SAF Learning
Remote Sensing & Geospatial Applications Bundle
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Enjoy this curated set of on-demand presentations that focus on remote sensing and geospatial applications. This bundle will be updated as new content becomes available.
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Presentations
2016
Real-time Tracking in Logging: Effects of Canopy, Aspect, and PDOP on GPS Movement Data and Geofence Accuracy - Eloise Zimbelman, University of Idaho
Multi-transmitter GPS Accuracy Analysis for Recording Swing Movements of Harvesting Machinery - Ryer Becker, University of Idaho
2017
Delineating Forested Wetlands and Crop Areas Using Synthetic Aperture Radar - Josef Kellndorfer; Earth Big Data, LLC.
Measuring and Monitoring Forest Structure and Biomass from Space - Sassan Saatchi, JPL CALTECH
NASA's GEDI Mission: Mapping Forest Height and Biomass from the International Space Station - Laura Duncanson, University of Maryland / NASA Goddard
2018
Use of Lidar-derived Forest and Topographic Characteristics to Classify Alternative Harvest System Options - Ryer Becker, University of Idaho
2019
Pre- versus Post-stratification in an Operational LiDAR Inventory - Scott Hillard, Minnesota Department of Natural Resources
Enabling Precision Forestry: A Nationwide Forest Inventory with SilviaTerra and Microsoft AI for Earth - Zack Parisa, SilviaTerra
Landscape Change across the Great Lakes: Petascale Computing of Satellite Stereo Imagery - Jennifer Corcoran, Minnesota Department of Natural Resources
LiDAR-Assisted Forest-Structure Products from Two Regions Using Area-Based and Single-Tree Analysis Methods - Mark Corrao; Northwest Management, Inc.
Reliability of Aerial-Photo Derived Maps of Forested Wetlands - Stephen Prisley, NCASI
2020
The Impact of GPS Receivers’ Accuracy on Spatial Point Pattern Analysis - Taeyoon Lee, University of Georgia
Airborne Hyperspectral Data Application in Health Stress Detection of Ash Trees - Catherine Chan, University of Maine
Cross-Site Remote Sensing Algorithms Produce Continental-Scale Observations on Density and Allometry for 180 Million Trees - Stephanie Bohlman, University of Florida
2021
Comparison of Merchantable Timber Estimates between Conventional Cruise Results and UAV-LiDAR Derived Stand Volume - Lane Gelhorn, Forsite Consultants Ltd.
2022
Harnessing the Power of CT Scanning to Identify Changes in Specific Gravity of Loblolly Pine - Mark Corrao, Northwest Management Inc.
A Felled Tree Validation of Tree Diameters and Heights Derived from Airborne LiDAR Data - Maxwell Schrimpf, Mississippi State University
2023
3DForests: Quantifying Aboveground Carbon Stocks and Fire Fuels to Inform Forest Management - Lisa Bentley, Sonoma State University
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Real-time Tracking in Logging: Effects of Canopy, Aspect, and PDOP on GPS Movement Data and Geofence Accuracy
Open to view video. | 19 minutes
Open to view video. | 19 minutes
Real-time GPS positioning and geofence alerts may improve situational awareness on active logging operations. However, the factors affecting real-time GPS systems are poorly understood. We report results of evaluating GPS motion data and the time-to-signal accuracy of geofences under various levels of canopy cover, aspect, and positional dilution of precision. Presented by Eloise Zimbelman, University of Idaho at the 2016 SAF National Convention in Madison, WI.
Multi-transmitter GPS Accuracy Analysis for Recording Swing Movements of Harvesting Machinery
Open to view video. | 22 minutes
Open to view video. | 22 minutes
Multi-transmitter GPS technologies incorporated into forest operations research creates unique opportuites to expand production studies outside of traditionally used sampling methods. This study explores the use and accuracy of GPS units in capturing the swinging movements of harvesting machinery . Additionally, the spatial data gathered from GPS units enables the exploration of real-time machinery mapping for production and safety applications. Presented by Ryer Becker, University of Idaho at the 2016 SAF National Convention in Madison, WI.
Delineating Forested Wetlands and Crop Areas Using Synthetic Aperture Radar
Open to view video. | 21 minutes
Open to view video. | 21 minutes
For many years, Synthetic Aperture Radar (SAR) has been used in research to identify forested wetlands and crop areas. The advantage of SAR over optical remote sensing techniques includes its it's all-weather observing capability. In the near future, quantifying forested wetlands and crop area will be demonstrated globally with NISAR. Presented by Josef Kellndorfer, Earth Big Data, LLC at the 2017 SAF National Convention in Albuquerque, NM.
Measuring and Monitoring Forest Structure and Biomass from Space
Open to view video. | 31 minutes
Open to view video. | 31 minutes
Post-2020 will witness a series of new observations from NASA and ESA spaceborne missions dedicated to measurements of aboveground forest structure and biomass (AGB). These measurements are designed to providr globally consistent estimates of forest aboveground biomass and monitor disturbance and recovery of forests. Presented by Sassan Saatchi, JPL CALTECH at the 2017 SAF National Convention in Albuquerque, NM.
NASA's GEDI Mission: Mapping Forest Height and Biomass from the International Space Station
Open to view video. | 27 minutes
Open to view video. | 27 minutes
NASA’s Global Ecosystems Dynamics Investigation (GEDI) is a full waveform LiDAR system that is scheduled for launch to the International Space Station (ISS) in December, 2018. This presentation gives an overview of GEDI's mission concept and algorithms for producing maps of forest height and aboveground biomass. Presented by Laura Duncanson, University of Maryland / NASA Goddard at the 2017 SAF National Convention in Albuquerque, NM.
Use of Lidar-derived Forest and Topographic Characteristics to Classify Alternative Harvest System Options
Open to view video. | 25 minutes
Open to view video. | 25 minutes
As innovative harvest systems are developed, the extent to which they can be utilized on the landscape based on machine capabilities is often unclear to forest managers. In response, spatial decision support models may aid land managers in choosing appropriate logging systems based on topography and stand characteristics. Lidar and inventory data from 91 sample plots were used to model site characteristics for 2627 stands in the Slate Creek drainage on the Nez Perce Clearwater National Forest in North-Central Idaho, USA. Five harvest systems were then integrated into a decision support tool to characterize sites where use of these systems was feasible. Self-levelling shovel harvester-based logging systems were included to determine potential sites where use of the systems is possible based on landscape and stand conditions. Lidar-derived predictions for volume and trees per hectare were determined with model accuracies of 76.4% and 70.3%, and together with topographic characteristics it was determined that shovel harvester-based options were feasible for over 30% of the study area. Additionally, increasing operable slope for ground-based systems by 10% increased the area harvestable by 21%. Feasible harvest system classification using lidar-derived products allows managers to better evaluate operable capabilities of alternative harvest system options and may aid in evaluating potential application and impact of new logging systems at the landscape level as they evolve. Presented by Ryer Becker, University of Idaho at the 2018 SAF National Convention in Portland, OR.
Pre- versus Post-stratification in an Operational LiDAR Inventory
Open to view video. | 24 minutes
Open to view video. | 24 minutes
Remote sensing based forest inventories are being contemplated by more administrators in the private and public sectors. Initially LiDAR based remote sensing data collection is being done on small scale pilot projects, with the idea of scaling to whole forest areas. Forest inventories to support model assisted inference, are often deployed based on regular arrays within these collects. This approach is often chosen due to a variety of factors, but relies on forest strata being applied to the area post plot collection. However this same number of plots may also be placed proportionally when some stratification information is known before inventory activities. These two strategies are referred to as pre and post stratification respectively. These strategies were applied in two separate LiDAR collections completed between 2017 and 2018 in northern Minnesota using Single Photon LiDAR (SPL). In one study area, plots were placed in an array across the study area, while in the other, strata were developed based on LiDAR data analysis, with plots being placed randomly using proportional allocation amongst strata. Impacts of these strategies on model performance relating plot measured forest attributes to LiDAR metrics, as well as inventory estimation in these two study areas will be reviewed. This is part of an ongoing study that is moving the MN DNR towards a remotely sensed inventory. Presented by Scott Hillard, Minnesota Department of Natural Resources, at the 2019 SAF National Convention, Louisville, KY.
Enabling Precision Forestry: A Nationwide Forest Inventory with SilviaTerra and Microsoft AI for Earth
Open to view video. | 29 minutes
Open to view video. | 29 minutes
In collaboration with Microsoft AI for Earth, SilviaTerra has developed a high-resolution forest inventory Basemap for the continental US. The dataset includes the species, diameter, and height of each stem in each 15 meter "pixel." This talk explores how different types of foresters are using Basemap to transform their work, from state foresters (inventory planning and monitoring) to consulting foresters (new client acquisition), and industrial foresters (acquisitions/divestitures). By providing instantly-available, enhanced forest inventory data, Basemap helps enable a new generation of "precision forestry" where foresters are able to tell rich, data-driven stories about land management and to quantify the elements of the forest that our society cares about the most. Presented by Zack Parisa, SilviaTerra, at the 2019 SAF National Convention, Louisville, KY.
Landscape Change across the Great Lakes: Petascale Computing of Satellite Stereo Imagery
Open to view video. | 27 minutes
Open to view video. | 27 minutes
Forests and wetlands are essential for recreational opportunities, wildlife habitat, flood mitigation, and filtering non-point source pollution in the Great Lakes basin. Existing forestry and wetland inventories are often outdated, inaccurate, and frequently misrepresent the location and underestimate the extent of these ecologically significant landscape features. Object-based image analysis (OBIA) has been used extensively for mapping landscape features. Recent advances in the availability of high-resolution stereo satellite imagery and surface elevation mapping approaches using petascale supercomputing have created new opportunities for mapping and monitoring landscape change within the basin. In this presentation, we expand on existing stereo image processing and OBIA-based mapping approaches by leveraging high-resolution remotely-sensed data and ancillary datasets to map and monitor changes in the landscape in the Great Lakes Basin. Attendees will be briefed on the data, methods, and preliminary results from our ongoing efforts to map and monitor these complex landscapes. Presented by Jennifer Corcoran, MN Department of Natural Resources, at the 2019 SAF National Convention, Louisville, KY.
LiDAR-Assisted Forest-Structure Products from Two Regions Using Area-Based and Single-Tree Analysis Methods
Open to view video. | 33 minutes
Open to view video. | 33 minutes
Light detection and ranging (LiDAR) data are increasingly available on forested lands across the United States. These point-cloud data differ in acquisition hardware and collection specifications, as well as the rigor, timing, and availability of field data for comparison. To illustrate this, we compare the unpublished TREE-D LiDAR processing workflow and results for a ~23,000-acre site in east-central Arizona to a similar-sized site in eastern Washington State. The Washington data were typical for publicly available datasets in the western U.S., with LiDAR data from two separate acquisitions (2014 and 2015), unknown acquisition specifications, and no synchronized field sampling. The Arizona data were better suited for LiDAR-assisted inventory—LiDAR acquisition parameters were controlled and optimized for forest inventory, extensive field data were acquired in close temporal proximity to the LiDAR flights, and these field data included individual-tree locations to accommodate individual-tree based analysis methods. At both sites, using a mix of open-source and proprietary software, we were able to identify large, dominant trees in the point cloud data, and predict volume and species probabilities at a pixel level. At the Arizona site, using dense point cloud data (12-20 pts/m2) and fixed radius plot data, we were able to predict these area metrics with better precision, as well as confidently identify location, height, species and DBH for individual trees. We discuss the value and limits of area-based LiDAR inventories, the potential and challenges associated with LiDAR-assisted individual-tree analysis, as well as data considerations for each approach. Presented by Mark Corrao, Northwest Management, Inc, at the 2019 SAF National Convention, Louisville, KY.
Reliability of Aerial-Photo Derived Maps of Forested Wetlands
Open to view video. | 9 minutes
Open to view video. | 9 minutes
Forested wetlands are critical components of the southern US forested landscape. Knowledge of their extent and trends in area is therefore crucial in ensuring the sustainability of ecosystem services derived from them. Spatial data on US forested wetlands are produced by the National Wetlands Inventory (NWI) program of the US Fish and Wildlife Service. The NWI program uses manual interpretation of aerial photography not only for the digital map layer of conterminous US wetlands but also for the periodic analyses of wetlands status and trends (S&T). Past S&T reports have attributed some forested wetland loss to silvicultural practices, so an understanding of the reliability of these map products is relevant to forest landowners and stakeholders. We conducted a study using photointerpreters trained in the NWI S&T methodology to (1) determine levels of variability between photointerpreters mapping the same area independently, (2) quantify agreement between photointerpreters in mapping of wetland change using time series of aerial photography, (3) assess uncertainty in wetland boundary delineation relative to S&T mapping standards, and (4) summarize the effects of 1-3 on estimates of change in forested wetland area. Substantial disagreement among photointerpreters is evident at the polygon and sample block level, but diminishes in relative effect as estimates from multiple sample blocks are aggregated. Anecdotal evidence indicates that different approaches to mapping may have a profound effect on results: mappers creating independent maps at two points in time had different levels of change than those who only mapped visible differences from prior delineations. Presented by Stephen Prisley, NCASI, at the 2019 SAF National Convention, Louisville, KY.
The Impact of GPS Receivers’ Accuracy on Spatial Point Pattern Analysis
Open to view video. | 18 minutes
Open to view video. | 18 minutes
Accurate data are essential for analysis and operations involving precision forestry. Global positioning system (GPS) receivers are one technology used for spatial data collection, and they have been widely employed to navigate and to collect various spatial information measurements. High quality spatial information is vital for spatial point pattern analysis. Nevertheless, many studies investigating the spatial distribution of tree (points) using data collected by GPS receivers have not considered the horizontal positional errors involved in the GPS data. The positional accuracy of GPS receivers produces potentially significant errors, especially in forested areas, due mainly to multipath signals. This study was conducted to investigate whether the errors inherent in GPS data can influence the results of spatial point pattern analyses. We collected the data at the pine seed orchard at Whitehall Forest in Athens, Georgia using three different types of GPS receivers: a mapping-grade receiver and two recreation-grade receivers (traditional; handheld-type and non-traditional types; GPS watch). The tree locations were recorded at each cardinal point of the stem (at North, South, East, and West side of the stem), and estimated at the middle point measurement of two of the cardinal points (North and South, East and West). The averaging of positions using all measurements at every cardinal point determined the center of each tree location. We compared these observed tree distributions and locations to high-quality control positions for each sample tree, obtained through precise field measurements and high-precision GPS base points. The result of this study helps to explain the impact of a GPS receiver’s general level of accuracy on spatial point pattern analysis and to inform forest managers and researchers about the advantages and disadvantages of using recreation-grade GPS receivers to support spatial point pattern analysis. Presented by Taeyoon Lee, University of Georgia at the 2020 SAF Virtual Convention.
Airborne Hyperspectral Data Application in Health Stress Detection of Ash Trees
Open to view video. | 26 minutes
Open to view video. | 26 minutes
Advanced detection of health stress in forests can prompt management responses to mitigate detrimental conditions such as drought and disease. Remote sensing has provided timely and reliable information covering large spatial extents, while new applications in hyperspectral data and imaging spectroscopy have shown potential in early stress detection. We build on previous work by assessing and integrating airborne spectral data, ground spectral data, and health classifications in ash trees of differing emerald ash borer (EAB) infestation scales in aims of accurately detecting health stress. Airborne scans and ground spectral data were collected within 3 days in late July, 2019 over 3 sites in southern New Hampshire. Ground sampled data were collected in November 2019 and include sampled ash classified on a scale of 1-5 (1=healthy, no major branch morality, 5=dead). The remotely sensed data will be validated through ground measurements and linked to the health classifications. Using methods in machine learning and statistical analysis, we aim to relate reflectance measurements to the health classifications, and ultimately to different intensities of EAB infestation. Our work seeks to utilize imaging spectroscopy in understanding tree stress signals, particularly in earlier stages to prevent the progression of adverse conditions such as EAB. The applications in this study however, are not limited to forests and have use in a multitude of other fields and scales. One of the greatest advantages of these technologies is the capability provided in monitoring and maintaining consistent health, relevant especially in fluctuating climate. As remote sensing techniques are advanced, the methods in monitoring and understanding forest health can as well. Presented by Catherine Chan, University of Maine at the 2020 SAF Virtual Convention.
Cross-Site Remote Sensing Algorithms Produce Continental-Scale Observations on Density and Allometry for 180 Million Trees
Open to view video. | 30 minutes
Open to view video. | 30 minutes
New developments in remote sensing technology have enabled measurements of the densities, sizes and important properties of trees on large spatial scales. However, most of these remote sensing applications are performed at single sites and use methods calibrated to local conditions, thus making it hard to use the same algorithm at other sites, particularly for forests with different structures. For very large scale (regional to continental) applications, methods that can be universally applied across many forest structures, even if trained in other locations, would allow rapid analysis of assessment of forest properties. Here we use remote sensing data (high resolution lidar and RGB camera images) from 45 sites of the National Ecological Observatory Network (NEON) to develop and test algorithms to quantify individual trees properties using a variety of algorithms that use cross-site training. These 45 sites include a huge variety of forest structures from desserts with sparse tree cover in the US Southwest to continuous tall canopies in the Pacific Northwest and Eastern US. In addition to in house, open source algorithm development, we have run data science competitions based on a common set of forest field and remote sensing data to both engage the computer science community to develop algorithms relevant to forest applications, and test which algorithms work best on a common data set. Finally, after applying our current best algorithms to the NEON sites, we discuss new insights from delineating nearly 180 million trees across the NEON sites, including continental-scale variation in tree allometry and density at an unprecedented scale. Presented by Stephanie Bohlman, University of Florida at the 2020 SAF Virtual Convention.
Comparison of Merchantable Timber Estimates between Conventional Cruise Results and UAV-LiDAR Derived Stand Volume
Open to view video. | 16 minutes
Open to view video. | 16 minutes
Companies and government are increasingly looking for cost-effective and accurate methods that can be used to determine pre-harvest stand volume and basal area. Conventional practices involving personnel in the field are trusted and while there are limitations, those limitations are well-known and understood. In contrast, drone-mounted sensors can rapidly collect extremely rich data within industrially viable time frames but new technology raises questions about best practices and practicality in general. The sensor data can include high point density LiDAR as well as multi-spectral imagery and those inputs can be used to provide volume estimates as well as species composition. This presentation will detail and compare the conventional and UAV-derived results for 5 different blocks in the Saskatchewan boreal forest and make recommendations for future investigations. Ground truth for the stand level assessment was collected in two independent timber cruises for each stand. This allowed an estimate of basal area by species, gross merchantable volume, and Lorey’s mean height (for trees >10cm DBH). We reviewed the UAV-derived inventory using three approaches. The first was to clip the individual tree points (all stems series) to the fixed radius plot in each stand. We then compiled these tree records as if they were field measurements, using the same compilation routine applied to the true field measurements at the fixed radius calibration plot. The second and third approaches were designed to investigate the fit at the whole stand level. Method number two was a simple summary of all LiDAR trees within the boundary of the AOI polygons. Method number three treated the individual tree records as a population to be sampled by the same grid of variable radius plots as was used to generate the timber cruise estimate. Presented by Lane Gelhorn from Forsite Consultants Ltd. at the 2021 SAF Virtual Convention.
Harnessing the Power of CT Scanning to Identify Changes in Specific Gravity of Loblolly Pine
Select the "View Video" button to begin. | 24 minutes
Select the "View Video" button to begin. | 24 minutes
This study is a comparison of conventional cruise derived, destructive sampling derived, and airborne laser scanning derived individual tree diameters and heights in a Pinus taeda sp. (loblolly pine) stand in Eastern Texas. Presented by Mark Corrao, Northwest Management Inc. at the 2022 SAF National Convention in Baltimore, MD.
A Felled Tree Validation of Tree Diameters and Heights Derived from Airborne LiDAR Data
Select the "View Video" button to begin. | 24 minutes
Select the "View Video" button to begin. | 24 minutes
We examine the role of the live crown in the transition from corewood to outerwood in loblolly pine. Specifically, we analyze the influence of initial spacing on radial variation of ring width and specific gravity using a high-resolution CT scanner. Presented by Maxwell Schrimpf, Mississippi State University at the 2022 SAF National Convention in Baltimore, MD.
3DForests: Quantifying Aboveground Carbon Stocks and Fire Fuels to Inform Forest Management
Select the "View Video" button to begin. | 22 minutes
Select the "View Video" button to begin. | 22 minutes
The 3DForests Project aims to evaluate the use of remote sensing techniques to rapidly and more accurately estimate aboveground biomass (AGB) for a range of tree species and estimate crucial fuels parameters to help validate or refine fuel treatment tools and fire behavior models across diverse California forests. Presented by Lisa Bentley, Sonoma State University at the 2023 SAF National Convention in Sacramento, CA.