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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

NASA ARMD Wildfire Management Workshop

In response to the increasing severity and cost of wildfires in the United States, the NASA Aeronautics Research Mission Directorate (ARMD) conducted a one-day Wildfire Management Workshop on May 13, 2021. The NASA Aeronautics Research Institute (NARI) at the NASA Ames Research Center organized the event. The goal of the workshop was to provide a forum for representatives from wildfire organizations (government, private, and academic) to raise important issues and discuss solutions. The proceedings consisted of presentations from the U.S. Forest Service, NASA Space Technology Mission Directorate, and NASA Applied Sciences Disasters Program. Also included were two sessions of breakout groups that focused on specific wildfire management issues. Some major themes that emerged from the breakout groups included data access and management, wildfire modeling, improving organizational collaboration, the use of aircraft in fighting fires and collecting data, and providing front line firefighters with the information tools they need to be effective. The output of the workshop will be used by ARMD to consider avenues that NASA might take to help with wildfire management.

Wildfire Management↗
NASA NTRS2021

NASA ARMD Wildfire Management Workshop

In response to the increasing severity and cost of wildfires in the United States, the NASA Aeronautics Research Mission Directorate (ARMD) conducted a one-day Wildfire Management Workshop on May 13, 2021. The NASA Aeronautics Research Institute (NARI) at the NASA Ames Research Center organized the event. The goal of the workshop was to provide a forum for representatives from wildfire organizations (government, private, and academic) to raise important issues and discuss solutions. The proceedings consisted of presentations from the U.S. Forest Service, NASA Space Technology Mission Directorate, and NASA Applied Sciences Disasters Program. Also included were two sessions of breakout groups that focused on specific wildfire management issues. Some major themes that emerged from the breakout groups included data access and management, wildfire modeling, improving organizational collaboration, the use of aircraft in fighting fires and collecting data, and providing front line firefighters with the information tools they need to be effective. The output of the workshop will be used by ARMD to consider avenues that NASA might take to help with wildfire management.

Wildfire↗
NASA NTRS2021

NASA ARMD Wildfire Management Workshop

In response to the increasing severity and cost of wildfires in the United States, the NASA Aeronautics Research Mission Directorate (ARMD) conducted a one-day Wildfire Management Workshop on May 13, 2021. The NASA Aeronautics Research Institute (NARI) at the NASA Ames Research Center organized the event. The goal of the workshop was to provide a forum for representatives from wildfire organizations (government, private, and academic) to raise important issues and discuss solutions. The proceedings consisted of presentations from the U.S. Forest Service, NASA Space Technology Mission Directorate, and NASA Applied Sciences Disasters Program. Also included were two sessions of breakout groups that focused on specific wildfire management issues. Some major themes that emerged from the breakout groups included data access and management, wildfire modeling, improving organizational collaboration, the use of aircraft in fighting fires and collecting data, and providing front line firefighters with the information tools they need to be effective. The output of the workshop will be used by ARMD to consider avenues that NASA might take to help with wildfire management.

Parimal Kopardekar↗
NASA NTRSDate not supplied

FIRMS US/Canada – An Extension of NASA Near Real-Time FIRMS for the Forest Service and Inter-agency Wildfire Management Community

As a part of broader, long time collaboration efforts between the two agencies, the USDA Forest Service has partnered withNASA for nearly 20 years to leverage near real-time MODIS and VIIRS fire products to support wildfire management. Thetimely availability of these data to the Forest Service and interagency partners indicate the location, extent, intensity and impactsof wildfire activity at a regional/national scale as well as informs decisions by fire managers regarding strategic planning andresponse to wildfire incidents.

fire monitoring↗
DOE OSTI2023

Managing Wildfire Risk and Promoting Equity through Optimal Configuration of Networked Microgrids

As climate change increases the risk of large-scale wildfires, wildfire ignitions from electric power lines are a growing concern. To mitigate the wildfire ignition risk, many electric utilities de-energize power lines to prevent electric faults and failures. These preemptive power shutoffs are effective in reducing ignitions, but they could result in wide-scale power outages. Advanced technology, such as networked microgrids, can help reduce the size of the resulting power outages; however, even microgrid technology might not be sufficient to supply power to everyone, thus forcing hard questions about how to prioritize the provision of power among customers. In this paper, we present an optimization problem that configures networked microgrids to manage wildfire risk while maximizing the power served to customers; however, rather than simply maximizing the amount of power served in kilowatts, our formulation also considers the ability of customers to cope with power outages, as measured by social vulnerability, and it discourages the disconnection of particularly vulnerable customer groups. To test our model, we leverage a synthetic but realistic distribution feeder, along with publicly available social vulnerability indices and satellite-based wildfire risk map data, to quantify the parameters in our optimal decision-making model. Our case study results demonstrate the benefits of networked microgrids in limiting load shed and promoting equity during scenarios with high wildfire risk.

distribution systems↗
DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

NASA’s Technology Development Program for Wildfire Science, Management, and Disaster Mitigation

NASA’s Earth Science Technology Office (ESTO) has established a new program called Technology Development for support of Wildfire Science, Management, and Disaster Mitigation (FireSense Technology), to develop innovative new technologies and capabilities to better predict, monitor and manage wildfires and their impacts. ESTO’s FireSense Technology program works in collaboration with NASA’s Applied Sciences Wildland Fire Program, the Aeronautics Research Mission Directorate (ARMD), the Space Technology Mission Directorate (STMD), and the Small Business Innovative Research (SBIR) program. The program will also work closely with interagency partners such as the National Oceanic and Atmospheric Administration (NOAA), the U.S. Department of Agriculture Forest Service, the California Department of Forestry and Fire Protection, the National Interagency Fire Center, and others. In this paper we will discuss the program objectives and provide an update on the technological developments to date.

Wildfires↗
NASA NTRS2012

CASA Forest Cover Change Data Sets

Deforestation and forest fires are global land cover changes that can be caused by both natural and human factors. Although monitoring forest fires in near-real time is critical for operational wildfire management, mapping historical wildfires in a spatially explicit fashion is also important for a number of reasons, including climate change studies (e.g., examining the relationship between rising temperatures and frequency of fires), fuel load management (e.g., deciding when and where to conduct controlled burns), and carbon cycle studies (e.g., quantifying how much CO2 is emitted by fires and for emissions reduction efforts under the United Nations programs for Reducing Emissions from Deforestation and Degradation -- REDD).

Mapping↗
NASA NTRS2024

Autonomous Drone Integration in Prescribed Fire Operations

With the surge in wildfire frequency and severity, the risk to firefighters, communities, and forests has escalated dramatically. Climate change and increased amounts of fuel have intensified these challenges, making wildfire management more important than ever. Prescribed burns, a controlled manner of burning land, are a crucial strategy for wildfire prevention, ecosystem management, and forest health. Nonetheless, traditional methods of implementing prescribed burns are labor-intensive, slow, and risk-laden due to human involvement. Our innovative approach leverages autonomous drone swarms to revolutionize prescribed burning methods. These advanced drones collaborate to ignite fires strategically, gather real-time data, and suppress sections of the fire as needed. By minimizing human proximity to the flames, our solution has the potential to significantly enhance operational efficiency and safety.

UAV systems↗
NASA NTRSDate not supplied

Mapping Surface Vapor Pressure Deficits From Geostationary Satellites for Fire Weather Monitoring

The increase in the wildfires were observed globally in accordance with global warming, and to real- time monitoring of wildfire risk in broad scale is demanded for wildfire management to prevent the spread of wildfires. Scientists invented a lot of indices to assess the wildfire risk. Vapor Pressure Deficit (VPD) is one of the most important meteorological components for those indices. Compared to other components of fire weather indices, VPD can change quickly from lower risk to higher risk even in sub-hourly. Therefore, real-time fire risk monitoring requires high-resolution and high- temporal VPD spatial map. Here, we developed VPD estimation method using the GOES Advanced Baseline Imager (ABI) data. Unlike the polar-orbital satellite data, the ABI can observe target region every 10 minutes, so that we can estimate VPD for fire weather in real-time. The method used to estimate VPD is same with the algorithm of NASA Earth Exchange Gridded Daily Meteorology (NEX- GDM), which estimate meteorological variables from ground weather observation and spatial variables based on random forest (RF). We calculated RF importance to select bands of ABI as input of the model. To validate our results, we compared the spatial pattern of our VPD data with the Real- Time Mesoscale Analysis (RTMA) data over the conterminous USA. We sought possibility of applying our method to the region where no real-time high-resolution weather data is available, such as South America. The developed method can produce real-time high-resolution high-frequent VPD data in the continental scale. The derived data from GOES ABI could contribute to improve the fire weather monitoring and lead to prevent wildfires.

Hirofumi Hashimoto↗