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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Orion Portable Fire Extinguisher Performance Testing Against a Laptop Lithium-Ion Battery Stored-Energy Fire-Method, Magnesium Fires, and Combustion By-Product Toxicity

As part of the qualification of the International Space Station (ISS) fine water mist portable fire extinguisher (PFE), several test methods were developed to determine firefighting capability against stored-energy sources. The most challenging of these devised stored-energy fire test methods proved to be the Lithium-ion (Li-ion) battery fire test scenario. The Orion crew capsule will utilize a different PFE technology from ISS (water spray rather than water mist), which spurred the need for the same type of evaluation focused on the sources of stored energy slated for use on Orion. Laptops were identified as a realistic source for stored-energy fires, requiring a modified Li-ion battery fire test scenario. In addition to open test cell (ambient oxygen concentration) testing to evaluate new proposed PFE performance, sealed chamber (20.9% and elevated oxygen concentration) testing was also performed. Chamber testing included combustion product sampling at various fire progression points for analysis and application to Orion emergency equipment design and response planning. The PFE stored-energy fire test methodology was modified and testing performed. Initial tests indicated ignition of the laptop magnesium laptop cases was possible. Additional tests were performed to characterize the laptop magnesium case fire behavior in various configurations. The new water spray PFE technology proved effective in extinguishing laptop stored-energy fires, and much was learned in the way these types of fires progressed. Findings indicate potential laptop magnesium case ignition mitigation strategies need to be further investigated.

Harper, Susana A.↗
NASA NTRS2019

The Global Fire Atlas of Individual Fire Size, Duration, Speed and Direction

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

global fire atlas↗
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 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 NTRS2019

Historical (1700–2012) Global Multi-Model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)

Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.

Li, Fang↗
NASA NTRS2024

Intermountain West Wildland Fires: Mapping Tree Mortality and Burn Patches using NASA Earth Observations to Determine Fire Risk and Inform Fire Management Practices

Within the intermountain west, monitoring fuel loads is a major concern for wildland fire management efforts. To address this concern, we partnered with the U.S. Forest Service to inform the agency which forested areas should be prioritized for prescribed burning and fuel reduction near human communities in the Bridger-Teton National Forest, Wyoming. We created burn maps, fuel load maps, and a tutorial document to identify forest impact trends and provide the partner with the tools to replicate project methods for expansion to other wildfire crisis strategy sites. These end products were made using two NASA Earth observations: Landsat 8 Operational Land Imager and Shuttle Radar Topography Mission. Based on our random forest analysis, our maps identified 998 acres within the Wildland Urban Interface that are predicted to have high fuel loading and high burn severity within the Bridger-Teton National Forest. Forested areas closer to heavily populated areas such as Jackson, Kelly, Moran, New Forks Lake, and Star Valley Ranch should be prioritized for fuel reduction. However, our random forest model analysis was limited to using vegetation and topographical indices with no field data for model validation. Therefore, future studies should use field data for model validation to improve model accuracy and additionally incorporate Global Ecosystem Dynamics Investigation data into models to create better predictions of forested areas with high fuel load and high burn severity.

Remote Sensing↗
NASA NTRS1993

Fire protection for launch facilities using machine vision fire detection

Fire protection of critical space assets, including launch and fueling facilities and manned flight hardware, demands automatic sensors for continuous monitoring, and in certain high-threat areas, fast-reacting automatic suppression systems. Perhaps the most essential characteristic for these fire detection and suppression systems is high reliability; in other words, fire detectors should alarm only on actual fires and not be falsely activated by extraneous sources. Existing types of fire detectors have been greatly improved in the past decade; however, fundamental limitations of their method of operation leaves open a significant possibility of false alarms and restricts their usefulness. At the Civil Engineering Laboratory at Tyndall Air Force Base in Florida, a new type of fire detector is under development which 'sees' a fire visually, like a human being, and makes a reliable decision based on known visual characteristics of flames. Hardware prototypes of the Machine Vision (MV) Fire Detection System have undergone live fire tests and demonstrated extremely high accuracy in discriminating actual fires from false alarm sources. In fact, this technology promises to virtually eliminate false activations. This detector could be used to monitor fueling facilities, launch towers, clean rooms, and other high-value and high-risk areas. Applications can extend to space station and in-flight shuttle operations as well; fiber optics and remote camera heads enable the system to see around obstructed areas and crew compartments. The capability of the technology to distinguish fires means that fire detection can be provided even during maintenance operations, such as welding.

Schwartz, Douglas B.↗
NASA NTRS2012

Fire-induced Carbon Emissions and Regrowth Uptake in Western U.S. Forests: Documenting Variation Across Forest Types, Fire Severity, and Climate Regions

The forest area in the western United States that burns annually is increasing with warmer temperatures, more frequent droughts, and higher fuel densities. Studies that examine fire effects for regional carbon balances have tended to either focus on individual fires as examples or adopt generalizations without considering how forest type, fire severity, and regional climate influence carbon legacies. This study provides a more detailed characterization of fire effects and quantifies the full carbon impacts in relation to direct emissions, slow release of fire-killed biomass, and net carbon uptake from forest regrowth. We find important variations in fire-induced mortality and combustion across carbon pools (leaf, live wood, dead wood, litter, and duff) and across low- to high-severity classes. This corresponds to fire-induced direct emissions from 1984 to 2008 averaging 4 TgC/yr and biomass killed averaging 10.5 TgC/yr, with average burn area of 2723 sq km/yr across the western United States. These direct emission and biomass killed rates were 1.4 and 3.7 times higher, respectively, for high-severity fires than those for low-severity fires. The results show that forest regrowth varies greatly by forest type and with severity and that these factors impose a sustained carbon uptake legacy. The western U.S. fires between 1984 and 2008 imposed a net source of 12.3 TgC/yr in 2008, accounting for both direct fire emissions (9.5 TgC/yr) and heterotrophic decomposition of fire-killed biomass (6.1 TgC yr1) as well as contemporary regrowth sinks (3.3 TgC/yr). A sizeable trend exists toward increasing emissions as a larger area burns annually.

Forest↗
NASA NTRS2017

Managing Fire Risk During Drought: The Influence of Certification and El Nino on Fire-Driven Forest Conversion for Oil Palm in Southeast Asia

Indonesia and Malaysia have emerged as leading producers of palm oil in the past several decades, expanding production through the conversion of tropical forests to industrial plantations. Efforts to produce "sustainable" palm oil, including certification by the Roundtable on Sustainable Palm Oil (RSPO), include guidelines designed to reduce the environmental impact of palm oil production. Fire-driven deforestation is prohibited by law in both countries and a stipulation of RSPO certification, yet the degree of environmental compliance isunclear, especially during El Niño events when drought conditions increase fire risk. Here, we used time series of satellite data to estimate the spatial and temporal patterns of fire-driven deforestation on and around oil palm plantations. In Indonesia, fire-driven deforestation accounted for one-quarter of total forest losses on both certified and noncertified plantations. After the first plantations in Indonesia received RSPO certification in 2009,forest loss and fire-driven deforestation declined on certified plantations but did not stop altogether. Oil palm expansion in Malaysia rarely involved fire; only 5 % of forest loss on certified plantations had coincident activefire detections. Interannual variability in fire detections was strongly influenced by El Nino and the timing of certification. Fire activity during the 2002, 2004, and 2006 El Nino events was similar among oil palm plantations in Indonesia that would later become certified, noncertified plantations, and surrounding areas. However, total fire activity was 75% and 66% lower on certified plantations than noncertified plantations during the 2009 and 2015 El Nino events, respectively. The decline in fire activity on certified plantations, including during drought periods, highlights the potential for RSPO certification to safeguard carbon stocks in peatlands and remaining forests in accordance with legislation banning fires. However, aligning certification standards with satellite monitoring capabilities will be critical to realize sustainable palm oil production and meet industry commitments to zero forestation.

Noojipady, Praveen↗
NASA NTRS2008

Measurement of Fire Radiative Energy from Space and Implications for Fire-Disaster Monitoring and Smoke Emissions Modeling

Measurement of fire radiative energy (FRE) release rate or power (FRP) from satellite provides a vital mechanism for distinguishing different strengths of fires. Analysis of 1-km resolution fire data, acquired globally by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor aboard the Terra and Aqua satellites from 2000 to 2006, showed instantaneous FRP values ranging between 0.02 MW and 1866 MW, to which simple thresholds can be applied to categorize fires by strength, in a similar fashion as the strengths of earthquakes and hurricanes. Analysis of regional mean FRP per unit area of land (FRP flux) shows that at peak fire season in certain regions, fires can be responsible for up to 0.2 W/m2 at peak time of day. When considered as the active fire contribution to the direct surface radiative forcing (RF) in the different fire regions, this order of magnitude of FRF fluxes is non negligible. It has been determined experimentally that the amount of FRE released by a fire over the course of its duration is directly proportional to the amount of biomass consumed by it. Furthermore, at the satellite observation scale, the rate of release of FRE (i.e. FRP) is proportional to the rate of biomass consumption, and that of emission of smoke particulates and eventually also other smoke constituents. Therefore, current research efforts are geared toward deriving simple parameterizations that will facilitate direct input of FRP measurements in models, not only to improve the accuracy of burned-biomass and smoke emissions estimations, but also to reduce the hitherto practiced heavy reliance on multiple indirect parameters with indeterminate uncertainties.

Ichoku, Charles↗
NASA NTRS2010

Effects of Lightning and Other Meteorological Factors on Fire Activity in the North American Boreal Forest: Implications for Fire Weather Forecasting

The effects of lightning and other meteorological factors on wildfire activity in the North American boreal forest are statistically analyzed during the fire seasons of 2000-2006 through an integration of the following data sets: the MODerate Resolution Imaging Spectroradiometer (MODIS) level 2 fire products, the 3-hourly 32-kin gridded meteorological data from North American Regional Reanalysis (NARR), and the lightning data collected by the Canadian Lightning Detection Network (CLDN) and the Alaska Lightning Detection Network (ALDN). Positive anomalies of the 500 hPa geopotential height field, convective available potential energy (CAPE), number of cloud-to-ground lightning strikes, and the number of consecutive dry days are found to be statistically important to the seasonal variation of MODIS fire counts in a large portion of Canada and the entirety of Alaska. Analysis of fire occurrence patterns in the eastern and western boreal forest regions shows that dry (in the absence of precipitation) lightning strikes account for only 20% of the total lightning strikes, but are associated with (and likely cause) 40% of the MODIS observed fire counts in these regions. The chance for ignition increases when a threshold of at least 10 dry strikes per NARR grid box and at least 10 consecutive dry days is reached. Due to the orientation of the large-scale pattern, complex differences in fire and lightning occurrence and variability were also found between the eastern and western sub-regions. Locations with a high percentage of dry strikes commonly experience an increased number of fire counts, but the mean number of fire counts per dry strike is more than 50% higher in western boreal forest sub-region, suggesting a geographic and possible topographic influence. While wet lightning events are found to occur with a large range of CAPE values, a high probability for dry lightning occurs only when 500 hPa geopotential heights are above 5700m and CAPE values are near the maximum observed level, underscoring the importance of low-level instability to boreal fire weather forecasts-

Peterson, D.↗
NASA NTRS2021

How fire patterns reveal uneven stabilization at the end of conflict: examining Syria's unusual fire year in 2019

In 2019, Syria experienced its largest outbreak of fires since 2003, when fire occurrence began to be monitored via the MODIS satellite instruments. Here, we combine remotely sensed data on fire, local climatic conditions, and vegetation with spatial data on violence, territorial control, and return of internally displaced persons (IDPs) to explore the underlying causes of Syria's fires at the sub-district, or ADM-3, level. We find evidence for a nuanced story: precipitation and stabilization in Kurdish-controlled territory fueled a bountiful wheat-growing year in 2019. This agricultural resurgence coincided with high fire activity, possibly revealing that people were attempting to jump-start agricultural livelihoods that were lost during the civil war. Restoration of labor forces from IDP returns was not a significant factor in this agricultural recovery. Meanwhile, most sub-districts outside Kurdish-controlled territory were free of fires, which explains the absence of a relationship between vegetation and fire. Thus, Syrian fire patterns in 2019 may be a signal of uneven stabilization, rather than destabilization.

Fire patterns↗
NASA NTRS2010

Stratifying Tropical Fires by Land Cover: Insights into Amazonian Fires, Aerosol Loading, and Regional Deforestation

This study analyzes changes in the number of fires detected on forest, grass, and transition lands during the 2002-2009 biomass burning seasons using fire detection data and co-located land cover classifications from the Moderate Resolution Imaging Spectroradiometer (MODIS). We find that the total number of detected fires correlates well with MODIS mean aerosol optical depth (AOD) from year to year, in accord with other studies. However, we also show that the ratio of forest to savanna fires varies substantially from year to year. Forest fires have trended downward, on average, since the beginning of 2006 despite a modest increase in 2007. Our study suggests that high particulate matter loading detected in 2007 was likely due to a large number of savanna/agricultural fires that year. Finally, we illustrate that the correlation between annual Brazilian deforestation estimates and MODIS fires is considerably higher when fires are stratified by MODIS-derived land cover classifications.

TenHoeve, J. E.↗
NASA NTRSDate not supplied

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

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

Suhani Dalal↗
NASA NTRS2013

Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data

High temporal resolution information on burned area is a prerequisite for incorporating bottom-up estimates of wildland fire emissions in regional air transport models and for improving models of fire behavior. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the evolution of nine large wildland fires. For each fire, local input parameters for the kriging model were defined using variogram analysis. The accuracy of the kriging model was assessed using high resolution daily fire perimeter data available from the U.S. Forest Service. We also assessed the temporal reporting accuracy of the MODIS burned area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared to 33% for MCD45A1 and 53% for MCD64A1.

Aqua↗
NASA NTRS2023

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

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

Remote sensing↗
NASA 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↗