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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data and Scripts associated with a manuscript on ecosystem responses to wildfires in the Columbia River Basin

This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wildfires identification: Semantic segmentation using support vector machine classifier

This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with $\mathcal{l}1$-loss and $\mathcal{l}2$-loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.

Pecha, Marek↗
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 OSTI2026

Adverse Neurological Effects of Wildfire Smoke

The respiratory and cardiovascular effects of smoke exposure are well documented from cigarette smoking and particulate matter (PM) associated with air-pollution studies, but epidemiological data also suggests exposure to high levels of woodsmoke correlate with central-nervous-system (CNS) dysfunction, including elevated rates of Alzheimer’s disease (AD) and dementia. Neuro-inflammation is believed to initiate the majority of Alzheimer’s and other neurodegenerative diseases attributed to environmental factors. We investigated whether inhaled PM and specific classes of compounds in wildfire smoke condensate extract (WSE) reach the brain and cause neuro-inflammation that can lead to progressive neurological degeneration, using isotope tracing of labeled particulate matter and major compound classes, biomarkers of blood-brain barrier (BBB) integrity, and biomarkers of neuro-inflammation. This project showed that inhalation exposure to PM and major compound classes present in WSE reached the brain through absorption in the nasal cavity and systemic circulation across the BBB. Twenty-four exposures of WSE to brain endothelial cells that form the main physical barrier of the BBB produced dose-dependent increases in biomarkers of inflammation and decreases in tight junction markers between cells. Intranasal exposure to WSE caused inflammatory disfunction in lung and brain. Biomarkers of inflammation increased and an enzyme inhibiting inflammation decreased. Similar patterns of chemical-induced changes in the lung and brain suggest either: (1) chemicals may act through shared molecular pathways upon entering lung and brain, or (2) that mediators originating in the lung circulate systemically to the brain and drive neuroinflammation. These findings warrant further mechanistic studies to delineate the temporal sequence of events to establish the lung-brain axis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Restoring Homes After Wildfires, Interviews with Practitioners about Common Cleanup Approaches and Knowledge Gaps

This document summarizes the key themes from our interviews with 10 remediation companies regarding common approaches used to remove odor and contaminants from homes impacted by fire and smoke damage Remediation companies are in agreement that thorough cleaning is the first step. Cleaning and removing contaminants from homes impacted by wildfires may be sufficient to eliminate smoke odor, which is the most important step when it comes to reducing exposure to any residues that may pose health risks to residents. Remediation companies differ in their approaches to addressing odors, such as using ozone generators, hydroxyl generators, chlorine dioxide treatment, thermal fogging and applying sealants. Their opinions of what works and why differs. There is little consensus on efficacy, safety, or application protocols. For example, some view ozone very favorably, while others would prefer using other methods. The influence of insurance companies in their coverage for some methods but not others also impact the decision about the approaches used. Remediation companies differ in their view on laboratory testing for contaminants, which may be partly influenced by insurance companies as well.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Grid Resiliency using Hydro-based Microgrids During Wildfires

The primary objective of this project is to assess the techno-economic viability of hydropower resources with supplementary investments, including microgrid controllers, storage solutions, load banks, additional generation like diesel generators, communication links, and transmission infrastructure. The aim is to enable these systems to operate autonomously (in islanded mode) and ensure grid resiliency during and after wildfire events. The project seeks to establish a comprehensive framework for setting up hydropower-based microgrids, from risk estimation to final investment decisions. These microgrids can provide critical support during emergencies and offer additional grid stability and revenue opportunities during regular operations and other system contingencies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM 2.5 Monitors with Optical Sensors

Air quality monitors using low-cost optical PM 2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements inside a well-ventilated lab impacted by the 2018 Camp Fire in California (USA). Using the Thermo TEOM-FDMS as reference, AFs of professional monitors were 0.85 for Grimm mini wide-range aerosol spectrometer, 0.25 for TSI DustTrak, and 0.53 for Thermo pDR1500; AFs for low-cost monitors were 0.59 for AirVisual Pro, 0.48 for PurpleAir Indoor, 0.46 for Air Quality Egg, and 0.60 for eLichens Indoor Air Quality Pro Station. We also compared public data from 53 PurpleAir PA-II monitors to 12 nearby regulatory monitoring stations impacted by Camp Fire smoke and devices near stations impacted by the Carr and Mendocino Complex Fires in California and the Pole Creek Fire in Utah. Camp Fire AFs varied by day and location, with median (interquartile) of 0.48 (0.44–0.53). Adjusted PA-II 4-h average data were generally within ±20% of PM 2.5 reported by the monitoring stations. Adjustment improved the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Intensive aerosol properties of boreal and regional biomass burning aerosol at Mt. Bachelor Observatory: larger and black carbon (BC)-dominant particles transported from Siberian wildfires

We characterize the aerosol physical and optical properties of 13 transported biomass burning (BB) events. BB events included long-range influence from fires in Alaskan and Siberian boreal forests transported to Mt. Bachelor Observatory (MBO) in the free troposphere (FT) over 8–14+ d and regional wildfires in northern California and southwestern Oregon transported to MBO in the boundary layer (BL) over 10 h to 3 d. Intensive aerosol optical properties and normalized enhancement ratios for BB events were derived from measured aerosol light scattering coefficients (σ scat ), aerosol light-absorbing coefficients (σ abs ), fine particulate matter (PM 1 ), and carbon monoxide (CO) measurements made from July to September 2019, with particle size distribution collected from August to September. The observations showed that the Siberian BB events had a lower scattering Ångström exponent (SAE), a higher mass scattering efficiency (MSE; Δσ scat /ΔPM 1 ), and a bimodal aerosol size distribution with a higher geometric mean diameter (D g ). We hypothesize that the larger particles and associated scattering properties were due to the transport of fine dust alongside smoke in addition to contributions from condensation of secondary aerosol, coagulation of smaller particles, and aqueous-phase processing during transport. Alaskan and Siberian boreal forest BB plumes were transported long distances in the FT and characterized by lower absorption Ångström exponent (AAE) values indicative of black carbon (BC) dominance in the radiative budget. Significantly elevated AAE values were only observed for BB events with <1 d transport, which suggests strong production of brown carbon (BrC) in these plumes but limited radiative forcing impacts outside of the immediate region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0

Abstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

[Re] Drivers of evapotranspiration from boreal wildfires

Computational reproducibility is a difficult challenge across science. I attempted to use R 3.6.1 to reproduce linear model fits, done originally using v2.6.0 for a 2009 paper on the drivers of large-scale forest evapotranspiration after wildfire. Model outputs were largely identical, aside from minor formatting changes, except for one–out of 12 total– regression in which the median residual value changed very slightly (in the sixth decimal place). I suggest that this essentially successful reproducibility is due to the relative simplicity of the script, its use of only base R functions, and R’s historically conservative approach to breaking changes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Cretaceous extinctions - Evidence for wildfires and search for meteoritic material

The results of analyses of the contents of deposits in the Cretaceous-Ternary (K-T) transition at three sites worldwide are discussed. The study was undertaken to examine the composition of the object which may have struck the earth, causing widespread biotic extinction. The data indicate that most of the parent body was destroyed on impact, a condition which would also hold true for comets, suggesting that comets were not a source of prebiotic life. A four-orders-of-magnitude excess of carbon in the K-T layer is considered in terms of its source, which is suspected to be deposits from wildfires. The consequent extinctions of species are regarded as possibly making the current nuclear winter scenarios too optimistic.

Wolbach, W. S.↗
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 NTRS1993

Mapping Wildfires In Nearly Real Time

Airborne infrared-sensing system flies over wildfire as infrared detector in system and navigation subsystem generate data transmitted to firefighters' camp. There, data plotted in form of map of fire, including approximate variations of temperature. System, called Firefly, reveals position of fires and approximate thermal intensities of regions within fires. Firefighters use information to manage and suppress fires. Used for other purposes with minor modifications, such as to spot losses of heat in urban areas and to map disease and pest infestation in vegetation.

Nichols, Joseph D.↗
NASA NTRS1993

Wildfire and MAMS data from STORMFEST

Early in 1992, NASA participated in an inter-agency field program called STORMFEST. The STORM-Fronts Experiment Systems Test (STORMFEST) was designed to test various systems critical to the success of STORM 1 in a very focused experiment. The field effort focused on winter storms in order to investigate the structure and evolution of fronts and associated mesoscale phenomena in the central United States. This document describes the data collected from two instruments onboard a NASA ER2 aircraft which was deployed out of Ellington Field in Houston, Texas from February 13 through March 15, 1992, in support of this experiment. The two instruments were the Wildfire (a.k.a. the moderate resolution imaging spectrometer-nadir (MODIS-N) Airborne Simulation (MAS)) and the Multispectral Atmospheric Mapping Sensor (MAMS).

Jedlovec, Gary J.↗
NASA NTRS1994

Monitoring changes in Greater Yellowstone Lake water quality following the 1988 wildfires

The fires that burned the Greater Yellowstone Area (GYA) during the summer of 1988 were the largest ever recorded for the region. Wildfire can have profound indirect effects on associated aquatic ecosystems by increased nutrient loading, sediment, erosion, and runoff. Satellite remote sensing and water quality sampling were used to compare pre- versus post-fire conditions in the GYA's large oliotrophic (high transparency, low productivity) lakes. Inputs of suspended sediment to Jackson Lake appear to have increased. Yellowstone Lake has not shown any discernable shift in water quality. The insights gained separately from the Landsat Thematic and NOAA Advanced Very High Resolution Radiometer (AVHRR) remote sensing systems, along with conventional in-situ sampling, can be combined into a useful water quality monitoring tool.

Lathrop, Richard G., Jr.↗