SAF Learning
Inventory & Biometrics Bundle
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Enjoy this curated set of on-demand presentations that focus on inventory and biometrics. This bundle will be updated as new content becomes available.
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Presentations
2017
Accuracy Assessment on Drone Measured Heights at Different Height Levels - I-Kuai Hung, Stephen F. Austin State University
Development of High Density LiDAR Derived Forest Inventory - Dennis Kepler, Minnesota Department of Natural Resources
2019
Development of Innovative Cost‐Saving Methodology for Forest Inventory in Minnesota - Jennifer Corcoran, Minnesota Department of Natural Resources
Examining Causal Factors Behind Improved Growth in Exotic Timber Species - Liam Gilson, Oregon State University
Rapid Assessment of Wildlife Habitat Conditions and Trends for Environmental Review - John Zobel, University of Minnesota
2020
Comparison of Small Area Estimation Methods Applied to Biopower Feedstock Supply in the Northern U.S. - Michael Goerndt, Missouri State University
Estimating Harvest Risk for Each Acre of the US South - Nan Pond; Silvia Terra, LLC
Predicting Growth of Eucalyptus marginata in a Mediterranean Climate Using an Individual-Based Modelling Approach - Shes Kanta Bhandari, The University of Western Australia
Rethinking the Maximum Stand Basal Area and SDI from the Aspect of Stand Dynamics - Dehai Zhao; University of Georgia Warnell, School of Forestry and Natural Resources
2021
Innovative Machine Learning Techniques to Create Individual Tree Inventories – Real-world Examples - Mike Parlow, Forsite Consultants Ltd.
2022
The eYield Forest Management Model: Capabilities and Assessment of Effectiveness - Pete Bettinger, University of Georgia
Digitization of Genetic and Tree Improvement Trials Data - Rebekah Shupe, Purdue University
Incorporating Competing Vegetation Metrics within Whole-Stand Growth and Yield Models - John Young, Plantation Management Research Cooperative
High Resolution Forest Inventory of a Dynamic Forest Restoration Block in Pennsylvania - Mike Kieser, Green Leaf Consulting Services, LLC
2023
Dendrochronology of Upper Mississippi River Floodplain Forests - Alan Toczydlowski, University of Minnesota
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Accuracy Assessment on Drone Measured Heights at Different Height Levels
Open to view video. | 30 minutes
Open to view video. | 30 minutes
A DJI Phantom drone was tested for height measurement at four different levels, 2, 5, 10, and 15 m along a height pole. The results revealed it measured height more accurately when it was landed before each measurement, while having the GPS on or off did not make any difference. Presented by I-Kuai Hung, Stephen F. Austin State University at the 2017 SAF National Convention in Albuquerque, NM.
Development of High Density LiDAR Derived Forest Inventory
Open to view video. | 30 minutes
Open to view video. | 30 minutes
Using cutting-edge technologies, a less expensive and highly robust inventory of the forest land base will be developed through a pilot study across a diverse ecological landscape with multiple ownerships in Minnesota. This pilot will assess methodology, accuracy, and costs, to evaluate the anticipated extension of this methodology statewide. Presented by Dennis Kepler, Minnesota Department of Natural Resources at the 2017 SAF National Convention in Albuquerque, NM.
Development of Innovative Cost‐Saving Methodology for Forest Inventory in Minnesota
Open to view video. | 27 minutes
Open to view video. | 27 minutes
Comprehensive forest inventory systems are the desire of multiple agencies throughout Minnesota, but the costs of establishing and maintaining such a system with boots on the ground, especially in light of an extensive and diverse forest land base, continue to be a challenge. This presentation will provide an overview of a recently completed pilot project in which we used cutting-edge technologies and advanced statistical methods to produce key forest inventory models across a diverse ecological landscape with multiple ownerships. This pilot project has assessed multiple methodologies to derive forest inventory metrics, explored accuracies and precision gained with different field campaign designs, and analyzed the costs associated with changing the way forest inventory is done when the extension of this new methodology is applied statewide in Minnesota. Presented by Jennifer Corcoran, MN Department of Natural Resources, at the 2019 SAF National Convention, Louisville, KY.
Examining Causal Factors Behind Improved Growth in Exotic Timber Species
Open to view video. | 24 minutes
Open to view video. | 24 minutes
Exotic species are often planted in intensive forestry settings, producing high yields over short rotations. Growth trials and other empirical methods have been used since the 19th century to identify potential species for these high-yield plantations, leading to numerous examples of exotic plantation forestry worldwide. Growth measurement has confirmed that many of the most common exotics, such as radiata pine, Douglas-fir, and many eucalypt species, exhibit improved volume growth in exotic settings compared to their native ranges. Relatively little research has focused on the causal factors behind these growth differences even in the most common exotics, and proposed hypotheses generally have not been conclusively tested. Here, we present a review of the current state of research into the environmental factors that seem to facilitate the dramatically accelerated growth rates of trees that have been moved from their native range to new geographic locations. Preliminary findings from a study conducted on operational plantings of Douglas-fir in New Zealand and Oregon will demonstrate how new techniques, such as genetic markers, can be applied to these research questions, potentially isolating confounding factors that have made determining causality difficult in the past. Presented by Liam Gilson, Oregon State University, at the 2019 SAF National Convention, Louisville, KY.
Rapid Assessment of Wildlife Habitat Conditions and Trends for Environmental Review
Open to view video. | 23 minutes
Open to view video. | 23 minutes
During the early 1990s, Minnesota funded a Generic Environmental Impact Statement (GEIS) to assess the implications of increased harvest rates in the state. Several environmental condition were considered and modeled, including wildlife habitat. A panel of over 25 wildlife professionals developed specific species-habitat relationships for 172 native, forest dependent wildlife species, including 138 birds, 22 small and medium mammals, four (4) large mammals, and eight (8) herptofauna. The models account for habitat quality and quantity using habitat suitability index (HSI) methodology and estimates of forestland extent. Recent refinements and updates to the model now allow for rapid assessment of habitat status and change (present or projected) during both exploratory and formal environmental review. Using USDA Forest Service, Forest Inventory and Analysis (FIA) data, this talk will also demonstrate the use of the habitat model (termed WHINGS – Wildlife Habitat Indicator for Native Genera and Species) to identify habitat trends in Minnesota over the last 40 years of forest management. Presented by John Zobel, University of Minnesota, at the 2019 SAF National Convention, Louisville, KY.
Comparison of Small Area Estimation Methods Applied to Biopower Feedstock Supply in the Northern U.S.
Open to view video. | 29 minutes
Open to view video. | 29 minutes
Increasing interest in utilization of forest biomass for bioenergy has prompted extensive contemporary research regarding costs, supply and technology for efficiently producing electricity and other forms of renewable energy. One challenge facing both researchers and users is obtaining precise estimates of available forest biomass within plausible supply areas for individual power plants. Due to the wide distribution of power plants poised to co-fire with forest biomass, assessing its availability requires methods that can yield precise and low-bias estimates of aboveground forest biomass and other key attributes at varying spatial scales. Small area estimation (SAE) methods have high potential to accomplish this due to the availability of national forest inventory data, combined with satellite imagery and other forms of remotely-sensed auxiliary information. The study assessed several indirect, direct and composite estimators of four forest attributes: aboveground tree biomass, biomass of small-diameter trees, biomass of tops and limbs, and volume at the county-level and within the estimated supply areas around power plants across 20 states in the contiguous Northern U.S. Composite estimators using both k-nearest neighbors imputation and multiple linear regression provided superior estimates of indicators of forest biomass availability based on both precision and bias at the county-level at sampling intensities as low as 10–20%, compared to the other SAE methods examined. The composite estimator using k-nearest neighbors imputation was subsequently shown to produce precise estimates of forest biomass availability for selected power plant supply areas. Presented by Michael Goerndt, Missouri State University at the 2020 SAF Virtual Convention.
Estimating Harvest Risk for Each Acre of the US South
Open to view video. | 28 minutes
Open to view video. | 28 minutes
We have developed a framework for estimating the risk of harvest across the US South. A high resolution, spatially-explicit approach facilitates differentiating risk of harvest across ownerships and parcels, and enables landscape-level planning for woodshed analysis, carbon capture estimation, and economic research. Beginning from a 30 m pixel estimate of forest stocking, we estimate volume, value, and then evaluate other criteria such as mill proximity, hydrology, ownership, past disturbance history, and conservation status. We will present the framework and calculations used, and invite collaboration from subject matter experts in identifying improvements to the approach. Applications of this effort will be demonstrated and discussed. Presented by Nan Pond, SilviaTerra, LLC at the 2020 SAF Virtual Convention.
Predicting Growth of Eucalyptus marginata in a Mediterranean Climate Using an Individual-Based Modelling Approach
Open to view video. | 26 minutes
Open to view video. | 26 minutes
Jarrah (Eucalyptus marginata) forest is one of the widespread and native forest types of Western Australia and has economic and ecological importance. This study aims to predict jarrah growth using tree-size (diameter, height) and neighbourhood competition. We also wanted to investigate how many neighbouring trees or what neighbourhood distance needed to be considered when accounting for competition.
We analysed correlation between growth and competition indices (CIs), to select the best CIs. Growth models were separately developed for thinned and unthinned stand using initial tree size and CIs. The developed models were validated using a subset of the data which was not used in model fitting. Tree-size was a significant predictor of growth. This prediction was improved when the competition was included in the model. Fit statistic (coefficient of determination) of the model ranged from 0.31 to 0.68. The model with spatial competition indices validated as being more accurate than with non-spatial indices. The model prediction can be optimized if 10 to 15 competitors (by number) or competitors within ~10 m (by distance) from the base of the subject tree are included in the model, which can reduce the time and cost of collecting the information about the competitors. As competition from neighbours was a significant predictor with a negative effect on growth, it is recommended including neighbourhood competition when predicting growth and considering thinning treatments to minimize the effect of competition on growth. These model approaches are likely to be useful tools for the conservations and sustainable management of jarrah forests. Presented by Shes Kanta Bhandari, The University of Western Australia at the 2020 SAF Virtual Convention.
Rethinking the Maximum Stand Basal Area and SDI from the Aspect of Stand Dynamics
Open to view video. | 22 minutes
Open to view video. | 22 minutes
The maximum stand basal area (BA) and maximum stand density index (SDI) are often used to express stand carrying capacity in forestry. But their definitions are inconsistent in the literature. In most previous studies, the maximum stand BA is associated with an upper boundary for a population of stands and is commonly derived from maximum size-density relationships (MSDRs). The implied maximum stand BA by MSDRs is non-decreasing and divergent, thus not good for expressing stand carrying capacity. For any individual stand, the pattern of BA (or SDI) trajectory has either a unimodal curve with a maximal value or an increasing curve with an asymptote. We refer this maximal value and asymptote as the maximum stand BA (or SDI) for that stand. Based on mathematical relationships among stand BA, thinning, and quadratic mean diameter trajectories, we have proven that stands would achieve their maximum BA only after they attain their maximum SDI, invalidating the assumption in MSDRs approach that stands attain the maximum stand BA while travelling along the maximum self-thinning trajectory. A total 283 of 551 nonthinned plots in six long-term loblolly pine experimental studies across the southern US had achieved maximum BA through the current measurement cycle. The effects of site quality, cultural intensity and planting density on the magnitude and timing of the maximum stand BA were examined. More intensively managed stands reach the maximum BA earlier and intensively managed stands on high-quality sites may attain smaller maximum BA. Stand planted at low densities (< 1480 trees ha-1) achieve lower maximum BA and at a later age. The range, average and standard deviation are, respectively, 25.5-61.7, 45.7 and 6.4 m2 ha-1 of maximum stand BA, and 8-36, 20 and 4.8 years of age at which stands attained the maximum BA. Presented by Dehai Zhao, University of Georgia Warnell School of Forestry and Natural Resources at the 2020 SAF Virtual Convention.
Innovative Machine Learning Techniques to Create Individual Tree Inventories – Real-world Examples
Open to view video. | 20 minutes
Open to view video. | 20 minutes
Remote sensing technologies can enable more efficient and profitable forestry operations. Combining technologies like LiDAR and multi-spectral imagery with machine-learning techniques can produce highly accurate individual tree inventories attributed with species, diameter at breast height, and merchantable volume. This presentation will showcase machine learning lessons learned across 3 real-world inventory projects. Key to each project’s success was the creation of machine learning descriptors required to achieve species identification. Descriptors are numeric representations of sample tree characteristics derived from the available remote sensing inputs. For the LiDAR descriptors, engineers created descriptors based on three general feature classes; geometry, density, and reflectivity. Following the LiDAR descriptors, engineers then looked at multi-spectral imagery. The latter yielded complimentary descriptors based on wavelength differences and color signatures. And finally, engineers captured descriptors derived from land base characteristics including wetness and sunlight mapping attributes. Once the bank of species identification descriptors is created, an optimization process is used to select the combination of those inputs that will yield the most accurate result in the field. This optimization process is critical to the project success. Inputs vary in their effectiveness based on a number of project realities. For example, certain terrain types favor some land base descriptors over others. Further, a predominantly conifer species mix may be more accurately attributed with more emphasis on geometric descriptors. Presented by Mike Parlow from Forsite Consultants Ltd. at the 2021 SAF National Virtual Convention.
The eYield forest management model: Capabilities and assessment of effectiveness
Select the "View Video" button to begin. | 15 minutes
Select the "View Video" button to begin. | 15 minutes
This presentation describes the eYield model, and provides examples of model simulations. Further, results from surveys designed to assess pre- and post-training user knowledge and satisfaction are presented. Presented by Pete Bettinger, University of Georgia at the 2022 SAF National Convention in Baltimore, MD.
Digitization of Genetic and Tree Improvement Trials Data
Select the "View Video" button to begin. | 15 minutes
Select the "View Video" button to begin. | 15 minutes
Thousands of genetic and tree improvement trials established in the mid-to-late twentieth century have been abandoned and forgotten, however, some still exist along with physical data. Our online database and data management suggestions can aid researchers with their projects and protect the integrity of future plantings. Presented by Rebekah Shupe, Purdue University at the 2022 SAF National Convention in Baltimore, MD.
Incorporating Competing Vegetation Metrics within Whole-Stand Growth and Yield Models
Select the "View Video" button to begin. | 20 minutes
Select the "View Video" button to begin. | 20 minutes
Loblolly pine production is often limited by the presence of competing vegetation (CV). Incorporation of CV growth information into whole-stand growth and yield model systems is pertinent for increasing the precision of loblolly pine growth estimates. Possible methods for updating current models to account for CV growth are illustrated. Presented by John Young, Plantation Management Research Cooperative at the 2022 SAF National Convention in Baltimore, MD.
High Resolution Forest Inventory of a Dynamic Forest Restoration Block in Pennsylvania
Select the "View Video" button to begin. | 19 minutes
Select the "View Video" button to begin. | 19 minutes
An innovative and collaborative approach to improve the ecological integrity of forests using reliable high resolution forest inventory solutions. This presentation will show how a high resolution forest inventory can enable the design of restorative silvicultural treatments to transform ecological and biological characteristics within targeted forest blocks. Presented by Mike Kieser, Green Leaf Consulting Services, LLC at the 2022 SAF National Convention in Baltimore, MD.
Dendrochronology of Upper Mississippi River Floodplain Forests
Select the "View Video" button to begin. | 23 minutes
Select the "View Video" button to begin. | 23 minutes
Floodplain forests of the Upper Mississippi River System are important and complex ecosystems currently threatened by hydrologic alterations, invasive species, and climate change. We are using tree ring and wood anatomy analysis to develop successional histories of these forests to guide restoration and management. Presented by Alan Toczydlowski, University of Minnesota at the 2023 SAF National Convention in Sacramento, CA.