Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “fire mapping”

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

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Observations of supermicron-sized aerosols originating from biomass burning in southern Central Africa

During the 3 years of the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign, the NASA Orion P-3 was equipped with a 2D stereo (2D-S) probe that imaged particles with maximum dimension (D) ranging from 10 < D < 1280 µm. The 2D-S recorded supermicron-sized aerosol particles (SAPs) outside of clouds within biomass burning plumes during flights over the southeastern Atlantic off Africa's coast. Numerous SAPs with 10 < D < 1520 µm were observed in 2017 and 2018 at altitudes between 1230 and 4000 m, 1000 km from the coastline, mostly between 7–11° S. No SAPs were observed in 2016 as flights were conducted further south and further from the coastline. Number concentrations of refractory black carbon (rBC) measured by a single particle soot photometer ranged from 200 to 1200 cm –3 when SAPs were observed. Transmission electron microscopy images of submicron particulates, collected on Holey carbon grid filters, revealed particles with potassium salts, black carbon (BC), and organics. Energy-dispersive X-ray spectroscopy spectra also detected potassium, a tracer for biomass burning. These measurements provided evidence that the submicron particles originated from biomass burning. NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) 3 d back trajectories show a source in northern Angola for times when large SAPs were observed. Fire Information for Resource Management System (FIRMS) Moderate Resolution Imaging Spectroradiometer (MODIS) 6 active fire maps showed extensive biomass burning at these locations. Given the back trajectories, the high number concentrations of rBC, and the presence of elemental tracers indicative of biomass burning, it is hypothesized that the SAPs imaged by the 2D-S are examples of BC aerosol, ash, or unburned plant material.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CESER: wildfire mapping

Multispectral satellite imagery has been demonstrated to accurately detect wildfires over a variety of land cover types at coarse- and medium-resolution (i.e., 1km to 30m), typically by detecting burned area after the fire has caused substantial damage. We developed an algorithm to map active fire (i.e., flame) in 2-meter resolution WorldView time-series imagery and monitor fire trajectory over time. Our results depict robust mapping across scenes collected from different fires over grassy and forested land cover, identifying fires as small as 4m 2 throughout the image. Operational deployment at scale may provide valuable near-real time maps of active fire for responders to leverage for timely, targeted mitigation efforts. This white paper describes the current capability specifications and indicates requirements for scaling.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors

Abstract Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Geology↗
DOE OSTI2024

Forecast of Wildfire Potential Across California USA Using a Transformer

Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.

Limber, Russell [ORNL]↗
DOE OSTI2022

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wildland Fires in the Subtropical Hill Forests of Southeastern Bangladesh

The first ever comprehensive study on wildland fires in Bangladesh is carried out to develop a fire prevention and prediction method. The major causes of huge wildland fires (88%) in the subtropical Chittagong Hill forest (43% of total) of southern Bangladesh are reported as shifting cultivation, grazing and unauthorized settlement. We used satellite hotspot (HS) data from 2003 to 2021 (a total of 54,669 HSs) to clarify the spatio-temporal structure of wildland fires. Fire weather conditions were analyzed using various weather data and synoptic-scale weather maps at different air levels. Fires concentrated from March through April or a transitional period from the dry season, caused by the Asian winter monsoon, to the wet season, due to the Asian summer monsoon. Fire occurrence depended on dry conditions and pre-monsoon showers and their timing. The difference in 925 hPa heights of high and low pressure systems may be attributed to the different types of the Asian winter and summer monsoons. The average maximum air temperature and incident shortwave solar energy in April were the highest and strongest, contributing fire-prone weather conditions. Based on the analysis, a fire prevention and prediction method will be developed, and this report may also facilitate the establishment of future CO 2 reduction measures for Bangladesh.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Climate Dynamics Preceding Summer Forest Fires in California and the Extreme Case of 2018

Recent record-breaking wildfire seasons in California prompt an investigation into the climate patterns that typically precede anomalous summer burned forest area. Using burned-area data from the U.S. Forest Service’s Monitoring Trends in Burn Severity (MTBS) product and climate data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over 1984–2018, relationships between the interannual variability of antecedent climate anomalies and July California burned area are spatially and temporally characterized. Lag correlations show that antecedent high vapor pressure deficit (VPD), high temperatures, frequent extreme high temperature days, low precipitation, high subsidence, high geopotential height, low soil moisture, and low snowpack and snowmelt anomalies all correlate significantly with July California burned area as far back as the January before the fire season. Seasonal regression maps indicate that a global midlatitude atmospheric wave train in late winter is associated with anomalous July California burned area. July 2018, a year with especially high burned area, was to some extent consistent with the general patterns revealed by the regressions: low winter precipitation and high spring VPD preceded the extreme burned area. However, geopotential height anomaly patterns were distinct from those in the regressions. Extreme July heat likely contributed to the extent of the fires ignited that month, even though extreme July temperatures do not historically significantly correlate with July burned area. While the 2018 antecedent climate conditions were typical of a high-burned-area year, they were not extreme, demonstrating the likely limits of statistical prediction of extreme fire seasons and the need for individual case studies of extreme years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Start-up and Emergency Shutdown Modeling for a Coal-fired 10MWe sCO₂ Power Plant

As part of a Phase II Front End Engineering Design (FEED) study for the DOE Fossil Fuel Large Scale Pilots program, investigations of novel start-up and emergency shutdown methods for a stoker-fed, coal-fired, 10MWe sCO₂ power plant proposal were performed. These investigations were performed using 1D transient system models of the plant created within the GT-SUITE system modeling platform. The system model components were created using vendor quotation data (heat exchangers), performance maps (turbomachines), or FMU models (fired heater). Previously, Echogen Power Systems (Echogen) has used a sCO₂ power cycle start-up method that fills the system with liquid CO₂ to facilitate easy use of an electrically-driven start pump to transition the system from initial CO₂ fill to turbo-compressor initialization. The novel start-up method presented attempts this transition with a minimal filling of the system with liquid CO₂, to reduce the total CO₂ inventory required. This method leaves the start pump vulnerable to a two phase inlet condition as system pressure is below the CO₂ saturation pressure for the condenser cooling water temperature (Tcw). System model cases at Tcw of 18°C, 26°C, and 32°C, corresponding to cold, design, and hot ambient days, were analyzed to determine if the SP inlet condition could be kept as a subcooled liquid and estimate the CO₂ inventory reduction amount./p> Stoker-fed, coal-fired heaters continue to emit heat for some time even after emergency shutdown from events such as a power failure. This heat emission would lead to a failure of the fired heater, as the metal overheats, if the CO₂ flow is shut off. The emergency shutdown method presented utilizes CO₂ vented by a controllable vent valve (CRV) to provide CO₂ cooling flow to the fired heater. To determine the effectiveness of this method at keeping the fired heater peak tubing metal temperature below the ASME material temperature limit, for pressures below 5 MPa, of 816 °C, system model cases with CRV diameters ranging from 4” to 10”, and an alternative CO₂ vent routing with CRV size of 6”, were investigated./p>

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Characterizing post-fire delayed tree mortality with remote sensing: sizing up the elephant in the room

Abstract Background Despite recent advances in understanding the drivers of tree-level delayed mortality, we lack a method for mapping delayed mortality at landscape and regional scales. Consequently, the extent, magnitude, and effects of delayed mortality on post-fire landscape patterns of burn severity are unknown. We introduce a remote sensing approach for mapping delayed mortality based on post-fire decline in the normalized burn ratio (NBR). NBR decline is defined as the change in NBR between the first post-fire measurement and the minimum NBR value up to 5 years post-fire for each pixel. We validate the method with high-resolution aerial photography from six wildfires in California, Oregon, and Washington, USA, and then compare the extent, magnitude, and effects of delayed mortality on landscape patterns of burn severity among fires and forest types. Results NBR decline was significantly correlated with post-fire canopy mortality (r 2 = 0.50) and predicted the presence of delayed mortality with 83% accuracy based on a threshold of 105 NBR decline. Plots with NBR decline greater than 105 were 23 times more likely to experience delayed mortality than those below the threshold (p < 0.001). Delayed mortality occurred across 6–38% of fire perimeters not affected by stand-replacing fire, generally affecting more areas in cold (22–41%) and wet (30%) forest types than in dry (1.7–19%) types. The total area initially mapped as unburned/very low-severity declined an average of 38.1% and generally persisted in smaller, more fragmented patches when considering delayed mortality. The total area initially mapped as high-severity increased an average of 16.2% and shifted towards larger, more contiguous patches. Conclusions Differences between 1- and 5-year post-fire burn severity maps depict dynamic post-fire mosaics resulting from delayed mortality, with variability among fires reflecting a range of potential drivers. We demonstrate that tree-level delayed mortality scales up to alter higher-level landscape patterns of burn severity with important implications for forest resilience and a range of fire-driven ecological outcomes. Our method can complement existing tree-level studies on drivers of delayed mortality, refine mapping of fire refugia, inform estimates of habitat and carbon losses, and provide a more comprehensive assessment of landscape and regional scale fire effects and trends.

Environmental Sciences & Ecology↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2025

Peatland fires in Alaska will double by the end of the century

During recent summers, warm and dry conditions have increased the occurrence of wildfires and potentially peat-fires across Alaska. Limitations in resolving the fine-scale distribution of peatlands and climate observations have constrained our ability to accurately predict peat-fire dynamics. Using a new high-resolution peatland map of Alaska, we evaluated the climate and environmental controls of past and future peat-fire activity. Ensemble machine learning models identified reduced soil moisture, higher temperatures, and evapotranspiration as key predictors of annual total burned peatland area (tenfold CV R 2 = 0.62, RMSE = 221.1 km 2 ). By the end of the twenty-first century, models forced with climate datasets from representative concentration pathways (RCPs) 4.5, 6.0, and 8.5 emission scenarios project a statewide doubling of burned peatlands (increasing 61–121%), with regional increases ranging from 25–165% in polar, 61–95% in boreal, and 102–106% in maritime ecoregions. These projections indicate that wildfires will progressively encroach further into organic-rich moist and wet peaty soils, potentially amplifying soil carbon release across Alaska.

climate-change ecology↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Vegetation transect surveys from 2022 and 2023 within the Kougarok Fire Complex (KFC) on the Seward Peninsula in Alaska, USA

This dataset contains recorded vegetation classifications from 16 transects within the Kougarok Fire Complex (KFC) area in Alaska. Each transect is 50 meters long, and vertical vegetation profiles were sampled every 50cm, resulting in 101 points recorded for each transect. At each point, a vertical rod was inserted in the ground and any living plant or substrate touching the rod was recorded from top to bottom. For each plant, the plant functional type (PFT) and species name was identified and recorded. The file "flmd.xlsx" provides file level meta data for all other included files. The file (transect_metadata.csv) contains descriptive information about each of the 16 transects (field observation year and date; latitude and longitude GPS measurements of the 0, 25, and 50 meter points; and whether that area burned during the 1971, 1997, 2002, 2015, and/or 2019 fires). The files (PFT_names.csv and species_names.csv) map the short hand labels for the plant function type (PFT) and species used in the transect surveys to their full names. The remaining files are named for each transect, and contain the results of the vegetation survey for each transect. These files contain the plant functional type and the species identified at each point. Points along the transect with more than one species at that point are listed vertically from top to bottom in these files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Elevated Forest Canopy Loss After Wildfires in Moist and Cool Forests in the Pacific Northwest

Wildfire frequency, intensity, and rate of spread are increasing across the Western U.S, resulting in more severe ecosystem impacts. Significant tree mortality can occur years after fire events, but this has received little attention compared to the immediate tree loss during a fire. We overlapped forest cover loss data with burn severity maps in the U.S. Pacific Northwest and quantified the total and delayed forest canopy loss after fires. We found that wildfires resulted in total canopy loss fraction (CLF) of 84%, 53%, and 22% within 3 years in areas burned at high, moderate, and low severity, respectively. The delayed canopy loss accounted for approximately 1/3, 1/2, and 2/3 of the total canopy loss for high, moderate, and low severity burns. Delayed canopy loss was greater in moist and cool areas than in dry and warm areas, likely because tree species in wetter environments were less adapted to survive when fires did occur. Across all forests, delayed CLF doubled as temperature increased from the climatological mean to a hot anomaly and tripled as vapor pressure deficit increased from a wet anomaly to a dry anomaly. Fire impacts on forest ecosystems are likely to intensify under future climate scenarios as wildfires expand into areas that historically experienced infrequent fires. The impacts can also be exacerbated by more frequent compound extreme events, such as droughts, heatwaves, and fires. These findings highlight the urgent need for targeted forest management strategies, particularly in mesic forests, to mitigate future fire impacts.

54 ENVIRONMENTAL SCIENCES↗