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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA

This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF), and Multi-Layer Perceptron (MLP) algorithms for LAI and BAPH modeling. The results showed that RF outperformed MLP by reducing the normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. The final variables selected for modeling of both LAI and BAPH indicated the superiority of Sentinel-2 variables over the Sentinel-1 SAR with minor contributions of site variables (mainly elevation). The red-edge spectral vegetation indices played a significant role in both LAI and BAPH estimation. We attained the lowest nRMSEs of 0.12, and 0.16 for the final LAI model of RS, and BF, respectively using Sentinel-2 and site variables. The lowest nRMSE for both RS and BF BAPH models was 0.12. As RS and BF are the primary host species for a cyclically occurring and most destructive pest of the region, eastern spruce budworm (Choristoneura fumiferana; SBW), these estimations will be useful to evaluate SBW dynamics in the region.

Forest inventory↗
NASA NTRS2020

DEVELOP Poster: Medicine Bow Disasters

The Medicine Bow National Forest (MBNF) consists of approximately 1,383,790 acres of forested land, grassland, and sagebrush steppe in southeastern Wyoming. Cheatgrass (Bromus tectorum), an invasive plant species in the Western US, occurs in the grasslands throughout MBNF. Cheatgrass is known to rapidly colonize disturbed sites and dramatically alter historic fire regimes and nutrient/water dynamics as well as outcompete native plant species that are important forage for mule deer (Odocoileus hemionus) and elk (Cervus canadensis). In 2012, the Squirrel Creek Fire burned approximately 10,587 acres of land within MBNF, exacerbating the spread of cheatgrass. In 2015, the Wyoming Ecological Forecasting DEVELOP team identified areas of high cheatgrass abundance within the fire boundary in order to guide US Forest Service (USFS) herbicide spraying efforts to reduce cheatgrass in 2016. This research used Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to create a 2019 probabilistic cheatgrass occurrence map. This map allowed an analysis of the effectiveness of aerial spraying to inform future land management techniques for the USFS. Based on the results of the Generalized Linear Model, we found that treated areas decreased in cheatgrass cover by 36% while untreated areas increased in cheatgrass cover by 6%, suggesting that herbicide treatment has been effective.

Disasters↗
NASA NTRS2022

Fire Island Water Resources: Assessing Sediment Dynamics and Turbidity Changes Along Fire Island National Seashore Using Satellite Data

Fire Island National Seashore has experienced damaging effects as a result of coastal erosion. Erosion has become an increasingly damaging problem that has led to the destruction of park and community infrastructure, contributed to rising groundwater tables, and posed a serious threat to a globally rare holly maritime forest. Beach nourishment project efforts have been made to mitigate damage, but dredging is expensive and can be thwarted by high rates of erosion. The NASA DEVELOP team partnered with the National Park Service and Fire Island National Seashore and used imagery from Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), and Sentinel-2 MultiSpectral Instrument (MSI) to analyze turbidity and sediment dynamics through surface reflectance data from 2000-2021. Imagery was atmospherically corrected using Atmospheric Correction for OLI Lite (ACOLITE) and visualized in SeaWiFS Data Analysis Systems (SeaDAS). Additionally, shoreline change was analyzed using high-resolution imagery from WorldView-2 acquired from Maxar for pre-Hurricane Sandy on July 25th, 2010 and post-Hurricane Sandy on December 18th, 2019. The results of this analysis showed that turbidity is highest in the winter seasons. The shoreline analysis estimated total shoreline loss of about 62 acres on the ocean side, and 11 acres on the bay side of the island. These results will be used to better inform future partner-designed shoreline management projects in the face of further erosion and sea level rise.

Kelly Young↗
DOE OSTI2025

Estimating evapotranspiration from deciduous broadleaf forests by considering the effect of foliar litter

Forest litter cover significantly alters the ground surface resistance, which influences the water and energy transfer processes between the land surface and atmosphere. Penman-Monteith (PM) model is widely used to estimate land surface evapotranspiration (ET). However, it was shown to overestimate ET during the period when foliar litter covers the ground surface. Therefore, incorporating the effect of foliar litter on ground surface resistance can potentially improve ET estimates. Here, in this study, we proposed a foliar litter surface resistance model to describe the effect of litter cover on water vapor transport, then incorporated the litter surface resistance into the PM model (noted as PM-EL model). The performance of the PM-EL model was evaluated using observations from 18 deciduous broadleaf forest (DBF) flux sites across global FLUXNET2015 datasets. Results showed that: (1) both the PM model and the PM-EL model are most sensitive to ground surface resistance among resistance parameters; (2) the proposed litter resistance model is capable of describing the seasonal dynamic of litter in DBF ; (3) the PM-EL model, has been shown to improve ET estimates with the coefficient of determination increased by 8.5 %, the mean absolute error and relative root mean square error decreased by 22.1 % and 20.9 %, respectively. The study contributes to our understanding of the effect of litter cover on ET estimation and provides insights into forest ecohydrology and land surface mass and energy transfer processes.

Deciduous Broadleaf Forest↗
DOE OSTI2021

Declining carbohydrate content of Sitka-spruce treesdying from seawater exposure

Abstract Increasing sea levels associated with climate change threaten the survival of coastal forests, yet the mechanisms by which seawater exposure causes tree death remain poorly understood. Despite the potentially crucial role of nonstructural carbohydrate (NSC) reserves in tree survival, their dynamics in the process of death under seawater exposure are unknown. Here we monitored progressive tree mortality and associated NSC storage in Sitka-spruce (Picea sitchensis) trees dying under ecosystem-scale increases in seawater exposure in western Washington, USA. All trees exposed to seawater, because of monthly tidal intrusion, experienced declining crown foliage during the sampling period, and individuals with a lower percentage of live foliated crown (PLFC) died faster. Tree PLFC was strongly correlated with subsurface salinity and needle ion contents. Total NSC concentrations in trees declined remarkably with crown decline, and reached extremely low levels at tree death (2.4% and 1.6% in leaves and branches, respectively, and 0.4% in stems and roots). Starch in all tissues was almost completely consumed, while sugars remained at a homeostatic level in foliage. The decreasing NSC with closer proximity to death and near zero starch at death are evidences that carbon starvation occurred during Sitka-spruce mortality during seawater exposure. Our results highlight the importance of carbon storage as an indicator of tree mortality risks under seawater exposure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mineral-Associated Organic Matter Concentration Beneath Northern Temperate Trees Varies by Mycorrhizal Type and Leaf Habit

Mycorrhizal fungi are important drivers of soil organic matter dynamics, but it can be difficult to isolate the effects of the fungi themselves from covarying traits of their host trees. For example, many trees with an evergreen leaf habit associate with ectomycorrhizal (ECM) fungi, while many deciduous tree species associate with arbuscular mycorrhizal (AM) fungi. Because leaf habit influences the quantity and quality of organic matter inputs to soil, it is often an important factor in soil carbon and nitrogen dynamics, and thus can mask the effects of mycorrhizal fungi on soil organic matter processes. We evaluated how tree mycorrhizal associations and leaf habit separately influence the amount and composition of mineral-associated organic matter (MAOM) and particulate organic matter (POM) in forest soils in New Hampshire and Vermont, USA. We measured carbon (C) and nitrogen (N) concentrations and C/N ratios of three soil density fractions beneath six tree species that vary in mycorrhizal association and leaf habit. We found lower concentrations of MAOM C and N beneath evergreen vs. deciduous trees, but only for tree species associating with AM fungi. Further, MAOM C/N was higher beneath evergreen trees and beneath trees with ECM fungi rather than AM fungi. Furthermore, these results add to the growing body of support for mycorrhizal fungi as mediators of soil organic matter dynamics, suggesting that the MAOM fraction is more sensitive to leaf habit beneath AM-associated versus ECM-associated trees. Because MAOM decomposition is thought to be less responsive than POM decomposition to changes in soil temperature and moisture, differences in the tendency of AM- and ECM-dominated forests to support MAOM formation and persistence may lead to systematic differences in the response of these forest types to ongoing climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

The NASA AfriSAR Campaign: Airborne SAR and Lidar Measurements of Tropical Forest Structure and Biomass in Support of Current and Future Space Missions

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1 hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform Lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

AfriSAR↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2026

IAM-FIRE: a Climate Emulator–Based Framework to Project Wildfire Impacts and Risks for Integrated Assessment Models

Most Integrated Assessment Models (IAMs) underrepresent dynamic feedbacks from climate-driven disturbances such as wildfires, potentially overestimating the permanence of land-based carbon sinks. In particular, representing the impacts of forest fires is becoming increasingly important, as these are expected to intensify in the coming years. We introduce IAM-FIRE (Integrated Assessment Model – Fire Impacts & Risks Emulator), a novel framework that enables the projection of wildfire burned area (BA) and carbon emissions (CE) directly from IAM outputs. IAM-FIRE combines a spatial climate emulator, land-use downscaling, vegetation productivity modelling, and an empirical fire model to generate global annual wildfire impacts for arbitrary socioeconomic and emissions scenarios at 0.5° resolution for the period 2020–2100. Calibrated against GFEDv5 observations and using inputs from the Global Change Analysis Model (GCAM), we report projections BA and CE derived from IAM-FIRE for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-6.6 and SSP5-7.6. The model reproduces historical global trends for total BA, including the observed global decline since the early 2000s, and for forest BA. Projected fire trajectories differ strongly among scenarios: total BA range from declines under SSP1-2.6 (-3.36 Mha yr-1) to increases under SSP3-6.6 (+1.6 Mha yr-1). Corresponding total CE show a similar divergence ranging from -15 to +10.6 TgC yr-1. Socioeconomic development exerts a dominant suppressing effect on wildfire impacts while climate change and CO2-driven increases in vegetation productivity amplify fire risk, particularly under high-emissions pathways. Compared with CMIP6 fire-enabled Earth System Models, IAM-FIRE exhibits greater sensitivity to radiative forcing and a stronger role for human-driven fire suppression, highlighting substantial structural uncertainties in future fire projections. By providing a computationally efficient and internally consistent approach to represent wildfire impacts within IAMs, IAM-FIRE enables systematic exploration of fire–climate–land feedbacks and supports improved assessments of mitigation permanence and climate risks in future integrated scenarios.

Rouhette, Theo↗
NASA NTRS1973

Management of natural resources through automatic cartographic inventory

The author has identified the following significant results. Over those parts of the ARNICA test site where ERTS-1 data were available, the search for correspondences between images and ground truth acquired by the vegetation and geology maps was quite positive. The probability of recognition of soil use types can be estimated at: (1) 100% for water plans, rivers, canals, swamplands, and wetlands; (2) 80%-100% for the major types of forestry, farmland zones, moorlands and pasturelands, and urbanization; (3) 20%-50% for communication lines; (4) 60%-80% for forestry species and organization of agricultural areas; (5) 40%-60% for finer discrimination between forest types and more accurate identification of cultivations; (6) 60%-90% for major geological features. These percentages will be improved upon as soon as it is possible to use the repetitive imagery. An early use of automatic cartography using ERTS-1 imagery was made possible for pine forests in the Central Pyrenees, the densitometric signature of which were particularly significant. Important observations were made in related fields of water resources, snow survey, estuary dynamics, and meteorology.

Rey, P.↗
NASA NTRS1994

Episodic nitrous oxide soil emissions in Brazilian savanna (cerrado) fire-scars

The seasonally burned cerrados of Brazil are the largest savanna-type ecosystem of South America and their contribution to the global atmospheric nitrous oxide (N20) budget is unknown. Four types of fire-scarred cerrado along a vegetation gradient from grassland to forest were investigated during the wet season of 1992/93. The effect of fire and subsequent water additions on epiodic emissions of N2O and the associated profile dynamic of soil/gas phase N2O concentrations were studied for several months. Additionally, the effect on episodic emissions of N2O of nitrate and glucose additions to a cerrado soil after fire and the associated profile dynamic of soil/gas phase N2O mixing ratios were determined. Finally, N2O episodic emissions in cerrado converted to corn, soybean, and pasture fields were investigated during one growing/wet season. Results showed N2O consumption/emission for the four fire-scared savanna ecosystems, for nitrogen and carbon fertilization, and for agriculture/pasture ranging from -0.3 to +0.7, 1.8 to 9.1, and 0.5 to 3.7 g N2O-N ha(exp -1) d(exp -1), respectively. During the wet season the cerrado biome does not appear to be a major source of N2O to the troposphere, even following fire events. However, the results of this study suggest that conversion of the cerrado to high input agriculture, with liming and fertilization, can increase N2O emissions more than ten fold.

Nobre, A. D.↗
DOE OSTI2023

Responses of soil organic carbon to climate extremes under warming across global biomes

The impact of more extreme climate conditions under global warming on soil organic carbon (SOC) dynamics remains unquantified. Here, in this study, we estimate the response of SOC to climate extreme shifts under 1.5 °C warming by combining a space-for-time substitution approach and global SOC measurements (0–30 cm soil). Most extremes (22 out of 33 assessed extreme types) exacerbate SOC loss under warming globally, but their effects vary among ecosystems. Only decreasing duration of cold spells exerts consistent positive effects, and increasing extreme wet days exerts negative effects in all ecosystems. Temperate grasslands and croplands negatively respond to most extremes, while positive responses are dominant in temperate and boreal forests and deserts. In tundra, 21 extremes show neutral effects, but 11 extremes show negative effects with stronger magnitude than in other ecosystems. Our results reveal distinct, biome-specific effects of climate extremes on SOC dynamics, promoting more reliable SOC projection under climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Oregon Wildfires: Integrating ECOSTRESS to Map & Analyze Vegetation Moisture for Wildfire Modeling

Wildfire season in the western USA is starting earlier and gaining in intensity. The Bootleg Fire in Southern Oregon began on July 6th, 2021, and burned over 1675 km2 before it was fully contained on August 15th, 2021. Evapotranspiration (ET) is one indicator of vegetation moisture and there is interest in using high-resolution ET products from ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in future wildfire modeling. In partnership with the Pacific Northwest National Laboratory and US Forest Service, the team examined ECOSTRESS ET for the two years before the Bootleg Fire and assessed the relationship between ET, topography, and vegetation. Remotely sensed data from Shuttle Radar Topography Mission (SRTM) and Global Ecosystem Dynamics Investigation (GEDI) along with ancillary data from the National Land Cover Database (NLCD) and Landscape Fire Resource Management Planning Tools (LANDFIRE) were incorporated. The team examined data in relation to soil burn severity from the Burned Area Emergency Response (BAER) program. From ET median composites for April 1st – July 5th, 2021 and 2019, the Bootleg Fire area showed a 7 mm/day decrease in ET and a relative 75% decrease in ET between 2019 and 2021. Approximately 6% of the Bootleg Fire area was identified as having a high soil burn severity and these areas were found predominantly in the evergreen forest land cover class and northward facing slopes with a mean ET decrease of 3 mm/day between 2019 and 2021. The team also analyzed ECOSTRESS Water Use Efficiency products as an additional vegetation moisture indicator of pre-fire conditions in the study area. The end products will allow the partners to assess if higher resolution vegetation moisture datasets from ECOSTRESS will improve wildfire modeling for other susceptible areas.

Brenna Hatch↗
DOE OSTI2026

Characterizing Turbulence at a Forest Edge: Comparing Sub-Filter Scale Turbulence Models in Simulations of Flow over a Canopy

In wildfires, atmospheric turbulence plays a major role in the transfer of turbulent kinetic energy. Understanding how turbulence feeds back into a dynamical system is important, down to the varying small scales of fuel structures (i.e. pine needles, grass). Large eddy simulations (LES) are a common way of numerically representing turbulence. The Smagorinsky model (1963) serves as one of the most studied sub-grid scale representations in LES. In this investigation, the Smagorinsky model was implemented in HIGRAD/FIRETEC, LANL’s coupled fire-atmosphere model. This study was motivated by the need to quantitatively investigate the vorticity budget equation in HIGRAD/FIRETEC. The Smagorinsky turbulent kinetic energy (TKE) was compared to FIRETEC’s 1.5-order TKE eddy-viscosity subgrid-scale model, known as the Linn turbulence model. This was done in simulations of flow over flat terrain with a homogeneous, cuboidal canopy in the center of the domain. Examinations of the modeled vertical TKE profile and turbulent statistics at the leading edge, and throughout the canopy, show that the Smagorinsky model provides comparable results to that of the original closure model posed in FIRETEC.

58 GEOSCIENCES↗
NASA NTRS1990

Seasonal dynamics in methane emissions from the Amazon River floodplain to the troposphere

Methane fluxes to the troposphere from the three principal habitats of the floodplain of the Amazon River main stem (open waters, emergent macrophyte beds, and flooded forests) were determined along a 1700-km reach of the river during the low-water period of the annual flood cycle (November-December 1988). Overall, emissions averaged 68 mg CH4/sq m per day and were significantly lower than similar emissions determined previously for the high-water period, 184 mg CH4/sq m per day (July-August 1986). This difference was due to significantly lower emissions from floating macrophyte environments. Low-water emissions from open waters and flooded forest areas were not significantly different than at high water. A monthly time series of methane emission from eight lakes located in the central Amazon basis showed similar results. The data were used to calculate a seasonally weighted annual emission to the troposphere from the Amazon River main stem floodplain of 5.1 Tg/yr, which indicates the importance of the area in global atmospheric chemistry.

Devol, Allan H.↗
DOE OSTI2023

Improving estimates of sub-daily gross primary production from solar-induced chlorophyll fluorescence by accounting for light distribution within canopy

Solar-induced chlorophyll fluorescence (SIF) has long been regarded as a proxy for photosynthesis and has shown superiority in estimating gross primary production (GPP) compared to traditional vegetation indices, especially in evergreen ecosystems. However, current SIF-based GPP estimations regard the canopy as a large leaf and seldom consider the impact of interactions among light, canopy structure, and leaf physiology. In this study, we proposed GPP estimation models with different descriptions of light–structure–physiology interactions (including the layered model, the two-leaf model, and the layered two-leaf model) and compared their performances with the big-leaf model using half-hourly (or hourly) observations at evergreen needleleaf forest sites. First, we found that the big-leaf model underestimated GPP, especially at noon. All models showed higher accuracy than that of the big-leaf model. Second, we investigated the diurnal dynamics of GPP estimations in each canopy layer and found that models with a two-leaf assumption captured the diurnal variations in GPP better than that with the layered assumption. Here we also deduced that the poor performance of the big-leaf model was related to its overestimation of the overall light stress on the redox state of PSII reaction centers (qL). Finally, we noticed that the qL at the canopy scale had lower sensitivity to light change than the single-leaf qL and that the light response of canopy-scale qL was influenced by the leaf area index during seasonal cycles. Overall, this study describes methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale. Further, it indicates the need to consider the description of light distribution within the canopy in next-generation terrestrial biosphere models, even if they incorporate SIF to constrain their parameterization. Thus, upscaling the established leaf-scale mechanistic SIF-GPP relationship or findings to canopy-scale applications still requires much work, especially when there are significant changes in environmental conditions and their within-canopy distributions.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2023

The eco-evolutionary role of fire in shaping terrestrial ecosystems

Fire is an inherently evolutionary process, even though much more emphasis has been given to ecological responses of plants and their associated communities to fire. Here, in this work, we synthesize contributions to a Special Feature entitled ‘Fire as a dynamic ecological and evolutionary force’ and place them in a broader context of fire research. Topics covered in this Special Feature include a perspective on the impacts of novel fire regimes on differential forest mortality, discussions on new approaches to investigate vegetation-fire feedbacks and resulting plant syndromes, synthesis of fire impacts on plant–fungal interactions, and a meta-analysis of arthropod community responses to fire. We conclude by suggesting pathways forward to better understand the ecological and evolutionary consequences of fire. These include developing ecological and evolutionary databases for fire ecology, integrating hierarchical genetic structure or phylogenetic structure, and developing new experimental frameworks that limit context-dependent outcomes.

54 ENVIRONMENTAL SCIENCES↗