|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.|
|Charles Perry||Can I get fries with that? Successes and continued challenges for BIGMAP||Wednesday||2a||Salon D/E||Large-scale computing applications in FIA||FIA entered into a partnership to deliver BIGMAP (Big Data Mapping and Analytics Platform): a cloud-based system for scalable production, analysis, and delivery of integrated geospatial products. Authoritative, science-based outputs support decision-making for private and public land management planning, resource allocation, and landscape sustainability. We will also review persisting challenges associated with partnerships and IT applications in a rapidly evolving political environment.||The Forest Inventory and Analysis (FIA) Program entered into a public:private partnership with the USDA FS CIO Geospatial Services Branch and Esri, Inc. to configure BIGMAP: the Big Data Mapping and Analytics Platform. We are entering the final year of our four-year agreement to deliver a prototype cloud-based system for scalable production, analysis, and on-the-fly delivery of massive, integrated, geospatial products. This solution is notable for its use of a commercial off-the-shelf (COTS) software solutions in a secure cloud environment. The resulting authoritative science-based products are designed to support decision-making for private and public land management planning, resource allocation, and landscape sustainability assessments. BIGMAP is designed to address several agency priorities, and this presentation will highlight several applications available for hands-on exploration during the digital engagement session. We will also review several persisting challenges associated with this type of partnership and IT applications in a rapidly evolving political environment.|
|Barry Wilson||Development of BIGMAP: a cloud-based national scale modeling, mapping, and analysis environment using Esri Raster Analytics||Wednesday||2a||Salon D/E||Large-scale computing applications in FIA||This presentation describes efforts to configure BIGMAP, a cloud-based national scale modeling and analysis environment built upon Esri software in Amazon's cloud. A pilot project was designed for testing BIGMAP with a set of use cases covering the range functionality required. This presentation focuses on use cases to support for national carbon reporting and small area estimation. It provides an overview of the architecture and configuration, example workflows and outputs, and lessons learned.||With the burgeoning remote sensing sector and the growing volume of imagery available at finer resolutions, new approaches are needed to support FIAs techniques research and national scale applications. Cloud-based solutions provide the flexibility to marshal the computing resources needed, and place algorithms and data together in a common environment.
This presentation summarizes efforts to configure the BIGMAP environment, built upon Esri software utilizing Amazon Web Services (AWS). A pilot project was designed to test this environment with a set of use cases intended to cover much of the range of functionality required. This presentation focuses on two of these use cases: a) support for national carbon reporting, and b) small area estimation.
The presenters will provide a brief overview of Esri Raster Analytics. They will discuss the process of architecting the AWS infrastructure and subsequent software configuration and performance testing of environment. They will address the technical challenges of taking use case workflows that were originally developed under different programming paradigms and adapting them to fit within BIGMAP. Finally, they will present some of the outputs produced to date and lessons learned.
|Sean Healey||Cloud computing supporting land cover change mapping||Wednesday||2a||Salon D/E||Large-scale computing applications in FIA||We highlight uses of a cloud platform for: detecting forest disturbances in the US; mapping land cover and use change across East Africa; and, merging a global sample of lidar data with wall-to-wall image data from Landsat to make statistical estimates of biomass over user-requested areas. Were learning lessons about forest cover and land use change that would have been inconceivable without huge expense just five years ago.||Since the beginning of the Landsat program in the 1970s, scientists have been using the platforms imagery to map land cover and land cover change. When the Landsat archive became free in 2008, algorithms using annual or even all acquisitions began to proliferate, with demonstrable benefits in sensitivity and accuracy. However, these algorithms have only begun to reach their potential as cloud computing has become more accessible. Several developments have driven the entire remote sensing field toward cloud platforms: 1) emerging evidence of the effectiveness of new, computationally intensive algorithms; 2) the sheer number of images now available; 3) the need to share ideas and code across increasingly diverse teams, and 4) the need for on-the-fly access to imagery for new user engagement applications.
We highlight uses of a cloud platform for: detecting forest disturbances in the US; mapping land cover and use change across East Africa; and, merging a global sample of lidar data with wall-to-wall image data from Landsat to make statistical estimates of biomass over user-requested areas. Were learning lessons about forest cover and land use change that would have been inconceivable without huge expense just five years ago.
|Kasey Legaard||Use of multi-objective machine learning and high performance computing to reduce prediction bias in forest maps||Wednesday||2a||Salon D/E||Large-scale computing applications in FIA||We have developed machine learning techniques that reduce systematic error when mapping forest attributes from multispectral satellite imagery and FIA plot data. Our approach combines support vector machines with a genetic algorithm to simultaneously minimize total and systematic error. Large-scale applications are supported through the use of modular and highly parallelized software executed on University of Maine cloud computing resources and computing clusters.||Methods used to produce maps from satellite imagery and forest inventory plot data generally result in patterns of error that are detrimental to many applications. We have developed machine learning techniques that reduce systematic error when mapping forest attributes from multispectral satellite imagery and geospatial data. Our approach is based on the optimization of support vector machines using a multi-objective genetic algorithm designed to simultaneously minimize total and systematic error. Using FIA plot data we have mapped tree species occurrence and relative abundance across Maine and obtained outcomes that compare well against other approaches. Our methods are highly generalizable and automatable, but also computationally demanding. Large-scale applications require use of high performance computing resources. We have developed efficient software for use on University of Maine cyberinfrastructure, including modular image processing and data handling workflows implemented on the cloud, and highly parallelized model training and map production code executed on a computing cluster. Our intention is to support a broad set of research and management applications through the production of high-quality spatial data at low cost.|
|Tony Chang||Utilizing cloud-computing for implementing a deep convolutional neural network based forest structure and classification model||Wednesday||2a||Salon D/E||Large-scale computing applications in FIA||Development and implementation of new biomass/carbon models require increased computational hardware that include large amounts of memory and access to GPUs. Contemporary cloud-compute platforms allow for such computational loads for parameter fitting state-of-the-art deep learning models. This research demonstrates a cloud-based approach to perform both classification and regression concurrently based on a large FIA training data set and these next generation models.||In recent years, the increased availability of high-resolution (<30 m) imagery and advancements in machine learning algorithms have opened up a new opportunity to fuse multiple datasets of varying spatial, spectral, and temporal resolutions. Here, we present a new model, based on a deep learning architecture, that performs both classification and regression concurrently, thereby consolidating what was previously several independent tasks and models into one stream. The model, a multi-task recurrent convolutional neural network that we call the Chimera, integrates varying resolution, freely available aerial and satellite imagery, as well as relevant environmental factors (e.g., climate, terrain) to simultaneously classify five forest cover types (conifer, deciduous, mixed, dead, none (non-forest)) and to estimate four continuous forest structure metrics (above ground biomass, quadratic mean diameter, basal area, canopy cover). We demonstrate the models ability and performance on the Azure cloud-computing platform for model training on 9967 georeferenced FIA field plots within California and Nevada, and then implement a large scale prediction for the Sierra Nevada region to understand the viability of this new approach.|
|David Bell||An integrated disturbance and vegetation mapping fra,ework highlights landscape level changes in forest dynamics during a multiyear drought||Wednesday||2a||Salon D/E||Large-scale computing applications in FIA||NEED||NEED|
|Chad Babcock||Remote sensing-assisted multivariate hierarchical spatial models for simultaneous prediction of multiple forest carbon pools||Wednesday||2b||Salon D/E||Advances in estimating carbon stocks and fluxes on forest land, woodlands, and urban trees across space and through time||We build a multivariate hierarchical spatial model to simultaneously predict six forest carbon pools across the continental US. Leveraging Landsat remote sensing information and cross-correlations between separate carbon pools, we are able to improve spatial prediction and generate small-area forest carbon estimates with better precision than current FIA procedures that use only field plot data.||The FIA monitors six forest carbon pools by taking repeat measurements on plots, including (1) live tree, (2) understory, (3) standing dead, (4) downed dead wood, (5) litter and (6) soil organic carbon. Some pools, such as live tree and standing dead, can be highly correlated with remote sensing data. However, other pools, such as litter and soil organic carbon, can be very weakly correlated with remote sensing products. All six forest carbon pools themselves typically are highly related, i.e., cross-correlated. These cross-correlations are useful information that, if appropriately modeled, can improve remote sensing-assisted prediction for all six pools. We demonstrate how a multivariate hierarchical spatial model can be paired with remote sensing data to predict multiple forest carbon pools simultaneously. Because we are using a unified multivariate framework for spatial prediction, we can explicitly model the cross-correlations between the six carbon pools. Our model is informed by FIA forest carbon pool plot data as response variables and Landsat-derived metrics as explanatory variables. Using this model framework, we make 30 meter spatial predictions for all NLCD-classified forest areas in the continental US.|
|Lucia Fitts||Effects of disturbances and land use change on carbon stocks in six US states||Wednesday||2b||Salon D/E||Advances in estimating carbon stocks and fluxes on forest land, woodlands, and urban trees across space and through time||This research focuses on how disturbances and land use change affect carbon stocks by first doing a model using a machine learning algorithm and a generalized mixed effects model to predict the probability of land use change in FIA plots from 2000 to 2017. Predictions from these models were used to create a regression with spectral information from Landsat imagery and produce a wall-to-wall probability map of land use change.||Forests serve as a large terrestrial carbon sink. Land use change is a major threat to forested areas, but the likelihood of change in forested areas is unknown. This research focuses on forest as a land use, and how forest conditions and population growth affect the carbon stocks in six U.S. states. The goals of the study are (1) to quantify the impacts of forest disturbances on the probability of forest becoming non-forest and (2) to quantify the resiliency of forest carbon stocks when faced with forest disturbances. FIA data was used to generate models with two approaches: a machine learning algorithm (random forest), and generalized mixed effects models. Predictions from these models were used to create a regression with spectral information from Landsat imagery and produce a wall-to-wall probability map of land use change. Results indicate that land use change from forests is more likely with increasing population and housing growth rates, and non-public areas have a higher probability of forest change. Furthermore, disturbances were not a major predictor of land use change. This study provides information for decision makers and land managers for designing policies and practices aimed at mitigating climate change.|
|Richard Birdsey||A User Protocol for Greenhouse Gas Inventories of Forests and Trees at Community Scale: Combining Forest Inventory and Remote Sensing||Wednesday||2b||Salon D/E||Advances in estimating carbon stocks and fluxes on forest land, woodlands, and urban trees across space and through time||A new GHG protocol is designed to support forestry-related decisions by communities for achieving net reductions in emissions. Land management decisions are typically made at small scales; yet, barriers to accessing and using data and models has prevented consideration of land management options. We have designed and tested a protocol that can be used by local planners to access publicly available data and compile estimates of GHG emissions and removals for forests and trees outside forests.||A new greenhouse gas (GHG) protocol is designed to support forestry-related decisions by communities (i.e., municipalities, counties, land-based cooperatives, large ownerships, states) for the purpose of achieving net reductions in GHG emissions. Land management decisions are typically made at relatively small scales such as U.S. counties; yet, barriers to accessing and using data and models has resulted in a lack of consideration of land management options in the hundreds of city and county climate action plans across the country. Working closely with city, county and municipal stakeholders, we have designed and tested a protocol that can be used by local planners to access publicly available data and compile estimates of GHG emissions and removals for forests and trees outside forests. Foundational data include the National Land Cover Data (NLCD) and Forest Inventory Data (FIA), both readily accessible for all areas of the conterminous U.S. The results of three case studies demonstrate how the protocol works. This protocol is associated with ICLEI - Local Governments for Sustainability, whose ClearPath is the online software platform used by US communities to report greenhouse gas inventories.|
|Nancy Harris||Mapping global climate mitigation potential from reforestation||Wednesday||2b||Salon D/E||Research suggests that reforestation is the single largest natural solution for mitigating climate change, but the geographic distribution of reforestations sequestration opportunity remains poorly characterized. Here we combine plot networks with machine learning to map near-term forest carbon sequestration potential with more geographic specificity than the regional estimates that currently exist.||Recent research suggests that reforestation is the single largest natural climate solution for mitigating climate change, and many countries have prioritized forest activities in their Nationally Determined Contributions (NDCs) to the Paris Agreement. However, the magnitude and geographic distribution of reforestations sequestration opportunity remains poorly characterized. One contributing factor is limited data on rates of biomass accumulation, which are spatially highly variable and influenced by numerous biophysical and management factors. By combining existing georeferenced field plot networks from around the globe (including FIA plots), biophysical predictor variables, and machine learning, we mapped current and potential future carbon sequestration potential of natural forest regeneration over the next 20-30 years with more geographic specificity than the regional estimates that currently exist. This type of information can help improve existing GHG inventories at subnational scales where management decisions often take place, and enable local decision makers to evaluate the feasibility, time frame, magnitude, and extent to which reforestation could contribute to and enhance NDCs and local Climate Action Plans.|
|Glenn Christensen||U.S. West Coast Forest Carbon Stocks and Flux: Results from recent analysis and using FIA data as basis informing state-led forest carbon emission mitigation policies||Wednesday||2b||Salon D/E||Following Californias lead, Oregon and Washington along the U.S. West Coast increasingly seek to adopt climate goals through specific strategies reducing carbon (C) emissions. The PNW Research Station developed new partnerships with Oregon and Washington that use FIA data to assess current C stocks and flux on forest land and estimate statewide rates of carbon dioxide (CO2) sequestration. Findings by state including notable differences in growth and mortality in the tree C pool are presented.||Following Californias lead, Oregon and Washington along the U.S. West Coast has increasingly sought to adopt climate goals through specific strategies to reduce carbon (C) emissions. Understanding the role forests play in each states overall C stocks and flux is critical to developing, implementing, and monitoring these strategies. Similar to the role FIA continues to play in California and our partnership with the California Department of Forestry and Fire Protection (CAL FIRE), the Pacific Northwest Research Station has developed new partnerships with the Oregon Department of Forestry and Washington State Department of Natural Resources to assess current C stocks and flux on forest land in order to estimate a statewide rate of carbon dioxide (CO2) sequestration. We present the latest findings from California, Oregon, and Washington including notable differences in growth and mortality in the tree C pool. The future direction of this work are discussed including state-led efforts improving the reporting and confidence of FIAs forest C stock and flux estimates.|
|Todd Morgan||Leveraging TPO data for HWP carbon storage in the PNW||Wednesday||2b||Salon D/E||Stakeholders in FIAs Pacific Northwest (PNW) region have interest in quantifying carbon (C) stored in harvested wood products (HWP) because of state climate goals regarding C emissions. University of Montana researchers have used timber product output (TPO) information and an IPCC based carbon storage model developed by the Forest Service to quantify C stored in landfills and products made from historic and recently harvested timber in California and Oregon, with a Washington study pending.||Stakeholders in FIAs Pacific Northwest (PNW) region have interest in quantifying carbon (C) stored in harvested wood products (HWP) because of state climate goals regarding carbon emissions. The FIAs Timber Product Output (TPO) program quantifies and characterizes timber used for products and disposition of mill residue, using mill surveys and logging utilization studies. In the PNW Region, this survey work is conducted by the University of Montanas Bureau of Business and Economic Research (BBER). Working with PNW-FIA, BBER researchers are using the Intergovernmental Panel on Climate Change (IPCC) production accounting approach to estimate HWP-C storage in California, Oregon, and Washington with a model developed by the Forest Service as well as timber harvest, primary products and residue generated by timber-processing facilities. Results from California and Oregon indicate HWP-C storage is a fraction of ecosystem C storage. However, being able to consistently quantify C stored in HWP through time and across political boundaries will allow stakeholders and policy makers to better understand the role that timber harvested and used for wood products plays in total forest carbon storage and flux.|
|Jeremy Fried||Propensity score matching analysis of FIA remeasurement across diverse West Coast forests yields insights on drivers of in-forest carbon sequestration||Wednesday||2b||Salon D/E||Marginal impact of stand structure, management and disturbance on above-ground net carbon increment was analyzed via propensity score matching on NFS and private FIA plot subsets in WA, OR and CA. Carbon increment associated positively with active management, site quality, and initial inventory everywhere. Even-aged stand structure elevated increment in coastal PNW. Fire, insect, and disease reduced increment inland. High QMD reduced stand level carbon accumulation everywhere.||To estimate stand structure, management and disturbance influence on above-ground net carbon increment on timberland, we developed matched subsets totaling 3920, not recently harvested, remeasured, forest service and private FIA plots in WA, OR and CA. Grouping plots into four regions (PNW coast/inland and PSW coast/inland), we applied propensity score matching (PSM) to generate comparable plot subsets for a better apples to apples evaluation of the marginal influence of those drivers on above-ground live tree carbon increment. As expected, carbon increment associated positively with site quality and initial inventory everywhere. Even-aged stand structure associated with higher increment only in PNW coastal. Negative impacts of fire, insect, and disease were evident in only inland regions. Higher quadratic mean diameter of trees reduced carbon increment in all regions. The more active forest management common on private timberlands elevated carbon increment in all regions, all else equal. PSM presents a promising approach for creating datasets amenable to evaluating the effect of discrete variables that arent randomly distributed across the forested landscape but are key to testing policy relevant forest management hypotheses.|
|Marin Palmer||Intensified FIA grid for BLM and NFS; partnership success in the Pacific Northwest||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||In 2018, OR BLM adopted the FIA sampling design as their strategic vegetation inventory. This expanded the partnership between PNW-FIA and NFS Region 6 to include OR BLM, integrating the vegetation inventories of both agencies. The FIA infrastructure enables both agencies to make inventory data available to the public and capitalize on FIAs national estimation tools, and is an example of the all lands approach to shared stewardship.||Forest Inventory and Analysis (FIA) is a key data source for monitoring late-successional old growth and associated wildlife habitat components in the Pacific Northwest. Through a strong partnership with the Pacific Northwest Research Station FIA program (PNW-FIA), NFS R6 implemented a 3x spatial intensification of FIA plots across all non-wilderness NFS R6 lands beginning in 2001, providing more detailed information about the status and trends of forest resources on NFS lands. Prior to FIA annual inventory, both NFS R6 and OR BLM had a similar inventory known as CVS (current vegetation survey).
In 2018, OR BLM adopted the FIA sampling design as their strategic vegetation inventory. This expanded the partnership between PNW-FIA and NFS Region 6 to include OR BLM, integrating the vegetation inventories of both agencies. The FIA infrastructure enables both agencies to make inventory data available to the public and capitalize on FIAs national estimation tools, and is an example of the all lands approach to shared stewardship.
|John Shaw||A New Source for FIA Data Suitable for Use in the Forest Vegetation Simulator||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||The Forest Vegetation Simulator (FVS) is an essential tool for National Forest planning, and also has a large user base outside National Forest Systems. A large portion of FVS users have an interest in using FIA data, but FVS-ready data have not been available for several years. A new translation and data delivery process makes FVS-ready data available for all plots available in FIADB, allowing users to download data by state and project FIA plot data at the condition, plot, or subplot level.||Not since the retirement of the Forest Inventory Mapmaker in 2008 has the FIA program provided users with FIA data prepared for direct use with the Forest Vegetation Simulator (FVS). The Forest Management Service Center, developers of FVS, provided a translator for several years, but upkeep was difficult in light of frequent changes to FIADB and regional variation in data. Other FIA-based tools, such as BioSum and DATIM, extract FIA data into FVS-usable format, but these translations are not readily usable to users of FVS as a stand-alone tool. The new process pre-translates all plots present in FIADB into FVS-ready format and stores the FVS-ready data in a separate schema. The tables are then added to FIADB tables in a single, state-level SQLite database and delivered through the FIA Datamart. Having FIADB and FVS-ready tables in a common database provides FVS users with capabilities not previously available. With a simple Group selection in the current FVS interface tools, Suppose and FVS Online/Onlocal, users can project FIA data by condition, whole plot, or subplot. A series of user guides will cover topics such as properties and manipulation of the FVS-ready tables, and guidance on FVS projections for use in forest planning.|
|Kevin Megown||Landscape Change Monitoring System - Delivery and Use For Forest Service (NFS)||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||This talk will briefly describe the LCMS outputs, the delivery of LCMS data and applications identified within the NFS Deputy area.||The Landscape Change Monitoring System (LCMS) is a Forest Service, Landsat based, annual national geospatial data product describing land cover change for every year from 1984 to the present. The LCMS product used together with other Forest Service enterprise data can support multiple business needs across FS Deputy areas, including R&D, NFS and S&P. This talk will briefly describe the LCMS outputs, the delivery of LCMS data and applications identified within the NFS Deputy area.|
|Alexa Dugan||Innovations in addressing carbon in NEPA and National Forest Planning||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||The U.S. Forest Service produced two nationally-consistent reports for all NFS units using FIA data and several empirical modeling tools to provide key information on forest carbon trends and influences. Drawing on the best available science and aligning with current policy, we developed four templates to serve different NFS needs. These templates have greatly increased efficiency, while improving the quality and legal defensibility of information used for decision making.||There has been a growing demand for forest carbon information to support national forest system (NFS) planning and National Environmental Policy Act disclosures. The U.S. Forest Service produced two carbon reports for all NFS units using Forest Inventory and Analysis (FIA) data and several empirical modeling tools to evaluate forest carbon trends and influences. Because these reports are nationally-consistent, we were able to develop a template-based approach to address carbon in decision making documents that align with current policy. This approach has improved understanding of forest carbon dynamics by managers and stakeholders. These templates have also greatly increased efficiency, while improving the quality and legal defensibility of information used for decision making. The templates work synergistically with one another and embody a number of innovations, creating an effective information delivery system that staff with even little training in carbon can use accurately. The data on which these templates are based are becoming dated and information needs and estimation methods have evolved, so we are looking to support the development of new FIA-based carbon reports to continue to serve NFS needs.|
|Melissa Hamid||Collaborative Governance Leadership Competencies in Federal Natural Resource Management Partnerships||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||This talk will review research on natural resource management partnerships and collaborative governance theory/frameworks. The talk will also present growing calls in the global literature to identify non-technical and interpersonal leadership competencies that could help improve future partnership outcomes.||Effective leadership is important to collaborative governance outcomes in Federal natural resource management partnerships. Within the natural resource field, there is a growing call in the literature to continue to explore interpersonal and other leadership competencies that could be leveraged by government to facilitate more effective collaborative partnership outcomes. It is not known how Federal government and natural resource management partner organization executives perceive the influence of leadership competencies on the formation and maintenance of effective collaborative governance processes and partnerships. There is a paucity of empirical information relating to leadership competencies that would benefit intended public outcomes involving collaborative governance partnerships. A qualitative descriptive study is planned to explore leadership competency perceptions from participants in natural resource management partnerships to study the phenomenon of collaborative governance.|
|Kristen Pelz||Integrating FIA data into National Forest management through the Forest Plan Revision process: Work in progress on the Salmon-Challis National Forest||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||On the Salmon-Challis National Forest, FIA data were used in the assessment, and with an interdisciplinary team analysis is being used to help with development of plan components and identify potential uses of data for monitoring. This presentation will outline what has worked, challenges of working with FIA data in the plan revision context, and potential monitoring plans for the future.||National Forests, under the current planning rule are required to use best available scientific information (BASI) for assessing forest conditions, developing a forest plan, and monitoring conditions as they relate to the plan. Forest Inventory and Analysis data can be one source of BASI, but because of the scale of data and lack of familiarity with it, the data can be difficult to integrate with other data sources. On the Salmon-Challis National Forest, FIA data were used in their assessment, and with an interdisciplinary team analysis is being used to help with development of plan components and identify potential uses of data for monitoring. This presentation will outline what has worked, challenges of working with FIA data in the plan revision context, and potential monitoring plans for the future.|
|Emrys Treasure||Evaluating Existing Longleaf Pine Ecosystem Condition with Forest Inventory and Analysis (FIA)||Wednesday||2c||Salon D/E||Building Strong Partnerships: NFS and FIA||The Range-Wide Conservation Plan for Longleaf Pine established condition-based restoration goals for 2025. Initial estimates were based on FIA forest-type and FSVeg data, with condition calls based on professional judgement. We used metrics developed in conjunction with NatureServe to assess condition on all FIA plots containing at least one longleaf pine using plot measurements. Though this approach likely overestimates longleaf area, it shows great promise in characterizing existing condition.||The Range-Wide Conservation Plan for Longleaf Pine established condition-based restoration goals to meet by 2025. The initial 2009 estimate of 3.4 million existing acres was based on a combination of FIA and local inventory data (i.e., FSVeg) for NFS lands. The split between maintain and improve/restore was based on professional judgement with limited sampling. Since then, NatureServe led an interagency effort to better define condition classes for longleaf pine ecosystems. These Open Pine Metrics score various criteria, which are combined to produce an overall condition class. There are 13 Open Pine Metrics, though FIA protocol only collects sufficient data to score 7 of them. We selected all FIA plots that contained at least one longleaf pine, and used the relevant FIA plot measurements to assign a condition. Preliminary results estimate considerably more longleaf pine area than previous estimates based on FIA forest-type, though this likely is an overestimate from including plots where longleaf ecosystems are not desired. Despite the need for further refinement, this approach shows promise in characterizing existing condition based on the best available science using the most robust data available.|
|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.|
|Jason Stoker||The 3D Elevation Program - useful for FIA?||Wednesday||4a||Hiwassee||Uncharted Outcomes: Delivering geospatial products with FIA||The 3D Elevation Program (3DEP) is a collaborative effort collecting lidar across the conterminous United States and territories to meet a wide range of stakeholder needs, with a goal of completing acquisition coverage by 2023. This presentation will give an overview of 3DEP, introduce new data access and tools, and open discussion as to the utility of these data to help meet FIA's mission.||The 3D Elevation Program - 3DEP - is a national program managed by the U.S. Geological Survey (USGS) on behalf of Federal and other partners to acquire high-quality three-dimensional elevation data with a goal to complete nationwide coverage by 2023 (lidar for the conterminous United States, Hawaii, and the U.S. territories; and IfSAR for Alaska). 3DEP is a cooperatively-funded program that provides a systematic approach for aligning federal, state and local investments to provide publicly available, high resolution elevation data to support a broad range of applications, including for USFS. Today, over 50 percent of the Nation has 3DEP-quality data available or in progress. This presentation will give an overview of 3DEP, introduce new data access and tools, and open discussion as to the utility of these data to help meet FIA's mission.|
|Bruce Cook||High-resolution data from NASA's G-LiHT Airborne Imager for remote inventory and science applications||Wednesday||4a||Hiwassee||Uncharted Outcomes: Delivering geospatial products with FIA||Airborne remote sensing data and inventory plots provide complementary information on forest structure, composition, and carbon stocks. In remote regions, such as interior Alaska, high-resolution airborne lidar and imagery capture gradients in forest resources that would be cost-prohibitive with standard plot-based approaches. Here, we highlight the unique information content from airborne data for forest inventory, forest science, and land management from the Tanana Inventory Unit.||The remoteness of interior Alaska presents a range of challenges for inventorying forest resources. Beginning in 2014, the USDA-Forest Service partnered with scientists at NASAs Goddard Space Flight Center to pilot a lidar-assisted forest inventory. This approach leverages the unique, high-resolution data from NASA Goddards Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager (www.gliht.gsfc.nasa.gov). In July 2014, we acquired 1 million hectares of G-LiHT data in strip samples spaced every 9 km across the Tanana Inventory Unit, an area the size of Arkansas (138,000 km2) that stretches west from the Canadian border past Denali National Park. 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 and hyperspectral data 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.|
|Karen Schlweeweis||Forest Service NLCD Tree Canopy Cover - Science for Future Products||Wednesday||4a||Hiwassee||Uncharted Outcomes: Delivering geospatial products with FIA||The information shared during this talk will describe the key science efforts, focus areas and applied science as the Forest Service plans to support a 2019 mid-cycle tree canopy cover change and the 5-year cycle 2021 product.||The Forest Service has supported a National Tree Canopy Cover for almost a decade. The data is a Landsat based, CONUS, Coastal Alaska, Hawaii, American Virgin Islands and Puerto Rico of tree canopy cover data and map products for epochs 2011 and 2016. Science and Research has always been a key foundation of the Forest Service National tree canopy cover team. These science efforts have supported the advancement, given prescriptive and perspectives for a production team. The information shared during this talk will describe the key science efforts, focus areas and applied science as the Forest Service plans to support a 2019 mid-cycle tree canopy cover change and the 5-year cycle 2021 product.|
|Stacie Bender||Forest Service NLCD Tree Canopy Cover - 2016 Data and Future Tree Canopy Cover Products||Wednesday||4a||Hiwassee||Uncharted Outcomes: Delivering geospatial products with FIA||This talk describes the key innovations used, the 2016 NLCD Tree Canopy Cover data, and outlines the future goals for supporting a 2019 midcycle as well as the future 2021 NLCD TCC data.||The Forest Service is a founding member of the MRLC and supports the development and maintenance of the NLCD Tree Canopy Cover data. As the NLCD TCC data steward, the Forest Service has recently delivered the 2016 NLCD Tree Canopy Cover suite to the MRLC. The Tree Canopy Cover data is one of many NLCD data that is core to multiple users and applications, demonstrated by the thousands of annual downloads. The making of the NLCD 2016 Tree Canopy Cover data required the use of many key and valued geospatial processes. These processes included efforts from the 2016 science team and innovations from the production team. This talk describes the key innovations used, the 2016 NLCD Tree Canopy Cover data, and outlines the future goals for supporting a 2019 midcycle as well as the future 2021 NLCD TCC data.|
|Mark Nelson||Mapping forest land use||Wednesday||4a||Hiwassee||Uncharted Outcomes: Delivering geospatial products with FIA||Forest estimates and map products are produced using a variety of definitions and minimum canopy cover thresholds. To better understand these differences we modelled a 30-m geospatial dataset of FIA forest land use. We modelled FIA forest site productivity to further stratify forest use pixels into FIA classes of timberland, reserved-productive, reserved-unproductive, and other forest. We compare maps of forest use versus forest cover and discuss definitional effects on area estimates.||Land use, land cover, and tree canopy cover all provide valuable insights into conditions and changes in forests. A variety of definitions and minimum canopy cover thresholds have been developed and applied to forest inventory estimates and map products. Estimates of forest land area from the Forest Inventory and Analysis (FIA) program show net gains over the past few decades, while estimates based on land cover maps show net losses. Gains in forest cover that occur gradually are more difficult to detect from image-based change analyses than from field observations, resulting under representation of gross gains and overestimation of net loss than is actually occurring. To better understand these differences we modelled a 30-m spatial resolution geospatial dataset of FIA forest land use from geospatial datasets of forest land cover, tree canopy cover, forest disturbance, and roads and developed areas. We modelled forest site productivity to further stratify forest land use pixels into FIA classes of timberland, reserved-productive, reserved-unproductive, and other forest. We compare maps of forest use versus forest cover and discuss definitional effects on area estimates.|
|Bharat Pokharel||Predictive Mapping of Trees Per Acre (TPA) Using a Non-parametric Approach||Wednesday||4a||Hiwassee||Uncharted Outcomes: Delivering geospatial products with FIA||Modeling stand level forest attributes such as trees per acre (TPA) are increasingly important for a large-scale forest management planning. We developed a predictive map of trees per acre (TPA) for each 68 USGS zones in R statistical computing environment to generate a continuous gridded raster map across the conterminous United States. The models explained over 30 percent variability with RMSE less than 110 trees per acre, and NLCD canopy cover was the most important predictor variable.||Stand level forest attributes such as trees per acre (TPA), biomass, volume and basal area are increasingly important for a large-scale forest management planning. Traditional field-based timber cruising is costly and time consuming, and requires high sampling intensity to capture the spatial heterogeneity in sample population. We hypothesized that variables derived from remote sensing data could be important predictors while estimating stand level attributes from pixel to landscape level. Landsat TM satellite imageries and their derivatives, national land cover dataset (NLCD), and digital elevation model were paired with Forest Inventory and Analysis (FIA) data from 2007 to 2011. We evaluated the use of non-parametric approach random forests to build a predictive model of TPA for each 68 USGS zones in R statistical computing environment to generate a continuous gridded raster map across the conterminous United States. The models explained over 30 percent variability, with RMSE less than 110 trees per acre. Among many other variables, NLCD canopy cover percent was the most important predictor variable while predicting TPA. This approach can be replicated for mapping various goods and services that forests provide to the society.|
|Sara Goeking||FIA 101: A training module for FIA staff and aspiring data users The process, the product, and future developments||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||Given increasing requests for FIA data for resource planning and management, this project seeks to develop a series of training modules that will build FIA knowledge and analytical capacity both in the short-term (to field staff) and the long-term (to external customers and new employees). This project focuses on meeting the short-term objective, while planning sufficient flexibility in training modules to enable their use by future employees and external customers.||In 2018, staff at the Interior West unit of the Forest Inventory & Analysis (FIA) program began developing The FIA Academy. This series of four self-guided training modules begins with basic information about FIAs purpose and sample design and progresses to advanced data analysis. Module 1 was completed in early 2019, with the title FIA 101: A self-study course and reference guide for FIA staff and aspiring data users. The development team was composed of highly motivated and experienced field staff, who wrote, organized, and presented content in Portable Document Format (PDF) with internal links to cross-referenced concepts and terminology. They worked with a steering team of analysis, information management, and data collection specialists under the principles of consensus and constructive feedback. Future work includes development of Modules 2, 3, and 4; revision of Module 1 based on 2019 feedback; and engagement with digital communication specialists for conversion of existing content to an interactive web-based platform. The purpose of this presentation is to increase awareness of this resource, and to solicit input on future development or revision with a more nationally-consistent perspective.|
|Robert Smith||Forestry web tools promote stakeholder access||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||We deployed an app for interagency users to quickly and accurately estimate biomass, carbon and nitrogen in ecologically important ground-layers (moss and lichen mats carpeting the forest floor). We illustrate three scenarios whereby stakeholders used the ground-layer app to describe: 1) fire effects in Pacific Northwest prairies, 2) functional diversity in Montana rangelands, and 3) nutrient status in interior Alaska forests. Accessible technologies can broaden participation in forestry.||Streamlining the estimation of forest attributes can benefit FIA data-consumers and data-contributing partners alike, while reducing demands on analysts. We deployed a free and open-source web application (||John Bertini||New developments in the Design and Analysis Toolkit for Inventory and Monitoring (DATIM)||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||The Design and Analysis Toolkit for Inventory and Monitoring (DATIM) is a comprehensive suite of applications and tools useful for forest management, inventory, and planning. In active development by Forest Service groups and partners, DATIM gains new features on a quarterly basis. This talk will discuss recent improvements to DATIM, including integration with EVALIDator, an improved Spatial Intersection Tool (SIT), a data export tool, and new report management features.||The Design and Analysis Toolkit for Inventory and Monitoring (DATIM) is an application in collaborative development by the National Forest System (NFS), Forest Inventory and Analysis (FIA), the University of Nevada, Las Vegas, and Southern Utah University. DATIM is a comprehensive suite of applications and tools useful for forest management, inventory, and planning. As DATIM matures, improvements are added on a quarterly basis. Recent features include: close integration with the FIA EVALIDator application, improved appearance and functionality for the Spatial Intersection Tool (SIT), data export tools, and report management functionality. DATIMs EVALIDator integration provides access to new estimates, and allows users to run reports using the latest FIADB data. The SIT tool provides a streamlined user interface to allow users to generate new attributes using their own spatial data, and will soon allow authorized users to securely intersect against real plot coordinates. DATIMs data export tool provides forest inventory datasets to users in an easy-to-use format. DATIMs report management tool allows users to edit and share their existing reports. This talk will demonstrate and discuss some of these new features and developments.|
|Hunter Stanke||rFIA: Unlocking the Potential of the FIADB in R||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||rFIA was written initially for a Inventory and Monitoring Division of the National Park Service project with the objective to assess status and trends of forest variables along the Appalachian National Scenic Trail and neighboring lands. Paucity of user-friendly, publicly accessible software to query and analyze FIA data in R, we decided to expand the scope of rFIA to accommodate a wide range of potential user objectives, and thus substantially increase the accessibility and use of the FIADB.||rFIA is an R package designed to simplify the estimation of forest variables using the Forest Inventory and Analysis (FIA) Database and unlock the flexibility inherent to the Enhanced FIA design. rFIA was designed to improve accessibility to the spatio-temporal estimation capacity of the FIA Database via space-time indexed summaries of forest variables within user-defined population boundaries, in a widely used, open-source programming language. With direct integration with other popular R packages (e.g., dplyr, sp, and sf), rFIA facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005), and has been validated against estimates and sampling errors produced by EVALIDator. Current development is focused on the implementation of spatially-enabled model-assisted estimators to improve population, change, and ratio estimates. We will demonstrate the flexibility and ease of implementation of rFIA by sharing our assessment of the status and trends in forest resources along the Appalachian National Scenic Trail corridor.|
|Gary Nebeker||Forest Service Historical Film Scanning Project - Much More Than An Aerial Photo Historical Archive||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||This talk focuses on the Forest Service film historical imagery, the importance of this enterprise data source and how this data is being scanned, made available through enterprise functions and used digitally.||The 3.5 million frames of historical Forest Service film span 60+ years, but are currently not available in digital format to readily support information needs. However, recent developments in film scanning and digital photogrammetry processing technologies has provided new ways to use aerial film. State of the art photogrammetric scanners can convert the film to high-resolution digital copies for processing to produce orthomosaics, 3D point clouds, and digital elevation models (DEMs). These products provide valuable spectral and elevation data for a host of projects supporting Forest Service uses. Consequently, the digital imagery compiled from historical Forest Service film holdings perhaps have even more value than contemporary digital airborne imagery since they provide a rare view into the past that allows land managers to better understand natural and anthropogenic impacts on the national forests. It is crucial that this collection of Forest Service film be preserved and made accessible for mapping and analysis in support of various agency and department initiatives and information needs that require data and trends information that can be elicited from the imagery.|
|Tracey Frescino||Demonstrating a progressive FIA through FIESTA: a bridge between science and production||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||FIESTA is an open-source, R estimation package designed for analysts that work with sample-based inventory data. The package contains a collection of functions that can access FIA databases, summarize inventory and spatial data, and generate estimates with associated sampling errors. FIESTAs flexible, open-source strategy allows for adaptability and integration with other R packages and other software platforms -- helping bridge the gap between science and FIA production.||FIESTA (Forest Inventory ESTimation and Analysis) is an open-source, R estimation package designed for analysts that work with sample-based inventory data from the USDA, Forest Service, Forest Inventory and Analysis (FIA) Program. With todays complex questions and fast-evolving technologies, we need a tool to efficiently process and translate multi-scale resource data to information. FIESTA was developed to support current available FIA tools, such as EVALIDator. It provides a flexible platform to accommodate unique research questions such as on-the-fly estimation over user-defined polygons. It enables the use of auxiliary data from a wide variety of remote sensing instruments. It also allows FIA to make use of statistical advances in areas such as model-assisted, model-based and small area estimation. The package contains a collection of functions that can access FIA databases, summarize and explore inventory data, compile spatial data, generate estimates with associated sampling errors, and automate reports and analyses. FIESTAs flexible, open-source strategy allows for accessibility, adaptability, and integration with other R packages and other software platforms -- helping bridge the gap between science and FIA production.|
|John Shaw||Bugs, Features, or What? Some Potential Opportunities to Tidy Up Parts of FIADB (and be Kind to Our Users)||Wednesday||4b||Hiwassee||Marketing FIA - Engaging new tools for traditional and nontraditional users||Since convenient access to FIADB was provided to the public, there has been a low-level controversy about what its designed for. The growing types and numbers of users lead to an ever-increasing diversity of applications of FIA data, potentially to benefit to FIA, but also at some risk of credibility. The credibility risk that comes from data quality is relatively low, but, the risk is potentially elevated when user expectations based on documentation and reasonable assumptions are not met.||In the process of writing programming specifications for a new data translation process from FIADB to the Forest Vegetation Simulator, a number of situations were encountered where programming logic according to available documentation, and sometimes reasonable assumptions about data properties, did not produce the desired results. This resulted in numerous programming and evaluation hours being used, perhaps unnecessarily, to ensure data integrity. In some cases the issues could be diagnosed by reviewing all values existing in a field, such as unusually long values or leading blanks. In other cases, unexpected situations appear to be by design for the purpose of producing commonly used estimates but with potential unintended consequences for users who use FIA data for other purposes and take the current documentation at face value. For some issues, resolution of the issues may can be addressed by clarification of documentation. However, there are some situations where the inconsistency between what database documentation appears to say and what it stored in FIADB raises questions about the purpose and use of certain variables. Several examples and possible solutions will be shown and discussed.|
|Songlin Fei||Spatial patterns and dynamics of forest invasion||Wednesday||4c||Hiwassee||Spatial dynamics of forest invasion||The ecological dynamics of forest invasions often vary across spatial scales making it challenging to provide recommendations for local to regional management. Advances in the use of forest inventory data, computational and statistical tools, and remote sensing technologies are providing an enhanced ability to understand how invasion dynamics vary across spatial scales. We discuss recent improvements in understanding forest invasion across scales and talk about remaining knowledge gaps.||Invasion of exotic organisms is a major threat to global forested ecosystems. The ecological dynamics of forest invasions often vary across spatial scales making it challenging to provide recommendations for local to national management. The use of forest inventory data, computational and statistical advances, and remote sensing technologies are providing an enhanced ability to understand how invasion dynamics vary across spatial scales. Recent advances in the understanding of forest invasion across scales will be covered, including support for common hypotheses of invasive success and the modelling of the drivers of spatial patterns of invasion. Finally, remaining gaps in knowledge that are needed to improve invasive management in US forests will be identified.|
|Basil Iannone III||Using FIA data to detect drivers of spatial heterogeneity in phylogenetic-invasion relationships||Wednesday||4c||Hiwassee||Spatial dynamics of forest invasion||We used FIA data to identify how environmental harshness, relative tree density, and environmental variability affect the degree to which evolutionary relatedness among trees contributes to resistance of forest plant invasions. Using data from 42,626 FIA plots and random mixed-effects models, we found decreased invasions in forests having less evolutionarily related native trees, and that this effect was most pronounced in forests having less harsh environments. These findings can guide studies||Forests having less evolutionarily related trees tend to be more resistant to plant invasion, although this resistance varies geographically. Here we use FIA data to identify drivers of this heterogeneity for the forests of the eastern United States. Using data from 42,626 FIA plots, we quantified spatial variability among the 91 ecological sections of the eastern US in relationships between plant invasion (species richness and cover) and the degree to which forests trees are evolutionarily related. We then modeled this variability in response to three variables known to affect invasions: environmental harshness (as estimated via tree height), relative tree density, and environmental variability. Invasive plant species richness and cover declined in forests having less evolutionarily related native trees; the size of this effect varied considerably across ecological sections. An ecological sections mean maximum tree height (MTH) and, to a lesser degree, SD in MTH explained from 47% to 63% of this variability. Therefore, we found that less evolutionarily related native tree communities better resist plant invasions in less harsh forests. These findings can guide investigations into the precise environmental variables that affect|
|Elizabeth LaRue||Forest structural diversity and plant invasion dynamics across North America||Wednesday||4c||Hiwassee||Spatial dynamics of forest invasion||Structurally diverse forest canopies are hypothesized to have high realized niche space. Canopy structural diversity may therefore contribute to the biotic resistance of recipient forest systems. We are testing whether structural diversity of forests across Eastern USA promote resistance to plant invasion.||Biotic resistance has been hypothesized to allow exotic species to invade ecosystems that have many unfilled niches, but species richness, a proxy of the number of niches filled, has often been found to facilitate invasion at large scales. Structural diversity is the arrangement of biotic structural elements within ecosystems and provides a direct measure of ecosystem niche space. Structurally diverse forest canopies have high realized niche space and may therefore contribute to the biotic resistance of recipient forest systems. We tested the hypothesis that structural diversity, as a direct measure of niche space, would promote biotic resistance across spatial scales versus species richness which would result in facilitation of invasion. We use over 45,000 Forest Inventory and Analysis plots to show that intermediate levels of forest structural diversity across the Eastern USA promote resistance to plant invasion. Ongoing analyses are examining whether these relationships between structural diversity and invasion are sensitive to spatial scale.|
|Jessica Elliott||Understanding the impacts of Emerald Ash Borer and forest structure on understory plant invasion||Wednesday||4c||Hiwassee||Spatial dynamics of forest invasion||Mortality of ash trees by EAB and its subsequent effects on forest structure are expected to influence the community composition and invasion of understory plants. Our project aims to examine the collective impacts of time since invasion by emerald ash borer (Agrilus planipennis), canopy structure, and ash tree mortality on understory plant invasion in Indiana.||Emerald ash borer (Agrilus planipennis) (EAB) is a significant threat to hardwood forests and can cause extensive ash tree (Fraxinus spp.) mortality in North America. Mortality of ash trees by EAB and its subsequent effects on forest structure are expected to influence the community composition and invasion of understory plants, but has been rarely investigated. LiDAR (Light Detection and Ranging) is a critical tool for visualizing and quantifying forest structure at large spatial scales. The goal of this project is to use LiDAR and field inventory to examine the impacts of EAB on canopy structure and ash tree mortality, and the subsequent invasion of understory non-native plants. First, we identified 537 established plots from the Indiana DNR Continuous Forest Inventory that contain ash trees. We then used aerial LiDAR collected from 2011 - 2013 across the state of Indiana to measure a suite of 15 metrics that describe different aspects of canopy structure. We further tested how forest canopy structure, time since EAB invasion, and ash tree mortality are correlated to understory plant species richness of native and invasive plants. Results of this research could be used for informing management of forests following EAB invasion.|
|Kevin Potter||The Most Invaded State in the Nation: Applying FIA Data to Assess Forest Impacts of Nonnative Plants and Pathogens in Hawaii||Wednesday||4c||Hiwassee||Spatial dynamics of forest invasion||We used FIA data to quantify the uniqueness and invadedness of Hawaii's forests. Three-fourths of plots contained at least one endemic tree species, and more than half had at least one nonnative. Low-elevation wet forests were the most invaded, with higher elevation wet and mesic forests dominated by native endemics. The most common tree was ??hi?a, an ecologically and culturally important species that is being decimated on the Big Island by rapid ??hi?a death, caused by exotic fungal pathogens.||Hawaii is unique in the United States both in the extent to which its native forests contain rare and endemic tree species, and in the degree to which they are imperiled by nonnative invasive species. We used the most recent Forest Inventory and Analysis (FIA) forest plot data from the state to quantify the uniqueness and invadedness of Hawaiian forests. Nearly 75 percent of the 238 forested plots contained at least one tree species endemic to Hawaii, and plots on average had 2.2 endemics (maximum: 10). Meanwhile, 54 percent of plots had at least one nonnative tree species (maximum: 8). Low-elevation wet forests were the most invaded, with higher elevation wet and mesic forests dominated by native endemic species. Elevation was, in fact, strongly positively related with native species importance value and negatively related with nonnative importance value on the Big Island. The most important tree species in the upland native forests (and the most commonly inventoried in the state) was ??hi?a (Metrosideros polymorpha), which is being decimated on the Big Island by rapid ??hi?a death, a wilt disease caused by exotic fungal pathogens. ??hi?a is a highly important tree species in Hawaii, both ecologically and culturally.|
|Qinfeng Guo||Tree diversity regulates forest pest invasion through both facilitation and dilution||Wednesday||4c||Hiwassee||Spatial dynamics of forest invasion||The tree diversity-pest invasion relationship is critical to invasion ecology and management but remains elusive. Here, we examine large unique datasets across the US and found unimodal tree-pest diversity relationships. Both facilitation and dilution appear to coexist but their relative strength varies with overall tree diversity. Our findings provide insight into the interaction between facilitation and dilution, which are critical for understanding the invasions of forests by nonnative pests.||Nonnative pests often cause cascading ecological impacts, leading to detrimental socio-economic consequences. However, it is still unclear how plant diversity may influence insect and disease invasions. Most studies to date have been on small-scales, and large-scale studies especially in natural ecosystems are extremely rare. Using subcontinental-level data, we examined the role of tree diversity on pest invasion across the conterminous USA. We find that the tree-pest diversity relationships is hump-shaped. Pest diversity increases with tree diversity at low tree diversity (facilitation or amplification), and is reduced at higher tree diversity (dilution). Thus, both facilitation and dilution operate simultaneously, but their relative strengths vary with overall diversity. Our findings demonstrate the role of native species diversity in regulating nonnative pest invasions.|