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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Vegetation Change Detection in Southern California Solar Energy Developments

Change detection from satellite sensor vegetation indices (VIs) presents an opportunity to monitor trends and disturbances at the regional scale for southern California's Mojave and Lower Colorado Deserts. Renewable energy sites are being constructed in this region on public lands under the Bureau of Land Management (BLM). We have developed a framework for VI change detection over the past two decades, with initial focus on three sites, Joshua Tree National Park, Mojave National Preserve, and a proximal group of Development Focus Areas (DFAs), for comparison between protected and development-targeted lands. Three Terra MODIS VIs (normalized difference [NDVI], enhanced [EVI], soil-adjusted [SAVI]) were evaluated in the Breaks for Additive Season and Trend (BFAST) setting for the regional MODIS 250-m resolution grid to estimate significant time series shifts (breakpoints) from February 2000 to May 2018. All three VIs tended to detect the maximum number of breakpoints at a grid location, but cross-correlations with precipitation and comparison with timing of wildfire burns near the study sites for breakpoint density (proportion of area with a breakpoint) showed that NDVI had the strongest response to these major disturbances, supporting its use for subsequent analysis. Time series of NDVI breakpoint change densities for individual solar energy sites did not have a consistent vegetation response following construction. Bootstrapping showed that the DFAs had significantly larger kurtosis and variance in the positive NDVI breakpoint distribution than did the protected sites, but there was no significant difference in the negative distribution for all three sites. The inconsistent post-construction NDVI signal and the large number of breakpoints overall suggested that the largest changes in vegetation cover density were tied to seasonal precipitation amounts. The distributional results indicated that existing site-specific conditions were the main control on VI responses, given the history of human disturbances in the DFAs. Although the results do not support persistent VI disturbances resulting from recent solar energy development, continued monitoring and examination of other ecological variables and surface temperatures will be vital to the long-term protection of this desert environment.

Potter, Christopher S.↗
DOE OSTI2025

Burn severity and vegetation type control phosphorus concentration, molecular composition, and mobilization

Shifting phosphorus (P) dynamics after wildfires can have cascading impacts from terrestrial to aquatic environments. However, it is unclear whether shifts in P composition or P concentration are responsible for changes in P dynamics post-fire. We used laboratory leaching experiments of Douglas fir forest and sagebrush shrubland chars to examine how the potential mobility of P compounds is influenced by different burn severities. Burning produced a 6.9- and 29-fold increase in particulate P mobilization but a 3.8- and 30.5-fold decrease in aqueous P released for Douglas fir forest and sagebrush shrubland, respectively. The mechanisms driving particulate- and dissolved-phase P compound mobilization were contrasting. Phosphorus compound mobilization in the particulate phase was controlled by solid char total P concentrations, while the aqueous phase was driven by solubility changes of molecular species. Nuclear magnetic resonance (NMR) and X-ray absorption near-edge structure (XANES) on the solid chars indicated that organic orthophosphate monoester and diester species were thermally mineralized to inorganic P moieties with burning in both vegetation types, which decreases P solubility. This coincided with the production of calcium- and magnesium-bound inorganic P compounds. With increasing burn severity there were systematic shifts in P concentration and composition – higher-severity chars mobilized P compounds in the particulate phase, although the magnitude of change was vegetation-specific. Our results indicate a post-fire transformation to both the composition of the solid charred material and how P compounds are mobilized, which may influence its environmental cycling and fate.

31P solution-state NMR↗
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↗
DOE OSTI2024

Systemic immunological responses are dependent on sex and ovarian hormone presence following acute inhaled woodsmoke exposure

Rural regions of the western United States have experienced a noticeable surge in both the frequency and severity of acute wildfire events, which brings significant challenges to both public safety and environmental conservation efforts, with impacts felt globally. Identifying factors contributing to immune dysfunction, including endocrinological phenotypes, is essential to understanding how hormones may influence toxicological susceptibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Oak Ridge National Laboratory EAGLE-I TM : Modeling Electric Utility County Customers for Situational Awareness

During natural hazard events (hurricanes, wildfires, earthquakes, etc.) and recent man-made events (e.g., cyber attacks), the exchange of near real-time, spatially refined data within the response community is critical. The EAGLE-I$^{TM}$ platform is one tool that facilitates this data for decision makers within the energy sector. While much information can be collected and integrated into the system directly, other pertinent data must be augmented by other derived data products to enhance the information and allow for a consistent evaluation of on-the-ground conditions. One such data set that requires the addition of other derived data is the electric utility customer outage data that is aggregated to the county level within the EAGLE-I application. Without a county customer data set, outages can only be compared on total counts, which gives greater importance to higher population outages. Including an electric utility customer data set at the county level allows for these outage counts to be converted to percent outages and brings a consistent classification of outages and equal importance to all outages. To achieve this, several available data sets were combined and spatial disaggregation techniques were employed to model customer estimates at the county scale. This paper presents the approach to produce this data for the United States and lessons learned from working with these disparate data sets. Data validation is provided, where possible, and limitations of the model and possible improvements are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Formalized Reasoning of Operational Volumes for Wildland Fire Fighting

This work is focused on the formalized reasoning of operational volumes as it relates to the current and future technologies developed by NASA to aid in wildfire fighting operations. One such technology is the unmanned aircraft system pilot kit (UASP-kit) developed by the Scalable Traffic Management for Emergency Response Operations (STEReO) project at NASA, which is used to increase situation awareness for a ground operator in the field. The UASP-kit utilizes operational volumes which represent mission areas and alerting volumes, to alert when another aircraft is within one of these volumes from received ADS-B data. This work is focused on developing a rigorous foundation for the concept of operational volumes for modeling and prototyping operations in such a tool as the UASP-kit. This includes establishing a class of algorithms to detect when an object is in an operational volume, and when an operational volume is intersecting or contained within another. Additionally, this work is focused on providing rigorous proof in an interactive theorem prover that the algorithms work as intended. Scenarios are presented that model current UASP-kit operations and extend past the current capabilities of the technology to modeling more complex scenarios such as mission planning.

Operational Volumes↗
DOE OSTI2021

Responses of Boreal Forest Ecosystems and Permafrost to Climate Change and Disturbances: A Modeling Perspective

The north circumpolar region contains a large amount of carbon. This carbon storage is vulnerable due to permafrost degradation and wildfire disturbances under ongoing and projected climate change. Climate warming and wildfires change soil organic horizons gradually or abruptly, and modify permafrost thermal-hydrology and biogeochemistry, ecosystem structures, functions, and capability of sequestrating rising atmospheric CO2. Land models do not fully take accounts of these interactions and its complexity in the high latitude. This chapter describes a terrestrial ecosystem model with dynamic organic soil module (DOS-TEM) and its unique freezing-thawing algorithm, and presents key results of its applications mainly in boreal forests of Alaska. The DOS-TEM explicitly considers interactions of soil thermal and hydrological processes, permafrost degradation and the direct and indirect effects of wildfire disturbances, in addition to soil–plant C and N cycles. We first introduce four modules of DOS-TEM, focusing on its disturbance module and coupling with a dynamic organic soil module. Then we describe and validate DOS-TEM’s freezing-thawing algorithm and development based on two-directional Stefan algorithm (TDSA). Finally, we apply the DOS-TEM at site and region scales, with a focus on model ability to dynamically simulate soil organic thickness under warming and wildfires, and consequent impacts on permafrost in the Yukon River Basin. We conclude that land surface model development is urgently needed to include other critical landscape processes, such as thermalkarst and other disturbances, to synchronize thermal-hydrological-biogeochemical processes, and to incorporate an advanced understanding of biospheric feedbacks to atmosphere and ecosystems. Such a complexity of modeling scope is plausible with advancement of high performance computing.

Yi, Shuhua↗
NASA NTRS2020

Seasonality of Biological and Physical Systems as Indicators of Climate Variation and Change

Evidence-based responses to climate change by society require operational and sustained information including biophysical indicator systems that provide up-to-date measures of trends and patterns against historical baselines. Two key components linking anthropogenic climate change to impacts on socio-ecological systems are the periodic inter- and intra-annual variations in physical climate systems (seasonality) and in plant and animal life cycles (phenology). We describe a set of national indicators that reflect sub-seasonal to seasonal drivers and responses of terrestrial physical and biological systems to climate change and variability at the national scale. Proposed indicators and metrics include seasonality of surface climate conditions (e.g., frost and freeze dates and durations), seasonality of freeze/thaw in freshwater systems (e.g., timing of stream runoff and durations of lake/river ice), seasonality in ecosystem disturbances (e.g., wildfire season timing and duration), seasonality in vegetated land surfaces (e.g., green-up and brown-down of landscapes), and seasonality of organismal life-history stages (e.g., timings of bird migration). Recommended indicators have strong linkages to variable and changing climates, include abiotic and biotic responses and feedback mechanisms, and are sufficiently simple to facilitate communication to broad audiences and stakeholders interested in understanding and adapting to climate change.

Seasonality↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗
NASA NTRS2009

Developing New Coastal Forest Restoration Products Based on Landsat, ASTER, and MODIS Data

This paper discusses an ongoing effort to develop new geospatial information products for aiding coastal forest restoration and conservation efforts in coastal Louisiana and Mississippi. This project employs Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in conjunction with airborne elevation data to compute coastal forest cover type maps and change detection products. Improved forest mapping products are needed to aid coastal forest restoration and management efforts of State and Federal agencies in the Northern Gulf of Mexico (NGOM) region. In particular, such products may aid coastal forest land acquisition and conservation easement procurements. This region's forests are often disturbed and subjected to multiple biotic and abiotic threats, including subsidence, salt water intrusion, hurricanes, sea-level rise, insect-induced defoliation and mortality, altered hydrology, wildfire, and conversion to non-forest land use. In some cases, such forest disturbance has led to forest loss or loss of regeneration capacity. In response, a case study was conducted to assess and demonstrate the potential of satellite remote sensing products for improving forest type maps and for assessing forest change over the last 25 years. Change detection products are needed for assessing risks for specific priority coastal forest types, such as live oak and baldcypress-dominated forest. Preliminary results indicate Landsat time series data are capable of generating the needed forest type and change detection products. Useful classifications were obtained using 2 strategies: 1) general forest classification based on use of 3 seasons of Landsat data from the same year; and 2) classification of specific forest types of concern using a single date of Landsat data in which a given targeted type is spectrally distinct compared to adjacent forested cover. When available, ASTER data was useful as a complement to Landsat data. Elevation data helped to define areas in which targeted forest types occur, such as live oak forests on natural levees. MODIS Normalized Difference Vegetation Index time series data aided visual assessments of coastal forest damage and recovery from hurricanes. Landsat change detection products enabled change to be identified at the stand level and at 10- year intervals with the earliest date preceding available change detection products from the National Oceanic and Atmospheric Administration and from the U.S. Geological Survey. Additional work is being done in collaboration with State and Federal agency partners in a follow-on NASA ROSES project to refine and validate these new, promising products. The products from the ROSES project will be available for aiding NGOM coastal forest restoration and conservation.

Spruce, Joseph P.↗
DOE OSTI2022

Earth, Wind, Fire, and Pollution: Aerosol Nutrient Sources and Impacts on Ocean Biogeochemistry

A key Earth system science question is the role of atmospheric deposition in supplying vital nutrients to the phytoplankton that form the base of marine food webs. Industrial and vehicular pollution, wildfires, volcanoes, biogenic debris, and desert dust all carry nutrients within their plumes throughout the globe. In remote ocean ecosystems, aerosol deposition represents an essential new source of nutrients for primary production. The large spatiotemporal variability in aerosols from myriad sources combined with the differential responses of marine biota to changing fluxes makes it crucially important to understand where, when, and how much nutrients from the atmosphere enter marine ecosystems. This review brings together existing literature, experimental evidence of impacts, and new atmospheric nutrient observations that can be compared with atmospheric and ocean biogeochemistry modeling. We evaluate the contribution and spatiotemporal variability of nutrient-bearing aerosols from desert dust, wildfire, volcanic, and anthropogenic sources, including the organic component, deposition fluxes, and oceanic impacts.

Marine & Freshwater Biology↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Postfire Biogeochemical Processes: Implications to Source Water Quality in Fire-Influenced Watersheds

Forested watersheds are instrumental in providing purified and reliable water to millions of people worldwide. The changing climate has increased the frequency and severity of global fire events. Forested watersheds and their ecosystem functions are greatly disrupted during fire activity. Postfire concerns in forested watersheds include unpredictable and potentially simultaneous alterations in source water quality and hydro-biogeochemical processes. Here, the degree of fire severity can complexly modify water quality through the production of fire-transformed constituents on the burned forest floor (i.e., nutrients, metal(loid)s, dissolved organic matter, and the formation of disinfection byproducts). Correspondingly, fire severity and postfire rainfall patterns can refine hydro-biogeochemical processes that influence the transport of the fire-transformed constituents (i.e., vegetation function, soil structure, hydrological pathways, and microbial communities). Postfire alterations to water quality and hydro-biogeochemical processes introduce further complexity with varying temporal influence, which ranges from months to decades. As postfire water quality and watershed response research progresses, it is essential to homogenize interdisciplinary expertise to bridge knowledge gaps between fields ranging from forest ecology, hydrology, microbiology, and geochemistry. A multidisciplinary approach in wildfire research will facilitate a comprehensive perception of the diverse water quality risks associated with fire activity and mitigate fire concerns on a global level.

Disinfection Byproducts↗
DOE OSTI2023

Long-Term Chemical Analyses in Biota Collected Upstream of the Pajarito Canyon Flood Retention Structure and Los Alamos Canyon Weir at Los Alamos National Laboratory

The Cerro Grande wildfire was a major disaster that burned during the summer of 2000 in the Jemez Mountains and parts of Los Alamos National Laboratory (LANL or Laboratory). As an immediate response to the fire, the United States Department of Energy generated a list of post-fire actions needed to mitigate the impacts of this disaster, including flooding and the potential for erosion. Two structures built post-fire include the Pajarito Canyon flood retention structure (FRS) and the Los Alamos Canyon weir. Environmental monitoring at these two structures has occurred since the early 2000s as part of ongoing efforts to evaluate impacts of the fire.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Use of VIIRS DNB Data to Monitor Power Outages and Restoration for Significant Weather Events

NASA fs Short-term Prediction Research and Transition (SPoRT) project operates from NASA's Marshall Space Flight Center in Huntsville, Alabama. The team provides unique satellite data to the National Weather Service (NWS) and other agencies and organizations for weather analysis. While much of its work is focused on improving short-term weather forecasting, the SPoRT team supported damage assessment and response to Hurricane Superstorm Sandy by providing imagery that highlighted regions without power. The team used data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The VIIRS low-light sensor, known as the day-night-band (DNB), can detect nighttime light from wildfires, urban and rural communities, and other human activity which emits light. It can also detect moonlight reflected from clouds and surface features. Using real time VIIRS data collected by our collaborative partner at the Space Science and Engineering Center of the University of Wisconsin, the SPoRT team created composite imagery to help detect power outages and restoration. This blackout imagery allowed emergency response teams from a variety of agencies to better plan and marshal resources for recovery efforts. The blackout product identified large-scale outages, offering a comprehensive perspective beyond a patchwork GIS mapping of outages that utility companies provide based on customer complaints. To support the relief efforts, the team provided its imagery to the USGS data portal, which the Federal Emergency Management Agency (FEMA) and other agencies used in their relief efforts. The team fs product helped FEMA, the U.S. Army Corps of Engineers, and U.S. Army monitor regions without power as part of their disaster response activities. Disaster responders used the images to identify possible outages and effectively distribute relief resources. An enhanced product is being developed and integrated into a web mapping service (WMS) for dissemination and use by a broader end user community.

Jedlovec, Gary↗
NASA NTRSDate not supplied

State of Wildfires

Fire is an essential component of ecosystems and acts as key driver of biogeochemical cycling with impacts on vegetation structure and composition, soil conditions, and climate feedbacks. Fire behavior and effects vary based on the types of fuel, fuel dryness, and frequency of ignition. Due to changing climate and land use patterns, fire danger is increasing in many regions globally, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Recurrent fires help to determine the distribution of trees and grasses, and overall fuel load, which inform the behavior of future fires. Process-based models can be used to capture multi-scale impacts at the forest stand-level up to the landscape level, and across minutes to centuries, but must capture variation in fire behavior and intensity as a function of the fuel and climate, from high intensity forest crown fires within boreal regions to low intensity rapid grass fires of the tropics. Fire model development demonstrates our ability to capture large scale fire influenced biogeography and vegetation distribution at the earth system scale through vegetation traits and fire feedbacks. At the individual stand scale the importance of interactions and feedbacks between above and below ground process is essential to capturing the vegetation dynamics across boreal forest systems. Improving the mechanics of including plant physiology and specifically live fuel moisture content is the next step to advancing the capability of process based models to inform fire research. Advances in the testing and creation of a mechanistic live fuel moisture model demonstrate the foundation for future live fuel dynamics research that can be informed by field and remote sensing information. Improved remote sensing, technological and modeling capabilities support a more comprehensive and cohesive fire response that will be better equipped to overcome current barriers and anticipate the new reality of fires in a warming world.

wildfires↗