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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Forest Fire Observation

Conditions on the perimeter of a forest fire can be obtained by use of airborne remote sensing techniques demonstrated by Ames Research Center. An Ames U-2 high-altitude survey aircraft served as an aerial fire observation system. Equipped with two types of sensors, the U-2 produces real-time infrared images of fireground scenes. Information acquired by the U-2's scanners defines the fire boundary and aids fire management decisions by showing the size, shape and direction of burn and the locations of hot spots in the fire zone. U-2 sends sensor date in digital form to an antenna at Ames Research Center. There the data is computer processed into images which are overlaid on U.S. Geological Survey topographical maps of the fire area. Maps are then transmitted by telecopy machine directly to fire control center. Whole process takes less than 10 minutes and the U-2 can provide information for up to five hours. Ames antenna can pick up signals from the U-2 anywhere within a 300 mile radius from Ames.

Source record↗
NASA NTRS2023

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wildfire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry (a Marin-based fire service workforce development program) and the Marin County Fire Department to develop models to assist with fire management. Using data from Sentinel-2A, PlanetScope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), our team developed a number of input data layers for three different models to evaluate wildfire severity. One model performed a suitability analysis with weights based on scientific literature; another model utilized a U-Net Convolutional Neural Network trained on previous fires in Marin and neighboring Sonoma County to predict the difference normalized burn severity; and the third inputted data layers into the FlamMap tool that outputs risk categories. We compared model outputs and performed a weighted overlay analysis to identify specific locations where a fireline could be constructed to interrupt the progress of an active fire. These tools will assist partners in preparing for and managing active wildfire situations.

Remote sensing↗
NASA NTRS1989

Expert systems applied to spacecraft fire safety

Expert systems are problem-solving programs that combine a knowledge base and a reasoning mechanism to simulate a human expert. The development of an expert system to manage fire safety in spacecraft, in particular the NASA Space Station Freedom, is difficult but clearly advantageous in the long-term. Some needs in low-gravity flammability characteristics, ventilating-flow effects, fire detection, fire extinguishment, and decision models, all necessary to establish the knowledge base for an expert system, are discussed.

Smith, Richard L.↗
NASA NTRSDate not supplied

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.

Suhani Dalal↗
NASA NTRS1989

Designing an infrared system to map and detect wildland fires

The 'Firefly' project is developing an infrared remote sensing system to provide near real-time wildland fire information for fire management and suppression. Recent technological advances in several areas now allow the design of an end-to-end, infrared system to map and detect wildland fires. The system components will include an airborne infrared sensor, automatic onboard signal and data processing, telecommunications link, and integration into a ground data terminal. The system will provide improved performance over current systems in terms of increased timeliness of data delivery, quantifiable accuracy, data consistency, reliability, and maintainability. The system will be the next generation of wildland fire mapping and detection system for the United States Forest Service.

Nichols, J. David↗
NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

Miller, Jerry↗
NASA NTRS1982

Forest management applications of Landsat data in a geographic information system

The utility of land-cover data resulting from Landsat MSS classification can be greatly enhanced by use in combination with ancillary data. A demonstration forest management applications data base was constructed for Santa Cruz County, California, to demonstrate geographic information system applications of classified Landsat data. The data base contained detailed soils, digital terrain, land ownership, jurisdictional boundaries, fire events, and generalized land-use data, all registered to a UTM grid base. Applications models were developed from problems typical of fire management and reforestation planning.

Maw, K. D.↗
DOE OSTI2023

Impacts of a Prescribed Fire on Air Quality in Central New Mexico

A short-duration but high-impact air quality event occurred on 28 November 2018 along the Rio Grande Valley of New Mexico. This fire occurred outside the typical wildfire season, and greatly impacted the air quality in Socorro, NM, and the surroundings. Measurements were taken during the event using an aerosol light scattering technique (integrating nephelometer) and a particulate mass concentration monitor (DustTrak PM optical monitor). The instruments sampled the ambient air during the event on the campus of the New Mexico Institute of Mining and Technology in Socorro, New Mexico. The peak values on a 5-min basis of light scattering and the PM mass concentration reached 470 Mm -1 and 270 µg/m 3 , respectively. We examined the meteorological context of the event using local meteorological data and back trajectories using the NOAA HYSPLIT model to determine atmospheric transport and possible sources. Several fires, both prescribed and wildfires, occurred in the region including a prescribed burn at Bosque del Apache National Wildlife Refuge (17 km south-southeast of the receptor site). The data suggest that the prescribed burn at Bosque del Apache was the dominant contributor due to transport evidence and the event’s narrow spatiotemporal extent. The increasing importance of restoring ecosystem function using prescribed fire in wildland fire management will likely lead to more frequent air quality impacts and sets up policy tradeoffs that require a balance between these public goals. This study examines the evidence of the effects of a prescribed fire in a protected wildland area impacting the air quality in a nearby populated area.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Forest Fire Mapping

The Fire Logistics Airborne Mapping Equipment (FLAME) system, mounted in a twin-engine and airplane operated by the U.S. Forest Service (USFS) of the U.S. Department of Agriculture (USDA), is an airborne instrument for detecting and pinpointing forest fires that might escape ground detection. The FLAME equipment rack includes the operator interface, a video monitor, the system's control panel and film output. FLAME's fire detection sensor is an infrared line scanner system that identifies fire boundaries. Sensor's information is correlated with the aircraft's position and altitude at the time the infrared imagery is acquired to fix the fire's location on a map. System can be sent to a fire locale anywhere in the U.S. at the request of a regional forester. USFS felt a need for a more advanced system to deliver timely fire information to fire management personnel in the decade of the 1990s. The Jet Propulsion Laboratory (JPL) conducted a study, jointly sponsored by NASA and USDA, on what advanced technologies might be employed to produce an end-to-end thermal infrared fire detection and mapping system. That led to initiation of the Firefly system, currently in development at JPL and targeted for operational service beginning in 1992. Firefly will employ satellite-reference position fixing and provide performance superior to FLAME.

Source record↗
NASA NTRS2023

Ad Hawk Aerial Connectivity Network

Wildfires in the United States have been increasing significantly in both frequency and size in recent decades, requiring maximal efficiency on the part of wildfire management organizations. However, while the methodology and technology to combat wildfires has improved, theconnectivityinfrastructure in more ruralregionsof the country is either incompatible with newertechnology or altogether non-existent. Due to this, many modern communication methods are rendered uselessin areas where connection is needed most, complicating the overall fire management procedure. To improve communication and logistics between responders on the front lines and headquarters, a team of NASA researchassociateshas developed the schematic for a rapidly deployable mobile ad-hoc internet-enabled Wi-Fi network that can bring stable internet accessto any desired area.

Wi-Fi↗
NASA NTRS2023

Ad-Hawk Aerial Connectivity Network: An Integrated and Empirical System Evaluation

Wildfires in the United States have been increasing significantly in both frequency and size in recent decades, requiring unprecedented resource and communication efficiency on the part of wildfire management organizations. However, while the methodology and technology to combat wildfires have improved, the connectivity infrastructure in rural regions of the country is either incompatible with newer technology or altogether non-existent. Due to this, many modern communication methods are rendered useless in areas where a connection is needed most, complicating the overall fire management procedure. To improve communication and logistics between responders on the front lines and headquarters, a team of NASA research associates has developed the schematic for a rapidly deployable mobile ad-hoc internet-enabled Wi-Fi network that can bring stable internet access to any desired area. This paper contains the testing and analysis of the Ad-Hawk project conceived in the summer of 2022 and developed in the summer of 2023.

Wildfire↗
NASA NTRSDate not supplied

New Technologies From NASA's FireSense Technology Program

NASA Wildland Fire Initiative - NASA’s Wildland Fire Initiative, is a NASA-wide effort focused on delivering NASA’s unique science and technological capabilities to operational agencies, striving towards measurable improvements in US wildland fire management. - The initiative involves the Science Mission Directorate (SMD), Aeronautics Research Mission Directorate (ARMD), the Space Technology Mission Directorate (STMD), including the Small Business Innovative Research (SBIR) program. - NASA works with interagency partners such as the National Oceanic and Atmospheric Administration (NOAA), the U.S. Department of Agriculture Forest Service (USFS), the California Department of Forestry and Fire Protection, the National Interagency Fire Center, and commercial partners.

FireSense↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles. Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited, largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire, vegetation, climate, and anthropogenic factors. Here, using data from satellite-derived fire observations and ecosystem model simulations, we performed a comprehensive investigation of the spatial and temporal dynamics of China's ecosystem fire disturbances and their carbon emissions over the past two decades (1997–2016). Satellite-derived results showed that on average about 3.47 - 4.53 x 10 4 km 2 of the land was burned annually during the past two decades, among which annual burned forest area was about 0.81 - 1.25 × 10 4 km 2 , accounting for 0.33-0.51% of the forest area in China. Biomass burning emitted about 23.02 TgC per year. Compared to satellite products, simulations from the Energy Exascale Earth System Land Model (ELM) strongly overestimated China's burned area and fire-induced carbon emissions. Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China's Daxing'anling region and subtropical dry forest in South Yunnan, as revealed by both the satellite product and the model simulations. Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China's ecosystem fire disturbances. Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences. Further studies are required to improve the methods of observing and modelling China's ecosystem fire disturbances, which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Demonstrating Acquisition of Real-Time Thermal Data Over Fires Utilizing UAVs

A disaster mitigation demonstration, designed to integrate remote-piloted aerial platforms, a thermal infrared imaging payload, over-the-horizon (OTH) data telemetry and advanced image geo-rectification technologies was initiated in 2001. Project FiRE incorporates the use of a remotely piloted Uninhabited Aerial Vehicle (UAV), thermal imagery, and over-the-horizon satellite data telemetry to provide geo-corrected data over a controlled burn, to a fire management community in near real-time. The experiment demonstrated the use of a thermal multi-spectral scanner, integrated on a large payload capacity UAV, distributing data over-the-horizon via satellite communication telemetry equipment, and precision geo-rectification of the resultant data on the ground for data distribution to the Internet. The use of the UAV allowed remote-piloted flight (thereby reducing the potential for loss of human life during hazardous missions), and the ability to "finger and stare" over the fire for extended periods of time (beyond the capabilities of human-pilot endurance). Improved bit-rate capacity telemetry capabilities increased the amount, structure, and information content of the image data relayed to the ground. The integration of precision navigation instrumentation allowed improved accuracies in geo-rectification of the resultant imagery, easing data ingestion and overlay in a GIS framework. We focus on these technological advances and demonstrate how these emerging technologies can be readily integrated to support disaster mitigation and monitoring strategies regionally and nationally.

Ambrosia, Vincent G.↗
NASA NTRSDate not supplied

(ODIN): An Open Source, Low-Latency Data Integration & Visualization Framework for the NASA System Wide Safety Project's Disaster Response Safety Demonstration Series

The Open Data Integration Framework (ODIN) is an open source, low latency data integration and visualization framework (https://github.com/NASARace/race-odin) developed under NASA’s System WideSafety Program to demonstrate new safety capabilities designed to improve US airspace operations. Safety demonstrations are a set of increasingly complex (from public safety perspective) disaster response scenarios under which air systems must operate with increased capacity and include: 1) Wildland fire response, 2) Hurricane relief and recovery, 4) Emergency medical delivery via UAS and 4) Urban disaster relief. To accommodate disaster response, ODIN is field deployable and can scale on one or more multi-core, commodity laptops operating with full to limited or intermittent internet connectivity, conditions likely encountered during operations. ODIN runs as webserver with local, persistent data storage to serve either public or a secured, ad hoc network (e.g., an incident command post). The current released ODIN, ODIN-Fire is tailored for wildland fire management incorporating information on satellite overpasses with links to the near real-time data and imagery from the respective agencies. Included are winds data, an important variable for emergency responders and airspace operations, and high-resolution wind forecasts generated by super-computing resources and ingested into ODIN. As an open-source project, ODIN has attracted interest from multiple entities. We will show how 1) a commercial field instrument and data provider uses ODIN to help users visualize, publish and integrate their in-situ sensor network data and 2) ODIN’s capabilities to ingest, integrate and display near-real time satellite data with air traffic and a USFS winds forecast model used in fire response and post-fire assessment. Within NASA ODIN demonstrated novel, near terminal airspace safety capabilities for a project close-out event and previously it monitored the national airspace in real-time to meet an agency milestone. ODIN is presently under development for the anticipated hurricane relief and response demonstration notionally scheduled for the 2025-27 time frame and is available from NASA's github at the above link.

Aeronautics↗
NASA NTRS2021

Reductions in NO2 Burden Over North Equatorial Africa from Decline in Biomass Burning in Spite of Growing Fossil Fuel Use, 2005 to 2017

Socioeconomic development in low- and middle-income countries has been accompanied by increased emissions of air pollutants, such as nitrogen oxides [NOx: nitrogen dioxide (NO2) + nitric oxide (NO)], which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, landscape biomass burning—another important NOx source—has declined in north equatorial Africa, attributed to changes in climate and anthropogenic fire management. Here, we use satellite observations of tropospheric NO2 vertical column densities (VCDs) and burned area to identify NO2 trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs—home to hundreds of millions of people—mean annual tropospheric NO2 VCDs decreased by 4.5% from 2005 through 2017 during the dry season of November through February. Reductions in burned area explained the majority of variation in NO2 VCDs, though changes in fossil fuel emissions also explained some variation. Over Africa’s biomass burning regions, raising mean GDP density (USD·km−2) above its lowest levels is associated with lower NO2 VCDs during the dry season, suggesting that economic development mitigates net NO2 emissions during these highly polluted months. In contrast to the traditional notion that socioeconomic development increases air pollutant concentrations in low- and middle-income nations, our results suggest that countries in Africa’s northern biomass-burning region are following a different pathway during the fire season, resulting in potential air quality benefits. However, these benefits may be lost with increasing fossil fuel use and are absent during the rainy season.

air pollution↗
DOE OSTI2020

Fire as a fundamental ecological process: Research advances and frontiers

Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire–dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above–ground ecology, (d) fire effects on below–ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives.

54 ENVIRONMENTAL SCIENCES↗