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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Comparative life cycle assessment of bioenergy in Japan from residual biomass-based wood pellets produced in the US Pacific Northwest

The US Pacific Northwest (PNW) faces an increase in wildfires due to forest overcrowding and climate change, posing significant environmental and public health risks. Traditional methods of managing surplus biomass, including prescribed burning, have increased air pollution and global warming. While in the PNW, residual woody biomass is being treated as waste, Japan’s growing demand for bio-based energy presents an opportunity to export value-added biomass as energy pellets. This study investigates whether producing wood pellets from the residual woody biomass from forest operations and sawmills for electricity generation in Japan is truly environmentally beneficial. Accordingly, we conducted a cradle-to-grave Life Cycle Assessment (LCA) to evaluate the environmental impact of residual pellets vs. coal for electricity generation. The assessment covered feedstock production, pellet processing, transportation, and combustion phases. Our findings indicate that replacing coal-based electricity in Japan with PNW residual pellet-based electricity can lower the Global Warming Potential (GWP) by about 90% for every unit of electricity displaced. Furthermore, the results show that repurposing the otherwise burnt harvest slash residues for pellet production would improve local air quality by reducing PM 2.5 and smog in the PNW. However, substituting coal with residual pellets marginally increased carcinogenic and ecotoxicity-related emissions. Based on these results, we conclude that substituting coal with residual wood pellets for electricity generation, particularly harvest-slash residual pellets, is environmentally beneficial across most impact categories, including the GWP. This research underscores how an export‑oriented pellet industry can help address the environmental challenges within the regional US wood products industry and global renewable energy supply.

Velappan, Hemalatha [Univ. of Washington, Seattle,↗
NASA NTRS2008

A VARI-Based Relative Greenness from MODIS Data for Computing the Fire Potential Index

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate the VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FP outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.

Schneider, P.↗
DOE OSTI2025

A worldwide aerosol phenomenology: Elemental and organic carbon in PM 2.5 and PM 10

Elemental carbon (EC), organic carbon (OC), and particulate matter (PM) concentrations in the inhalable (PM 10 ) and fine (PM 2.5 ) size fractions are measured worldwide, albeit with different analytical methods. These measurements from many researchers were collected and analyzed for Africa, America, Asia, and Europe for 2012–2019. EC/PM, OC/PM, and OC/EC ratios were examined based on region, site type, and season to infer potential sources and impacts. These analyses demonstrate that carbonaceous materials are important PM constituents throughout the world. Mean EC/PM ratios were lowest in PM 10 in Sahelian Africa and Europe (∼0.01), highest (>0.07) in PM 2.5 at urban sites in North America, South America, and Japan. Mean OC/PM ratios were lowest in PM 10 in the Sahel (∼0.06) and in PM 2.5 in China and Thailand (0.10), and highest in central and eastern Europe (∼0.3) and North America (∼0.4). OC/EC ratios were elevated in western and northern Europe, and at regional background sites in North America. EC/PM increased with PM 10 in Thailand, while OC/PM increased with higher PM mass in Thailand, India, and North America, highlighting the specific contribution of carbonaceous aerosols to PM pollution in these regions. At European and North American background sites, OC/EC ratios increased with PM mass. Higher OC/EC ratios in dry periods indicate influence of wildfires, prescribed burns, and secondary aerosol formation. Elevated wintertime EC/PM ratios coincide with residential heating in temperate climate zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
DOE OSTI2026

Environmental Extremes: Building a New Research Partnership between the University of Nevada Reno and the Pacific Northwest National Laboratory (Final Report)

This project focused on strengthening research partnership and collaboration between the University of Nevada, Reno (UNR) and the Pacific Northwest National laboratory (PNNL) in two focused areas: wildfires and hydrology in the context of environmental extremes. The goal was to overcome barriers and bring UNR university researchers, students, and postdocs up to speed and entrained into DOE/SC/BER /Earth and Environmental System Science Division’s (EESSD) environmental research enterprise. UNR continues to work with the Earth and Biological Sciences Directorate (EBSD) at PNNL to create a broad new collaborative research program connecting scientists and research faculty at the two institutions. This research partnership has been designed to increase the capabilities and velocities of both institutions by developing long-term relationships between researchers from each institution, exposing university researchers to the deep capabilities in the DOE National Laboratories and user facilities, and establishing a pipeline of skilled and experienced graduate students and postdoctoral researchers ready to work alongside DOE scientists to take on grand challenge science problems. These collaborations are intended to ultimately benefit the public by advancing scientific discovery, unleashing the scientific potentials through collaboration and connection, enriching the technical and scientific competitiveness of our nation, and enabling the development of a skilled workforce to tackle the critical scientific challenges that face our nation’s security, infrastructure, and prosperity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Grand Valley Ecological Forecasting II: Forecasting Trends in Pinyon-Juniper and Sagebrush Habitat Relative to Wildfire, Drought, Beetle Disturbance, and Treatment Impact for Management Planning

Disturbances and landcover change in pinyon-juniper and sagebrush ecosystems are exacerbated by environmental conditions such as variability in climate characteristics. Our DEVELOP team partnered with the National Park Service (NPS) in Colorado National Monument and the Bureau of Land Management (BLM) in McInnis Canyons and Dominguez-Escalante National Conservation Areas to investigate disturbances to land cover. NPS partners were interested in identifying areas at risk of pinyon-juniper die-off or encroachment by invasive species. The BLM partners prioritized identifying areas suitable for fire reduction and prevention treatment. To address these concerns, we forecasted landcover change in the Grand Valley region of Colorado using NASA Earth observation data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua. We collected and analyzed these data in conjunction with Term I of this project. We found that the primary drivers of forecasted landcover change in the study area were aspect and elevation. Our forecasted land cover maps, created using the Idrisi TerrSet Land Change Modeler, indicated that by 2040, ecosystems within partner management areas will likely see tree encroachment on shrublands. These maps addressed the needs of our partners by showing potential habitat suitability trends, which will inform management planning.

Bill Curtiss↗
DOE OSTI2024

Soil microbiome feedbacks during disturbance-driven forest ecosystem conversion

Abstract Disturbances cause rapid changes to forests, with different disturbance types and severities creating unique ecosystem trajectories that can impact the underlying soil microbiome. Pile burning—the combustion of logging residue on the forest floor—is a common fuel reduction practice that can have impacts on forest soils analogous to those following high-severity wildfire. Further, pile burning following clear-cut harvesting can create persistent openings dominated by nonwoody plants surrounded by dense regenerating conifer forest. A paired 60-year chronosequence of burn scar openings and surrounding regenerating forest after clear-cut harvesting provides a unique opportunity to assess whether belowground microbial processes mirror aboveground vegetation during disturbance-induced ecosystem shifts. Soil ectomycorrhizal fungal diversity was reduced the first decade after pile burning, which could explain poor tree seedling establishment and subsequent persistence of herbaceous species within the openings. Fine-scale changes in the soil microbiome mirrored aboveground shifts in vegetation, with short-term changes to microbial carbon cycling functions resembling a postfire microbiome (e.g. enrichment of aromatic degradation genes) and respiration in burn scars decoupled from substrate quantity and quality. Broadly, however, soil microbiome composition and function within burn scar soils converged with that of the surrounding regenerating forest six decades after the disturbances, indicating potential microbial resilience that was disconnected from aboveground vegetation shifts. This work begins to unravel the belowground microbial processes that underlie disturbance-induced ecosystem changes, which are increasing in frequency tied to climate change.

Nelson, Amelia R.↗
DOE OSTI2023

Developing a microwave-driven reactor for ammonia synthesis: insights into the unique challenges of microwave catalysis

Rapid development of new ammonia (NH 3 ) synthesis techniques that enable modular, intermittent production is essential to actualizing NH 3 's potential as a clean energy carrier, since contemporary methods are configured to centralized, continuous production methods with high emissions and are incompatible with renewable sources. Here, in this mission, microwave-driven catalysis is promising for its ability to enhance reaction kinetics and apply targeted heating for efficient energy use. However, owing to an incomplete understanding of the interaction between microwave fields and catalyst beds, the development of such microwave-catalysis systems remains underexplored and challenging. This paper investigates the 10× scale-up of a microwave-based NH 3 synthesis reactor using numerical and experimental approaches, achieving the largest reported microwave-driven NH 3 reactor to date. Results elucidate phenomena unique to microwave processes that are only predictable through numerical modeling, including how a catalyst's dielectric properties influence microwave field distribution by affecting penetration depth and how energy utilization can be poor even with sufficient attenuation. These dynamics change with scale, constrain reactor geometry, and potentially hamper performance. Nonetheless, we demonstrate a production rate of 56.6 g NH$_3$ per day, the highest reported NH 3 synthesis rate for laboratory-scale alternative techniques; correspondingly, the benchmark energy efficiency achieved in this paper (45.6 g NH$_3$ kW h -1 ) is the highest reported for such reactors of sufficient scale. Even with this exemplary energy efficiency, energy losses were found in excess of 50%, an issue resolvable through scale and reactor design. The efficiencies imparted by microwaves were key in these achievements, warranting further investigation toward development of microwave-driven NH 3 systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ~ 450 % compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Identifying Chemical Aerosol Signatures Using Optical Suborbital Observations: How Much Can Optical Properties Tell us about Aerosol Composition?

Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).

Meloe S F Kacenelenbogen↗
DOE OSTI2024

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Rahimi, Stefan (ORCID:0000000331884462)↗
NASA NTRS2017

The Development of a Thermally Enhanced Emergency Fire Shelter

Since its founding in 1905, the U.S. Forest Service has been responsible for maintaining public lands. The Forest Service and other public lands agencies respond to an average of 73,000 wildfires per year, and responding firefighters are required to carry a number of safety gear items, including the M2002 emergency fire shelter. The emergency fire shelter is intended to serve as a last resort means of protection in case a firefighter's escape route has been compromised in the face of an approaching flame front. No fire shelter deployment tragedy has been more costly than the 2013 Yarnell Hill fire in Arizona, where 19 members of the Granite Mountain Hotshots perished. After the tragedy at Yarnell Hill, the Forest Service decided to expedite the next redesign cycle of the fire shelter in order to improve its ability to withstand direct contact with flames. Engineers at NASA Langley Research Center have spent the better part of a decade developing flexible thermal materials for use in inflatable aerodynamic decelerators and have demonstrated their performance in the IRVE-2 and IRVE-3 flight programs (Inflatable Reentry Vehicle Experiment). NASA engineers recognized an opportunity to leverage their experience and knowledge with flexible thermal protection systems to potentially improve the fire shelter's resistance to direct flame contact, and have been working directly with the U.S. Forest Service to achieve this goal. They launched the CHIEFS project (Convective Heating Improvement for Emergency Fire Shelters) in 2014. Over the past three years, CHIEFS has screened over 270 unique material layups, and tested over 30 unique full scale shelter concepts in an effort to achieve a game changing improvement to the thermal protection of the fire shelter, while maintaining minimal mass and volume. This paper will discuss CHIEFS' 1st and 2nd generation fire shelter development efforts and test results.

Fody, Joshua M.↗
NASA NTRSDate not supplied

Flight Test Exploration of Integrated Wildfire Response Operations with Crewed and Uncrewed Air Assets

Since 2016, Japan Aerospace Exploration Agency (JAXA) and NASA have partnered to investigate the safe and efficient integration of uncrewed aircraft systems (UAS) in disaster relief operations. Current disaster response operations would benefit from the use of UAS but are limited by the need for safe airspace integration technologies and enabling regulations. The work presented in this paper focuses on the safe integration of UAS and helicopters in active wildfire disaster response operations. The authors propose the use of mission planning and traffic management technologies, including onboard pilot situation awareness support technologies, to coordinate airspace operations between crewed and uncrewed aircraft (e.g., UAS). A representative wildfire scenario including a helicopter and UAS was developed. The helicopter mission included a flight path that mimicked the siphoning of water from a natural water reservoir (lake) and a subsequent water drop over vegetation along the progressing edge of a fire perimeter. Simulated UAS were assumed to conduct aerial ignition flights in the vicinity as part of a controlled burn of the vegetation ahead of the fire front to create a natural barrier between the fire and a nearby populated area. A situation awareness tool was developed and used to support the helicopter pilots’ task to stay clear of the volume of airspace where the UAS was conducting its operation while efficiently conducting the fire suppression mission. A flight test using JAXA’s experimental helicopter was conducted in August 2022 to validate the developed technology. The advantages of the developed technology were demonstrated, and pilot feedback confirmed that real-time awareness of the UAS operation airspace helped the flight planning and execution of a safe mission that minimized the flight time loss due to the potential interference with the UAS. Post-flight interviews with the pilots indicated no sufficient increase in the pilots’ workload and provided ideas for future technology development. Future research will provide recommendations to standards development organizations to support considerations for crewed-uncrewed interactions in disaster-response operations.

disaster response↗
NASA NTRS2005

Apex Reference Manual 3.0 Beta

Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.

Freed, Michael A.↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events, and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, enable detection of atmospheric features during daytime operations with a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
DOE OSTI2023

Characterizing post-fire delayed tree mortality with remote sensing: sizing up the elephant in the room

Abstract Background Despite recent advances in understanding the drivers of tree-level delayed mortality, we lack a method for mapping delayed mortality at landscape and regional scales. Consequently, the extent, magnitude, and effects of delayed mortality on post-fire landscape patterns of burn severity are unknown. We introduce a remote sensing approach for mapping delayed mortality based on post-fire decline in the normalized burn ratio (NBR). NBR decline is defined as the change in NBR between the first post-fire measurement and the minimum NBR value up to 5 years post-fire for each pixel. We validate the method with high-resolution aerial photography from six wildfires in California, Oregon, and Washington, USA, and then compare the extent, magnitude, and effects of delayed mortality on landscape patterns of burn severity among fires and forest types. Results NBR decline was significantly correlated with post-fire canopy mortality (r 2 = 0.50) and predicted the presence of delayed mortality with 83% accuracy based on a threshold of 105 NBR decline. Plots with NBR decline greater than 105 were 23 times more likely to experience delayed mortality than those below the threshold (p < 0.001). Delayed mortality occurred across 6–38% of fire perimeters not affected by stand-replacing fire, generally affecting more areas in cold (22–41%) and wet (30%) forest types than in dry (1.7–19%) types. The total area initially mapped as unburned/very low-severity declined an average of 38.1% and generally persisted in smaller, more fragmented patches when considering delayed mortality. The total area initially mapped as high-severity increased an average of 16.2% and shifted towards larger, more contiguous patches. Conclusions Differences between 1- and 5-year post-fire burn severity maps depict dynamic post-fire mosaics resulting from delayed mortality, with variability among fires reflecting a range of potential drivers. We demonstrate that tree-level delayed mortality scales up to alter higher-level landscape patterns of burn severity with important implications for forest resilience and a range of fire-driven ecological outcomes. Our method can complement existing tree-level studies on drivers of delayed mortality, refine mapping of fire refugia, inform estimates of habitat and carbon losses, and provide a more comprehensive assessment of landscape and regional scale fire effects and trends.

Environmental Sciences & Ecology↗
NASA NTRS2005

A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.

Ramapriyan, Hampapuram↗
DOE OSTI2021

Pyrolyzed Substrates Induce Aromatic Compound Metabolism in the Post-fire Fungus, Pyronema domesticum

Wildfires represent a fundamental and profound disturbance in many ecosystems, and their frequency and severity are increasing in many regions of the world. Fire affects soil by removing carbon in the form of CO2 and transforming remaining surface carbon into pyrolyzed organic matter (PyOM). Fires also generate substantial necromass at depths where the heat kills soil organisms but does not catalyze the formation of PyOM. Pyronema species strongly dominate soil fungal communities within weeks to months after fire. However, the carbon pool (i.e., necromass or PyOM) that fuels their rise in abundance is unknown. We used a Pyronema domesticum isolate from the catastrophic 2013 Rim Fire (CA, United States) to ask whether P. domesticum is capable of metabolizing PyOM. Pyronema domesticum grew readily on agar media where the sole carbon source was PyOM (specifically, pine wood PyOM produced at 750°C). Using RNAseq, we investigated the response of P. domesticum to PyOM and observed a comprehensive induction of genes involved in the metabolism and mineralization of aromatic compounds, typical of those found in PyOM. Lastly, we used 13 C-labeled 750°C PyOM to demonstrate that P. domesticum is capable of mineralizing PyOM to CO 2 . Collectively, our results indicate a robust potential for P. domesticum to liberate carbon from PyOM in post-fire ecosystems and return it to the bioavailable carbon pool.

59 BASIC BIOLOGICAL SCIENCES↗