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NASA NTRSDate not supplied

Firesense: Supporting Operational Partners Through Co-Development

Fire is a natural disturbance and fundamental to many ecosystems, but, across the United States and many areas globally, fires have become more common, larger and more likely to occur at the same time. This has the potential to challenge resource allocation and response and requires coordinated integration of technology and tools at the spatial and temporal scales required by practitioners who make wildland fire management decisions. In the US wildfire is currently managed across state, federal, and tribal agencies who leverage various datasets to guide management decisions. FireSense, a NASA Science Mission Directorate project, aims to develop and deliver trailblazing technology and tools for use before, during, and after wildland fires by working together with land managers and practitioners. FireSense is focused on turning information into solutions by expanding partnerships and collaborations with operational agencies to inform and deliver data and technology to support decisions for wildland fire management. Through implementation across the US and with campaign activities in a variety of landscapes, coordinated research and development work will build upon adaptive and use-inspired approaches that can be put into operation. By leveraging and complementing partner activities through coordinated investment, we support a more comprehensive and cohesive response that can advance fire science to better understand fire behavior and effects through integrated measurement, monitoring and modeling. We present updates from in-progress technology development and field campaigns within the US which include sampling and application across scales with information from multiple sensors collocated with field measurements and highlighting the importance of cross-scale observations during the fire lifecycle. Coordinated investment in new science and technology for the complete fire lifecycle supports a comprehensive and cohesive fire response that help to overcome barriers and anticipate and manage the new reality of extreme fires in a warming world.

Jacquelyn Shuman↗

NASA NTRSDate not supplied

Wildland Fire Mitigation: The Role of Airspace Management

Wildland fire is a significant problem on six continents. As wildland fires become increasingly widespread, dangerous, and costly, advances in aerial technology will make a significant difference. Leveraging Unmanned Aircraft Systems Traffic Management (UTM) success, NASA is conducting aerial suppression research and development (R&D) and is collaborating across government, industry, and the academic community to co-develop cutting-edge technology, procedures, and best practices. Join Dr. Parimal "PK" Kopardekar, Director of the NASA Aeronautics Research Institute (NARI), and learn more about how to maximize the utilization of airspace for wildfire management and mitigation.

Wildland fire↗

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.↗

DOE OSTI2026

Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition

Background Fire is a foundational ecological process that shapes ecosystem structure, diversity, and resilience. Quantifying paleofire regime attributes such as frequency, severity, and intensity is essential for understanding the historical range of variability in fire behavior and its ecological effects. While frequency and severity are often reconstructed in paleofire studies, quantitative reconstructions of fire intensity remain limited. Recent work has shown that maximum pyrolysis temperature—a proxy for fire intensity—and plant species type can be inferred from charcoal using transmission Fourier-transform infrared (FTIR) spectroscopy. However, the sample preparation for transmission FTIR is destructive and time-consuming, limiting application and reuse of materials for other analyses. We evaluated reflectance FTIR spectroscopy as a non-destructive alternative for reconstructing combustion temperature and plant species from laboratory-generated charcoal. We also examined the influence of contrasting airflow environments (ambient air versus nitrogen-rich) on pyrolysis temperature and plant species reconstruction prediction accuracies and compared predictive performance between a novel, neural network–based deep learning model with the traditional modern analogue technique (MAT) using k-nearest neighbor functions. As proof of concept, we apply our enhanced methodology to ancient charcoal to demonstrate applicability at improving long-term fire regime reconstructions and the ability to link paleofire records with contemporary fire ecology. Results Our analysis shows that transmission and reflectance FTIR spectra yield comparable spectral profiles. However, sample preparation for reflectance FTIR is minimal and non-destructive, unlike transmission FTIR which is destructive. We demonstrate that oxygen environments improved reconstruction accuracy relative to nitrogen-rich conditions. Finally, our deep learning neural network (DL) achieved testing accuracies of 98.7% for temperature and 96.2% for species identification, outperforming MAT’s k-NN approach (89.8% and 65.9%, respectively). A Shapley importance analysis identified 5 key spectral regions that greatly influenced the model’s temperature or species categorization. When applied to ancient charcoal, our results show historic fires from the most recent past primarily burned at low intensities (400–500 °C), reflective of natural fire regimes in ponderosa pine forests. Our results corroborate charcoal morphology data that suggests all ancient charcoal originated from burned woody plant types. Conclusions By combining reflectance FTIR spectroscopy with a deep learning approach, we provide the first accuracies high enough to confidently identify both species and temperature from laboratory-produced charcoal, improving quantitative reconstructions of fire intensity and fuel composition from paleofire records. This opens a wide range of research into the link between fire and larger drivers (i.e., climate or human) and greater ecological understanding of fire regimes beyond that of burn scars or recent observations. These methodological improvements have direct relevance for fire management by improving interpretation of historical fire behavior, informing fuel–fire relationships, and providing a scalable analytical framework applicable to both long-term ecological studies and contemporary fire science.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

NASA Wildland Fires

See attached presentation slides. This short talk (less than 10 minutes) is to introduce the NASA Applied Science Wildland Fire Management program to a general audience. Links to the NASA program homepage are shared.

NASA↗

NASA NTRS1990

High altitude aircraft remote sensing during the 1988 Yellowstone National Park wildfires

An overview is presented of the effects of the wildfires that occurred in the Yellowstone National Park during 1988 and the techniques employed to combat these fires with the use of remote sensing. The fire management team utilized King-Air and Merlin aircraft flying night missions with a thermal IR line-scanning system. NASA-Ames Research Center assisted with an ER-2 high altitude aircraft with the ability to down-link active data from the aircraft via a teledetection system. The ER-2 was equipped with a multispectral Thematic Mapper Simulator scanner and the resultant map data and video imagery was provided to the fire command personnel for field evaluation and fire suppression activities. This type of information proved very valuable to the fire control management personnel and to the continuing ecological research goals of NASA-Ames scientists analyzing the effects of burn type and severity on ecosystem recovery and development.

Ambrosia, Vincent G.↗

NASA NTRS1987

Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park

An unsupervised computer classification of vegetation/landcover of Olympic National Park and surrounding environs was initially carried out using four bands of Landsat MSS data. The primary objective of the project was to derive a level of landcover classifications useful for park management applications while maintaining an acceptably high level of classification accuracy. Initially, nine generalized vegetation/landcover classes were derived. Overall classification accuracy was 91.7 percent. In an attempt to refine the level of classification, a geographic information system (GIS) approach was employed. Topographic data and watershed boundaries (inferred precipitation/temperature) data were registered with the Landsat MSS data. The resultant boolean operations yielded 21 vegetation/landcover classes while maintaining the same level of classification accuracy. The final classification provided much better identification and location of the major forest types within the park at the same high level of accuracy, and these met the project objective. This classification could now become inputs into a GIS system to help provide answers to park management coupled with other ancillary data programs such as fire management.

Cibula, William G.↗

NASA NTRS1994

Development and implementation of a scrub habitat compensation plan for Kennedy Space Center

Kennedy Space Center (KSC), located on Merritt Island on the east coast of central Florida, is one of three remaining major populations of the Florida Scrub Jay (Aphelocoma coerulescens coerulescens), listed as threatened by the U.S. Fish and Wildlife Service (USFWS) since 1987. Construction of new facilities by the National Aeronautics and Space Administration (NASA) on KSC over the next five years has the potential to impact up to 193 ac (78.1 ha) of Scrub Jay habitat. Under an early consultation process with the Endangered Species Office of the USFWS, NASA agreed to a compensation plan for loss of Scrub Jay habitat. The compensation plan required NASA to restore or create scrub on KSC at a 2:1 ratio for that lost. The compensation plan emphasized restoration of scrub habitat that is of marginal or declining suitability to Scrub Jays because it has remained unburned. Although prescribed burning has been conducted by the USFWS Merritt Island National Wildlife Refuge (MINWR) for more than ten years, significant areas of scrub remain unburned because they have been excluded from fire management units or because landscape fragmentation and a period of fire suppression allowed scrub to reach heights and diameters that are fire resistant. For such areas, mechanical cutting followed by prescribed burning was recommended for restoration. A second part of the restoration plan is an experimental study of scrub reestablishment (i.e., creation) on abandoned, well drained agricultural sites by planting scrub oaks and other scrub plants. The compensation plan identified 260 ac (105 ha) of scrub restoration in four areas and a 40 ac (16 ha) scrub creation site. Monitoring of restoration sites required under the plan included: establishing permanent vegetation sample transects before treatment and resampling annually for ten years after treatment, and color banding Scrub Jays to determine territories prior to treatment followed by monitoring reproductive success and survival for ten years after treatment. Monitoring scrub creation sites included determining survival of planted material for five years and establishing permanent transects to follow vegetation development for ten years after planting. Scrub Jay monitoring of creation sites is incorporated with that of adjacent restoration sites.

Schmalzer, Paul A.↗

NASA NTRSDate not supplied

Emissions Characterization and Smoke Transport of A Prescribed Fire

Under the background of climate change, vast regions of the world will face a hotter and probably drier future that favors ignition and spread of wildfires. This poses additional challenges to the communities who are fighting the ever-larger wildfires. Fire and smoke models are valuable tools to fire managers and decision-makers to mitigate the impact of fires. Existing fire modeling systems bear large uncertainties due to the difficulty in collecting data to characterize fire behavior and smoke transport. More observations are urgently needed for improvement of fire modeling and reduction in model uncertainty. The coordinated prescribed burn experiment, such as the Fire and Smoke Model and Measurement Evaluation Experiment (FSMMEE), will allow the concurrent collection of critical measurements of fuel, fire behavior, smoke, and meteorology to better understand and model fires. In this preliminary investigation, we employed the data from the prescribed burn experiment at Langdon Mountain, Utah, in November 2019, to reconstruct the fire emissions and characterize the smoke transport. The NASA Unified Weather Research and Forecasting Model (NU-WRF) was utilized to assist in identifying the potential pre-fire remote sensing capabilities, such as fuel load, moisture, and fire radiative power, which are helpful to model development and advancement. Along the way, two sets of NU-WRF experiments would be done by applying either a default fire emissions inventory (i.e., NASA’s Quick Fire Emissions Dataset, QFED) or reconstructed fire emissions using the data collected from the prescribed fire. The results would help answer the questions such as “How do reconstructed emissions compare to QFED emissions and their impact on plume transport”.

NU-WRF↗

NASA NTRS1991

Firefly system concept

The 'Firefly' project has developed and implemented an infrared (IR) remote sensing prototype system based on the concept presented. The Firefly system produces images through smoke that will provide near real-time wildland fire information for fire management and suppression. The prototype will be tested through the 1991 fire season. Results of the testing will be incorporated into the final system design for operational use at the end of 1992.

Nichols, J. D.↗

NASA NTRS1990

A feasibility study: California Department of Forestry and Fire Protection utilization of infrared technologies for wildland fire suppression and management

NASA's JPL has completed a feasibility study using infrared technologies for wildland fire suppression and management. The study surveyed user needs, examined available technologies, matched the user needs with technologies, and defined an integrated infrared wildland fire mapping concept system configuration. System component trade-offs were presented for evaluation in the concept system configuration. The economic benefits of using infrared technologies in fire suppression and management were examined. Follow-on concept system configuration development and implementation were proposed.

Nichols, J. D.↗

DOE OSTI2020

The impact of interventions in the global land and agri-food sectors on Nature’s Contributions to People and the UN Sustainable Development Goals

Interlocked challenges of climate change, biodiversity loss and land degradation require transformative interventions in the land management and food production sectors to reduce carbon emissions, strengthen adaptive capacity, and maintain or increase food production to 2050. However, deciding which interventions to pursue and understanding their relative synergies with and trade-offs against different social and environmental goals has been difficult without direct comparisons across a range of possible actions. This study examined a series of 40 different options, implemented through land, value chain or risk management, for their relative impacts across 18 Nature’s Contributions to People (also known as ecosystem services) and the 17 Sustainable Development Goals. We find that a relatively small number of interventions show significant positive synergies with both SDGs and NCPs; these include improved cropland management, improved grazing land management, improved livestock management, agroforestry, agricultural diversification, increased soil organic carbon content, reduced soil erosion, salinization and compaction, fire management, reduced landslides and hazards, reduced pollution, management of invasive species, sustainable source, and reduced post-harvest losses. Several interventions show strong negative impacts on either SDGs, NCPs or in some cases, both; these include bioenergy and BECCS, afforestation, and some risk sharing measures, like commercial crop insurance. Our results demonstrate that a better understanding of benefits and trade-offs of comparative policy approaches can help decision-makers choose the most effective, or at the very minimum, benign interventions for implementation in specific contexts.

Mcelwee, Pam↗

DOE OSTI2022

Past fires and post-fire impacts reconstructed from a southwest Australian stalagmite

Speleothem records of past environmental change provide an important opportunity to explore fire frequency and intensity in the past, and the antecedent climatic conditions leading to fire events. Here, fire sensitive geochemical signals in a stalagmite from Yonderup Cave, a shallow cave in Western Australia, are compared to well-documented wildfire events that occurred in recent decades. The results are extended to identify wildfires during the growth interval of the speleothem (1760 CE–2005 CE). Principal component analysis of the stalagmite time-series revealed distinct peaks in a combination of phosphorus and metal (aluminium, zinc, copper and lead) concentrations in response to known fire events, which are interpreted to have come from ash. Varying responses in the geochemical signal in the pre- and post-European colonisation period are likely linked to changes in land management, fire frequency and fire intensities. Of note is what we infer to be a particularly intense fire event concluded to have occurred in 1897 ± 5 CE, which climate sensitive proxies (δ18O and bedrock-derived elements) indicate was preceded by a multi-decadal dry period that began in the late 1860s. The intensity of the deduced fire event is supported by a peak in P that is 6.3 times higher than elsewhere in the speleothem, accompanied by a peak in Zn. It was also inferred that changes to the surface-cave hydrology occurred as a result of this event, indicated by increased input of colloidal organic material onto the stalagmite that was interpreted to be caused by shallow karst bedrock fracturing from the heat-induced deformation that is observed after intense fires. These findings show the potential for speleothems to provide dated records of fire intensity and recurrence intervals. Further development could lead to a better understanding of the climate-fire relationship and the effects of land-management practices on wildfire frequency and intensity.

58 GEOSCIENCES↗

NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Joseph C Coughlan↗

NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

Guillaume P Brat↗

NASA NTRS2019

NASA's Fire Information for Resource Management System (FIRMS): Near Real-Time Global Fire Monitoring Using Data from MODIS and VIIRS

NASA's Fire Information for Resource Management System (FIRMS) provides near-real time active fire / hotspot products from MODIS and VIIRS to users in over 160 countries. The goal of FIRMS is to meet the needs of natural resource and protected area managers that face considerable challenges in obtaining timely satellite-derived information on fires burning within and around their management area. FIRMS has been reliably providing active fire / hotspot data in easy to use formats since its inception in 2006. Fire information is provided through a web map interface, email alerts, a web mapping service, and a range of downloadable files (SHP, CSV, KML and JSON). FIRMS data are used directly by end users and by brokers who take the data and add value to it before re-distributing it.FIRMS was initially developed by the University of Maryland in 2006; it was funded by the United Nations FAO and NASA's Applied sciences program under a NASA ROSES call. In 2012 FIRMS became integrated in to NASA's Land Atmosphere Near real-time Capability for EOS (LANCE); a virtual system that leverages NASAs existing science processing capabilities to deliver NRT data from ten instruments within 3 hours of satellite overpass. This presentation will describe the FIRMS system, provide an overview of the system, briefly describe some of the known applications and describe plans to further integrate the data in to NASA's Global Imagery Browse Services (GIBS) public mapping services and Worldview website.

active fire/hotspots↗

NASA NTRS2004

Modeling Forest Understory Fires in an Eastern Amazonian Landscape

Forest understory fires are an increasingly important cause of forest impoverishment in Ammonia, but little is known of the landscape characteristics and climatic phenomena that determine their occurrence. We developed empirical functions relating the occurrence of understory fires to landscape features near Paragominas, a 35- yr-old ranching and logging center in eastern Ammonia. An historical sequence of maps of forest understory fire was created based on field interviews With local farmers and Landsat TM images. Several landscape features that might explain spatial variations in the occurrence of understory fires were also mapped and co-registered for each of the sample dates, including: forest fragment size and shape, forest impoverishment through logging and understory fires, source of ignition (settlements and charcoal pits), roads, forest edges, and others. The spatial relationship between forest understory fire and each landscape characteristic was tested by regression analyses. Fire probability models were then developed for various combinations of landscape characteristics. The analyses were conducted separately for years of the El Nino Southern Oscillation (ENSO), which are associated with severe drought in eastern Amazonia, and non-ENS0 years. Most (91 %) of the forest area that burned during the 10-yr sequence caught fire during ENSO years, when severe drought may have increased both forest flammability and the escape of agricultural management fires. Forest understory fires were associated with forest edges, as reported in previous studies from Ammonia. But the strongest predictor of forest fire was the percentage of the forest fragment that had been previously logged or burned. Forest fragment size, distance to charcoal pits, distance to agricultural settlement, proximity to forest edge, and distance to roads were also correlated with forest understory fire. Logistic regression models using information on fragment degradation and distance to ignition sources accurately predicted the location of lss than 80% of the forest fires observed during the ENSO event of 1997- 1998. In this Amazon landscape, forest understory fire is a complex function of several variables that influence both the flammability and ignition exposure of the forest.

Alencar, A. A. C.↗

DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗