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NASA Stennis Flashback: Shuttle Team Achieves Unprecedented Milestone


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Preparations for Next Moonwalk Simulations Underway (and Underwater)

As chief of test operations at NASA’s Stennis Space Center, Maury Vander has been involved in some long-duration propulsion hot fires – but he still struggles to describe a pair of 34-minute space shuttle main engine tests conducted onsite in August 1988.

“When you stop and think about it, …” Vander begins, then pauses. “In 34 minutes, I can leave work and drive home to Slidell (15-20 miles west in Louisiana) and be relaxing in my recliner in that amount of time.”

Vander’s struggle is understandable when one considers the numbers. On Aug. 3 and Aug. 15, operators at the Thad Cochran Test Stand (B-1) at NASA Stennis near Bay St. Louis, Mississippi, fired a space shuttle main engine for a total of 2,017 seconds each day, more than four times as long as the engine fired (500 seconds) during a typical space shuttle launch.

In terms of propulsion firings, nothing else comes close. The next-longest duration appears to have occurred in 2001, when a Progress M1-5 engine was fired for about 22 minutes to help deorbit the Russian space station Mir.

Vander still wonders at the south Mississippi feat. “The ability to juggle the type of challenges seen over the course of 30-plus minutes is amazing,” he said. “And you are not talking about 21st century technology either. You are talking about rather simplistic stuff not far removed from the 1960s, so there was an art to operating that type of equipment. But, they pulled it off.”

NASA Stennis may have been the only place such a firing could have been conducted.

It had the needed test facility. The Thad Cochran (B-1) stand featured a larger liquid oxygen tank to support the test and was equipped with a diffuser that allowed operators to throttle the engine to lower power levels, thus conserving fuel. The stand also had a larger dock area for additional propellant barges needed for test support.

Each 34-minute test required about 600,000 gallons of liquid hydrogen and 230,000 gallons of liquid oxygen. Careful coordination ensured proper propellant flow from barges. “We still had old pumps for the barges, as opposed to the new ones that have variable drives to help control flow,” Vander noted. “The pumps back then were basically on/off pumps. If they were running, they were pretty much running wide open. That posed a challenge for controlling flow. It was a real art to orchestrate everything for such a long period of time.”

In addition, the NASA Stennis High Pressure Gas Facility had to ensure proper volume and flow of gases to support the tests. Teams at the High Pressure Water Facility had to manage uninterrupted flow from the 66-million gallon reservoir to the test stand. “All of these were challenges they had to think their way through and logistically make happen,” Vander said.

The test team had to maintain constant vigilance of such operations. “You are always monitoring, trying to figure out what could go wrong,” Vander said. “At any given moment, you may have to react and deal with a problem. To think of those people sitting in front of computer screens, gauges, and such, watching and making sure their responsibilities were covered for 30-plus minutes, is just amazing.”

The teams were driven by a compelling factor. The nation was just recovering from the Challenger tragedy of 1986. Space shuttle Discovery would launch NASA’s return to flight in late September. Space shuttle Atlantis was scheduled to launch later in the year, but there was an issue with the fuel preburner injector on one of the engines. To resolve the matter, operators needed to record 8,000 seconds of hot fire on the injector. They decided to compile the time as efficiently as possible.

Engineers at NASA’s Stennis Space Center conduct one of two 2,017-second tests of a space shuttle main engine on the Thad Cochran Test Stand (B-1) in August 1988. The tests still stand as the longest duration propulsion hot fires at the center and perhaps anywhere. The tests – almost 34 minutes each – were more than four times longer than space shuttle main engines fired during an actual launch.
NASA/Stennis

By the conclusion of the Aug. 15 test, just 340 more seconds of testing was needed to resolve the injector issue. As it did throughout the shuttle program, NASA Stennis teams delivered on propulsion test needs, resolving the issue to clear Atlantis for launch in early December.

From 1975 to 2009, the center tested every space shuttle main flight engine and all engine upgrades, and also helped troubleshoot various performance issues. NASA Stennis now tests the RS-25 engines produced by Aerojet Rocketdyne, an L3Harris Technologies company, to support launches of NASA’s SLS (Space Launch System) rocket on Artemis missions to the Moon and beyond.

“The people were proud of the work they did, yet humble,” Vander said, looking back at the record of the shuttle era. “You had to pull some of the stuff they did out of them when you were talking with them. Once they opened up, though, there were all kind of lessons there that we are still building on today.”

For information about NASA’s Stennis Space Center, visit:

Stennis Space Center – NASA

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Aug 05, 2024
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NASA Stennis Communications
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      Luthcke explained that V2.1 improves on precision orbit determination, precision attitude determination, tracking point modeling, time tags, and oscillator calibration. Looking ahead, V3.0 will enhance range bias calibration, improved pointing bias calibration, and modifications to L1A, L1B, L2A, and L2B products. Luthcke also discussed updates to the L3 data product, which include corrected timing and range bias, improved positioning and elevation, and a wall-to-wall 1-km (0.62-mi) elevation map to be released alongside V3.0.
      Tony Scaffardi [GSFC—GEDI Mission Director] provided an update on the Science and Mission Operations Center since its post-hibernation return to science operations on June 3, 2024. He addressed various on-orbit events that may have briefly disrupted data collection and reviewed upcoming ISS altitude plans. As of March 2025, each of the instrument’s three lasers logged over 22,000 hours in firing mode, collecting more than 20 billion shots each, with 72% of that time directly over land surfaces. As of April 2025, 95,346 hours of science data have been downlinked, averaging 51.21 GB of data per day.
      Bryan Blair [GSFC—Deputy PI and Instrument Scientist] concluded this section of the meeting with a discussion of GEDI instrument status, reporting that all three lasers are operating nominally and that both the detectors and digitizers continue to perform well. He noted that the laser pulse shapes have remained stable since the mission began, indicating consistent system performance over time. Blair also addressed the inherent challenges of operating in space, such as radiation exposure, and emphasized the importance of designing systems for graceful degradation. A recent firmware update was successfully applied to all three digitizers, and no life-limiting concerns have been identified to date.
      Competed Science Team Presentations – Session I
      Jim Kellner [Brown University—GEDI Co-I] kicked off the Competed Science Team (CST) presentations with an overview of his work investigating the role of stratification and quality filtering to improve GEDI data products and the impact of stratification error on prediction. He explained how GEDI quality filtering and aboveground biomass density (AGBD) model selection and prediction rely heavily on stratification by plant functional type (PFT) and geographic world region. Thus, evaluation and improvement of stratification and quality filtering will help maximize the number of usable GEDI shots, some of which are potentially excluded unnecessarily. To support these improvements, Kellner is exploring replacement of the current 1-km (0.62-mi) stratification layer with a 30-m (98-ft) product derived from Landsat and similarly upgrading the 500-m (1640-ft) phenology stratification layer to a 30-m (98-ft) Landsat version. These changes aim to improve the L4A footprint-level AGBD estimates in particular, but flow through to the GEDI L4B data product.
      Birgit Peterson [United States Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center] presented her research on the decomposition of GEDI waveforms to derive vegetation structure information for 3D fuels and wildfire modeling, emphasizing the importance of consistent and comprehensive information on vegetation status for effective wildland fire management. Canopy structure data, like that provided by GEDI, can play a key role in developing physics-based fire behavior models, such as QUIC-Fire. With study sites in South Dakota, the Sierra Nevada, and dispersed around the southeastern United States, Peterson’s work aims to demonstrate how vegetation structure parameters needed to run the QUIC-Fire model can be derived from GEDI waveform data.
      David Roy [Michigan State University] shared updates on his CST project leveraging GEDI data to improve understanding of species-specific tropical forest regrowth in central Africa. Focusing on the Mai Ndombe region of the Democratic Republic of the Congo (DRC), the project aims to quantify forest regrowth by integrating GEDI-derived structural data with satellite and airborne laser scanner (ALS) based maps of forest height. Roy emphasized the potential of secondary and recovering forest conservation as a low-cost mechanism for carbon sequestration and climate change mitigation. GEDI data combined with satellite maps provides new opportunities to quantify forest regrowth and carbon sequestration in secondary forests at finer detail, although high species diversity and varying regrowth rates can be complex to assess with remote sensing. Roy also presented a 2025 paper validating the GEDI relative height product in the DRC and at two US temperate forest sites with a simple method to improve the GEDI canopy height using digital terrain heights measured by airborne laser scanning (ALS). 
      Perspectives I
      After the morning CST presentation session, meeting attendees heard the first perspectives presentation from Amanda Whitehurst [NASA Headquarters (HQ] GEDI Program Scientist and NASA Terrestrial Ecology Program Manager]. Whitehurst is new to the GEDI Program Scientist role; she used this opportunity to officially introduce herself to the ST and expressed her enthusiasm for the work ahead  She commended the GEDI team on the impressive accomplishments of the mission to date, and spoke about the exciting potential for continued data collection and scientific discovery through the program.
      Matteo Pardini [DLR] shared his perspective on the potential of combining synthetic aperture radar (SAR) with lidar data to improve four-dimensional (4D) forest structure mapping. He highlighted DLR’s TerraSAR-X and TanDEM-X missions, which have been acquiring interferometric data since 2007 and 2010, respectively. Both missions are expected to continue acquiring data through 2028. The TanDEM-X Global Digital Elevation Model, covering 150 million km2 (58 million mi2) with approximately 1-m (3-ft) accuracy, can be used to derive forest height and biomass. The fusion of TanDEM-X and GEDI data can improve biomass estimates – see Figure 2 – and help researchers parameterize the relationship between coherence and forest structure. Pardini also previewed the upcoming BIOMASS mission, which will operate at a lower frequency and be able to penetrate vegetation, providing complementary information to the TerraSAR-X and TanDEM-X missions.
      Figure 2. Biomass estimates over the Amazon basin at 25-m (82-ft) resolution derived using a fusion of data from NASA’s GEDI and DLR’s TanDEM-X missions. Figure credit: Wenlu Qi CST Presentations – Session II
      Chris Hakkenberg [University of California, Los Angeles (UCLA)] opened the second CST presentation session with a discussion on his research using GEDI to characterize fuel structure, burn severity, and post-fire response across the regions of California affected by wildfire. He began by highlighting significant land cover changes resulting from wildfires in recent years that are visible as enormous [greater than 100 km2 (38 mi2)] conversions from forest to grass/scrub in the National Land Cover Dataset. Hakkenberg’s project aims to examine the role of fuel structure in driving fire severity patterns, improve burn severity maps using GEDI for change detection, and characterize post-fire response using data from Landsat 5, 7, and 8 and GEDI. He noted that while fire behavior is heavily dependent on weather, topography, and fuels – only fuels can be actively managed. GEDI provides valuable insights into forest fuel structure by measuring canopy volume (total fuel quantities) and vertical continuity (how fire may spread through those volumes). Hakkenberg and his team found that vertical fuel continuity metrics were stronger predictors of severity than fuel volume, especially in extreme weather conditions, and are most closely related to the high-burn severities that can delay long-term recovery. Finally, Hakkenberg presented research that combines GEDI and Landsat to improve burn severity assessments, which will be the focus on the next phase of this research project.
      Sean Healey [U.S. Forest Service (USFS)—GEDI Co-I] presented an overview of the Online Biomass Inference using Waveforms and iNventory (OBI-WAN) project. OBI-WAN provides globally consistent estimates of biomass and carbon, as well as changes in these estimates over time, for user-defined areas and periods of interest rather than fixed 1-km (0.62-mi) squares. The project leverages GEDI L4A models to predict biomass at the footprint level and uses this dense collection of footprints to create local-level biomass models with Landsat (assuming consistent calibration of Landsat through time). To quantify uncertainty in change estimates, OBI-WAN employs a statistical method called bootstrapping, which can be embedded into customized accounting systems through a powerful programming interface accessed through Google Earth Engine.
      Data Product Status I
      Michelle Hofton [UMD—GEDI Co-I] and Sarah Story [GSFC] returned to the topic of GEDI data product status. They presented an update on the GEDI L2A product, which includes ground topography and canopy height measurements. Encouragingly, preliminary testing shows that GEDI’s post-storage performance has remained consistent with pre-storage. Hofton explained GEDI observations are compared with high-quality intersections with the Land, Vegetation, and Ice Sensor (LVIS) (an airborne lidar) data in order to assess GEDI data quality and accuracy.  She highlighted the use of bingos – pairs of GEDI waveforms believed to be spatially coincident in vegetated areas – as a valuable tool for assessing geolocation and waveform errors as well as algorithm performance. As of December 2024, more than one million bingos had been collected. Hofton and Story concluded with a preview of anticipated updates to the L2A product for V3.0, including new quality flags for data cleaning and a refined algorithm selection approach. 
      John Armston [UMD—GEDI Co-I] presented on GEDI L2B data, which provides gridded footprint-level [25-m (82-ft) resolution] metrics such as canopy cover (see Figure 3), plant area index (PAI), plant area volume density (PAVD), and foliage height diversity (FHD). Waveform analysis will remain largely unchanged from V2.0. He shared that the upcoming V3.0 release will differ from V2.0 in that it will use GEDI-derived canopy-ground reflectance ratios — rather than values derived from NASA LVIS — to estimate canopy cover, thus allowing for spatial variability. Waveform analysis will remain largely unchanged from V2.0. Armston also presented 1-km (0.62-mi) leaf-on and leaf-off gridded L2B canopy cover fraction maps using both pre- and post-storage data (April 2019–November 2024), explaining how post-storage data were used to fill gaps. Additionally, the mission team has mapped GEDI canopy cover distributions using a H3-indexing API developed by Tiago de Conto [UMD], which are being used to improve GEDI L2A algorithms for ground detection. V3.0 will offer a more direct measure of canopy structure to complement L2A relative height metrics by improving quality flags and including relative canopy height metrics. Finally, the team presented progress on the independent validation of GEDI L2B V3.0 algorithms and products using the GEDI FSBD and NASA LVIS campaign data from Costa Rica, Gabon, French Guiana and the United States.
      Figure 3. GEDI L2B leaf-on canopy cover fraction map derived from data obtained April 2019–October 2024. Figure credit: John Armston Jamis Bruening [UMD] shared the final data product update of the day. He discussed GEDI’s L4B gridded aboveground biomass density (AGBD) product, which is a 1-km (0.62-mi) raster dataset representing area-level estimates of mean AGBD and associated uncertainty across the mission’s range of observation. GEDI’s L4B estimates are derived from the footprint-level L4A AGBD predictions through one of two statistical modes of inference. Currently, hybrid estimation is used to generate L4B. This approach uses GEDI data as the sole input and requires at least two GEDI tracks in a 1-km (0.62-mi) grid cell to produce a mean estimate. The hybrid estimator also provides a standard error, accounting for both model variance in the L4A predictions and GEDI’s sampling uncertainty. To address gaps in GEDI’s coverage where hybrid estimates cannot be produced, the team has begun implementing an alternative inference mode, called generalized hierarchical model-based (GHMB) estimation. GHMB incorporates auxiliary imagery, such as Landsat, SAR, and GEDI’s L4A predictions, to infer mean biomass and its standard error. Although the addition of post-storage data has increased GEDI’s coverage, GHMB remains essential for producing a complete, gap-free 1-km (0.62-mi) AGBD map. Both hybrid and GHMB approaches will soon be used together to generate a global, gap-free L4B product. Users can expect the release of V3.0 L4B estimates – featuring hybrid and GHMB models of biomass inference using both pre- and post-storage data – later in 2025.
      What’s Next?
      Day one concluded with John Armston, who presented on the potential new satellite laser altimetry mission called Earth Dynamics Geodetic Explorer (EDGE), which was competitively selected for a Phase A Concept Study under NASA’s Earth Systems Explorer Announcement of Opportunity. If selected, EDGE would launch in 2030 and operate for a two-year mission, providing a critical link between current and future satellite laser altimetry missions.
      Armston explained that EDGE addresses two of the targeted observables identified in the 2017 Earth Science Decadal Survey – terrestrial ecosystem structure and ice elevation. It provides a dramatic improvement in coverage and resolution over current active missions by operating in a Sun-synchronous orbit that will enable the direct measurement of change in the three-dimensional (3D) structure of vegetation and the surface topography of ice at the spatial and temporal scales needed to observe the driving processes. EDGE will provide fine-scale detail of ecosystem structure in some of the world’s most critical and challenging-to-quantify regions, including the boreal, transforming the field’s understanding of global terrestrial ecosystem structure and its response to natural and anthropogenic change over all of Earth’s wooded ecosystems. 
      DAY TWO
      Data Product Status II/Extended and Demonstrative Products I
      Jim Kellner began day two with an L4A footprint-level AGBD product update. His presentation focused on current product status and planned evaluation of and improvements to the L4A algorithm. Since the last STM, L4A V2.1 was updated to include data through MW 311 (through November 2024) and is now available to end users. V3.0, along with an updated Algorithm Theoretical Basis Document (ATBD), is expected later in 2025. The revised ATBD will outline enhancements to the waveform simulator, quality filtering, stratification, and model selection thanks in part to the availability of on-orbit data. V3.0 will also benefit from the ingestion of approximately 35% more simulated waveforms that passed quality assurance and quality control in the FSBD, significantly expanding training data coverage, particularly in Africa and North America. Kellner noted that users should be aware of key differences between L2 and L4 quality flags; L4 flags account for factors such as sensitivity, water presence, urban conditions, and phenology. Additionally, updates to the selected models may lead to changes in AGBD estimates, which will be more clearly communicated in the V 3.0 release. Comparing pre- and post-storage data, Kellner and his team found that AGBD estimates remain stable across both periods. He encouraged users to review the updated ATBD upon release to fully understand the changes and their implications.
      Tiago de Conto [UMD] presented the new GEDI L4C WSCI product, which was released in May 2024 and available through the ORNL DAAC – see Figure 4. This footprint-level metric captures the amount and variability of canopy structure in 3D space, reflecting the richness of structural information underlying any given GEDI observation. It synthesizes multiple structural attributes into a single metric and incorporates elements of both vertical and horizontal variability. WSCI models are trained at the PFT level (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, and the combination of grasslands, shrubs, and woodlands) using crossovers of GEDI and airborne lidar point clouds. While WSCI tends to scale with canopy height, the relationship varies across biomes. Looking ahead, de Conto previewed forthcoming WSCI–SAR fusion work designed to produce wall-to-wall maps that are suitable for applications, such as change detection. Early fusion results using data from the European Union’s Copernicus Sentinel-1 (a synthetic aperture radar mission) and the Japan Aerospace Exploration Agency’s (JAXA) Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-PALSAR) show stable prediction performance across different biomes and time periods as well as consistent performance against airborne lidar wall-to-wall reference data.
      Figure 4. The global frequency distribution of GEDI L4C Waveform Structural Complexity Index. Figure credit: Tiago de Conto Paul May [South Dakota School of Mines and Technology] presented his work predicting interpolated waveforms, along with their associated uncertainties, over USDA Forest Inventory and Analysis (FIA) field plots across the contiguous United States (CONUS). This project aims to develop regression models that convert GEDI’s waveform data into measurements of key forest attributes and enhance monitoring capabilities for a variety of applications. The resulting data product – GEDI-FIA Fusion: Training Lidar Models to Estimate Forest Attributes – was released June 2025 and is publicly available through the ORNL DAAC.
      Sean Healey presented ongoing work on the GEDI L4D Imputed Waveform product, led by postdoctoral researcher Eugene Seo [Oregon State University]. This product aims to generate a wall-to-wall 30-m (98-ft) resolution map of GEDI waveforms across the globe in 2023. To achieve this, Seo, Healey, and Zhiqiang Yang [USFS] are using a k-nearest neighbor (k-NN) imputation approach to address areas without GEDI observations. The model operates at a 10-km (6-mi) scale but draws neighbors from a surrounding 30×30 km (19×19 mi) window. The resulting 30-m (98-ft) resolution imputed waveform map is aligned with Landsat data from 2023. Users can expect the release of the L4D product later in 2025.
      GEDI Applications and Perspectives II
      Neha Hunka [European Space Agency] shared her work using GEDI to fill gaps in the Republic of Sudan’s National Forest Inventory (NFI) in support of their Forest Reference Level (FRL) report to the United Nations Framework Convention on Climate Change (UNFCCC). Using existing NFI data for calibration, Hunka and colleagues developed a geostatistical model-based approach that interpolates between NFI sample units, allowing predictions of AGBD to be made in areas of interest – see Figure 5. (Hunka was lead author on a 2025 paper in Remote Sensing of Environment that describes a similar approach to what she described in this presentation.) UNFCCC called for the modeling approach to be transparent and replicable. Hunka emphasized the importance of access to and preparation of covariate data and called for greater capacity-building and knowledge-transfer support to help other countries adopt GEDI in their reporting. Sudan’s submission marks the first time GEDI data has been used in an FRL report.
      Figure 5. A geostatistical model-based approach uses data from the Republic of Sudan’s National Forest Inventory (NFI) for calibration and interpolates between NFI sample units, allowing predictions of aboveground biomass density where desired. Figure credit: Neha Hunka Forests cover about 30% of Earth’s land area, store over 80% of terrestrial biomass and carbon, and absorb around 30% of anthropogenic carbon dioxide (CO₂) emissions. While storing carbon in forests can help mitigate carbon emissions, deforestation, disturbances, shifting global economy, and low confidence in forest carbon credits add risk and uncertainty to this strategy. By monitoring forest biomass, some of these risks can be alleviated. Stuart Davies [Smithsonian Institution] joined the STM to present his work on GEO-TREES, a global forest biomass reference system aiming to provide high-quality, publicly available ground data from a network of long-term forest inventory sites to improve biomass mapping on the global scale. Despite many Earth observing (EO) missions focused on forest biomass, a lack of standardized ground reference data has hindered accurate validation. GEO-TREES addresses this need, by fostering collaboration between carbon monitoring, biodiversity research, and EO communities. The project includes 100 core sites and 200 supplementary sites across tropical and temperate regions, selected to represent environmental and human-use gradients, with greater emphasis on sampling in the tropics. Each core site follows Committee on Earth Observation Satellites (CEOS) protocol and includes three types of measurements: forest plot inventory plot, terrestrial laser scanning, and ALS.
      CST Presentations – Session III
      Atticus Stovall [GSFC] shared first-year findings from his research on post-fire disturbance forest recovery in Mediterranean ecosystems – specifically Spain and Portugal – where the frequency and intensity of wildfires have significantly increased in the 21st century. Using Iberian Forest Inventory ALS data and GEDI footprint data, Stovall and his team showed that GEDI can be used to assess post-fire change as well as evaluate degradation patterns from increasing fire recurrence and intensity. Stovall shared examples demonstrating the use of GEDI to detect both immediate fire effects as well as recovery after disturbance, including stand-replacement and understory clearing. By overlaying disturbance maps with GEDI data, the team observed that recovery rates differ across height class. Looking forward, they plan to investigate how recovery rates vary across environmental gradients and incorporate field plot data to validate their findings.
      KC Cushman [ORNL] presented on biomass calibration and validation (cal/val) activities for the NISAR mission, which launched in July 2025. She outlined the general approach to the NISAR biomass algorithm, which uses multiple observations from NISAR every year to produce annual biomass estimates at 1-ha (0.004 mi2) resolution. Cal/val efforts will use ALS to link sparse field data to larger landscapes with estimates at two or more sites in 15 different ecoregions. NISAR has supported cal/val field plot data collection in Spain, South Africa, and various National Ecological Observatory Network (NEON) sites in addition to ALS campaigns at Agua Salud, Panama, near the Los Amigos Biological Station, Peru, in the Chaco ecosystem, Argentina, and near Madrid, Spain.
      Chi Chen [Rutgers University] presented his work exploring vertical acclimation of vegetation canopy structure and photosynthetic activities using GEDI data. Chen’s research aims to generate gap-free, high-temporal-frequency canopy profile data and to develop a novel framework that integrates GEDI observations into a multi-layer canopy process model. By training a random forest model with spatially discontinuous GEDI PAVD profiles and multiple features, e.g., multiband spectral reflectance, tree height, and forest type, Chen and his team successfully estimated spatially continuous PAI profiles across different canopy heights. The team cross-validated their predicted PAI with GEDI PAI, NEON PAI, and LAI measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on NASA’s Terra and Aqua platforms. These data could be used to study seasonal variation in different canopy heights. Using a Global Multilayer Canopy OPTimization (GMC-OPT) model, they also found that GEDI-informed data has the potential to identify the vertical position of “net” seasonal leaf turnover –  ultimately improving the accuracy of estimates of carbon and water fluxes.
      CST Presentations – Session IV
      Marcos Longo [Lawrence Berkeley National Laboratory (LBNL) in transition to Brazilian National Institute for Space Research (INPE)] and his team presented a proposed project that integrates GEDI data with process-based models to assess the impact of wildfires on forest structure, recovery, and ecosystem function. As western US forests face increasing wildfire risk due to drier climates, more human ignitions, and a legacy of fire suppression, changes in forest structure, composition, and function are likely to become more detectable over time. This project, under the leadership of Robinson Negron Juarez [University of California, Irvine and LBNL—PI] aims to quantify biomass changes in mixed conifer forests across California, Oregon, and Washington using both ALS and GEDI data. The team plans to use GEDI L2A and L2B data to assess immediate fire impacts on forest structure, investigate post-fire forest recovery, and establish relationships between forest structure and fire intensity/severity. This information will inform process-based models – e.g., the U.S. Department of Energy’s Functionally Assembled Terrestrial Ecosystem Simulator (FATES) – and support a better understanding of forest resilience under fire disturbance regime changes.
      Ovidiu Csillik [Wake Forest University] presented work using GEDI and ALS to investigate biomass and structural changes in tropical forests. The work, conducted with Michael Keller [NASA/ Jet Propulsion Laboratory (JPL), USFS—PI], aims to use models to quantify changes in tropical forest biomass and evaluate understanding of topical forest productivity drivers. The project will use ALS data from over the Brazilian Amazon and other sites in Brazil, Gabon, French Guiana, Costa Rica, Mexico, and Borneo alongside GEDI data over pantropical regions around the globe. Csillik and Keller are currently conducting ALS–ALS and GEDI–GEDI comparisons, and are planning to estimate aboveground biomass change from ALS–ALS, ALS–GEDI, and GEDI–GEDI comparisons at both regional and pantropical scales from 2008–2026.
      Zhenpeng Zuo [Boston University (BU)] presented his team’s research, led by Ranga Myneni [BU—PI], using mechanistic model-GEDI integration to map potential canopy top height (pCTH) and inform forest restoration planning. Predicting restoration potential is challenging, as empirical, deep-learning, and mechanistic methods vary in their accuracy, interpretability, and spatial detail. This project uses a mechanistic model based on water use and supply equilibrium, calibrated using GEDI canopy height metrics, to predict pCTH. The team found this approach produced robust pCTH predictions and shows that the Eastern US has vast restorable areas. Future work will expand to dynamic modeling to incorporate disturbance risks and effects under different climate scenarios.
      DAAC Reports
      Rupesh Shrestha [ORNL DAAC] presented the status of GEDI L3 and L4 datasets at the ORNL DAAC – see Figure 6. Since the 2023 STM, three new datasets have been released: L4B (country-level summaries of aboveground biomass), L4C (footprint level waveform structural complexity index), and a GEDI-FIA (fusion dataset for training lidar models to forest attributes). In total, almost 34,000 unique users have downloaded GEDI L3 and L4A-C data 13,770,648 times, with L4A being the most popular at 13.1 million downloads. As of April 30, 2025, all GEDI footprint-level datasets from L1–L4 are available with data through mission week 311 (November 2024), besides L4C. Users can look forward to a GEDI L4D Imputation Dataset later in 2025 along with the much-anticipated V3.0 GEDI data product release. All levels of GEDI data can now be accessed in one place through the NASA Earthdata Search and Data Catalog. In addition to the data products themselves, data tools and services, publications citing GEDI data and GEDI data tutorials and workshops can be found at the ORNL DAAC website. The ORNL DAAC provides data user support through the Earthdata Forum, or via their email uso@daac.ornl.gov.
      Figure 6. GEDI L3 and L4B data projected on NASA WorldView/GIBS API. Explore the program here. Figure credit: Rupesh Shrestha Jared Beck [Land Processes Distributed Active Archive Center (LP DAAC)] presented on GEDI data products at the LP DAAC, a USGS–NASA partnership that archives and distributes lower-level GEDI products (GEDI L1B, L2A, and L2B). The LP DAAC has distributed over seven petabytes of GEDI data so far, and is now exclusively distributing GEDI data through NASA’s Earth Data Cloud. GEDI L2A is the most popular of the products in terms of terabytes of distribution. Like the ORNL DAAC, data user support also flows through the NASA Earthdata Forum. Tutorials can be found on GitHub. All levels of GEDI data can now be accessed in one place through the NASA Earthdata search and data catalog options.
      GEDI Extended and Demonstrative Products II
      Scott Goetz [NAU] discussed his team’s research leveraging GEDI data for biodiversity applications, emphasizing its potential to help improve species distribution models and the high value of understanding forest structure for conservation assessments. He highlighted a 2022 Nature Ecology & Evolution article showing that forests with higher structural integrity and cover reduced the extinction risk for over 16,000 threatened or declining species. Another 2023 study in Nature demonstrated how biodiversity indicators, such as habitat cover, canopy structure, and human pressures, can influence the effectiveness of protected areas. In order to have a wider variety of gridded products to work with for species distribution models, Pat Burns [NAU], Chris Hakkenberg, and Goetz developed the Gridded GEDI Vegetation Structure Metrics and Biomass Density at Multiple Resolutions product that has been released through the ORNL DAAC and Google Earth Engine along with a data descriptor paper published in a Nature Scientific Data paper – see Figure 7. Burns elaborated that, relative to fusion products, gridded GEDI products performed better when measuring structure, especially in the understory. The team is now comparing species distribution models in mainland Southeast Asia using fusion versus solely GEDI data.
      Figure 7. GEDI mean foliage height diversity (FHD) map using shots from April 2019 to March 2023 at 6-km (4.7-mi) spatial resolution. Red boxes indicate the approximate location of airborne lidar used for intercomparison. Three insets show GEDI mean FHD at finer spatial resolution [1 km (0.62 mi)] as well as more detailed airborne lidar coverage (red polygons). From left to right the insets show: Sonoma County, California, Coconino National Forest, Arizona, and Sumatra/Borneo. Figure credit: From Patrick Burns et al (2024) Nature Scientific Data Perspectives III
      STM attendees concluded day two with a perspective talk from Marc Simard [JPL], who showcased a range of studies demonstrating diverse applications of GEDI data, opportunities for its improvement, and potential for informing future scientific research. Drawing on his own work, Simard shared examples of using GEDI data for cal/val of global Digital Elevation Measurement (DEM) and Digital Terrain Model (DTM), mapping global mangrove heights, monitoring forest growth, and analyzing hydrological processes. In more detail, he explained how he led the development of a 12-m (39-ft) spatial resolution global mangrove height product using GEDI and TanDEM-X data. Additionally, he discussed a study evaluating tree growth rates in the Laurentides Wildlife Reserve in Quebec, Canada using both GEDI and ALS. The analysis revealed an average growth rate of approximately 32 ±23 cm (12 ±9 in) per year. Finally, he presented a paper under review examining water level detection and hydrological conditions in coastal regions using GEDI alongside Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) data. In closing, Simard emphasized that GEDI datasets can help identify critical data and knowledge gaps, guiding the development of new missions – e.g., the Surface Topography and Vegetation (STV) mission concept called for in the 2017 Earth Science Decadal Survey report. As described in the STV Study Team Report, the mission would focus on elevation and vertical structure to study the solid Earth, cryosphere, vegetation structure, hydrology, and coastal geomorphology.
      DAY THREE
      CST Presentations – Session V
      Jody Vogeler [Colorado State University] opened the final CST presentation session with an overview of her research using GEDI data fusions to characterize post-fire landscapes and understand habitat refugia for the threatened Canada lynx (Lynx canadensis). This project builds on her team’s phase-I work, which produced 30-m (98-ft) resolution gridded GEDI fusion maps across six Western U.S. states to support habitat and diversity applications related to cavity-nesting birds, small mammals, and carnivore–prey relationships. The team is now focusing on validating and improving their GEDI fusion products within post-disturbance landscapes, specifically post-fire. Using this data, Vogeler and her team aim to better understand how post-fire structural information from GEDI improves their ability to understand lynx behavior–habitat relationships across early post-fire landscapes. This information can help evaluate what structural attributes determine post-fire refugia patch use by lynx. Next steps for this work include integrating GEDI V3.0 data upon its release, identifying new GEDI metrics and derived products, and incorporating lynx radio-collar data into their analyses. Vogeler also presented her work as co-PI on a NASA Ecological Conservation Project in Greater Kruger National Park, South Africa, where she and her team are developing spatial monitoring tools to support management and conservation planning.
      Jingfeng Xiao [University of New Hampshire] provided updates on his team’s research using GEDI data to understand how structural diversity influences productivity and carbon uptake of forests in the United States. The project aims to assess GEDI’s ability to quantify structural diversity, investigate how that diversity regulates forest productivity and carbon uptake, and understand its role in resilience of forest productivity to drought. When analyzing the relationships of gross primary production (GPP) and evapotranspiration (ET) with canopy structure metrics, the team found that increased canopy structure complexity positively affected GPP and ET and reduced their seasonal variability. They also found that greater canopy complexity improved ecosystem resistance to drought. As part of the project, the team also produced 1-km (0.62-mi) resolution gridded maps of GPP and ET.
      Lei Ma [UMD] delivered the final talk of the STM, presenting his project that integrates GEDI observations with mechanistic ecosystem modeling to quantify forest regrowth in a changing climate. Ma used GEDI data and the Ecosystem Demography (ED) model in his research and found that height and aboveground biomass (AGB) regrowth rates can be derived by combining GEDI and land-use and land-cover change data. Ma found that regrowth rates derived from different inputs are generally consistent at large scales but variable at fine scales. Notably, regrowth rates showed temporal dependence, decreasing by roughly 50% every decade. Lastly, Ma and his team found that spatial variation in height and AGB regrowth rates can be partially explained by environmental conditions and disturbance frequency.
      Conclusion
      The 2025 GEDI STM was especially exciting, as it came on the cusp of post-storage data being processed and released as V2.1. Additionally, it marked the first time the new CST cohort presented on their research and joined breakout sessions with the wider GEDI team. The meeting highlighted the mission’s ongoing success and scientific value following hibernation on the ISS. Looking ahead, data users can anticipate the V3.0 product release later in 2025.
      Talia Schwelling
      University of Maryland College Park
      tschwell@umd.edu
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      Last Updated Aug 18, 2025 Related Terms
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