Administration (EIA) was used for the residential fuelwood component of TPO reporting, however EIA data are not available on the same spatial scale as other TPO estimates nor do they provide geographic sourcing information. FIA is developing, testing, and implementing a new methodology to estimate residential fuelwood use that will better align with other TPO estimates. Using data from the EIAs Residential Energy Consumption Survey, Census American Community Survey, the National Climatic Data Center, and EIAs home heating fuel prices, volumetric data for annual residential fuelwood usage in TPO will be modeled to the county level as well as species distribution and county-of-origin information using ancillary TPO and FIA data.|
|Brett Butler||Timber Products Output Field Data Collection Methods: Past, Present, and Future||Tuesday||3b||Sequoyah||Advances in timber products monitoring||The past and current methods used to collect TPO data from mills will be reviewed. Potential future data collection protocols, based on the current science of survey design and implementation, will then be discussed. Specific components to be considered include questionnaire design, the use of incentives, and data collection modes.||The FIA Timber Products Output program has been collecting data from primary wood processing facilities across the U.S. for decades. The TPO program has recently switched to a sample-based data collection protocol and it is time to thoroughly review the field data collection methods. Traditional techniques often involved visits to mill locations and working with mill managers to complete the questionnaires. More recent effort have started to rely more on self-administered, mail-back questionnaires. This presentation will summarize the past and current methods used to collect TPO data. Potential future data collection protocols based on the current science of survey design and implementation will be discussed along with methods for testing these new approaches. Specific components to be considered include questionnaire design, the use of incentives, and data collection modes.|
|Rebekah Zehnder||Southern Timber Supply Analysis: Forest Inventory Data for All||Tuesday||3b||Sequoyah||Advances in timber products monitoring||The Southern Timber Supply Analysis web application summarizes USDA Forest Service Forest Inventory and Analysis (FIA) data for a user-defined supply area. The easy-to-use application produces estimates of the amount of timberland and standing timber, growth, and removals within a user-specified distance or trucking time of a site of interest in the U.S. South. Southern Timber Supply Analysis broadens delivery of FIA data, simplifying the process of examining forest inventory and sustainability.||The Southern Timber Supply Analysis web application summarizes USDA Forest Service Forest Inventory and Analysis (FIA) data for a user-defined supply area. The application produces estimates of the amount of timberland and standing timber, growth, and removals within a user-specified distance (50, 75, or 100 miles) or trucking time (1, 1.5, or 2 hours) of the users site of interest in the U.S. South. The analysis can be filtered by state and ownership, and timber quantities can be displayed by volume or by green weight. The results can be downloaded in a PDF report. The application also contains pre-made statewide reports available for download.
Designed specifically for ease of use, Southern Timber Supply Analysis simplifies the process of examining forest inventory levels and sustainability within a custom area. It presents the results equivalent to running numerous EVALIDator queries in a matter of seconds, with very little effort required by the user. The information available through Southern Timber Supply Analysis will support economic development, conservation and sustainability efforts, and state forestry agencies and associations.
|David Walker||Data needs and availability for developing and testing national scale biomass estimators||Tuesday||3c||Sequoyah||Development and implementation of new volume/biomass/carbon models for the FIA program||Felled-tree records were compiled from legacy sources and new field campaigns in order to build a database suitable for developing and testing models for use in FIA national volume, biomass and carbon inventories. Some of the issues associated with the complex data set will be addressed, including the use of both US and Canadian data, dealing with missing observations within trees, and existing gaps in tree species, size, and specific geographic locations where no data are yet available.||A two-track approach involving legacy data compilation and new data collection was pursued to provide sufficient data for developing and testing models to use in FIAs national-scale biomass estimation. Legacy data sources included agency, university, and industry studies from the US, as well as government studies from both provincial and national agencies in Canada. The main data type involved stem taper and volume measurements, mainly from felled-tree studies conducted over the past 120 years in the US and Canada. Weight and biomass data including wood properties such as specific gravity comprised the second largest type of data, almost exclusively from felled-tree studies conducted in North America over the past 60 years. Major gaps in legacy data tree species, size, and geographic location were addressed by conducting field campaigns at strategic locations in the US over a period of about eight years since 2011. In total, data from nearly a quarter-million trees were compiled, about one-tenth of which include biomass measurements. Despite existing needs to be addressed in ongoing model development, the database is among the largest known collections of felled-tree records ever compiled for national-scale model development.|
|David Affleck||Alternative Modeling Strategies for Estimating Tree Biomass Across a Nationwide System of Inventory Plots||Tuesday||3c||Sequoyah||Development and implementation of new volume/biomass/carbon models for the FIA program||We review the biological, practical, and statistical and limitations of alternative model systems for estimating volume and component biomass at the tree-level over the US Forest Inventory & Analysis (FIA) plot network. Developed under FIAs National Biomass Estimators Project include systems that model|
1) total tree mass directly or indirectly;
2) mass as a function of volume, or independently of volume; and
3) trans-species or individual species trends.
|We review the advantages and limitations of alternative model systems for estimating tree volume and biomass over the Forest Inventory & Analysis (FIA) plot network. Systems proposed under FIAs National Biomass Estimators Project have been structured according to biological and dimensional principles. Yet user needs and FIA protocols have also figured prominently in development and evaluation. Chief among the former are demands for being data-driven, for additivity among biomass components, and for smoothness across administrative boundaries, as well as the desirability of compatibility between stem volume and mass estimates. Considerations related to inventory protocols include retrospective applicability and the economics associated with collection of alternative variables. Similarly, the form, quantity, and distribution of tree data, as well as inherent data dependence structures, have motivated alternative statistical specifications. We thus describe the biological, practical, and statistical strengths of systems that model
1) total tree mass directly or indirectly;
2) mass as a function of, or independently of, volume; and
3) trans-species or individual species trends.
Instances of these are explored further in this session.
|Philip Radtke||Evaluating Modeling systems for National-Scale Biomass Estimators: A Scorecard Approach with Preliminary Results||Tuesday||3c||Sequoyah||Development and implementation of new volume/biomass/carbon models for the FIA program||Modeling approaches for live tree aboveground biomass (AGB) estimators will be evaluated for desirable modeling properties: a) component additivity; b) compatibility of multiple attribute predictions; c) greatest accuracy in predicting AGB for major species; d) performance of models used to predict for minor species; e) well-behaved prediction patterns in models when used to extrapolate to very large trees; and f) effectiveness of incorporating stand or site-level predictors.||Numerous approaches will be evaluated to ensure desirable modeling properties in live tree aboveground biomass (AGB) estimators for national forest and carbon inventories, including: a) component additivity for stem and branch wood or bark, branches, and foliage; b) compatibility of volume, taper, biomass, and specific gravity predictions; c) accuracy in predicting AGB for species well-represented in model fitting data sets and for those lacking data; d) performance of models used to predict for species lacking adequate data; e) well-behaved prediction patterns in models when used to extrapolate beyond the range of observed data; and f) the effectiveness of incorporating stand and site-level predictors to reduce prediction uncertainty.
A scorecard approach has been developed for delivering concise yet complete information to aid in the model evaluation process. The scorecard approach enumerates categorically which properties proposed modeling systems are designed to achieve and provides a means for reporting quantitative measures of models performance. Examination of both the intended design and measured performance of proposed models will lead to a transparent modeling solution with highly favorable predictive abilities.
|David MacFarlane||Functional, species-specific or hybrid groups for new tree models for FIA plots?||Tuesday||3c||Sequoyah||Development and implementation of new volume/biomass/carbon models for the FIA program||This talk describes ideas, data and models are presented from several studies to examine alternative, tree-functional approaches to species-specific models, along with hybrid approaches that use both species and functional types to better characterize tree to tree variation, both within and between species, across the large spatial domain of FIA.||The Forest Inventory and Analysis (FIA) program of the USDA Forest Service is compiling continent-wide data to create new mass and volume models to be applied to trees on FIA inventory plots. FIA will need to consider how these models should be constructed to capture variation in tree attributes across the vast array of tree species, forest ecosystems, climatic regions and forest disturbance regimes that comprise the scope of FIAs inventory. In practical terms, this means allowing model coefficients and model forms to vary and, traditionally, this would mean allowing model coefficients to vary by species or groups of species. Here, ideas, data and models are presented from several studies to examine alternative, tree-functional approaches to species-specific models, along with hybrid approaches that use both species and functional types to better characterize tree to tree variation, both within and between species, across the large spatial domain of FIA. A new species form type volume modeling approach, developed for the Michigan state forest inventory system, is presented, as case study of the potential utility of a species-functional group approach for both volume and mass estimation for trees on FIA plots.|
|Krishna Poudel||Approaches to Estimate Individual Tree Aboveground Biomass||Tuesday||3c||Sequoyah||Development and implementation of new volume/biomass/carbon models for the FIA program||For the past six years, the FIA along with university partners across the nation have worked together to compile long-term legacy data as well as to collect new set of destructively sampled data to develop new models. This unified national-scale dataset was used to test different approaches to estimate total aboveground live tree biomass as well as the biomass of aboveground components.||Forest ecosystems contribute substantially to global climate change mitigation by sequestering and storing carbon. Forest carbon inventories are obtained by using tree and area measurements along with biomass equations. Therefore, large-scale forest biomass estimation is important to assess the role of forestry sector in mitigating climate change impacts. Methods to estimate total aboveground biomass as well biomass of different tree components (stem, bark, branch, and foliage) were developed using large dataset compiled from studies over the years. Missing components in the dataset were imputed using species-specific or combined-species Dirichlet imputation. Alternative model formulations and fitting techniques for obtaining biomass estimates were tested. Specifically, the independent estimation of biomass is compared with the biomass estimates derived from volume estimates that are obtained using volume equations fitted in this study. Error produced by the methods developed in this study are compared with the error produced by the Component Ratio Method currently being used for official U.S. forest carbon inventories.|
|Aaron Weiskittel||National Scale Biomass Estimator (NSBE) Project: Next steps, implications, and future timeline||Tuesday||3c||Sequoyah||Development and implementation of new volume/biomass/carbon models for the FIA program||The National Scale Biomass Estimator (NSBE) has been a joint collaborative project between several universities and the US Forest Service Forest Inventory & Analysis (FIA) program. The overall goal is to refine FIAs approach to estimating aboveground tree biomass and carbon for the primary species in the US using existing and newly collected data. Current efforts have shifted to evaluating alternative have shifted to evaluating alternative modeling approaches based on this available data.||The National Scale Biomass Estimator (NSBE) has been a joint collaborative project between several universities and the US Forest Service Forest Inventory & Analysis (FIA) program. The overall goal is to refine FIAs approach to estimating aboveground tree biomass and carbon for the primary species in the US using existing and newly collected data. Acquiring and strategically collecting biomass data was an initial focus of the project, while current efforts have shifted to evaluating alternative modeling approaches based on this available data. A current target for the project is to deliver a revised methodology to FIA for estimating tree biomass in 2020. Implementation and testing of this revised methodology by FIA will then occur with particular focus on communicating the revisions to FIAs broad array of key stakeholders. However, preliminary results indicate rather significant shifts in total biomass for certain species and regions, which have important implications for US biomass and carbon estimates. This presentation will review past accomplishments, current plans for next steps in the coming year, and the broader implications of revising FIAs approach to estimating biomass nationally.|
|Miranda Mockrin||Supporting the USFS WUI research agenda: Framing the session||Wednesday||3a||Sequoyah||Supporting the USFS WUI research agenda||The wildland-urban interface, that area where houses meet or mix with undeveloped natural areas, is widespread and growing rapidly. At 10% of the conterminous U.S. in area and 33% of all homes in the US, the WUI is diverse, and we lack a comprehensive understanding of ecological and social conditions within this environment. The FIA program can make valuable contribution to our understanding of WUI forest conditions and family forest managers, furthering our national program of WUI research.||The wildland-urban interface, that area where houses meet or mix with undeveloped natural areas, is widespread and growing rapidly. As of 2010 the WUI covered 9.5% of the conterminous U.S. and included 33% of all homes. From 1990 to 2010, the WUI expanded by more than 189,000 km2, an area larger than Washington State. We know that WUI expansion in the U.S. occurs overwhelmingly as a result of housing growth, but we lack a comprehensive understanding of ecological and social conditions within the WUI. Much of the current research and management focuses on wildfire risk, but the WUI is widespread, including in areas that are not fire-prone, and the ramifications of residential development on natural resources extend far beyond implications for wildfire management. As the national inventory of forest extent and conditions, the FIA Program is well positioned to provide additional insight into forest conditions, invasive species distribution, carbon sequestration benefits, and family forest owners within the WUI. This broad perspective on datasets, WUI conditions, and management issues will contribute to a national assessment of WUI research being conducted by the Washington office of Forest Service R&D.|
|Rachel Riemann||Wildland-urban interface (WUI) analysis using datasets related to urbanization and fragmentation of forest land||Wednesday||3a||Sequoyah||Supporting the USFS WUI research agenda||Urbanization and fragmentation are dominant regional stressors on forests, and they are particularly relevant in the WUI. These factors reduce forest ecosystem resilience in the face of stresses associated with climate change, and increasing demand for ecosystem services. A series of geospatial datasets has been carefully selected to characterize the fragmentation, urbanization, and landscape context of forest land in the US. We will report on what they have been able to tell us about the WUI.||Urbanization and fragmentation are dominant forest stressors with substantial ecological, economic, cultural, and human health implications and they are particularly relevant in the WUI. They reduce forest ecosystem resilience in the face of stresses associated with climate change, and in the face of increasing human demand for processing (air/water pollution), use (recreation), and extractive ecosystem services (timber, non-timber and wildlife forest products). Measuring and monitoring the status and change in forest fragmentation, urbanization, and landscape conditions has become an invaluable tool in both understanding the impacts of these stressors and characterizing their nature and extent. This information has become vital for planning, management decisions, predicting future impacts, and policy development at all scales. Much work has gone into selecting and fine-tuning a series of geospatial datasets to characterize the fragmentation, urbanization, and landscape context associated with forest land in the US. We describe these datasets, how they fit together, and report on what they have been able to tell us about the WUI.|
|Nancy Sonti||Using FIA plot data to characterize forest in the WUI||Wednesday||3a||Sequoyah||Supporting the USFS WUI research agenda||The wildland-urban interface is rapidly expanding into forested areas across the United States, leading to widespread forest fragmentation. FIA data can be used to characterize forest condition in the WUI compared to wildland forest. In this presentation, we explore differences in forest structure, species composition, and ownership between wildland forest and areas of new or persistent WUI. We also look at regional differences across the Northern Research Station geographic footprint.||The wildland-urban interface (WUI) is rapidly expanding into forested areas across the United States, leading to widespread forest fragmentation. Forest Inventory and Analysis data can be used to characterize forest in the WUI, and to examine whether WUI forest conditions are different from wildland forest that remains outside the WUI. Based on FIA data circa 2013, 16% of total forestland area in the conterminous United States was in the WUI, of which one-third became WUI since 1990.
In this presentation, we explore differences in forest structure and species composition between areas that are outside the WUI (wildland forest), that have been in the WUI since 1990 (persistent WUI), or that have more recently been characterized as WUI (since 1990). Is the WUI associated with certain forest types or stand size? And how might that relationship vary by region? We also look at patterns in forest ownership among these WUI classes, finding that WUI is more likely to develop on private or local public lands rather than state or federal lands. Finally, we highlight regional differences in these trends across the Northern Research Station geographic footprint impacted by a combination of ecological and social drivers.
|Cassandra Kurtz||Does the WUI predict invasive plant occurrence on FIA plots?||Wednesday||3a||Sequoyah||Supporting the USFS WUI research agenda||A strong correlation of invasive plant presence to housing density and natural vegetation dominance reflects the important role of human disturbance on ecosystems. Fragmentation of uninhabited, vegetated lands may increase invasive spread into formerly uninvaded areas. To help curb the spread into less invaded areas, careful monitoring to eradicate new populations and reducing numbers elsewhere is important. Using the WUI map along with site monitoring can help managers care for the resources.||Invasive plants are spreading across ecosystems causing concern and threatening the future environment. We explored the occurrence of invasive forest plants on FIA plots in the eastern US in relation to the WUI class which contained the plots, with and without adjusting for site and landscape variables known to influence invasion rates. Invasion was defined as the presence of at least one individual of any invasive plant species. Compared to an overall invasion rate of 58%, a smaller rate (38%) was observed only for plots in WUIs that were uninhabited and dominated by natural vegetation, and the highest rates (~85%) were observed in the WUI classes with low to medium housing density that were not dominated by natural vegetation. To account for potentially confounding effects of ecological province, site quality, forest cover fragmentation, and distance from road, we used logistic regression to estimate adjusted odds of invasion among four housing density classes and two classes of natural vegetation dominance. The results indicated that both housing density and natural vegetation dominance are strong predictors of the occurrence of invasive forest plants on FIA plots.|
|Jacqueline Dias||Family Forest Owners Along The Urban-Rural Spectrum||Wednesday||3a||Sequoyah||Supporting the USFS WUI research agenda||Family Forest Owners living in areas of high housing density face different pressures and decisions compared to those living in more rural areas. We combine landowner information from the National Woodland Owners Survey with the landscape context of the Wildland-Urban Interface to better understand how landowner characteristics vary across this gradient.||Family Forest Owners (FFOs) hold a plurality of forest land in the United States. Much of this land falls within the Wildland-Urban Interface (WUI), the intersection of housing density and wild vegetation. FFOs living in areas of high housing density (i.e., urban areas) face different pressures and decisions compared to those living in rural areas, and the WUI classifications allows for comparison of dynamics along a gradient within this critical area. As land continues to be developed and the WUI expands to new areas, it is important to understand who the landowners are in these areas and how they plan for and manage their land. Using data from the US Department of Agriculture Forest Service National Woodland Owner Survey, we can contextualize landowner responses within the WUI landscape. We analyze a range of FFO characteristics and behaviors to quantify how they vary across the WUI, including recreational use, forest management, and demographics.|
|Grant Domke||Toward estimating carbon stocks and stock changes across the wildland urban interface||Wednesday||3a||Sequoyah||Supporting the USFS WUI research agenda||Carbon sequestration from live trees in forests, woodlands, and settlements in the United States offsets more than 7 percent of total greenhouse gas emissions each year. How much of this carbon is sequestered and stored in the wildland urban interface (WUI)? This study combines FIA data with auxiliary information to estimate ecosystem carbon stocks and stock changes within and beyond the WUI with particular emphasis on carbon dynamics associated with land change.||* This is for an organized session on WUI coordinated by Rachel Reimann et al.
Carbon sequestration from live trees in forests, woodlands, and settlements in the United States offsets more than 7 percent of total greenhouse gas emissions each year. How much of this carbon is sequestered and stored in the wildland urban interface (WUI)? How are total carbon stocks distributed across the ecosystem pools within the WUI and on the margins of these landscapes? How are carbon stocks and stock changes within the WUI quantified in national reports? This study combines FIA data with auxiliary information to estimate ecosystem carbon stocks and stock changes within and beyond the WUI with particular emphasis on carbon dynamics associated with land change. We will describe how FIA data and auxiliary information were used to classify the WUI and estimate carbon stocks on forest land, woodlands, and settlements. We will also point to future research needs to better characterize carbon stock changes in these dynamic landscapes.
|Jack Triepke||ICE - Land Use, Land Cover and Agent of Change Data for NFS||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||This talk discusses a project utilizing historical scans of the NFS Region 3, Lincoln National Forest, implications for NFS, and practical methods used to support ICE measurements of the Forest Service scanning their historical imagery.||The Forest Service has invested in measuring land use, land cover and agent of change using the NAIP imagery source the Image Change Estimation project. The information provided from the ICE project is unique to supporting multiscale monitoring designs. The ICE project provides information to Forest Service R&D, S&P, and NFS Deputy Areas. Key support and reliance comes from the FIA program; using the FIA plot locations allows for design based estimators, LU/LC/AC data to be estimated on all lands, supports consistent/unbiased intensification across all lands, allows for monitoring change on permanent plots, and is nationally implemented. The NFS uses of ICE measures allows for temporal measures forward through time. As the Forest Service scans its historical aerial photos, the ability to measure land use, land cover and change agent estimates back through time supports tracking supporting a historical context through multiple decades. This talk discusses a project utilizing historical scans of the NFS Region 3, Lincoln National Forest, implications for NFS, and practical methods used to support ICE measurements of the Forest Service scanning their historical imagery.|
|Andrew Lister||Using biannual change detection maps for prestratification of photointerpreted survey samples for area change estimation||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||Map-based estimates often lack useful uncertainty metrics. For a forest inventory survey unit in Georgia, Landsat-based biannual change maps were used to prestratify plot data on cover change class derived from Landsat temporal signatures made with the TimeSync software. The goal of the project was to assess the accuracy of the change map, produce corrected area estimates of change, and assess the feasibility and costs of using this method for operationally producing biannual change estimates.||Monitoring land use and land cover change with ground plots or remote sensing is important to environmental managers and policymakers. With estimates directly from maps, traditional uncertainty indices are difficult to interpret through the lens of sampling theory. Estimates from design-based ground plot samples are interpretable using sampling theory, however they can be more expensive and are not wall-to-wall. The current study seeks to leverage the strengths of remote sensing- and plot-based area change estimation. For a forest inventory survey unit in Georgia, Landsat-based biannual change maps derived through time series analysis were used to prestratify plot data on cover change class derived from Landsat temporal signature graphics assembled using the TimeSync software. The goal of the project was to assess the accuracy of the biannual change product, produce corrected area estimates of change from that product, and assess the feasibility and costs of using this method for operationally producing biannual change estimates. Results will be presented and implications for the US Forest Services Forest Inventory and Analysis business practices will be discussed.|
|Joseph McCollum||Land use: ICE vs. P2||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||Image-based Change Estimation (ICE) is a relatively new photointerpretation project created by FIA. The ICE process involves collecting land use, land cover and disturbance information on FIA plots using two dates of aerial imagery. FIA already collects similar information through its normal business operations. Here ICE and FIA land use calls are compared across multiple states to evaluate potential for using the data sets to form blended estimates in areas with high nonresponse rates.||Image-based Change Estimation (ICE) is a relatively new photointerpretation project created by the U.S. Forest Inventory and Analysis (FIA) program. The ICE process involves collecting land use, land cover and disturbance information on FIA plots by photo interpreting aerial images collected at two points in time. The FIA program already collects similar information through its standard process of checking the forest status of plots prior to field visitation (referred to as double sampling for post-stratification). This redundancy results in two similar, but different data sets which can lead to conflicting estimates of forest land area and other variables. In this presentation we compare ICE and FIA data from multiple states to evaluate the level of agreement for five general land use categories (forest, agriculture, developed, water, and other). Results show overall agreement ranged from 66% in Connecticut to 96% in the U.S. Virgin Islands. Although agreement varied significantly by state, the results for forest, agriculture and developed were generally high enough to indicate potential for using ICE and FIA interchangeably in areas where non-response rates are high.|
|Scott Pugh||TESTING FOR TEMPORAL CHANGE IN FOREST INVENTORY & ANALYSIS PROGRAM (FIA) DATA USING THE DIFFERENCE TESTER ON THE WEB||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||Online analytical tools from FIA provide current inventory and change estimates that guide management and policy decisions for our Nations forest resources. Temporal comparisons are made using these tools but the tools do not account for covariance. A new online tool called Difference Tester accounts for covariance when comparing estimates of a chosen attribute between two consecutive inventory cycles (z-score and p-value indicate likelihood of a statistical difference).||Online analytical tools from FIA provide current inventory and change estimates that guide management and policy decisions for our Nations forest resources. A broad range of temporal comparisons are of interest to analysts. For example, an increase in the number of standing dead trees can indicate a forest health issue. Often, these comparisons have covariance because the estimates are derived from observations on the same plots over time (not independent). Hypothesis testing for statistically significant differences should account for covariance but FIA tools to date have not offered this benefit. A new online tool called Difference Tester (|
|Karen Schleeweis||Proximate Causes of Forest Loss 2001-2011 in a Landscape Mosaic Context||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||We aim to evaluate gross and intact forest loss nationally and their causal processes. Here we use two dates of the new epoch of National Land Cover Database (NLCD) land cover change maps, the annual North American Forest Dynamics (NAFD) attribution maps and the Landscape Mosaic methodology. Results are given in terms of regional differences and Landscape context. An advantage of this neighborhood approach is that it alleviates many issues related to per-pixel comparisons of maps.||The areas of forest loss are of concern for many Earth System applications. Equally important are changes in the spatial arrangement of forest. To really understand these dynamics and their proximate causes and landscape context is invaluable. Here we use two dates of the new epoch of National Land Cover Database (NLCD) land cover change maps, 2001 and 2011, to analyze changes in gross and interior forest. We combine these metrics with signals, measured in 4.41-ha neighborhood, of proximate causal processes. NLCD maps provide land cover change classes and North American Forest Dynamics (NAFD) attribution maps provide classification of fire, harvest, and insect/stress for forest canopy cover loss events nationally (CONUS). Annual NAFD attribution data are subset to the same 2001-2011 period for analysis. An advantage of this neighborhood approach is that it alleviates many of the issues related to per-pixel map comparisons. We discuss variations in changing forest patterns and proximate causes across the Resources Planning Act (RPA) regions and Landscape Mosaic categories, which locates and measures dominant land cover and the degree of land cover heterogeneity at a specified scale.|
|Zhiqiang Yang||Classification of satellite time series parameters instead of individual images supports temporally coherent model-assisted estimation at annual time steps||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||We demonstrate the use of time series analysis to support design-based inference by estimating the area of tree cover over time for major regions of the United States, combing annual CCDC-based maps of tree cover with a national-scale random sample of tree cover history collected using the TimeSync tool.||There are mature methods for using maps to support design-based estimation of forest characteristics. An obstacle to applying these model-assisted methods every year is the lack of temporally coherent maps. When land cover models are applied to individual images from a series of years, image noise makes it likely that cover class will fluctuate implausibly through time. This cause big problems for model-assisted change estimates.
We produced the first national time series analysis of the Landsat satellite record (1985-2018), using the CCDC algorithm. This analysis fits harmonic functions to all cloud-free acquisitions for each pixel (1000+ images), allowing us to see beyond image-specific noise. Parameters of these functions remain the same until a land cover change is detected. Applying models to time series parameter makes map calls both more accurate by accounting for the entire growing year and more stable between detected changes.
We demonstrate the use of time series analysis to support design-based inference by estimating the area of tree cover over time for major regions of the United States, combing CCDC-based maps of tree cover with a national-scale random sample of tree cover history collected using the TimeSync tool.
|Olaf Kuegler||Monitoring Change in the Pacific Islands: Non-traditional Estimates of Change||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||PNW-FIA has measured the forest in the U.S. affiliated Pacific Islands since 2001 and recently completed a full remeasurement of these surveys. Interest in traditional FIA components of changes (growth, removals and mortality) is very limited. However, monitoring changes in forest structure and biodiversity as well as any increases of invasive species are of paramount interest.||PNW-FIA has measured the forest in the U.S. affiliated Pacific Islands (American Samoa, Guam, Republic of Palau, Commonwealth of Northern Mariana Islands (CNMI), Federated States of Micronesia (FSM), and Republic of the Marshall Islands (RMI)) since 2001 and recently completed a full remeasurement of these surveys. Apart from changes in food crops (e.g., change in number Coconut trees), interest in traditional FIA components of changes (growth, removals and mortality) is very limited. However, monitoring changes in forest structure and biodiversity as well as any increases of invasive species are of paramount interest.
The Micronesia Challenge (www.miconesiachallenge.org) is a commitment by Palau, FSM, RMI, Guam and CNMI to conserve at least 30% of the near-shore marine resources and 20% of the terrestrial resources across Micronesia by 2020". In 2013, the Micronesia Conservation Trust started to fund the collection of additional plots in order to monitor the progress of conservation efforts.
While the FIA database structure is designed to allow the estimation of the traditional components of change, it can also be used to estimate change in forest structure, biodiversity as well as changes in invasive species.
|Mauricio Vega-Araya||Costa Rica's Land Use, Land Cover, and Ecosystems Monitoring System (SIMOCUTE).||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||This work presents the state of progress of SIMOCUTE in Costa Rica, emphasizing the use of tools such as Collect Earth Online (CEO), the image-based change estimation (ICE) tool and FIESTA (Forest Inventory ESTimation and Analysis), an R package developed by the US Forest Service.||Costa Rica implemented a National Forestry Inventory (NFI) in 2014 as part of its MRV processes. This inventory was implemented by the Costa Rica's forest office. Among others products, the NFI generated a systematic gird (SG) of points. Field plots were established on a subset of these. The SG has formed the basis of the countrys National Land Use, Land Cover, and Ecosystems Monitoring System (SIMOCUTE). SIMOCUTE aims to be the countrys official platform for coordination and institutional and sectoral integration, to facilitate the management and distribution of information and data related to the countrys land use, land cover, and ecosystems. In addition to the NFI, SIMOCUTE includes a mapping subsystem and a land use and land cover monitoring system based on photo-interpreting these attributes from the grid of points. The latter subsystem generates tabular estimates of the areas of the countrys different land uses and land covers, and changes in them through time.
This work presents the state of progress of SIMOCUTE in Costa Rica, emphasizing the use of tools such as Collect Earth Online, the image-based change estimation tool and Forest Inventory ESTimation and Analysis, an R package developed by US Forest Service.
|Todd Schroeder||Developing county-level harvest and conversion rates for southeastern forests using Landsat time series and U.S. Forest Inventory and Analysis (FIA) plots||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||In the southeastern U.S. reliable estimates of harvest area and volume are needed at larger scales and smaller time-steps than what FIAs plot sample alone can reliably produce. In this study Landsat disturbance maps and time series metrics are combined with FIA plots to estimate harvest and conversion rates for 67 counties in Georgia. Results are used to discuss advantages of agent specific predictors, and the benefits of using model-assisted approaches to improve estimation of rare events.||Known as the nations wood basket, the southeastern United States contains some of the most biologically diverse and dynamic landscapes in the country. In order to help sustainably manage the effects of forest management reliable estimates of harvest area and volume are needed at spatial and temporal resolutions that are typically smaller than what FIAs plot sample alone can reliably address. Remote sensing techniques, particularly the use of Landsat time series algorithms, have surfaced as an effective way to map the location, extent, and timing of forest harvest activities and other types of disturbance. In this study Landsat disturbance maps and time series metrics are used to develop Fay-Herriot small-area estimation models to predict annual harvest and conversion (i.e. loss of forest to non-forest use) rates for 67 counties in Georgia from 1986 to 2010. Results are used to discuss the advantages of using agent specific predictor variables, as well as the benefits of combining remote observations with FIA plots to improve annual estimation of rare events. Potential for using county-level harvest estimates in timber products studies, FIA reporting and other applications are also discussed.|
|Paul Patterson||Photo-based or Pixel-based Change Estimation?||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||The precision of photo-based and mapped-based (pixel-based) estimates of change categories within a geographic region are compared. A statistically based method for increasing the precision of photo-based estimates of change are presented. Finally the statistical and technical properties of the photo-based and pixel based methods for change estimation are compared.||Aerial photography may be used to estimate change within a landscape. This involves establishing a base sample of photo-plots and interpreting the characteristics of a grid of points within each of the photo-plots. The points within the photo-plot give an estimate of the proportion of the change categories on photo-plot; which are combined for the overall estimate. In many situations (REDD+) when using the base sample the precision of the estimates of the change categories are not sufficient. Another method to estimate change is to create a disturbance map (e.g., from the Landsat stack), then using a probability sample of the pixels evaluate the pixel level accuracy of the map, and finally using the accuracy assessment output construct an estimate of the proportion of each change category. A sample size based method for increasing the precision of the photo-based estimates will be presented. This will be followed by a discussion of the statistical and technical properties of the photo-based and pixel based methods.|
|Nicholas Nagle||Developing FIA survey weights with high spatial and temporal resolution by calibration to Landsat time series||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||Estimates of FIA plot characteristics are desired at spatial and temporal scales not directly supported by the plot sample design. A method based on penalized maximum entropy is presented that allows the calibration of survey weights to large sets of auxiliary data. Here this method is used to produce survey weights that have been calibrated to Landsat time series for two FIA regions covering 67 counties in Georgia. Results are used to discuss the pros and cons of entropy-based design-weights.||FIAs plot sample is not designed for the spatial and temporal resolution of annual, wall-to-wall maps or small area estimates. While many auxiliary data are available for calibrating FIA survey weights to small domains, many of these auxiliary data are collinear or noisy, making it difficult to use standard survey weighting approaches. A penalized maximum entropy approach is presented for survey weight generation. Like LASSO, this approach allows the efficient use of collinear and noisy auxiliary variables. Compared to LASSO, the penalized maximum entropy has the desirable property of producing non-negative survey weights, but does not possess the same desirable sparseness properties as LASSO. We demonstrate the application of this methods to produce a time series of survey weights for 67 counties in Georgia that are calibrated to Landsat time series that have been categorized using a recently published non-linear, trajectory fitting algorithm. Although entropy-based design weights are slightly less precise than LASSO, they can be used with multiple forest attributes to produce estimates which are internally consistent such that sub-populations sum to larger regions, and tabular estimates match spatial estimates.|
|Gretchen Moisen||Comparing and combining observation systems for land use and land cover change in Georgia||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||Using data collected in the north central section of Georgia, we compare four sources of land use and land cover change data, including that from FIA plots, ICE plots, Timesync plots, as well as Landsat-based maps. We explore ways in which these four sources can be combined through model-assisted estimation methods, as well as through logistically harmonized systems, to produce the best information with an eye toward cost.||FIA has access to a variety of observation systems for understanding land use and land cover change in the US. There is the network of ground plots, photo-based observations collected through Image-based Change Estimation (ICE) methodology, Landsat-based observations collected through Timesync, as well as a variety of change map products. Using data collected in the north central section of Georgia, we compare how four sources of land use and land cover change information differ in terms of the story they tell about land use and land cover dynamics. That is, for each of the observation systems we assess the ability to detect net and transitional change estimates through time that are statistically different than zero, as well as trends that are significantly increasing or decreasing. We also explore ways in which these four sources can be combined through model-assisted estimation methods, as well as through logistically harmonized systems, to produce the best information with an eye toward cost.|
|Jonathan Knott||Distributional shifts of regional forest communities in the eastern U.S.||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||We identified regional forest communities of the eastern U.S., and we assessed distributional shifts of these communities. We found 11 of 12 communities have shifted their distributions, either in their centroid or area. We assessed the impact of forest- and climate-related predictors on changes in forest community distribution but found that, although significant, the model performed poorly, indicating the lack of a strong community-level response to climate change.||The impact of climate change on forests is often measured by species-level changes, but much less is known about community-level responses. Here, we identified 12 regional forest communities of the eastern U.S. using data from the Forest Inventory and Analysis Program and the Latent Dirichlet Allocation topic model. In addition, we detected distributional shifts by evaluating movement in community centroid and changes in community area over the last three decades. We found 11 of 12 communities had significant shifts in centroid (eight in longitude, seven in latitude), and 5 of 12 communities had significant changes in area (three expansions, two contractions). Mixed-effects models revealed significant forest-related (fire frequency, basal area, and nitrogen deposition) and climate-related (temperature, precipitation, and precipitation change) predictors of changes in community distribution, but the model performed poorly (marginal R2 = 0.07). This may reflect resilience of forest communities to climate change, but may also indicate a lag between climate change and community-level responses. By identifying changes in forest communities, our results can provide useful information for managers of vulnerable forest communities.|
|Luca Morreale||Using non-forest conditions to identify forest fragments and quantify tree growth across a changing landscape||Wednesday||3b||Sequoyah||Understanding landscape change using FIA data||This research aims to quantify the effects of forest fragmentation on tree growth and mortality in temperate forests across the northeastern US. Using FIA measurements of forest and non-forest subplot conditions and the locations of individual trees, we are able to identify forest edges and quantify differences between trees at the edge and those in the forest interior. Forest fragmentation is a pervasive landcover regime that needs to be included in our understanding of forest ecosystems.||Forest fragmentation is a ubiquitous land cover change phenomenon with important implications for the health and function of forested ecosystems worldwide. Designing studies that capture both the variability of regional land use regimes, as well as accurately quantifying ecosystem responses on the level of individual trees presents a distinct challenge. The USFSs Forest Inventory and Analysis (FIA) database is uniquely suited to address this multiscale research challenge due to the density, spatial range and detailed, on-the-ground measurements. The purpose of this study is to quantify the magnitude and spatial variability of tree growth and mortality responses to forest fragmentation in the northeastern US. Our research leverages the spatially explicit FIA measurements of forest and non-forest subplot conditions and the locations of individual trees. The condition-boundary dataset allows us to compare trees near the forest edge to those in the interior to understand both organismal and community responses to edge stimuli, such as increased sunlight. This study characterizes ecosystem responses to fragmentation across a range of forest types and land cover adjacencies.|
|Yingfang Wang||Using FIA Volume and Biomass Equations through National Volume Estimator Library||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||FIA volume and biomass equations have been incorporated into the National Volume Estimator Library (NVEL). The NVEL was programmed in FORTRAN and compiled into a dynamic link library and a shared library. The library is available to use in Windows, Linux, and Android applications. The NVEL Excel Addins has functions to calculate cubicfoot and boardfoot volume. The NVEL has built-in interface functions for use with C#, VB, C/C++, Python, and R program. It also calculates tree component biomass.||FIA volume and biomass equations were programmed in the National Information Management System (NIMS) Oracle database and were not accessible outside of NIMS. The National Volume Estimator Library (NVEL) has been expanded to incorporate all FIA volume and biomass equations. The NVEL was programmed in FORTRAN and compiled into a dynamic link library (DLL) and a shared library. The library is available to use in Windows, Linux, and Android applications. The NVEL Excel Addins functions can be easily configured to calculate total cubic foot volume from the stump to the tip, merchantable cubic foot volume from the stump to a specified top diameter, boardfoot volume, and topwood volume for tree data stored in Excel spreadsheet. For profile model equations, there are also functions to calculate diameters at a given height, the height to a given diameter on the stem, and log diameters and volumes. The NVEL has built-in interface functions for use with C#, VB, C/C++, Python, and R program. The NVEL also has the National Biomass Estimator Library (NBEL) combined with it. It contains published biomass equations to calculate tree component biomass. The FIA existing volume and biomass equation number could be used in NVEL.|
|Lucas Clay||South Carolina Forests Potential to Contribute to the California Carbon Market||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||Private forest management can greatly affect forest carbon stocks in SC. The California Carbon Market gives a monetary value to sequestered carbon. This study aims to educate landowners on how forest management can increase carbon stocks. FIA data was used in the Forest Vegetation Simulator to project carbon stocks for 130 forested plots in SC. 10 management practices were simulated to see how carbon stocks differed. This is beneficial for forest landowners interested in a carbon project.||South Carolina (SC) has a variety of different forest types, and they all have potential to sequester a certain amount of carbon. Private forest landowners control a significant portion of the overall forestland in SC, and their management efforts can maintain or improve forest carbon stocks. Currently, the California Carbon Market gives a monetary value to sequestered carbon. One carbon credit is equal to one metric ton of carbon, and is currently worth around $15.00. This study aims to educate forest landowners about various forest management practices that contribute to increasing carbon stocks by looking at various forest types SC and their current and projected carbon stocks. Forest Inventory Analysis (FIA) data was used in the Forest Vegetation Simulator to project carbon sequestration for 100 years for 130 forested plots in SC. 10 different management practices were employed to see the variance in carbon sequestration. Results showed that carbon sequestration would increase for certain management practices such as thinning and prescribed fire. This data will be beneficial for forest landowners interested in a carbon project and those interested in seeing how different management practices affect carbon sequestration.|
|Dehai Zhao||Developing new taper, volume and biomass model systems for FIA to meet the needs of diverse clients||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||We present the structures, conditions, and estimation methods that are required to achieve the compatibility and additivity of taper, volume and biomass model systems. The main subtopics include (1) new variable-top merchantable volume and weight equations; (2) how to develop and estimate the completely compatible taper and volume equations; and (3) strategies for developing additive biomass component and biomass allocation equations.||FIA is striving to develop/update estimators of volume, biomass, and carbon contents of trees and tree components for the major tree species of the United States. In addition to total volume and total biomass, some clients need to accurately estimate biomass of each component for improving stand carbon/biomass estimates or nutrient accounting due to the variation of carbon and nutrient concentrations among different components, and some industrial partners are more interested in estimating volumes or weights of various portions of tree boles with changing utilization standards. It is desirable and possible to develop taper, volume and biomass model systems to meet such a variety of needs. Here, we present the structures, conditions, and estimation methods that are required to achieve the compatibility and additivity of the model systems. The main subtopics include (1) new variable-top merchantable volume and weight equations; (2) how to develop and estimate the completely compatible taper and volume equations; and (3) strategies for developing additive biomass component and biomass allocation equations.|
|Matthew Russell||Development of downed woody debris diameter and length models for application in forest biomass and carbon estimation||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||Historical measurements in FIAs downed and dead wood inventory were used to develop new models of large- and small-end diameters and length using decay class, species, and diameter at transect. Despite a number of changes in FIA measurement protocols on downed woody debris, these models can be used to quantify carbon attributes, estimate fuel loads, and determine wildlife habitat.||An accurate assessment of the dimensions of downed woody debris (DWD) are required to obtain reliable estimates of the volume, biomass, and carbon attributes of forest dead wood. Measurement protocols in FIAs downed and dead wood inventory have changed tremendously over the past two decades, with current procedures forgoing detailed measurements on DWD pieces such as large- and small-end diameters and length. Despite this, over 400,000 observations of DWD pieces have been collected by the FIA program across the US that contain these detailed measurements. These historical data were used to develop new models of DWD end-point diameters and length using simpler measurements such as the decay class, species, and diameter at transect. Models were fit with linear quantile mixed models and were subsequently used to determine plot-level volume, biomass, and carbon stocks in the DWD pool. Results indicate important differences in plot-level attributes depending on whether direct measurements or modeled estimates were used to determine DWD diameter and length. These findings have important implications in quantifying carbon attributes, estimating fuel loads, and determining wildlife habitat.|
|Andrew Gray||Disentangling direct disturbance impacts from other carbon fluxes on remeasured plots on the West Coast||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||The impacts of disturbance on forest carbon (C) are a common concern after a large fire event or insect outbreak. However, estimates often vary wildly. We explore alternative approaches to estimating immediate and longer-term impacts of disturbance. The mechanistic approach estimated pre- and post-disturbance growth, decay, and combustion of different C pools. The plot-matching approach compares trajectories of C pools on disturbed and undisturbed plots that share the same starting conditions.||The impacts of disturbance on forest carbon (C) stores are a common concern among managers, policy-makers, and the public, especially after a large fire event or insect outbreak. However, estimates of impacts often vary wildly, confuse short- and long-term impacts, or dont consider all C pools. Our objectives were to explore alternative approaches to estimating immediate and longer-term (e.g., 10 years) impacts of disturbance, with a focus on fire on the West Coast, USA (i.e., California, Oregon, and Washington). We compiled remeasured tree, dead wood, and forest floor estimates from 11,892 inventory plots distributed across 33.4 million ha of forestland and assessed changes in forest C pools by disturbance severity. The mechanistic approach estimated pre- and post-disturbance growth of live trees, decay of dead wood, combustion of live and dead vegetation parts, and combustion and accumulation of forest floor. The plot-matching approach compares trajectories of C pools on disturbed and undisturbed plots that share the same starting conditions. We discuss the strengths and limitations of each approach in terms of assumptions and data availability.|
|Jeremy Fried||Extending BioSum to optimize multi-decade forest restoration and evaluate biochar facility feasibility in the Upper Klamath Basin||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||Capacity of national forests in the Upper Klamath Basin to generate a biochar feedstock supply under restoration management regimes designed to enhance fire resistance was analyzed using Forest Inventory and Analysis data. The BioSum framework, extended with custom scripting for landscape optimization, showed that modeled treatments elevated fire resistance on most forested area, usually at positive net revenue, producing sufficient feedstock to keep a biochar facility operating for 20 years.||Capacity of the dry national forests of southern Oregon and northern California to generate a sustainable and economically feasible supply of biochar feedstock under restoration management regimes designed to enhance fire resistance was analyzed using Forest Inventory and Analysis data. The BioSum analysis framework, combined with recently developed effectiveness metrics, offered a compelling foundation for this analysis, which, thanks to BioSums modular design, could be extended with custom scripting to more accurately represent wood value recovered and to encompass landscape scale optimization of the treatment program over time. This case illustrates the value of inventory-linked, open analytic tools adaptable to problems not originally envisioned at the onset of tool development, and the mutual benefits that flow from collaborating with users with different analytic perspectives. Implemented with tethered harvest systems, modeled treatments elevated fire resistance on most forested area; the most effective ones usually more than covered treatment cost with sales of wood. At current, 100-yr management intervals, biochar feedstock is sufficient to supply at least one large-scale facility over the next 20 years.|
|Paul Patterson||Evaluation of Estimates of Biomass from Initial On-orbit Data of the Global Ecosystem Dynamics Investigation Lidar Mission||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||In Dec 2018 the NASA GEDI (Global Ecosystem Dynamics Investigation) mission installed a full-waveform lidar instrument on the International Space Station for the purpose of measuring global forest structure. The waveforms are collected in spatially discontinuous footprints. Based on the first 6 months of on-orbit GEDI science data a post-launch evaluation of the GEDI footprint level above ground biomass density (ABGD) estimators and hybrid estimate of the mean AGBD within regions is presented.||In December 2018 the NASA GEDI (Global Ecosystem Dynamics Investigation) mission installed a full-waveform lidar instrument on the International Space Station for the purpose of measuring global forest structure. The waveforms are collected in footprints that are spatially discontinuous; the waveform data is expected to be strongly correlated with aboveground forest biomass density (AGBD in Mg/ha). Using simulated GEDI waveforms, models of AGBD have been constructed for the footprints by plant functional types within the six continental regions. The estimates for the discontinuous footprint level AGBD will be combined to estimate the mean AGBD within geographical regions, e.g., 1 km grid cell, administrative unit, national park, etc. using a hybrid estimator. Using a diverse set of global sites a simulation study was conducted to empirically assess the statistical properties of hybrid estimator and the proposed estimated variance. In this talk we present an overview of the first 6 months of on-orbit GEDI science data. Based on the on-orbit GEDI science data a post-launch evaluation of the GEDI footprint level AGBD biomass estimators and hybrid estimate of the mean AGBD with geographical regions will be presented.|
|Ram Deo||Evaluating forest biomass inventory models based on integration of fixed-radius and variable-radius plot data with different modes of LiDAR and Photogrammetric metrics||Thursday||3a||Sequoyah||Biomass and Carbon: New Techniques, Big Potential, and Novel Equations||The area of interest has three types of LiDAR and DAP metrics. We collected aboveground biomass data at 51 locations using one-tenth acre, one-twenty-fourth acre (i.e. FIA subplot size) and BAF5 coverage areas that were concentric. AGB models were developed by relating the three types of LiDAR and DAP metrics separately with the fixed and variable plot AGB data. Modeling and analyses are ongoing.||LiDAR and digital aerial photogrammetry (DAP) data have become an integral part in enhanced forest inventories (EFI). However, the sensor characteristics, season of data acquisition and field sampling techniques (e.g., plot size, fixed radius vs variable radius plots) can affect the accuracy of spatial inventory models. This study was implemented in Lake County, MN where an overlap area (~ 20,000 ac) had DAP and three types of LiDAR data: high density linear LiDAR (55 points/m2), Single Photon LiDAR (33 points/m2) and low density linear LiDAR (1.5 points/m2). We evaluated aboveground forest biomass (AGB) models developed using DAP-derived predictors and grid metrics derived from the three types of LiDAR. The spatial predictors were integrated with field sampling data based on fixed and variable radius plots that were measured in summer 2018 by MN DNR at 51 locations using concentric 1/10 acre, 1/24 acre (i.e., the size of FIA subplot) and BAF 5 prism plots. AGB was calculated for the three plot sizes and the three types of AGBs were separately related to the three types of LiDAR and DAP metrics in Random Forest modeling framework to obtain the inventory models. The modeling and analyses are underway.|