Search NASA⌕ Search

SEARCH · Search NASA

Results for “WILDFIRE PREDICTING”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Using Soil Moisture Information to Better Understand and Predict Wildfire Danger: A Review of Recent Developments and Outstanding Questions

Soil moisture conditions are represented in fire danger rating systems mainly through simple drought indices based on meteorological variables, even though better sources of soil moisture information are increasingly available. This review summarises a growing body of evidence indicating that greater use of in situ, remotely sensed, and modelled soil moisture information in fire danger rating systems could lead to better estimates of dynamic live and dead herbaceous fuel loads, more accurate live and dead fuel moisture predictions, earlier warning of wildfire danger, and better forecasts of wildfire occurrence and size. Potential uses of soil moisture information in existing wildfire danger rating systems include (1) as a supplement or replacement for drought indices, (2) for live and (3) dead fuel moisture modelling, (4) for estimating herbaceous fuel curing, and (5) for estimating fuel loads. We identify key remaining research questions and note the logistical challenge of convincing wildfire professionals of the importance of soil moisture compared with more familiar wildfire danger metrics. While obstacles remain, the path forward is clear. Soil moisture information can and should be used to improve fire danger rating systems and contribute to more effective fire management for the protection of communities and ecosystems worldwide.

fuel properties↗
NASA NTRSDate not supplied

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

Xiaohua Pan↗
NASA NTRS2001

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Roberts, Dar A.↗
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↗
NASA NTRS2023

Impacts of Estimated Plume Rise on PM 2.5 Exceedance Prediction During Extreme Wildfire Events: A Comparison of Three Schemes (Briggs, Freitas, and Sofiev)

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM 2.5 ) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM 2.5 concentrations near the source region but higher AOD and PM 2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM 2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg m−3) predictions. Higher plume heights generally produce larger downwind PM 2.5 exceedance areas. The PM 2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM 2.5 concentration and predicted PM 2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM 2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.

Yunyao Li↗
NASA NTRS2020

Great Basin Ecological Forecasting II: Assessing and Forecasting Live Fuel Moisture Content of Wildfire Fuels for the Eastern Great Basin to Improve Wildfire Timing and Severity Predictions

The eastern Great Basin (EGB) extends throughout the states of Arizona, Colorado,Idaho, Utah, and Wyoming, covering approximately 411,000 km2. In recent years, wildfires in the EGB have increased in frequency and size, representing a growing concern for our partners at the Bureau of Land Management (BLM), the National Weather Service (NWS), and the Great Basin Coordination Center (GBCC). Live fuel moisture (LFM) is an important factor in predicting wildfire risk, as dry vegetation requires less energy to combust than wet vegetation. Land managers currently derive LFM levels from just 165 in situ sites in the EGB. In order to provide partners with a more accurate assessment of LFM, the team used data from the National Elevation Dataset, Aqua and Terra Moderate Resolution Imaging Spectroradiometer, and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite. These datasets include vegetation indices, evapotranspiration, and topographic variables, which were used to create biweekly forecasts of LFM throughout the EGB. An accuracy assessment was conducted using historical in situ data from our partners at the BLM and the GBCC. This model allowed our partners to make informed decisions regarding resource allocation in response to the predicted timing and severity of wildfires in the EGB.

Ecological Forecasting↗
NASA NTRS2020

Great Basin Ecological Forecasting II: Assessing and Forecasting Live Fuel Moisture Content of Wildfire Fuels for the Eastern Great Basin to Improve Wildfire Timing and Severity Predictions

The eastern Great Basin (EGB) extends throughout the states of Arizona, Colorado, Idaho, Utah, and Wyoming, covering approximately 411,000 sq.km. In recent years, wildfires in the EGB have increased in frequency and size, representing a growing concern for our partners at the Bureau of Land Management (BLM), the National Weather Service (NWS), and the Great Basin Coordination Center (GBCC). Live fuel moisture (LFM) is an important factor in predicting wildfire risk, as dry vegetation requires less energy to combust than wet vegetation. Land managers currently derive LFM levels from just 165 in situ sites in the EGB. In order to provide partners with a more accurate assessment of LFM, the team used data from the National Elevation Dataset, Aqua and Terra Moderate Resolution Imaging Spectroradiometer, and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite. These datasets include vegetation indices, evapotranspiration, and topographic variables, which were used to create biweekly forecasts of LFM throughout the EGB. An accuracy assessment was conducted using historical in situ data from our partners at the BLM and the GBCC. This model allowed our partners to make informed decisions regarding resource allocation in response to the predicted timing and severity of wildfires in the EGB.

Ecological Forecasting↗
NASA NTRS2020

Fuel availability not fire weather controls boreal wildfire severity and carbon emissions

Carbon (C) emissions from wildfires are a key terrestrial–atmosphere interaction that influences global atmospheric composi-tion and climate. Positive feedbacks between climate warming and boreal wildfires are predicted based on top-down controls of fire weather and climate, but C emissions from boreal fires may also depend on bottom-up controls of fuel availability related to edaphic controls and overstory tree composition. Here we synthesized data from 417 field sites spanning six ecoregions in the northwestern North American boreal forest and assessed the network of interactions among potential bottom-up and top-down drivers of C emissions. Our results indicate that C emissions are more strongly driven by fuel availability than by fire weather, highlighting the importance of fine-scale drainage conditions, overstory tree species composition and fuel accumulation rates for predicting total C emissions. By implication, climate change-induced modification of fuels needs to be considered for accu-rately predicting future C emissions from boreal wildfires.

X J Walker↗
NASA NTRS2022

Chile Disasters: Automating Wildfire Risk and Occurrence Mapping in Google Earth Engine to Improve Wildfire Detection and Response Time Efforts

Wildfires in Chile in the last decade were the worst on record, destroying homes and livelihoods, polluting the air, and displacing whole towns. To predict locations where wildfires were likely to start, the Corporación Nacional Forestal (CONAF) created a wildfire risk model within ArcGIS Pro and Google Earth Engine (GEE) that utilized the NOAA Global Forecast System (GFS) and the NASA Shuttle Radar Topography Mission (STRM) 90-meter datasets. The previous CONAF model was very resource-heavy and time-intensive to run. NASA DEVELOP, in partnership with CONAF, automated the previous model and transferred it fully into GEE where all Earth observation datasets could be used without downloading. The new model substantially reduced the runtime. The final model was used to create a near real-time wildfire monitoring application as well as fire severity maps. The end products will be used by CONAF for wildfire prediction and management to prevent more destruction in the future.

Maria De Los Santos↗
NASA NTRS2017

An Assessment of Land Surface and Lightning Characteristics Associated with Lightning-Initiated Wildfires

Can we use modeled information of the land surface and characteristics of lightning beyond flash occurrence to increase the identification and prediction of wildfires? Combine observed cloud-to-ground (CG) flashes with real-time land surface model output, and Compare data with areas where lightning did not start a wildfire to determine what land surface conditions and lightning characteristics were responsible for causing wildfires. Statistical differences between suspected fire-starters and non-fire-starters were peak-current dependent 0-10 cm Volumetric and Relative Soil Moisture comparisons were statistically dependent to at least the p = 0.05 independence level for both polarity flash types Suspected fire-starters typically occurred in areas of lower soil moisture than non-fire-starters. GVF value comparisons were only found to be statistically dependent for -CG flashes. However, random sampling of the -CG non-fire starter dataset revealed that this relationship may not always hold.

wildfire↗
NASA NTRS2018

Using NASA's Remote Sensing Datasets and Land Information System to Characterize Lightning Initiated Wildfires

Can we use modeled information of the land surface and characteristics of lightning beyond flash occurrence to increase the identification and prediction of wildfires? The goals of this study are to: combine observed cloud-to-ground (CG) flashes with real-time land surface model output; and compare data with areas where lightning did not start a wildfire to determine what land surface conditions, rainfall observations, and lightning characteristics were responsible for causing wildfires.

White, Kristopher↗
NASA NTRSDate not supplied

Proposition to Optimize Fire Stations for Wildfires

The rising trend in wildfire occurrence and severity has put a strain on wildfire management organizations by spreading out limited resources to meet increasing demand. There has been extensive prior research and data collection to determine the areas of highest risk and to predict regional wildfire damages based on historical trends. Our group aims to utilize this data to best determine fire station placement, optimizing where resources are allocated to reduce the time and investment needed to effectively mitigate wildfires. Using existing research, we are able to calculate optimal fire station locations by utilizing a single-objective facility location problem algorithm incentivized for cost reduction. Further work would be needed to refine the algorithm to accommodate for more realistic factors including access to water and roadways as well as better accounting for the costs involved, but our work serves as a proof of concept and lays the foundation for future research. Implementation of this algorithm would allow public fire planning agencies (such as CAL FIRE and Forest Service) to shift resources to where they would be most effective.

Wildfires↗
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↗
NASA NTRS2018

GEOS-CF Model Assessment of PM2.5 During Wildfires: Inferring the Impact of PM2.5 Exposure on Adverse Respiratory & Cardiovascular Conditions

Exposure to fine particulate matter (PM2.5) can aggravate pre-existing respiratory and cardiovascular conditions. When PM2.5 is inhaled it can cause damage to the lungs such as reduced lung function and shortness of breath. After being inhaled PM2.5 can enter the bloodstream and cause harm to the heart. One major natural source of PM2.5 exposure is from wildfire smoke. The particulates within the smoke from the wildfires can spread from the initial source region, potentially impacting communities both near and far. During and after wildfire events, PM2.5 levels can exceed the WHO air quality guidelines (10 gm^3 annual mean; 25 gm^3 daily mean), becoming hazardous to an individual's health. Global models can be used to simulate the emission and transport of these particulates and subsequently they can be valuable to air quality forecasting in highly polluted areas. The NASA Goddard Earth Observing System (GEOS) version 5 Composition Forecast (GEOS-CF) system has been used to produce near-real time air quality forecasts of atmospheric composition at a high global resolution of 25 km. The GEOS-CF system utilizes the GEOS weather forecast model coupled with GEOS-Chem (version 11) chemistry module to provide analyses and forecasts of various toxic air pollutants, including PM2.5. The GEOS-CF simulated high levels of PM2.5 (40 gm^3 to 250 gm^3 ), exceeding the WHO guidelines, during multiple recent regional and global wildfire seasons, including the 2017 Seattle, WA and Los Angeles, CA wildfire seasons, and biomass burning events in India. Furthermore, the GEOS-CF simulated PM2.5 applied to a human health assessment model, BenMAP (The Environmental Benefits Mapping and Analysis Program, version 1.3), estimates the impact on adverse respiratory health conditions due to PM2.5 exposure from wildfires. The GEOS-CF predicted PM2.5 during the wildfire season with the corresponding BenMAP results provides an assessment of the human health impact of PM2.5 exposure.

Saunders, Emily↗
NASA NTRS2018

NASA's GEOS Composition Model Assessment of PM2.5 During Wildfires: Inferring the Impact of PM2.5 Exposure on Adverse Respiratory & Cardiovascular Conditions

Particulate matter pollution is a mixture of solid and liquid droplets floating in the air that can lead to reduced air quality and increased adverse health impact. Fine particulate matter (PM2.5) can be emitted into the air from anthropogenic sources such as the burning of fossil fuels, motor vehicles, and powerplant emissions. Exposure to PM2.5 can aggravate pre-existing respiratory and cardiovascular conditions. When PM2.5 is inhaled it can cause damage to the lungs such as reduced lung function and shortness of breath. After being inhaled PM2.5 can enter the bloodstream and cause harm to the heart. One major natural source of PM2.5 exposure is from wildfire smoke. The particulates within the smoke from the wildfires can spread from the initial source region, potentially impacting communities both near and far. During and after wildfire events, PM2.5 levels can exceed the WHO air quality guidelines (10 m.g/m^3 annual mean; 25 m.g/m^3 daily mean), becoming hazardous to an individual's health. Global models can be used to simulate the emission and transport of these particulates and subsequently they can be valuable to air quality forecasting in highly polluted areas. The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-CF) system has been used to produce near-real time air quality forecasts of atmospheric composition at a high global resolution of 25x25 km2. The GEOS-CF system utilizes the GEOS weather forecast model coupled with GEOS-Chem (version 11) chemistry module to provide analyses and forecasts of various toxic air pollutants, including PM2.5. The GEOS-CF simulated high levels of PM2.5 (40 m.g/m^3 to 250 m.g/m^3 ), exceeding the WHO guidelines, during multiple recent regional and global wildfire seasons, including the 2017 Washington State and Northern and Southern California wildfire seasons. Furthermore, the GEOS-CF simulated PM2.5 applied to a human health assessment model, BenMAP (The Environmental Benefits Mapping and Analysis Program, version 1.3), estimates the impact on adverse respiratory health conditions due to PM2.5 exposure from wildfires. The GEOS-CF predicted PM2.5 during the wildfire season with the corresponding BenMAP results provides an assessment of the human health impact of PM2.5 exposure.

Saunders, Emily↗
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↗