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NASA NTRSDate not supplied

NASA's New Wildland Fire Earth Observation Science & Applications Programmatic Developments

In 2021, the U.S. National Aeronautics & Space Administration (NASA) initiated new programmatic elements within the Science Mission Directorate (SMD) and the Aeronautics Research Mission Directorate (ARMD) focused on supporting wildland fire science and applications improvements, employing the vast array of NASA scientific knowledge, airborne and space-borne Earth Observations (EO) capabilities, technology development (sensor systems, etc.), and large framework modeling efforts. Within the Science Mission Directorate, the NASA Earth Science Division (ESD) will focus on improving our understanding of wildland fire through EO tools and applying rigorous-tested modeling and results of that research into operational use. The ESD Wildfire strategy is to invest in new technology and to better integrate NASA’s satellite, airborne, and ground-based observations with wildfire models to provide the wildfire stakeholders with the information they need to make informed decisions about the pre-, active-, and post-fire conditions. The Applied Science Program has restarted the Wildland Fire Applications Program with a focus on engaging wildland fire management and the fire science community in transitioning EO science efforts into routine use by land management entities at the local, state, national and international level. The NASA Aeronautics Research Mission Directorate will focus on arenas where their aeronautics science and engineering outcomes can benefit the fire management community as well, specifically in the innovative development of Uncrewed Aircraft systems, congested mixed-use platform airspace management issues, new platform configurations supporting wildland fire missions, and other aeronautics-related science / engineering capabilities which may benefit the fire management community. In total, these developments represent a major thrust forward, supporting the goals of utilizing NASA science to benefit humankind. This presentation will highlight the various wildland fire science focus areas identified through collaborations with the wildland fire science and management community and highlight the plans of this new NASA focus area.

NASA↗
NASA NTRSDate not supplied

Anthropogenic Pathways for Modeling and Managing Future Arctic Fires

Wildland fires, including extreme fire events and seasons, are becoming more common in the boreal and Arctic regions due to climate change. Current climate modeling approaches do not include country- or region-specific socioeconomic pathways that specifically address the drivers of and potential mitigation techniques for wildland fires. Forest management, energy extraction, and tourism, together with firefighting capacity and readiness as well as fuels treatment, can have a significant impact on future wildland fire risks and impacts. To assess the impacts of anthropogenic factors and to align with previous work done on shared socioeconomic pathways (SSPs), climate pathways for future wildfires up to 2050 were created for the states that compose the original Arctic Council countries: Canada, the United States, the Kingdom of Denmark, Iceland, Sweden, Norway, and Finland as well as the Russian Federation (with whom the other seven countries withdrew participation from in May 2022 due to the invasion and ongoing war in Ukraine). High and low fire activity and risk pathways for all states comprising the Arctic were made, with expert ‘best guess’ pathways for each state created separately to represent the middle of road. The low activity and low fire risk pathways, named “We Got This”, assume active fire suppression via citizenry participation and official land management, efficient and extensive fuel treatments, and consistent and active wildland firefighting for each new ignition. The high activity and high fire risk pathways, named “Let It Burn”, assume nearly the opposite, due to lack of government and community response and no action on climate change drivers that increase wildland fire risk. The ‘best guess’ pathway, named “The Fire Will Come”, indicates that some countries are currently on the pathway for less fire compared to other Arctic and Boreal states but not a ‘no-fire’ future. For example, in the Nordic countries, human ignition sources from tourism, timber and energy extraction, summer cottages, and expanding wildland-urban intermix due to exurban growth may increase. In North America, these same risks will apply but also may see an expansion of agriculture that increases the likelihood of open burning in croplands. Drier fuel condition and extreme heat events due to climate change create favorable conditions for extreme wildfires from any ignition source. Throughout the Arctic and boreal lightning is expected to increase, increasing the risk of tundra fires in addition to forest fires in hard-to-reach locations that are more difficult to coordinate and execute wildland firefighting. To move the future Arctic fire SSPs forward, several short-term and long-term actions must be completed. Certain data needs are required, like a harmonized pan-Arctic and pan-boreal fuels geospatial product, while also a need to refine and socialize current definitions of fire seasons and fire management – including developing an open-source system to track and share innovation, mitigation, and adaptation strategies across Arctic states.

Arctic↗
NASA NTRS2017

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Ecosystems

The combined effects of repeated fires, climate, and landscape features (e.g., edges) need greater focus in fire ecology studies, which usually emphasize characteristics of the most recent fire and not fire history. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells that represented potential territories because frequent fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities between states varied annually as functions of environmental covariates. Covariates included vegetative type, edges, precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presenceabsence of fire covariate, but also fire history covariates: time since the previous fire, the maximum fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Measuring territory quality states and environmental covariates each year combined with multistate modeling provided a useful empirical approach to quantify the effects of repeated fire in combinations with environmental variables on transition probabilities that drive management strategies and ecosystem change.

Breininger, David R.↗
NASA NTRS2018

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Habitat

The combined effects of fire history, climate, and landscape features (e.g., edges) on habitat specialists need greater focus in fire ecology studies, which usually only emphasize characteristics of the most recent fire. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights, which are dynamic because of frequent fires. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells (that represented potential territories) because fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities vary between states as functions of environmental covariates. Covariates included vegetative type, edges (e.g., roads, forests), precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presence/absence of fire covariate, but also fire history covariates: time since the previous fire, the longest fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Edges reduced the effectiveness of fires in setting degraded scrub and flatwoods into earlier successional states making mechanical cutting an important tool to compliment frequent prescribed fires.

Breininger, David R.↗
NASA NTRS2024

Córdoba Wildland Fires: Assessing Fire Risk Factors in Córdoba, Argentina using Earth Observations

In recent years, Córdoba, Argentina has experienced intensified wildfire activity, with fires in 2020 alone scorching over 300,000 hectares within the province. Potential causes for the increased burn area include climate change, the expanding wildland-urban interface (WUI), and inadequate fire management practices. Previous studies have produced fire frequency maps for the region, but gaps remain in understanding the environmental parameters influencing fire behavior and growth. We partnered with the Instituto Nacional de Tecnología Agropecuaria (INTA) to address these gaps by utilizing NASA Earth observing data to analyze key wildfire risk factors. Using a combination of data inputs from Soil Moisture Active Passive (SMAP), Shuttle Radar Topography Mission (SRTM), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG), and Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS), we created a ten-year baseline using environmental variables to determine anomalies that influenced the fires of 2020. Baseline data were used to calculate the statistical significance of the environmental factors as precursors to wildfires. We found that the normalized difference vegetation index (NDVI) and precipitation were the strongest indicators for the September 2020 wildfires. Using the environmental risk factors, we created a wildfire risk map for the province of Córdoba, which can be used to enhance our partner’s fire management strategies and decision-making processes.

wildland fires↗
NASA NTRSDate not supplied

Exemplifying the Usability of NASA Earth Observations to Analyze Potential Risk Factors that Predispose Wildfires in the Rural-Urban Areas of Córdoba, Argentina

In recent years, Córdoba, Argentina has experienced intensified wildfire activity, with fires in 2020 alone scorching over 300,000 hectares within the province. Potential causes for the increased burn area include climate change, the expanding wildland-urban interface, and inadequate fire management practices. Previous studies have produced fire frequency maps for the region, but gaps remain in understanding the parameters influencing fire behavior and growth. This project partnered with the Instituto Nacional de Tecnología Agropecuaria to address these gaps by utilizing NASA’s remote sensing capabilities to analyze key wildfire risk factors. Using a combination of data inputs from Soil Moisture Active Passive (SMAP), Shuttle Radar Topography Mission (SRTM), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG), and Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS), a ten-year baseline was created using environmental variables to determine anomalies that influenced the fires of 2020. Of these anomalies, the baseline data were used to calculate the statistical significance of the environmental factors to the wildfires. This study found that the normalized difference vegetation index (NDVI) and precipitation were the strongest indicators for the September 2020 wildfires. Using the environmental risk factors, this project created a wildfire risk map for the province of Córdoba, which can be used to enhance partner’s fire management strategies and decision-making processes.

Chassety Raines↗
NASA NTRS2021

COVID-19 Lockdowns Drive Decline in Active Fires in Southeastern United States

Fire is a common ecosystem process in forests and grasslands worldwide. Increasingly, ignitions are controlled by human activities either through suppression of wildfires or intentional ignition of prescribed fires. The Southeastern United States leads the nation in prescribed fire, burning ca. 80% of the countries extent annually. The COVID-19 pandemic radically changed human behavior as workplaces implemented social-distancing guidelines and provided an opportunity to evaluate relationships between humans and fire as fire management plans were postponed or cancelled. Using active fire data from satellite-based observations, we found that in the Southeastern United States, COVID-19 led to a 21% reduction in fire activity compared to the 2003-2019 average. The reduction was more pronounced for federally managed lands, up to 41% below average compared to the past 20-years (38% below average compared to the past decade). Declines in fire activity were partly affected by an unusually wet February before the COVID-19 shutdown began in mid-March 2020. Despite the wet spring, the predicted number of active fire detections was still lower than expected, confirming a COVID-19 signal on ignitions. In addition, prescribed fire management statistics, reported by US federal agencies, confirmed the satellite observations, and showed that following the wet February and before the mid-March COVID-19 shutdown, cumulative burned area was approaching record highs across the region. With fire return intervals in the Southeastern United States as frequent as 1-2 years, COVID-19 fire impacts will contribute to an increasing backlog in necessary fire management activities, affecting biodiversity and future fire danger.

Biological Sciences↗
NASA NTRSDate not supplied

Moon to Mars (M2M): Exploration Atmosphere

As humans leave the bounds of Earth to explore the lunar surface and beyond, crew will don extravehicular activity (EVA) suits to learn more about these extraterrestrial environments, establish sustained presence, and perform needed upgrades and maintenance to their space vehicle and habitation systems. Spacefaring vehicle and habitation design will need to support these EVA excursions while ensuring crew health and safety. A crucial technological design advancement towards this goal is the use of a lower pressure exploration atmosphere (EA) that enables high efficiency EVA, rather than the sea level atmosphere of 14.7 psia, 21% oxygen (O 2 ) found on the International Space Station, Shuttle, and most other Russian and Chinese space vehicles and stations. Early space vehicles (Mercury through Apollo Programs) used a 5 psia, 100% O 2 environment, which eliminated the need for pre-EVA denitrogenation protocols, simplified the life support system to a single gas, and saved structural mass. For longer duration missions (Skylab), a diluent gas was added, changing the atmosphere to 5 psia, 70-74% O 2 to prevent atelectasis while remaining normoxic. As in-flight science became a top priority, Shuttle and ISS atmospheres were chosen to operate at sea level allowing for simpler ground-based study control conditions. Consequently this led to long pre-EVA denitrogenation protocols involving up to 4 hours of O 2 prebreathe because the EVA suit still operated at a low pressure of 4.3 psid. To increase operational efficiency, the Shuttle was retroactively certified to operate using 10.2 psia, 26.5% O 2 , reducing O 2 prebreathe time to 40-75 min. Current plans for M2M habitats on the Lunar surface require EVA, thus EA recommendation became 8 psia and 32% O 2 but was revised to 8.2 psia and 34% O 2 to decrease hypoxia exposure. Unfortunately, the benefits of EA in support of safe and efficient EVAs comes with the challenge of fire management in a higher-than-normal O 2 % environment. Although known for decades, the recommended forward work to address fire management has only recently begun. Current flammability tests include examining material propagation and ignition sources as well as fire mitigation processes to better understand these properties for proposed new EA environments. Fire safety, DCS risk, and mission design all contribute to the multifaceted parameters of EA. Thus while it is clear that EA is required to achieve the goals of future exploratory space missions, final specifications are still being evaluated for optimizing crew health and safety.

space atmosphere↗
NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗
NASA NTRS2007

Estimation of Forest Fuel Load from Radar Remote Sensing

Understanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar imagery acquired over a large area of the Yellowstone National Park (YNP) by the AIRSAR sensor, to estimate the distribution of forest biomass and canopy fuel loads. Semi-empirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, canopy fuel weight, canopy bulk density, and foliage moisture content. These estimates when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy, and when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R(sup 2) = 85 for the canopy fuel weight, R(sup 2)=.84 for canopy bulk density and R(sup 2) = 0.78 for the foliage biomass.

Saatchi, Sassan↗
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.↗
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↗