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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

A Search for Soot from Global Wildfires in Central Pacific Cretaceous-Tertiary Boundary and Other Extinction and Impact Horizon Sediments

Hypotheses of global wildfires following the Cretaceous-Tertiary (KT) boundary impact are supported by high concentrations of elemental carbon (3.6 mg cm-3) and soot (1.8 mg cm-2) in DSDP Site 465, which was located several thousand kilometers from potential continental sources at 65 Ma. Soot is not preserved at four other central Pacific KT localities, but this is attributed to loss during oxic diagenesis. We find no evidence for wildfires related to major impacts in the late Eocene or to Ir anomalies and extinctions in the late Cenomanian.

Wolbach, Wendy S.↗
NASA NTRS2003

Wildfires Dynamics in Mid-Siberian Larch Dominated Communities

The longterm wildfire dynamics, including fire return interval (FRI), in Siberian larch communities were examined. A wildfire chronology encompassing the 15th through the 20th centuries was developed from analyzing tree stem fire scars. Two methods were used to calculate the time interval between fires: 1) direct counting of annual tree growth rings between stem fire scars and 2) the next earlier fire date was estimated from growth ring analysis and added to the first estimate. Average FRI determined from stem fire scar dating was 82 plus or minus 7 using Method I or 95 plus or minus 7 when age of the next earlier fire was inferred from observed larch regeneration structure (Method II). FRI was also found to be dependent on site topography. FRI on north-east facing slopes was 86 plus or minus 11 years (105 plus or minus 12). FRI on south-west facing slopes was significantly less at 61 plus or minus 8 (73 plus ot minus 8) years. Flat terrain showed little difference between methods 68 plus or minus 14 (70 plus or minus 13). This was also the case for bogs, but FRI was much longer; 139 plus or minus 17 (138 plus ot minus 18). The maximum number of annual fires occurred with periods of 36 and 82 years on average. The temporal trend in the FRI decreased from 101 years in the 19 th century to 65 years in the 20th century. The effect of post-fire forest recovery on depth to permafrost was also estimated. After initial melting from increased local temperatures permafrost depth decreased at a rate of 0.3 cm/yr on average as forest canopies developed.

Kharuk, V. I.↗
NASA NTRS2008

Improved Airborne System for Sensing Wildfires

The Wildfire Airborne Sensing Program (WASP) is engaged in a continuing effort to develop an improved airborne instrumentation system for sensing wildfires. The system could also be used for other aerial-imaging applications, including mapping and military surveillance. Unlike prior airborne fire-detection instrumentation systems, the WASP system would not be based on custom-made multispectral line scanners and associated custom- made complex optomechanical servomechanisms, sensors, readout circuitry, and packaging. Instead, the WASP system would be based on commercial off-the-shelf (COTS) equipment that would include (1) three or four electronic cameras (one for each of three or four wavelength bands) instead of a multispectral line scanner; (2) all associated drive and readout electronics; (3) a camera-pointing gimbal; (4) an inertial measurement unit (IMU) and a Global Positioning System (GPS) receiver for measuring the position, velocity, and orientation of the aircraft; and (5) a data-acquisition subsystem. It would be necessary to custom-develop an integrated sensor optical-bench assembly, a sensor-management subsystem, and software. The use of mostly COTS equipment is intended to reduce development time and cost, relative to those of prior systems.

McKeown, Donald↗
NASA NTRS2010

Infrared Sensor on Unmanned Aircraft Transmits Time-Critical Wildfire Data

Since 2006, NASA fs Dryden Flight Research Center (DFRC) and Ames Research Center have been perfecting and demonstrating a new capability for geolocation of wildfires and the real-time delivery of data to firefighters. Managed for the Western States Fire Mission, the Ames-developed Autonomous Modular Scanner (AMS), mounted beneath a wing of DFRC fs MQ-9 Ikhana remotely piloted aircraft, contains an infrared sensor capable of discriminating temperatures within 0.5 F (approx. = 0.3 C), up to 1,000 F (approx. = 540 C). The AMS operates like a digital camera with specialized filters to detect light energy at visible, infrared, and thermal wavelengths. By placing the AMS aboard unmanned aircraft, one can gather information and imaging for thousands of square miles, and provide critical information about the location, size, and terrain around fires to commanders in the field. In the hands of operational agencies, the benefits of this NASA research and development effort can support nationwide wildfire fighting efforts. The sensor also provides data for post-burn and vegetation regrowth analyses. The MQ-9 Unmanned Aircraft System (UAS), a version of the Predator-B, can operate over long distances, staying aloft for over 24 hours, and controlled via a satellite-linked command and control system. This same link is used to deliver the fire location data directly to fire incident commanders, in less than 10 minutes from the time of overflight. In the current method, similarly equipped short-duration manned aircraft, with limited endurance and range, must land, hand-carry, and process data, and then deliver information to the firefighters, sometimes taking several hours in the process. Meanwhile, many fires would have moved over great distances and changed direction. Speed is critical. The fire incident commanders must assess a very dynamic situation, and task resources such as people, ground equipment, and retardant-dropping aircraft, often in mountainous terrain obscured by dense smoke.

Pestana, Mark↗
NASA NTRS2017

NASA Wrangler: Automated Cloud-Based Data Assembly in the RECOVER Wildfire Decision Support System

NASA Wrangler is a loosely-coupled, event driven, highly parallel data aggregation service designed to take advantageof the elastic resource capabilities of cloud computing. Wrangler automatically collects Earth observational data, climate model outputs, derived remote sensing data products, and historic biophysical data for pre-, active-, and post-wildfire decision making. It is a core service of the RECOVER decision support system, which is providing rapid-response GIS analytic capabilities to state and local government agencies. Wrangler reduces to minutes the time needed to assemble and deliver crucial wildfire-related data.

decision support↗
NASA NTRS2017

Classification of Wildfires from MODIS Data Using Neural Networks

Wildfires are destructive to both life and property, which necessitates an approach to quickly and autonomously detect these events from orbital observatories. This talk will introduce a neural network based approach for classifying wildfires in MODIS multispectral data, and will show how it could be applied to a constellation of low-cost CubeSats. The approach combines training a deep neural network on the ground using high performance consumer GPUs, with a highly optimized inference system running on a flight-proven embedded processor. Normally neural networks execute on hardware orders of magnitude more powerful than anything found in a space-based computer, therefore the inference system is designed to be performance even on the most modest of platforms. This implementation is able to be significantly more accurate than previous neural network implementations, while also approaching the accuracy of the state-of-the-art MODFIRE data products.

Artificial Intelligence↗
NASA NTRS2019

Progress and Challenges in Quantifying Wildfire Smoke Emissions, Their Properties, Transport, and Atmospheric Impacts

Wildfire is a natural and integral ecosystem process that is necessary to maintain species composition, structure, and ecosystem function. Extreme fires have been increasing over the last decades, which have a substantial impact on air quality, human health, the environment, and climate systems. Smoke aerosols can be transported over large distances, acting as pollutants that affect adjacent and distant downwind communities and environments. Fire emissions are a complicated mixture of trace gases and aerosols, many of which are short‐lived and chemically reactive, and this mixture affects atmospheric composition in complex ways that are not completely understood. We present a review of the current state of knowledge of smoke aerosol emissions originating from wildfires. Satellite observations, from both passive and active instruments, are critical to providing the ability to view the large‐scale influence of fire, smoke, and their impacts. Progress in the development of fire emission estimates to regional and global chemical transport models has advanced, although significant challenges remain, such as connecting ecosystems and fuels burned with dependent atmospheric chemistry. Knowledge of the impact of smoke on radiation, clouds, and precipitation has progressed and is an essential topical research area. However, current measurements and parameterizations are not adequate to describe the impacts on clouds of smoke particles (e.g., CNN, INP) from fire emissions in the range of representative environmental conditions necessary to advance science or modeling. We conclude by providing recommendations to the community that we believe will advance the science and understanding of the impact of fire smoke emissions on human and environmental health, as well as feedback with climate system.

I N Sokolik↗
NASA NTRSDate not supplied

Wildfire Emergency Response Hazard Extraction and Analysis of Trends (HEAT) through Natural Language Processing and Time Series

A methodology for Hazard Extraction and Analysis of Trends (HEAT) is proposed and conducted on a data set of wildfire incident response forms, known as ICS-209-PLUS.The HEAT processes: (1) extract a set of hazards from a data set, (2) calculate hazard-relevant metrics in a primary analysis, (3) analyze trends over time in metrics using timeseries, and (4) examine potential explanations for metric trends using a secondary analysis. Hazards are extracted from narrative data in the ICS-209-PLUS based on a framework previously developed by the authors, using natural language processing. Metrics examined for each hazard include operational time to occurrence, rate of occurrence, frequency, and severity. Primary results include a taxonomy of hazards present in the data set with relevant quantitative metrics. The most frequent hazards identified are environmental and include hazardous terrain. Most hazards occur on average between 35-55% containment. Incidents with hazards tend to have a higher average severity score when compared to the average score for all incidents. Time series of the metrics and relevant predictors, including fire characteristics, fire intensity, and operations, are created to facilitate further analysis. Secondary results used to determine which factors best predict hazard frequency include a correlation matrix and regression analysis. These findings are relevant to safety for current, as well as emerging wildfire operations, and are an exploratory first step in developing historical data-driven risk assessment models.

Sequoia R. Andrade↗
NASA NTRS2022

Evaluation and Improvement of System-of-Systems Resilience in a Simulation of Wildfire Emergency Response

Because of the increasing threat that wildfires pose, there is interest in leveraging new technologies to improve firefighting. Specifically, Unmanned Aerial Systems (UAS) and UAS Traffic Management (UTM) promise to improve firefighters’ situational awareness, coordination, communications, safety, and strategy. While these technologies could be beneficial, there has been little formal investigation into how much benefit would occur and whether these benefits would outweigh hazards introduced by these systems. To better understand the impacts of these technologies, this paper presents a high-level dynamic simulation for evaluating wildfire response performance and resilience incorporating fire propagation, surveillance and communication, response planning, and the resulting mitigation actions. This simulation is then used to study the impact of communications and surveillance improvement, considering (1) the effect on fire containment and ground crew injuries and (2) the effect of introduced and existing disruptive fault scenarios. Simulating this model over a large number of scenarios finds that these changes can improve containment and reduce ground crew injuries. While these improvements generalize over both existing and introduced single-fault scenarios and thus result in a more resilient system, they could be negated if the introduced communications infrastructure is prone to full-scale outages.

modeling↗
NASA NTRS2023

Machine Learning Framework for Hazard Extraction and Analysis of Trends (HEAT) in Wildfire Response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction andAnalysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident responseforms. The HEAT framework produces safety- and risk- relevant analyses, consisting of: (1) a set of hazards extractedfrom text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-styletable and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potentialpredictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards doc-umented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEATcan be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply theframework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire re-sponse arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrixidentifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

natural language processing↗
NASA NTRS2022

Simultaneous Characterization of Wildfire Smoke and Surface Properties With Imaging Spectroscopy During the FIREX-AQ Field Campaign

We introduce and evaluate an approach for the simultaneous retrieval of aerosol and surface properties from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS-C) data collected during wildfires. The joint National Aeronautics and Space Administration (NASA) National Oceanic and Atmospheric Administration Fire Influence on Regional to Global Environments and Air Quality field campaign took place in August 2019, and involved two aircraft and coordinated ground-based observations. The AVIRIS-C instrument acquired data from onboard NASA's high altitude ER-2 research aircraft, coincident in space and time with aerosol observations obtained from the Aerosol Robotic Network (AERONET) DRAGON mobile platform in the smoke plume downwind of the Williams Flats Fire in northern Washington in August 2019. Observations in this smoke plume were used to assess the capacity of optimal-estimation based retrievals to simultaneously estimate aerosol optical depth (AOD) and surface reflectance from Visible Shortwave Infrared (VSWIR) imaging spectroscopy. Radiative transfer modeling of the sensitivities in spectral information collected over smoke reveal the potential capacity of high spectral resolution retrievals to distinguish between sulfate and smoke aerosol models, as well as sensitivity to the aerosol size distribution. Comparison with ground-based AERONET observations demonstrates that AVIRIS-C retrievals of AOD compare favorably with direct sun AOD measurements. Our analyses suggest that spectral information collected from the full VSWIR spectral interval, not just the shortest wavelengths, enables accurate retrievals. We use this approach to continuously map both aerosols and surface reflectance at high spatial resolution across heterogeneous terrain, even under relatively high AOD conditions associated with wildfire smoke.

FIREX-AQ↗
NASA NTRS2022

Unexpected Repartitioning of Stratospheric Inorganic Chlorine after the 2020 Australian Wildfires

The inorganic chlorine (Cl y ) and odd nitrogen (NO y ) chemical families influence stratospheric O 3 . In January 2020 Australian wildfires injected record-breaking amounts of smoke into the southern stratosphere. Within 1-2 months ground-based and satellite observations showed Cl y and NO y were repartitioned. Compared to decadal means, lower stratospheric (LS) HCl columns declined by ~30% between March and May while ClONO 2 columns increased by 40-50%. The Cl y perturbations recovered from June to October. The LS NO 2 decreased from February to April, consistent with sulfate aerosol reactions, but returned to typical values by June - months earlier than the Cl y recovery. Transport tracers show an air mass change explains the observed O 3 decrease after April. Simulations assuming wildfire smoke behaves identically to sulfate aerosols couldn’t reproduce observed Cl y changes, suggesting that their composition and chemistry differs from sulfate aerosols. This undermines our ability to predict ozone in a changing climate.

middle atmosphere chemistry and composition↗
NASA NTRS2022

Emission Factors and Evolution of SO2 Measured From Biomass Burning in Wildfires and Agricultural Fires

Fires emit sufficient sulfur to affect local and regional air quality and climate. This study analyzes SO2 emission factors and variability in smoke plumes from US wildfires and agricultural fires, as well as their relationship to sulfate and hydroxymethanesulfonate (HMS) formation. Observed SO2 emission factors for various fuel types show good agreement with the latest reviews of biomass burning emission factors, producing an emission factor range of 0.47–1.2 g SO2 kg^(−1) C. These emission factors vary with geographic location in a way that suggests that deposition of coal burning emissions and application of sulfur-containing fertilizers likely play a role in the larger observed values, which are primarily associated with agricultural burning. A 0-D box model generally reproduces the observed trends of SO2 and total sulfate (inorganic + organic) in aging wildfire plumes. In many cases, modeled HMS is consistent with the observed organosulfur concentrations. However, a comparison of observed organosulfur and modeled HMS suggests that multiple organosulfur compounds are likely responsible for the observations but that the chemistry of these compounds yields similar production and loss rates as that of HMS, resulting in good agreement with the modeled results. We provide suggestions for constraining the organosulfur compounds observed during these flights, and we show that the chemistry of HMS can allow organosulfur to act as an S(IV) reservoir under conditions of pH > 6 and liquid water content >10^(−7) g sm^(−3). This can facilitate long-range transport of sulfur emissions, resulting in increased SO2 and eventually sulfate in transported smoke.

Sulfur↗
NASA NTRSDate not supplied

NASA Airborne Observations in Support of Wildfire Science and Applications

The NASA Science Mission Directorate Earth Science Division Airborne Science Program enables access to modified aircraft in support of NASA projects. The Program supports researchers and engineers by enabling testing of new instruments, calibration of on-orbit satellite instruments with airborne sensors, and to support process studies and model development. This talk will provide an overview of the Program capabilities, review past NASA flight projects that have focused on wildfires, and discuss how the Program is poised to support the FireSense project and interagency efforts to incorporate new technologies and observations into the wildfire management community. Past missions including the Western States Fire Mission and FIREX-AQ will be discussed, an overview of relevant NASA sensors will be provided, and an introduction to upcoming flight activity will be presented. Data telemetry, communications, and data standards will also be discussed towards improving timely distribution of data products to land managers.

Matthew M Fladeland↗
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 NTRSDate not supplied

June 2023 Canadian Wildfire Effects on Hampton Roads VA using ASDC Data

In June of 2023 thousands of acres of wildfires spread across northwestern Canada. This event emitted huge amounts of smoke, and pollutants that travelled east across the North America, the Atlantic ocean and eventually all the way to Continental Europe. This smoke created a dangerous air quality event throughout the Atlantic Coast. As climate change increases the prevalence of wildfires more cities and states will be affected by the dangerous smoke and pollutants produced by these events. In this poster we documented and investigated the June 06th Air Quality Event over Hampton Roads, VA. To perform a comparative analysis, we utilized two data sources: NASA’s Atmospheric Science Data Center we used the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), the Deep Space Climate ObserVatoRy (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC), data product, and the EPA’s ground-based Air Quality System (AQS).

Air Quality↗
NASA NTRSDate not supplied

Spatial Structure of Precipitation and Lightning in the Vicinity of Wildfire Ignitions in the United States

The horizontal storm structure surrounding 92512 lightning ignited wildfires is examined in the mid to eastern sections of the United States from 2003-2015 using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV gauge corrected radar precipitation mosaic, and the US Forest Service’s Fire Occurrence Database. Though lightning flash density peaks strongly around fire ignitions on the instantaneous 1 km scale, on the hourly 10 km scale both the lightning and precipitation peaks are typically offset from fire ignitions. As expected, lightning density is higher and precipitation lower around ignition points compared to non-ignition points. The average spatial distribution of total lightning flashes around fire ignitions is symmetrical, while that of precipitation and positive flashes is not. The results of regressing positive and negative currents against ignition efficiency are ambiguous. These results can aid in modeling storms that are likely to produce the "dry lightning" associated with wildfire ignitions.

Brian Vant-Hull↗
NASA NTRS2020

Ensemble-Based Deep Learning for Estimating PM2.5 over California with Multisource Big Data Including Wildfire Smoke

Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g. wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.

air quality↗