|Thomas Brandeis||Assessing tree mortality probability in harvested hardwood stands using long-term forest inventory data||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||Retention trees in eastern US hardwood forests were 1.35 times more likely to die during or after partial harvesting than trees in unharvested stands, representing a 2.5% of volume removed. 83.7% of post-harvest mortality trees were under 11 inches DBH, which may have consequences for the future condition of these stands if not taken into account when planning management. These effects were seen equally with commercial and non-commercial species, and on both public and private lands.||Partial harvesting (here defined as removal of ? 50% of pre-harvest volume) is the predominant silvicultural scheme applied to hardwood forest types in the eastern US. Future stand conditions are largely reliant on trees retained after harvest so ensuring the survival of good quality trees can be essential to meeting long-term management goals. Retained tree mortality due to damage caused by harvesting activity should be minimized or taken into account when planning. We quantify partially harvested stand characteristics and post-harvest mortality using data from 27,671 forested conditions, 276,883 trees with a DBH ? 5 inches of which 15,061 trees were cut and utilized. On average 23.3% of stand volume was harvested with an additional 2.5% of volume lost to harvesting-caused mortality. The likelihood of mortality was 1.35 times higher (95% CI of 1.29 to 1.42) for trees retained in stands that had undergone partial harvesting than for trees growing in unharvested stands. 83.7% of post-harvest mortality trees were under 11 inches DBH. The increased odds of mortality were similar for both commercial and non-commercial species, and for trees on both privately and publicly-owned lands.|
|John Zobel||Assessing long-term implications of forest management on wildlife habitat in Minnesota.||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||The Wildlife Habitat Indicator for Native Genera and Species (WHINGS) is a forest habitat model that allows for rapid assessment of habitat based on forest inventory data and projected forest conditions. This talk will demonstrate the use of WHINGS across 40 years of statewide inventory data from the FIA program. Results will address long-term statewide forest management trends and their direct impact on wildlife habitat in Minnesota.||As part of the comprehensive evaluation of alternative harvesting scenarios, the Minnesota Generic Environmental Impact Statement (GEIS) used or created several methods to assess specific environmental conditions, including forest wildlife habitat. Over 25 wildlife professionals were involved in the development of the habitat models based on Forest Inventory and Analysis (FIA) data, and results were incorporated into the final GEIS report. Subsequently, the original models were updated and refined into the Wildlife Habitat Indicator for Native Genera and Species (WHINGS), a forest habitat model that allows for rapid assessment of habitat based on forest inventory data and projected forest conditions. The model uses Habitat Suitability Index (HSI) methodology for native, forest dependent wildlife species, including 138 birds, 22 small and medium mammals, four (4) large mammals, and eight (8) herptofauna. This talk will demonstrate the use of WHINGS across 40 years of statewide inventory data from the FIA program. Results will address long-term statewide forest management trends and their direct impact on wildlife habitat in Minnesota.|
|Courtney Giebink||Updating the Forest Vegetation Simulator with climate response recorded in tree rings||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||The Forest Vegetation Simulator (FVS), a widely used growth & yield model, currently lacks the ability to directly characterize the effect of climatic variability on diameter increment, and subsequently demographic processes. We integrate FIA remeasurement data, tree-ring data, and their climatic response to improve the FVS diameter increment model.||The Forest Vegetation Simulator (FVS) is a widely used growth & yield model that uses tree and plot data to simulate growth, mortality, and response to treatments for forest stands to inform management decisions. Although a climate extension of FVS modifies forest development based on spatial species distribution models, FVS lacks the direct effect of climate variation on diameter growth. We plan to use the climate response recorded in tree rings, complemented by inventory data from the ecologically unbiased Interior West-Forest Inventory and Analysis (IW-FIA) program to parameterize the diameter growth for several species in the Utah variant of FVS. Current models predict a ten-year growth increment using a multiple linear regression. Updated models will predict an annual growth increment with climate as a predictor using a mixed effects model. All focal species show positive sensitivity to precipitation variability and negative sensitivity to temperature variability, with differing strengths in response. The inclusion of climate in the diameter growth models will provide more accurate projections, allowing foresters to anticipate stand vulnerability to climate change and adaptively manage to increase stand resiliency.|
|Mark Brown||Mangrove Inventory Comparisons, Findings from Multi-Agency Tests in Florida||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||Ground based inventory of mangrove forests is difficult due to accessibility issues, time constraints, and hazardous conditions. To mitigate these issues and improve data collection, a collaborative multi-agency study was initiated. The study compared two plot designs, two diameter methods, and remotely sensed data with ground data. This presentation describes study parameters and findings from the methods compared. The study concludes with recommended changes to mangrove inventory methods.||Traditional ground based inventory of mangrove forests can be difficult and time consuming. Tides, mud flats, prop roots, and tree densities impede foot travel. To mitigate these issues and improve data collection, the Southern Research Station (SRS) initiated a collaborative study with the Florida Forest Service (FFS) and the National Aeronautics and Space Administration (NASA) to test alternative methods for mangrove inventory. Consultation with Mexicos National Forestry Commission (CONAFOR) mangrove inventory provided the diameter method studied. This presentation describes study parameters and findings from methods compared. First, inventory a one point plot design to reduce extensive traverse involved with current 4 subplot design. Second, measure tree diameter at 1.0 feet above highest prop root when encountered at or above normal diameter at breast height (dbh), to alleviate difficult measurement at 3.5 feet above that point under current methods. Third, synchronize NASA flyovers of mangrove plot locations using global positioning system (GPS) coordinates and Goddard LiDAR Hyperspectral Thermal (G-LiHT) airborne imaging to test viability of remotely sensed data to represent inaccessible mangrove plot locations in Florida.|
|Lance Vickers||Are current seedling demographics poised to regenerate northern US forests?||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||Detailed seedling inventories from 24 northern US states were examined for plausible regeneration outcomes following overstory removal. We examined the likelihood of meeting two fundamental regeneration objectives: 1) securing future forest and 2) securing upper canopy species. Almost half the plots lacked adequate seedlings to regenerate a stand after canopy removal and over half risked compositional shifts. In all, some regeneration difficulty may occur on two-thirds of the plots examined.||Securing desirable regeneration is essential to sustainable forest management, yet failures are common. Detailed seedling measurements from a new NRS-FIA regeneration indicator inventory across 24 northern US states were examined for plausible regeneration outcomes following overstory removal. The examination included two fundamental regeneration objectives: 1) stand replacement- securing future forest and 2) species maintenance- securing upper canopy species. Almost half the plots lacked adequate seedlings to regenerate a stand after canopy removal and over half risked compositional shifts. Based on those advance reproduction demographics, regeneration difficulties could occur on two-thirds of the plots examined. The remaining one-third were regeneration-ready. However, compared to historical norms, increased small-tree mortality rates reduces that proportion. Not all forest types rely on advance reproduction and results varied among the forest types examined. Some variability was associated with browsing intensity, as areas of high deer browsing had a lower proportion of regeneration-ready plots.|
|Kevin Potter||Leveraging the Forest Inventory and Analysis Design in a National, Spatially Explicit Assessment of Forest Tree Genetic Degradation Risk||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||For a national sustainability reporting effort, the USDA Forest Service needs to assess the Number and Geographic Distribution of Forest-Associated Species at Risk of Losing Genetic Variation and Locally Adapted Genotypes. To address this Montréal Process genetic diversity indicator, we use Forest Inventory and Analysis data to determine, for each species, the ratio of mature trees to saplings within provisional seed transfer zones, which encompass areas with similar environmental conditions.||Genetic diversity is essential for forest tree species because it provides a basis for adaptation and resilience to environmental stress and change. The Montréal Process, which the USDA Forest Service uses as a forest sustainability assessment framework, incorporates genetic variation among its indicators of biological diversity, including the following: Number and Geographic Distribution of Forest-Associated Species at Risk of Losing Genetic Variation and Locally Adapted Genotypes. To address this indicator, we intersect Forest Inventory and Analysis data with climatically and edaphically defined provisional seed zones, which encompass areas with similar geology, climate, vegetation and soils. These zones are proxies for among-population adaptive variation in tree species under the assumption that their adaptive genetic variation is associated with the environmental conditions that define the seed zones. We determine, for each species, the ratio of mature trees to saplings within each seed zone as an indicator of insufficient regeneration that could lead to the loss of genetic variation. The results offer insights into which species and which areas of the country may be experiencing degradation of genetic diversity.|
|Dacia Meneguzzo||Trees outside forests: where are they and what are they doing?||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||Forests in the agriculturally-dominant Great Plains region are not like traditional forests. Their placement is often intended to provide a specific ecological service, such as conserving soil, protecting crops, livestock, and humans, or improving water quality. We present mapping methodologies, output products, and data describing the non-traditional forest resource for multiple states in the central U.S. This endeavor provides information at a new scale appropriate for nontraditional forests.||Forests in the agriculturally-dominant Great Plains region are not like traditional forests. Their placement is often intended to provide a specific ecological service, such as conserving soil, protecting crops, livestock, and humans, or improving water quality. While these tree features often fail to meet the definition of "forest" employed by the Forest Inventory and Analysis program, they are viewed as such by the region's land managers. Windbreaks are a prime example; they are critically important yet little information describing their extent and location is available in formats (e.g., maps) that are useful for resource professionals and decision-makers. Collaborative research with the USDA National Agroforestry Center has led to the development of high-resolution tree cover maps as well as value-added map products depicting ecological services provided by trees outside forests (TOF). Mapping methodologies, output products, and data describing the TOF resource for multiple states in the central U.S. are presented. This endeavor is the first of its kind in the region and provides information at a new scale that is appropriate for inventory, monitoring, and decision-making related to nontraditional forests.|
|Hyeyoung Woo||Estimating wildfire effects with propensity score and spatial matching||Tuesday||1a||Cherokee||FIA and ecology research: Shifting ranges, forest management and stand development||Three propensity and spatial matching approaches were implemented to match burned Forest Inventory and Analysis plots with unburned plots based on topography, climate, and land cover variables. These matched plots were then used to estimate wildfire effects on standing tree carbon. The performance of the three matching methods was compared and we discuss advantages of the regional approach to wildfire effect estimation over existing case studies of individual fires.||Wildfire effects on standing tree carbon are often estimated based on observational studies of wildfires. Many of these studies focus on extreme, large wildfires that gained public interest and research support. Using Forest Inventory and Analysis (FIA) data in Oregon and Washington, USA, we identified plots (n=611) that burned in large forest wildfires within the Pacific Northwest between 2001 and 2016. We matched them with unburned FIA plots using three approaches: 1) propensity score matching based on topography, climate, and land cover variables, 2) spatial matching, and 3) distance-adjusted propensity score matching. With these matched plots we estimated wildfire effects with a quasi-experimental approach for the entire area that burned during that period. Therefore, our results provide estimates of wildfire effects that are representative of all wildfires across Oregon and Washington, USA. We discuss 1) the performance of the three different propensity score matching approaches for estimating wildfire effects from matched inventory plots, and 2) how this regional approach to wildfire effect estimation provides complementary information to that from detailed fire effects studies conducted as case studies in individual fires.|
|Robert Jespersen||Assessing potential chronology bias in the FIA increment core record with increment core data from densely sampled forest plots across North America||Tuesday||1b||Cherokee||Forests of Interior Alaska Insights from the Tanana Unit||We examined the potential for bias in increment core datasets collected through low density selective sampling strategies (LDSS), as practiced in some FIA units. Using datasets collected via high density sampling (HDS) from forests across North America, we compared the chronologies developed from HDS datasets with those developed from LDSS subsets. Preliminary results suggest that LDSS chronology bias varies strongly by species.||Increment cores collected through the United States Forest Inventory and Analysis (FIA) program offer the potential for unique insights into forest growth and its sensitivity to climate. While FIA plots are randomly located, trees selected for coring on FIA plots are generally a small number (n~5 trees) of dominant or co-dominant individuals whose growth patterns may or may not reflect the broader population. We took advantage of tree-ring data from densely sampled plots (n~30-60 trees) to examine the potential for chronology bias emerging from sampling primarily dominant and co-dominant trees. Data from densely sampled plots were acquired for the Tanana Valley of interior Alaska and from forested sites throughout North America. Preliminary results suggest the potential for chronology bias might vary strongly by species. Further analyses will examine the relative chronology quality from dominant and codominant trees across forest types, tree functional groups, and sampling designs. Our findings of bias, or lack thereof, may help inform the use and interpretation of FIA tree-ring chronologies.|
|Hans Andersen||Use of high-resolution, multi-sensor airborne remote sensing to support forest inventory in interior Alaska||Tuesday||1b||Cherokee||Forests of Interior Alaska Insights from the Tanana Unit||In this presentation, we describe how advanced, high-resolution, and multi-sensor airborne remote sensing data (airborne laser scanning, hyperspectral imaging, ultra-high resolution ( 3 cm pixels) stereo imagery) collected by NASA-Goddard are being used to support the FIA inventory in interior Alaska, with particular emphasis on the development of tools to make these technologies more accessible to end users.||Given the rapid development of several complementary advanced geospatial technologies (e.g. laser scanning, hyperspectral imaging, direct georeferencing) the role of airborne remote sensing in forest inventory is also continually changing. In this presentation, we describe how advanced, high-resolution, and multi-sensor airborne remote sensing data (airborne laser scanning, hyperspectral imaging, ultra-high resolution ( 3 cm pixels) stereo imagery) collected by NASA-Goddard are being used to support the FIA inventory in interior Alaska, with particular emphasis on the development of tools to make these technologies more accessible to end users. First, we describe how stereo imagery from a high-res mapping camera can be used to support field operations in the Susitna-Copper inventory unit, including detailed forest condition class mapping. Second, we describe how a combination of airborne lidar and hyperspectral imagery can be used to increase the reliability of inventory reporting for the Tanana inventory unit through the use of model-assisted survey regression estimators, including the development of an R package to facilitate calculations and presentation of tabular results.|
|Bruce Cook||High-resolution data from NASA's G-LiHT Airborne Imager for remote inventory and science applications||Tuesday||1b||Cherokee||Forests of Interior Alaska Insights from the Tanana Unit||Interior Alaska accounts for 15% of U.S. forest lands, yet the remoteness of interior Alaska presents a range of challenges for inventorying forest resources. Beginning in 2014, the USDA-Forest Service partnered with NASAs Goddard Space Flight Center to pilot a lidar-assisted forest inventory. This approach leverages the unique, multi-sensor, high-resolution data from NASA Goddards Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager (www.gliht.gsfc.nasa.gov).||Interior Alaska accounts for 15% of U.S. forest lands, yet the remoteness of interior Alaska presents a range of challenges for inventorying forest resources. Beginning in 2014, the USDA-Forest Service partnered with NASAs Goddard Space Flight Center to pilot a lidar-assisted forest inventory. This approach leverages the unique, multi-sensor, high-resolution data from NASA Goddards Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager (www.gliht.gsfc.nasa.gov). During 2014-19, G-LiHT flights have collected strip samples of data spaced every 9 km across three inventory units (Tanana Valley, Susitna-Copper River, and Southwest). Within these strips, the USFS implemented a 1/5th intensity grid of FIA plots. Here, we discuss the role of 1 m resolution airborne lidar, hyperspectral, thermal, and fine-resolution (<4 cm) stereo photographs for connecting the dots between inventory plot locations, including analyses of forest biomass, species composition, and trajectories of vegetation succession following stand-replacing wildfires. G-LiHT data also sample gradients in elevation, soils, and disturbance history needed to evaluate changing permafrost, insect outbreaks, expanding shrub biomass, and wildlife habitat.|
|Grant Domke||Toward estimating litter and soil carbon stocks on forest land in Alaska||Tuesday||1b||Cherokee||Forests of Interior Alaska Insights from the Tanana Unit||*I received an invitation to present this work in a session on Interior AK organized by Sean Cahoon and Hans Andersen.|
Soil organic carbon is the largest terrestrial carbon (C) sink on earth and management of this pool is a critical component of global efforts to mitigate atmospheric C concentrations. Here we describe how FIA data have been harmonized with auxiliary biophysical and geospatial data to develop models for predicting litter and soil carbon stocks on forest land in Alaska
|Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on earth and management of this pool is a critical component of global efforts to mitigate atmospheric C concentrations. Soil organic carbon is also a key indicator of soil quality as it affects essential biological, chemical, and physical soil functions such as nutrient cycling, water retention, and soil structure and maintenance. Much of the SOC on earth is found in forest ecosystems and is thought to be relatively stable. That said, several studies have documented the sensitivity of SOC to global change drivers, particularly in the northern circumpolar region where approximately 50% of the global SOC is stored. The Forest Inventory and Analysis (FIA) program within the United States Department of Agriculture, Forest Service has been measuring litter and soil attributes in forests of the US since 2001. These data have recently been harmonized with auxiliary biophysical and geospatial data to develop models for predicting litter and soil carbon stocks on forest land in the conterminous US. In this study we expand that work to estimate litter and soil carbon stocks and associated uncertainties for managed forest land in Alaska.|
|Lixia Lambert||FIA Use in Developing the Forest Sustainable and Economic Analysis Model (ForSEAM)||Tuesday||1c||Cherokee||Advances in modeling distributions, structure, and sustainability||The use of Forest Inventory and Analysis (FIA) database in developing the Forest Sustainable Economic Analysis Model (ForSEAM) to conduct spatial optimization analysis on timber resource for renewable energy development.||The Forest Sustainable and Economic Analysis Model (ForSEAM) is a dynamic linear optimization model developed to determine where conventional wood and energy feedstocks could be acquired from timberland. The model is compartmentalized into three sections including supply, demand, and sustainability. The supply component includes timber-sector production activities for 305 production regions in the lower 48 U.S. states. Each region is composed of a set of production activities including sawlog, pulpwood, and energy feedstock (woody biomass). The model currently considers two sources of energy feedstock: 1) logging residue and 2) removal of whole pulpwood and non-merchantable trees as energy feedstock. The growth rates and yields of different size and types of timber were calculated based on information from the Forest Inventory and Analysis (FIA) database. The conventional timber demand component is based on U.S. Forest Service Scenarios determined by the U.S. Forest Products Module. The sustainability component insures that harvest in each region does not exceed annual timber growth, forest tracts are located within reasonable distance of the roads,and current year forest attributes reflect previous period harvests and removals.|
|Kathryn Baer||Species distribution models predict shifting climatic suitability for important subsistence species in interior Alaskan forests||Tuesday||1c||Cherokee||Advances in modeling distributions, structure, and sustainability||Changing climatic conditions in subarctic forests may shift the distribution of understory vegetation, including important berry-producing species. We utilized occurrence records of berry species from Interior Alaska FIA data paired with current climate data and future climate models to estimate berry species distributions in the present and predict future shifts. Models predicted a decrease in highly suitable habitat along with shifts towards higher elevations and away from population centers.||Rapidly changing climatic conditions in subarctic forests have the potential to drive dramatic shifts in the distribution and abundance of their component vegetation. Of particular concern in Alaska is the potential for changes in the distribution and productivity of berry-producing understory species, which represent an important cultural and subsistence resource. FIA inventory data for Interior Alaska offer a unique dataset from which models can be constructed to estimate current berry species distributions and project how these distributions may shift under future climate change. In this study, we utilized records of berry species presence and absence paired with current climate data to estimate the current extent of berry species distributions. We then applied these models to three global circulation models for future climate to predict how these distributions might shift in the study area under future climatic conditions. Models predicted a decrease in the incidence of highly suitable habitat along with shifts towards higher elevations and away from population centers. These results indicate that subsistence berry species may become less abundant in interior Alaska and suggest areas for intensified future monitoring.|
|Margaret Evans||Using forest inventory data to build demography-driven models of the geographic distribution: a case study of Pinus edulis (two-needle piñon)||Tuesday||1c||Cherokee||Advances in modeling distributions, structure, and sustainability||We present a case study using FIA data to parameterize a demographic range model a model of the geographic distribution of Pinus edulis based on core variables of the annualized FIA Program along with PRISM climate data. This demography-driven range model suggests that negative density-dependent regulation, in addition to climatic factors, are important in shaping the distribution, and further that fire plays an important role in excluding this species from more productive forest types.||Climate change is expected to affect species distributions. Species distribution models based on occurrence data, using bioclimatic predictors alone, are widely criticized as phenomenological. Here we view the FIA database as the worlds largest repository of demographic data on trees, and use these data to parameterize a demographic model, an integral projection model, for a species whose entire geographic distribution is contained within the FIA domain, Pinus edulis (two-needle piñon). We evaluate competing hypotheses for piñons distribution: climate alone limits this species vs. climate and competition are both important limiting factors. We find support for the importance of both, with respect to the response of vital rates (growth, mortality, and regeneration) to variation in climatic and competitive conditions, and in terms of the fit between projected population growth rate and observed occurrences of Pinus edulis. Drawing on both disturbance ecology and a deep time perspective on the importance of fire in shaping the evolution of pines, we further suggest that fire may a missing factor that leads to a mismatch between occurrence data and predicted population growth rate above a mean annual precipitation of ~750 mm.|
|Patricia Manley||Data solutions for large-scale modeling and planning: Using FIA-based SilviaTerra Data for resilience planning in the central Sierra Nevada||Tuesday||1c||Cherokee||Advances in modeling distributions, structure, and sustainability||The Tahoe Central Sierra Initiative is a collaborative of 10 organizations in California that is restoring forest resilience across 2.4 M acres of high value watersheds. SilviaTerra data was identified as a singular, high quality data source for vegetation data across the entire landscape. Our detailed assessment addressed forest structure and composition, wildlife habitat, and timber supply over 100 years using the Landis II dynamic growth model to inform restoration options.||Western coniferous forests are exhibiting substantial stress from lack of fire, drought, and changing climates. Large-scale mortality events have been increasing in extent and frequency, and they are raising concerns that the opportunity to restore forest resilience may be slipping away. A common barrier to increasing the pace and scale of restoration is the availability of vegetation data that is consistent across large areas and multiple ownerships, and that describes forest conditions with sufficient detail, accuracy, and precision to support management actions. The Tahoe Central Sierra Initiative, a collaborative of Federal, State and non-profit organizations in California was formed to restore forest resilience across 2.4 M acres of mixed land ownership in some of the most ecologically and socially important watersheds in California. SilviaTerra data was identified as a singular, high quality data source for vegetation data across the entire landscape that provided spatially explicit, high resolution data on forest structure and composition. We conducted a detailed assessment of forest structure and composition, wildlife habitat, and timber supply, and then modeled changes in these parameters over 100 years using the Landis|
|David Bell||An application of generalized joint attribute modeling for modeling forest structure and composition||Tuesday||1c||Cherokee||Advances in modeling distributions, structure, and sustainability||Wall-to-wall maps of forest structure and composition are needed for landscape monitoring and planning. We apply a new hierarchical Bayesian modeling approach for complex, multivariate ecological data to predict multivariate forest inventory structure and composition based on environment and multispectral remote sensing. We compare the results of this model with those of an existing multivariate nearest neighbor imputation approach.||Wall-to-wall maps of forest structure and composition are needed for landscape monitoring and planning. Any such mapping strategy should generate statistically rigorous estimates of multiple response variables (e.g., species-level abundances) that are consistent with emergent properties (e.g., total abundance and species richness). In this study, we will apply the generalized joint attribute modeling (GJAM) approach, a hierarchical Bayesian modeling method for complex, multivariate ecological data, to predict multivariate Forest Inventory and Analysis structure and composition data based on environmental gradients and multispectral remote sensing. We will compare GJAM with an existing multivariate approach (gradient nearest neighbor imputation) in terms of (1) individual species basal area, (2) total tree basal area, and (3) total species richness. Comparisons will include plot-level accuracy assessments as well as comparisons of resulting map products, including both mean predictions and prediction precision (e.g., standard deviations). By comparing GJAM with existing nearest neighbor imputation methods, we will provide new guidance regarding the mapping of complex, multivariate forest attribute datasets.|
|Brian Miranda||Combining FIA data with the LANDIS forest simulation model to improve projections||Tuesday||1c||Cherokee||Advances in modeling distributions, structure, and sustainability||FIA data has been used widely with the LANDIS forest simulation model to provide information about current forest conditions and to calibrate and validate model initialization and growth parameters. The USDA Forest Service Northern Research Station is now pursuing improved integration between the LANDIS modeling community and the FIA research program to draw on the strengths of these two lines of research.||Forest simulation models allow users to project potential forest conditions into the future under different conditions and scenarios. Forest inventory and analysis (FIA) data provides rigorous information about past forest conditions. When used in combination, FIA data and forest simulations models, such as LANDIS, can utilize the information from the recent past to inform the projections of future conditions. FIA data has been used widely in LANDIS simulation studies to provide information about current forest conditions and to calibrate and validate model initialization and growth parameters. The USDA Forest Service Northern Research Station is now pursuing improved integration between the LANDIS modeling community and the FIA research program to draw on the strengths of these two lines of research. FIA data can be used to develop seamless forest condition maps meeting the specific model requirements for the simulation starting conditions, and for statistically rigorous evaluation of model behavior at different spatial scales utilizing the FIA data structure. In turn, the LANDIS model has the potential to inform aspects of the FIA carbon accounting by modeling growth and disturbance processed between plot measurement years.|
|Mark Majewsky||Urban FIA: Integrating all lands inventories into the national work flow||Wednesday||1a||Cherokee||Urban FIA update and latest results||Installing plots in urban areas is just the first step in FIA's vision of an All trees, All lands, All the time approach to the long-term monitoring of our nation's trees. This session will cover the status of the program as well as efforts being made and challenges encountered while folding FIAs plots in urban areas into the existing national work-flow from prefield, field, data processing, analysis, and on to data delivery while thinking regionally but acting nationally across four units.||ABSTRACT.--The Forest Inventory and Analysis (FIA) Program of the USDA Forest Service reports on the status and trends in forest land health, growth, area, location, and ownership. The 2014 Farm Bill instructs FIA to Implement an annualized inventory of trees in urban settings, including the status and trends of trees and forests, and assessments of their ecosystem services, values, health, and risk to pests and diseases. Urban areas implementation started in Baltimore, MD, and Austin, TX, during the 2014 field season and expanded into 35 cities in 24 States; 15 of which have both their proposed cities and all their urban areas active as of the 2019 field season. Installing plots in urban areas is just the first step in FIA's vision of an All trees, All lands, All the time approach to the long-term monitoring of the nation's trees. This session will cover the status of the program as well as the efforts being made, and challenges encountered, while folding FIAs plots in urban areas into the existing national work-flow from prefield, field, data processing, analysis, and on to data delivery while taking an approach of thinking regionally while acting nationally as a unified national program representing four regional units.|
|Tonya Lister||Advances in urban FIA processing, data availability, tools, and products||Wednesday||1a||Cherokee||Urban FIA update and latest results||Recognizing the importance of urban forests, FIA initiated an annualized urban inventory program in 2014. After five years of program implementation, a number of cities now have nearly complete cycles of baseline data and data distribution frequency will soon be annual. In this presentation we describe advances in the processing of urban FIA data including database development, data review, publication, the development of analytical tools, and automated 5-year reporting.||FIA initiated an annualized urban inventory in 2014 and the program has grown to include urban forest monitoring in 35 cities, across 24 states, with new cities added each year. During this period of implementation, there has been limited published urban data available. However, a number of cities now have nearly complete cycles of baseline data and data distribution frequency will soon be annual. In this presentation we describe advances in the processing of urban FIA data including database development, the implementation of a data review process, data publication, the development of analytical tools, and automated 5-year reporting. An overview of the data release schedule and reporting timeline are also presented. We conclude with a discussion of future directions in urban data delivery, analysis, and reporting.|
|Rebekah Zehnder||My Citys Trees: Delivering Information from Urban FIA Data||Wednesday||1a||Cherokee||Urban FIA update and latest results||My Citys Trees delivers Urban FIA data to a broad audience in a user-friendly interface, making the complex database accessible to average users. The information presented in the application provides a basis for strengthening urban forest management and advocacy efforts by empowering city government, non-profit organizations, and consultants with valuable data that is easy to access and understand.||My Citys Trees delivers Urban FIA data to a broad audience in a user-friendly interface, making the complex database accessible to average users. The use of themes to break down city-wide estimates into selected areas of the city remains a main feature of My Citys Trees. Themes, such as ecoregions, watersheds, or income level, are selected independently for each city to reflect local resource issues. Available estimates include tree counts, carbon storage, energy savings, and more.
The web application received some major updates in 2019, including improved functionality and additional features as well as data for more cities. Information on the status of Urban FIA in participating cities is available on the map. The revamped app has better reporting capability users are now able to produce a full report or one-page summary for their area in PDF format and share it directly from the application. Estimates now include breakdowns by diameter class and land use in addition to ownership. Data for San Diego and San Antonio are now available along with Austin and Houston data. The 2019 release of My Citys Trees is designed to work seamlessly across devices of all sizes, from smart phones to desktops.
|Nancy Sonti||Urban National Landowner Survey: Results and Application from Pilot Cities||Wednesday||1b||Cherokee||Urban FIA update and latest results||The Urban National Landowner Survey is designed to capture attitudes and behaviors of urban residential landowners in order to enhance our understanding of urban forest values and management practices. This talk will discuss some of the challenges and opportunities encountered in the process of developing and implementing the survey in Texas and Wisconsin pilot cities, as well as the application of this data to inform targeted outreach to community stakeholders in urban forestry.||The Urban FIA program was created to inventory the nations urban trees and forests, expanding the FIA sampling frame beyond the FIA definition of forestland. Alongside the plot-based Urban FIA data collection, development of an Urban National Landowner Survey (analogous to FIAs National Woodland Owner Survey) captures the attitudes and behaviors of urban residential landowners regarding their neighborhood and community, as well as their propertys trees and other green spaces. A more complete understanding of private residential land management both within and across cities can help policymakers, natural resource managers, and private industry in their efforts to create, sustain, and make productive use of urban trees and other vegetation. This talk will discuss some of the challenges and opportunities encountered in the process of developing and implementing the Urban National Landowner Survey in pilot cities, as well as the application of this data to inform targeted outreach to community stakeholders in urban forestry. Pilot cities include Austin, Texas and the cities of Green Bay, Madison, Milwaukee, and Wasau, Wisconsin.|
|Michael Galvin||Going with the flow: tracking urban wood flows to test an Urban TPO approach||Wednesday||1a||Cherokee||Urban FIA update and latest results||The National Renewable Energy Laboratory estimates that over 41,000,000 tons of urban wood|
waste are generated annually in the U.S. With an inventory of urban trees, of those that generate urban wood waste (tree care
companies), and of those that process urban wood, we can understand the scope and potential
of your urban wood waste stream. We apply this model to Baltimore, share results, and also discuss approaches for intensification.
|Urban wood waste is a plentiful, underutilized resource. The National Renewable Energy Laboratory estimates that over 41,000,000 tons of urban wood waste are generated annually in the U.S., and that tree care crews generate ~ 1,000 tons of
urban wood waste per crew per year. Reports indicate that most of it is chipped or used for firewood.
We examined three models of various levels of intensity for an urban analog to the FIA TPO, which we subsequently refer to as Urban TPO. We found that the most basic level of Urban TPO, focusing on wood waste generators and volumes, would be most appropriate for a national program.
With an inventory of urban trees, of those that generate urban wood waste (tree care companies), and of those that process urban wood, we can understand the scope and potential of an urban wood waste system. We apply this model to Baltimore and share our results and their limitations. We also discuss potential approaches for intensification by including urban wood processors, producers, and customers.
|Kathryn Baer||An Inventory of San Diegos Forest Resources: Urban FIA in the Wild West||Wednesday||1a||Cherokee||Urban FIA update and latest results||In 2017, San Diego, California became the first urban area in FIAs PNW Region to be inventoried as part of the Urban FIA (UFIA) initiative. This presentation will highlight key findings of the San Diego UFIA analysis and report, including species composition and distribution of the citys urban forest and the ecosystem services provided. Attributes of San Diegos urban forest will be compared to results from urban areas within different ecoregions previously inventoried by the UFIA program.||In 2017, San Diego, California became the first urban area in FIAs Pacific Northwest Region to be inventoried as part of the Urban FIA (UFIA) initiative outlined in the 2014 Farm Bill. A full complement of 200 plots were selected in San Diego, of which 190 were sampled by UFIA crews from September to November of 2017. The results of this inventory will be released in 2019 in what is anticipated to be the first standardized UFIA report. This presentation will describe novel techniques utilized in the San Diego urban inventory, including methods for the consistent measurement of the citys many palm trees. We will highlight key findings of the San Diego UFIA analysis and report, including descriptions of the species composition and distribution of the citys urban forest and the ecosystem services it provides. Attributes of San Diegos urban forest will be compared to those of two urban areas within FIAs Southern Region that were previously inventoried as part of the UFIA initiative: Houston and Austin, Texas. These comparisons will include a discussion of differences in tree cover and regeneration among urban areas within different ecoregions, and unique threats to the sustainability of San Diegos urban forest.|
|Thomas Brandeis||Implementing Urban FIA in the U.S. Virgin Islands||Wednesday||1a||Cherokee||Urban forest research and advances in urban inventory and monitoring||Forest cover in the US Virgin Islands provides watershed protection, endemic species conservation and support for an economy heavily dependent on tourism. FIA currently does not capture the ecosystem services contribution of trees in urban areas in this landscape dominated by low density, dispersed development. In the upcoming fourth forest inventory in these Caribbean islands, data collection will transition from forest only to applying urban FIA protocols on all sampling points.||Forest cover is of particular importance on Caribbean islands. Trees capture rainfall and stabilize soils, improving aquifer recharge and protecting fringing coral reefs vital to fisheries and tourism. It preserves endemic plant and animal species and mitigates ambient temperatures on these subtropical islands. The FIA program began inventorying forests in the US Virgin Islands (USVI) in 2004 with an intensified sample (6-12 times for a total of 106 sampling points), remeasured these plots in 2009 and 2014, and will again in late 2019. While the islands of St. Croix (53,870 ac. total area; 56% forested, 50, 601 people), St. Thomas (20,480 ac., 44% forested, 51,634 people) and St. John (12,800 ac., 81% forested, 4,170 people) vary in size and population, they share similar dispersed, low density development. 46% of FIA sampling points on St. Croix fall within the Christiansted urban cluster (UC) and 70% of points on St. Thomas fall within the Charlotte Amalie UC. Trees on the islands developed lands also contribute ecosystem services and the goal of the urban FIA program is to quantify them. In this latest cycle, all USVI sampling points will treated as falling within an UC and have the full suite of urban FIA data collection.|
|James Westfall||Urban tree specific gravity and ash content: a case study from Baltimore, Maryland USA||Wednesday||1b||Cherokee||Urban forest research and advances in urban inventory and monitoring||Wood samples from various tree species in the city of Baltimore, MD USA were analyzed for basic specific gravity (SG) and ash content (AC). Some species appeared to have either higher or lower SG than their forested counterparts. Overall, the use of forest-based SG may produce estimates of urban woody biomass that are 510% too low. Most of the species studied had higher AC than their forested counterparts. Further work is needed to refine results and better support urban tree inventories.||Interest in conducting urban tree inventories and quantifying the associated wood resource has accelerated at a pace faster than supporting research needs can be identified and accomplished. To examine potential differences in wood properties between trees grown in forest and urban settings, wood samples were collected from various tree species in the city of Baltimore, MD USA. The samples were analyzed for basic specific gravity (SG) and ash content (AC). The results from urban trees were compared with published values from studies based on forest-grown trees. There was no general trend in the results for SG; however, urban Acer rubrum L. and Fraxinus spp. appeared to have higher SG and Quercus palustris Münchh. lower SG than their forested counterparts. Based on these results, the use of existing forest-based SG data may produce weight estimates of urban woody biomass that are 5 10% too low. Conversely, most of the species studied had higher AC than their forested counterparts. Further work is needed on a wider range of species and geographic locations to refine the results and better support the analysis of urban tree inventories.|
|David MacFarlane||New approaches and considerations for developing tree measurements and models for urban FIA plots.||Wednesday||1b||Cherokee||Urban forest research and advances in urban inventory and monitoring||This talk will explore ideas and data from initial investigations in the Northern FIA region that show some important differences in urban (open-grown) versus rural (forest-grown) trees, which should be considered when developing urban tree measurement protocols and models.||As the Forest Inventory and Analysis (FIA) program enhances its national plot network to include comprehensive annual forestry inventory and monitoring data for trees growing in urban areas, it will facilitate analyses of tree resources along an urban to rural gradient. While a tree is a tree, at some level, FIA may need new measurements and/or models unique to some urban environments to allow for unbiased comparisons of plots in urban versus rural areas, in terms of tree merchantability and quality, risk assessment, and ecosystem services provided. Here, we present results from initial investigations in the Northern FIA region that show some important differences in urban (open-grown) versus rural (forest-grown) trees, which should be considered when developing urban tree measurement protocols and models. Also discussed are data needs for developing urban-tree-specific models, including non-destructive methods based on terrestrial LiDAR and photography.|
|Jason Henning||Evaluation of simplified crown measures for Urban FIA||Wednesday||1b||Cherokee||Urban forest research and advances in urban inventory and monitoring||An evaluation of replacing the new urban measurement of percent foliage absent within the crown silhouette with an adaptation of the existing compacted crown ratio measurement. Initial evaluation suggests that this approach leads to an underestimate of individual tree leaf surface area of approximately 2%.||The incorporation of i-Tree methods in Urban FIA inventories has added several new field measurements. Many of these new measurements are associated with assessing crown volume, a key driver of leaf surface area and ecosystem service models within the i-Tree tools. These new variables include perpendicular crown widths, crown base heights, and the foliage absent within the silhouette of the live crown. These additional variables can be challenging to collect, especially in closed canopy settings where overlapping crown boundaries can be difficult to discern from the ground. We suggest a method to replace the new percent foliage absent measure with an adaptation of the existing compacted crown ratio measure. Initial evaluation shows that this approach leads to underestimates of individual tree leaf surface area by an average of 2% and underestimates of leaf surface area at the plot level by an average of 7%. Eliminating the need to assess percent absent has the potential to save time in the field while relying on the established compacted crown ratio measurement. The modest underestimates of leaf surface area will lead to conservative estimates of ecosystem services from the i-Tree models.|
|James Westfall||Crown width models for tree species growing in urban areas of the U.S.||Wednesday||1b||Cherokee||Urban forest research and advances in urban inventory and monitoring||Models to predict crown widths for urban trees were developed using data from 49 cities across the U.S. The effort consisted of fitting mixed models for 29 species groups that encompassed 964 species. Cities were considered as random effects and were statistically significant for 22 of the 29 groups. The presentation will also include comparisons with other published crown width models for both forested and urban environments.||Crown widths of trees growing in urban areas are of considerable importance as an overall indicator of tree health and also serve as an important factor when assessing ecosystem services such as carbon sequestration, air quality, energy efficiency, and aesthetic values. Unfortunately, assessing tree crown width in urban environments is often challenging, which compromises the accuracy and consistency of measurements for this key variable. To circumvent this issue, models to predict crown widths for urban trees were developed using data from 49 cities across the U.S. The effort consisted of fitting mixed models for 29 species groups that encompassed 964 species. Cities were considered as random effects and were statistically significant for 22 of the 29 groups. The presentation will also include assessments of using crown width predictions instead of observed values for leaf area index (LAI) calculations within the iTree software suite, as well as comparisons with other published crown width models for both forested and urban environments.|
|Georgios Arseniou||The use of fractal dimension to study ecological traits of trees in US cities.||Wednesday||1b||Cherokee||Urban forest research and advances in urban inventory and monitoring||We analyzed a national urban tree database and found that urban-tree fractal dimensions are significantly correlated with drought and shade tolerance and leaf mass per unit area and sensitive to local- (e.g., distance from buildings) and regional- scale factors (e.g., mean annual temperature).||There is growing interest in understanding the nature of urban trees, because they provide multiple social and ecological services (e.g., temperature regulation, carbon storage). Functional traits of urban trees may be very different from trees growing in rural forests, because urban environments can be very stressful, due to conditions like excessive moisture and heat, barriers to root growth and pruning, but also more favorable, because trees in cities may receive additional watering, fertilization and other care. Here, we studied the fractal dimension of urban tree crowns, a metric related to tree form and function. We analyzed a national urban tree database (McPherson et al. 2016) and found that urban-tree fractal dimensions are significantly correlated with drought and shade tolerance and leaf mass per unit area and sensitive to local- (e.g., distance from buildings) and regional- scale factors (e.g., mean annual temperature). The results suggest that urban forest monitoring programs (like urban FIA) may benefit from measuring fractal dimension, which helps enhance understanding of urban tree physiology and may provide important information for managing urban trees under different local and regional environmental conditions.|
|Susannah Lerman||i-Tree Wildlife: a tool for evaluating bird habitat potential in the urban forest||Wednesday||1b||Cherokee||Urban forest research and advances in urban inventory and monitoring||The i-Tree wildlife module evaluates bird habitat potential in urban forests using i-Tree field data. It demonstrates how mitigation strategies and forest assessment tools can enhance habitat in urban forests. The tool quantifies how birds respond to different urban forest characteristics and management strategies, can guide management goals, and can provide habitat suitability summaries for urban FIA reports. Ultimately, the tool aims to improve the urban forest for the benefit of birds.||As more land becomes slated for urban development, identifying effective urban forest management tools becomes paramount to ensure the urban forest provides habitat to sustain bird and other wildlife populations. To address this need, we developed a wildlife tool for i-Tree to 1) provide detailed information on habitat requirements for a variety of songbirds, 2) to evaluate the bird habitat potential at ecoregional scales, and 3) to guide habitat improvement plans. The tool quantifies and qualifies available habitat based on data, and summarizes bird habitat relationships for variables that directly relate to these datasets. These data served as the basis for generating habitat suitability equations for predicting the probability of a species occurrence for the selected habitat variables. The bird habitat models calculate specific targets (e.g., canopy percent or dead wood density) geared towards urban foresters and planners when determining how to manage the urban forest for wildlife. Further, because of the flexibility of the tool and associated habitat models, the i-Tree wildlife module has the potential to integrate the habitat outputs in urban FIA reports.|
|Robert McGaughey||Overview of Digital Aerial Photogrammetry||Wednesday||1c||Cherokee||Use of photogrammetrically-derived 3D point clouds to support large-area forest inventory and monitoring||Digital aerial photogrammetry (DAP) is a technique that derives 3D information from overlapping aerial images. DAP products include digital surface models and 3D point clouds. The combination of broad-scale image acquisition programs such as the National Agriculture Imagery Program (NAIP), DAP-derived data products, and existing terrain data offer the ability to characterize vegetation height and canopy cover over large areas for relatively low cost.||Digital aerial photogrammetry (DAP) is a technique that derives 3D information from overlapping aerial images. The required overlap can be obtained using a series of images captured with frame-based cameras or from the multiple look angles captured by continuous scanners (push-broom sensors). DAP products include digital surface models and 3D point clouds. DAP-derived data is often compared to lidar. However, DAP can only produce measurements for areas in an image that are well illuminated and that contain enough contrast to allow image matching to work. This means that DAP-derived data products can contain gaps and only represent the ground surface in open areas. Combined with USGS 3DEP elevation data, DAP-derived data products offer the ability to characterize vegetation height and canopy cover over large areas for relatively low cost. Such information has many applications in the FIA context including pre-field conditions assessment, improving the precision of county-level estimates, and reducing bias associated with non-sampled plots.
This presentation will present an overview of DAP technology, describe the basic data products, and compare the technology to lidar.
|James Ellenwood||The potential for augmenting statewide forest estimations with canopy height from remotely sensed products the status of national programs||Wednesday||1c||Cherokee||Use of photogrammetrically-derived 3D point clouds to support large-area forest inventory and monitoring||Lidar and digital photogrametric data is widely available and it is possible to produce a nationwide Canopy Height Model (CHM) at a 1-m resolution. Benefits to the Forest Inventory and Analysis program include the potential to improve Image-based Change Estimation (ICE) and pre-field efficiencies; the potential to incorporate better measurements in non-forest areas such as agriculture and urban forest areas; and improved small area estimation and modeling.||Since the initial development of LIDAR and digital aerial photogrammetric technologies, their use has been limited to local project assessments and analysis. In recent years, a number of developments have increased the potential for the use of these datasets for larger-area assessments. The National 3D Elevation Program (3DEP) allows for the cooperation among federal agencies and partners to join forces in funding common and adjacent areas to improve project costs and gather more usable data. The National Agriculture Imagery Program (NAIP) allows for the optional purchase of the digital aerial photogrammetric point clouds, as of 2018. The University of Minnesota Polar Geospatial Center is conducting a large-area digital photogrammetric project based upon all available US Government purchased imagery from Digitalglobe for global coverage. It is possible to produce a nationwide Canopy Height Model (CHM) at a 1-m resolution. Benefits to the Forest Inventory and Analysis program include the potential to improve Image-based Change Estimation (ICE) and pre-field efficiencies; the potential to incorporate better measurements in non-forest areas such as agriculture and urban forest areas; and improved small area estimation and modeling.|
|Andrew Lister||Comparing the relationship between tree canopy height information from LiDAR, phodar and forest inventory data in northeastern forests||Wednesday||1c||Cherokee||Use of photogrammetrically-derived 3D point clouds to support large-area forest inventory and monitoring||The measurement of tree height with phodar over large areas is relatively recent, and little is known about its usefulness compared to LiDAR. The current study assesses phodars potential by comparing LiDAR and phodar-based height products with height information from forest inventory plots from Connecticut and Maryland. Bivariate correlation between the data types and other metrics will be compared with the goal of assessing the utility of phodar for various FIA business processes.||Detailed tree canopy height information is valuable to natural resource monitoring, science and management specialists. Canopy height is commonly measured from the ground on forest inventory plots, but this information is expensive and prone to error. Alternatively, 3-d models of canopy height and structure can be made using aerial LiDAR or stereo pairs of digital aerial photographs, sometimes referred to as phodar. The operational use of phodar over large areas is relatively recent, and there is thus a gap in the understanding of how phodar compares to LiDAR or ground measurements with respect to producing useful canopy height products. The current study seeks to fill this gap by comparing data from wall-to-wall LiDAR and phodar-based height products with height information from US Forest Service Forest Inventory and Analysis (FIA) plots in the northeastern states of Connecticut and Maryland. Bivariate correlation between the data types and other metrics will be compared with the goal of assessing the utility of phodar for various FIA business processes. The goal of this pilot analysis is to assess the potential for this new technology to provide useful 3-d vegetation structure information in northeastern forests.|
|Benjamin Branoff||Evaluating the potential of NAIP point clouds to support operational forest inventory applications in the southeastern U.S.||Wednesday||1c||Cherokee||In this presentation we evaluate the quality of 3D point clouds developed through photogrammetric processing of National Agriculture Imagery Program (NAIP) stereo imagery. NAIP canopy height surfaces for Tennessee and Virginia are compared with measured tree heights collected in the field by FIA, and with canopy heights developed from airborne Lidar data. Issues affecting data quality are evaluated and used to recommend standards needed for broader use of 3D point clouds within the FIA program.||The U.S. Forest Inventory and Analysis (FIA) program regularly collects tree and stand-level data to produce statistical estimates which are used to analyze the current status and condition of Americas forests. Land cover data from the National Land Cover Database (NLCD) are typically used to post-stratify these estimates but the information in these maps is often more related to forest area than tree density or size, thus there is a need for alternative products that can help operationally improve estimates of forest structure-related variables like volume and biomass. In this presentation we evaluate the quality of 3D point clouds developed through photogrammetric processing of high resolution stereo imagery collected by the National Agriculture Imagery Program (NAIP). NAIP canopy height surfaces developed for the states of Tennessee and Virginia are compared with measured tree heights collected in the field by FIA, as well as with canopy height products developed with airborne Lidar data. Issues affecting data quality such as image acquisition date, post-processing techniques and precision of field GPS locations are evaluated and used to recommend standards needed for broader use of 3D point clouds within the FIA program.|
|Jacob Strunk||An Examination of DAP, Landsat, and Environmental Variables for Modeling Volume, Biomass, and Carbon on FIA Plots||Wednesday||1c||Cherokee||Digital Aerial Photogrammetry (DAP) for WA State was evaluated in combination with Landsat and environmental gradients to model forest volume, live biomass, and live carbon. For 570 plots in Washington State we used principal components, linear regression, and variable selection to evaluate relationships among data types. Initial results indicate that use of multiple auxiliary datasets enables superior mapping products to support forest monitoring, management, and planning efforts.||Digital Aerial Photogrammetry (DAP) has recently received increased interest for forest inventory augmentation due to its low cost and availability over large areas including the entire conterminous USA every 2-3 years. There are additional opportunities for large area inventory augmentation, such as the integration of DAP with satellite imagery (e.g., Landsat multispectral data) and environmental gradients (e.g., slope, aspect, elevation, and temperature and precipitation attributes). This study explores the relationships between DAP, Landsat, and environmental gradients and contrasts their abilities to independently and jointly explain variation in measured tree volume, aboveground live biomass, and live carbon on Forest Inventory and Analysis plots. For 570 plots in Washington State, we looked at associations between principal components for the three auxiliary datasets, fit linear models for volume, biomass, and carbon to principal components, and performed a variable selection exercise with all subsets regressions. Initial results indicate that integration of multiple sources of auxiliary information in forest mapping efforts enables superior products to support forest monitoring, management, and planning efforts.|
|Nick Eliopoulos||Estimation of Tree Diameter at Breast Height Using Close Range Stereo Photogrammetry||Wednesday||1c||Cherokee||Video footage taken using a stereo camera was used to report the diameter at breast height. Our algorithm involves performing a frame-by-frame analysis of each image in the video footage to report a diameter at breast height. Depth information from each frame is extracted and interpreted independently without generating a point cloud. Our method reported a diameter at breast height root mean square error of 1.32 cm over 40 trees, with footage taken 3 meters away from each tree.||Forestry inventory analysis is time-consuming and expensive. Contemporary solutions such as terrestrial laser scanning are not convenient for small-scale landowners due to their cost. State of the art solutions involving the use of stereo photogrammetry have the advantage of being mobile, relatively low-cost, and do not require training to use. Our method captures the mobility and low-cost benefits of stereo photogrammetry, while surpassing diameter at breast height accuracy compared to similar groups. Our improvement is an algorithm that is used with stereo footage to report diameter at breast height. Two types of video footage were recorded for use in our algorithm: video captured standing still, and video captured in motion walking through a plot. The best diameter at breast height root mean square error reported for video standing still was 1.32 cm over 40 trees. Our algorithm produced a root mean square error of 1.11 cm on the video captured in motion, which included 18 trees. Footage taken standing still over 20 trees took 5 minutes to record, but only 28.7 seconds for our algorithm to report diameters for each tree.|
|Demetrios Gatziolis||Evaluating the utility of pushbroom photogrammetry-derived point clouds for estimating tree canopy cover||Wednesday||1c||Cherokee||Tree canopy cover is a parameter challenging to measure in the field, yet integral to many forest inventory operations and data analyses. Remotely sensed data conducive to an accurate and precise estimate of cover, namely LiDAR, are typically too costly, especially over large areas. Digital Aerial Photogrammetry (DAP) for NAIP stereo imagery has emerged as a potentially economically feasible alternative. We evaluate the potential of DAP for tree canopy cover estimation across Washington State.||Tree canopy cover is an important biological and ecological parameter often used as a criterion for land classification and other purposes. Definitions of forestland, a critical parameter in assessing rates of forest gain, loss and degradation, are based on a minimum cover threshold (e.g. 10 percent). Because it is time consuming and challenging to measure it with acceptable accuracy and precision during field visits of inventory plots, canopy cover is often estimated via remote sensing. LiDAR data arguably yield the best estimates, but their acquisition cost often leads to sporadic availability. Manual, photointerpretation-based estimates from airborne imagery, such as the one acquired periodically by the NAIP Program for the continental US, require substantial analyst involvement and are susceptible to overestimation owing to the wide field of view and minimal overlap between image swaths. Limitations to photo-interpretation can be potentially overcome by Digital Aerial Photogrammetry (DAP) for NAIP stereo imagery, with tree canopy cover estimates obtained by processing the point clouds generated by DAP. We evaluate this potential with dense LiDAR point clouds co-temporal to the NAIP imagery in the State of Washington.|
|Mark Nelson||FIA contributions to Montréal Process Criteria & Indicators for monitoring biological diversity||Thursday||1a||Cherokee||FIA contributions to monitoring biological diversity||Forests support a variety of ecosystems, species, and genes collectively referred to as biological diversity along with their ecological processes. In the United States, conservation of forest-associated biological diversity is monitored through Montréal Process (MP) Criteria & Indicators, including three indicators each for ecosystem, species, and genetic diversity. Forest Inventory and Analysis products contribute to MP monitoring of biological diversity in a variety of ways.||Conservation of biological diversity is critical for maintaining many ecological services. Biological diversity includes diversity within species, between species, and of ecosystems, as defined by the Convention on Biological Diversity. Many countries have implemented programs for monitoring biological diversity across these scales. The Montréal Process (MP) was developed to provide a standard, international framework for assessing the sustainability of temperate and boreal forest ecosystems across twelve countries, including the United States. The first of seven MP criteria is Conservation of Biological Diversity, containing nine indicators, three each for ecosystem, species, and genetic diversity. Data, information, and knowledge produced by the U.S. Forest Service Forest Inventory and Analysis program contribute to MP monitoring at national, regional, and state scales. We present the MP, other regional and state variations of indicators, and corresponding applications of FIA for monitoring biological diversity. This presentation provides introduction and context for subsequent presentations in this session.|
|Randall Morin||Use of Forest Inventory to Assess Trends in Habitat Abundance for the Indiana Bat across Forests of the Northern United States||Thursday||1a||Cherokee||FIA contributions to monitoring biological diversity||FIA data can support estimates of habitat abundance for some wildlife species whose habitat requirements have corresponding FIA attributes of composition and structure. The federally endangered Indiana bat (Myotis sodalis) lives in hardwood and hardwood-pine forests, requiring particular tree characteristics for roosting habitat features. We estimated habitat distribution and trends for Indiana bat, and demonstrate a multi-scale tool for presenting habitat information.||Forest Inventory and Analysis (FIA) data has long been used to address broad classes of forest habitat types and address global, national, and regional biodiversity assessments. For example, forest inventory data can be used estimate the current status of and trends in early-successional forest. In cases where specific habitat requirements related to trees are well documented for forest-associated species it becomes possible to make habitat estimates for specific wildlife species. The Indiana bat (Myotis sodalis) was listed as endangered by the US Fish & Wildlife Service in 1967. Indiana bats live in hardwood and hardwood-pine forests across much of the midwestern, southern, and mid-Atlantic regions of the United States. They have specific requirements for the number of live and standing dead trees per acre that are necessary to provide suitable and optimal habitat for roosting and nesting. Here we analyze forest inventory data distributed across the eastern USA to estimate the current distribution and trends of suitable and optimal forest habitat for the Indiana bat and demonstrate a digital tool for sharing this information at multiple spatial scales.|
|Alexa McKerrow||Joining USGS GAP species-habitat relationships to FIA via USNVC Macrogroups||Thursday||1a||Cherokee||FIA contributions to monitoring biological diversity||Nationally consistent population trend data are available for some forest-associated species, but not for others. Species-specific habitat abundance and change estimates provide indirect indices of biological diversity. To support such estimates we joined the GAP species-habitat relationships database to FIA database using attributes of composition and structure within U.S. National Vegetation Classification macrogroups; we incorporated geospatial datasets to further refine these associations.||Forest Inventory and Analysis (FIA) data provide opportunities for monitoring biological diversity by creating linkages between forest characteristics and wildlife habitat. Tree species data recorded during FIA field visits provide direct estimates of tree species distribution, abundance, and change. Some forest-associated terrestrial vertebrate species also have nationally consistent population trend data (e.g., Breeding Bird Survey), but many do not. For those species, diversity can be monitored indirectly via species-specific estimates of habitat abundance and change. Most forest species-habitat relationships include detailed attributes of forest composition and structure, which FIA data can provide if species habitat data can be efficiently linked to FIA via common attributes. We joined USGS Gap Analysis Program (GAP) species-habitat relationships at the macrogroup level to FIA using the recently added U.S. National Vegetation Classification (USNVC) macrogroups in the FIA condition attributes across the Eastern US. GAP species-habitat relationships data were recently completed for birds, mammals, reptiles, and amphibians. To further refine GAP-USNVC-FIA linkages we incorporated vertebrate species range maps.|
|James Berdeen||Estimating potential abundance of wood duck nest cavities||Thursday||1a||Cherokee||FIA contributions to monitoring biological diversity||Wood duck (Aix sponsa) hens nest in tree cavities. FIA annual inventories do not record the presence of tree cavities. Therefore, we upscaled an intensive field survey of tree cavities to FIA data on tree species, size, and health status to estimate tree cavity abundance and trends in northern Minnesota. Numbers of trees >= 22 cm DBH in seven species most associated with cavities increased from 1990 to 1999-2003, then decreased during subsequent annual inventories through 2014-2018.||Tree cavities provide nesting substrate for Wood duck (Aix sponsa) hens. Habitats used by wood ducks in Minnesota have changed in recent decades. We initiated a study to explain the variation in suitable cavity presence in the broader Laurentian Mixed Forest Province of north central Minnesota between 1990 and 2014-2018, and used these results with FIA data to make inferences about temporal changes of cavity abundance in northern Minnesota. We measured 7,869 trees >22 cm DBH in Cass County, Minnesota. We classified 223 cavities as suitable and 111 as marginally suitable for nesting. Data were sparse for large DBH trees of all species, so we surveyed additional plots to obtain sufficient data on large-DBH stems (>40 cm for early and mid-successional species, >50 cm for late successional species). Logistic regression models fit to these data explained the variation of cavity presence in seven common tree species for which there was adequate cavity data. The estimated number of these trees increased between 1990 and 1999-2003, then decreased during subsequent periods. We will discuss our projections of the temporal change in cavity abundance.|
|Andrew Hartsell||THE IMPACTS OF VARYING SPATIAL SCALE IN DETERMINNG PREDICTORS OF TREE SPECIES DIVERSITY USING NESTED WATERSHEDS AND FOREST INVENTORY DATA IN THE SOUTHEASTERN UNITED STATES||Thursday||1a||Cherokee||FIA contributions to monitoring biological diversity||This study uses FIA data and nested watershed to investigate the impact of spatial scale on ecological processes. Multi-response permutation procedures (MRPP), non-metric multidimensional scaling (NMS), and regression trees were used. Getis-Ord Gi* hot-spot analysis was utilized to determine if diversity hot and cold spots clustered. Finally, ordinary least squares, spatial lag, and spatial error models were developed.||The relationship between tree species diversity and various climatic, environmental, and anthropogenic factors in the southeastern United States in multifaceted and complex. Key among these is the impact of plantation forestry, agricultural establishment, and urban development on three measures of tree species diversity: species richness, Shannon-Wiener index, and Simpsons index. Forest Inventory and Analysis data as the source of tree species data and nested watersheds from the Watershed Boundary Dataset (WBD) were used as spatial boundaries. The measures of diversity were calculated for the differing watershed scales. Multivariate analysis and spatial analysis techniques were incorporated to understand how possible predictors and covariates of tree species diversity vary with spatial scale. Multivariate analysis techniques such as multi-response permutation procedures (MRPP), non-metric multidimensional scaling (NMS), and regression trees were used to assess relationships within the data. A species-area curves was created to determine appropriate spatial scales. Getis-Ord Gi* hot-spot analysis was utilized to determine if diversity hot and cold spots clustered, as well as if clustering patterns changed in regards to changes in|
|Christopher Looney||Variation in individual-tree growth across species mixtures provides evidence of complementarity effects in Interior West forests||Thursday||1a||Cherokee||FIA contributions to monitoring biological diversity||In some forest types, species complementarity could potentially enhance productivity and resilience to climate change. We used Interior West FIA data to model the growth-effects of complementarity, stand structure, and tree characteristics on ponderosa pine, Douglas-fir, quaking aspen, and Engelmann spruce. Preliminary results suggest that species mixtures enhance individual-tree growth depending on shade tolerance. Stand structure may also modify complementarity effects.||A central challenge to contemporary forest management is maintaining forest productivity in the face of climate change. Research suggests species diversity could be used to enhance both stand productivity and resilience through complementarity effects (e.g. facilitation) However, this research has focused on European forests, with limited North American examples. To fill this knowledge gap, we investigated the relative influence of complementarity, stand structure, and tree characteristics on the growth of four major species in the Interior West: ponderosa pine, Douglas-fir, quaking aspen, and Engelmann spruce. Using 10yr FIA remeasurements, we created individual-tree (>4.9 in. DBH) models and examined the growth of each of the four species along community gradients. Our preliminary results indicate that only shade-tolerant Engelmann spruce and Douglas-fir increase in growth with decreasing stand purity. Light partitioning between shade-tolerant and intolerant species, modified by stand structure, may drive mixture effects on growth. These implications of species mixtures for individual-tree growth and vigor will help guide future efforts to sustain function across the Adaptive Silviculture for Climate Change (ASCC) Network.|
|John Coulston||Overview of small area estimation concepts and applications||Thursday||1b||Cherokee||The small area estimation conundrum||Small area estimation techniques are a tool for increasing precision of estimates. Here we provide an overview of small area estimation concept and applications. We further compare and contrast small area estimation concepts with FIA user perspective on small area estimation products.||Small area estimation techniques have recently gained attention in forest inventory applications. Pursuant to the 2014 Farm Bill initiative on improving sub-state estimates, the Forest Inventory and Analysis program invested in research on small area techniques. However there is substantial variation in what partners, and FIA staff themselves, consider as small areas. Further, there is lack of common understanding of what small area techniques offer and how they may differ from other approaches. In an effort to understand the variation in what partners and stakeholder are after with respect to small area, we conducted a survey to highlight the various user needs. In this presentation we will provide an overview of small area estimation concepts and applications, and summarize the results of a recent user community survey about small area estimation needs, applications, and perceptions.|
|Aaron Stottlemyer||State Perspectives on Small Area Estimation||Thursday||1b||Cherokee||The small area estimation conundrum||While FIA data have traditionally been used to produce population estimates over large geographic areas, there is considerable interest in using modeling approaches that combine remotely sensed data with FIA plot data to estimate forest attributes in small areas. In this presentation we will share the perspectives of multiple states to clarify the range of needs to help guide future work in small area estimation.||The Forest Inventory and Analysis (FIA) program collects data on forest ecosystems across the United States and its territories at an intensity of approximately one plot every 6,000 acres. While FIA data have traditionally been used to produce population estimates over large geographic areas, there is considerable interest in using modeling approaches that combine remotely sensed data with FIA plot data to estimate forest attributes in small areas. In this presentation we will share the perspectives of multiple states to clarify the range of needs to help guide future work in small area estimation.|
|Richard Guldin||An Overview of Small Area Estimation Research Outside the United States: The International Perspective||Thursday||1b||Cherokee||The small area estimation conundrum||Small area estimation (SAE) research outside of the United States has been centered in Europe--both in transnational organizations, such as the European Union, and in individual countries. The impetus for SAE research is coming from government policy-makers responsible for core national statistics across domains (e.g, demographic data for political jurisdiction or business data for economic sector). SAE research on forest conditions parallels work in other domains, but defined by geography.||Small area estimation (SAE) research outside of the United States has been centered in Europe--both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SAE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information for political jurisdictions (domain) or business data for an economic sector (domain). SAE research on forest statistics is typically a subset of core environmental statistics, defined by a geography domain. Whatever the domain--geography, economic sector, political jurisdiction--the same principles and basic statistical research are driving ongoing SAE research activities in other countries. Policy makers who seek better estimates of conditions and trends for smaller areas in a domain than can be provided at acceptable levels of precision from contemporary sampling frames are often the impetus behind ongoing SAE research activities. This but another example of how researchers at the science-policy interface seek to build better bridges to inform policy-makers decisions.|
|Steve Prisley||A perspective on small area estimation from the commercial forestry sector||Thursday||1b||Cherokee||The small area estimation conundrum||This presentation will present the case for small area estimation and other approaches to meet the increasing need for fine-grain data by the commercial forestry sector. While substantial techniques research has already been done, there remains confusion about terminology, desired products, and soundness of various approaches.||Obtaining reliable forest inventory data for smaller geographic areas and with finer grain is of increasing interest in the commercial forestry sector. Commercial forestry entities need inventory data to estimate potential wood supply for facilities, to develop strategic responses to supply disruptions by hurricanes or wildfires, and to anticipate markets for forest products to inform investment decisions. A wealth of relevant resource data at fine spatial grain is readily available- including high-resolution aerial imagery at increasing temporal frequencies, detailed soils maps, ownership parcel boundaries, transportation networks, topography and derivative layers, and many others. Yet it is apparent that we have not yet realized the potential to use inventory data from sparse samples (like the FIA plot network) to inform estimates for finer geographic and categorical domains. Numerous approaches have been tested (classical small area estimation, downscaling, imputation, and interpolation, among others), but there is little consensus on a best way forward. This presentation will make the case for focused research and operational testing of scientifically sound approaches to small area estimation to meet information needs of th|
|Priya Shahani||Needs for Fine-Scale Vegetation Data on National Forest System Lands||Thursday||1b||Cherokee||The small area estimation conundrum||The US Forest Service has numerous needs for fine-scale vegetation data on National Forest Service lands that are difficult to address, given limited staff time available for conducting field surveys. These data needs relate to land management and monitoring to support both condition-based management and adaptive management decision making. We will provide an overview of some of these needs in different parts of the country.||The US Forest Service has numerous needs for fine-scale vegetation data on National Forest Service lands that are difficult to address, given limited staff time available for conducting field surveys. These data needs relate to land management and monitoring to support both condition-based management and adaptive management decision making. We will provide an overview of some of these needs in different parts of the country.|