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368 results for “wildfires”
Evidence for multi-decadal fuel buildup in a large California wildfire from smoke radiocarbon measurements
Open the record for dataset details and reuse information.
Lake exposure to wildfire smoke in North America, 2019-2021
Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing ash within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and underrecognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019-2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes >=10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.
California Wildfire Resilience Core Metrics Rating Process and Results
The California Wildfire & Forest Resilience Task Force (Task Force) is producing a toolkit that can support organizations to prioritize, plan and implement actions to lessen wildfire risk to communities and improve broader statewide ecosystem resilience. As part of this effort, a core set of metrics needed to be identified to report resilience progress. The Task Force's Science Advisory Panel engaged in a rapid Delphi process, collecting expert opinion via surveys to provide content knowledge and science support for this process. This data archive provides content 1) for transparency, to share as much of our workflow as is feasible; 2) for others to pull from as an example if they want to do a similar process; 3) to provide all the detailed results on metrics if a reader wants to look into the ratings for and definition of a particular metric. The archive supports a report and paper summarizing and describing our Delphi method and the results regarding the specific metrics considered by our experts.
Long-term changes in tundra carbon balance following wildfire, climate change and potential nutrient addition, a modeling analysis.
A study investigating the mechanisms that control long-term response of tussock tundra to fire and to increases in air temperature, CO2, nitrogen deposition and phosphorus weathering. The MBL MEL was used to simulate the recovery of three types of tussock tundra, unburned, moderately burned, and severely burned in response to changes in climate and nutrient additions. The simulations indicate that the recovery of nutrients lost during wildfire is difficult under a warming climate because warming increases nutrient cycles and subsequently leaching within the ecosystem. The study was published in Ecological Applications (in press, 2016). This dataset is the long term archive of the results published in the paper. The full dataset has been broken into two parts because of the number and size of the files. Part 1 contains MBL MEL executable, a model description file in word, and the input files to run the simulations. Part 2 contains the output files for all simulations. Both Part 1 and Part 2 contain several different types of files. In Part 1 the comma separated ascii file included with the dataset is one of the many driver files used for the simulations. The variable descriptions below describe the variables in that file and all the driver files. In Part 2 the comma separated ascii file included with the dataset is one of the many output files from the simulations. The variable descriptions below describe the variables in that file and all the output files. To access all the files in the dataset be sure to download the two zip files described in the Methods section below. Note that the full download is large, over 700 MB for each part. Permanent Archive of the data published in Jiang, et al., in press, Modeling long-term changes in tundra carbon balance following wildfire, climate change and potential nutrient addition, Ecological Applications.
Human influences on wildfire in Alaska from 1988 through 2005-Shapefile Outline for Interior Alaska
Data shows the outline for Interior Alaska used in my study. It's based on the Omernik-Bailey ecosytem classification and includes mostly the boreal ecozone without the Ogilvie mountains subzone.
Interior Alaska long-term effects of climate and wildfire on permafrost soil temperature regimes: hourly data, 2013-2018
This dataset contains the hourly output from soil temperature sensors at depths from 5 to 150 cm . Three pairs of burned and unburned sites were instrumented in 2013 to measure permafrost temperature dynamics of the surface permafrost.
Estimates of Forest Structure, Successional Trajectory, and Carbon and Nitrogen Pools Across the 2004 Wildfire Network Sites
This dataset contains stand structure and ecosystem carbon and nitrogen pool data measured in 2005 and 2006. It includes in situ measurements of post-fire residual above and belowground carbon and nitrogen pools, reconstructions of pre-fire pools and estimates of losses. Successional trajectory was calculated from measurements of tree seedling density and biomass made in 2017.
Global ECMWF Fire Forecasting system - sample data for wildfires in Attica (Greece) on 23-26 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:</p> <ul> <li> <p>ECMWF_EFFIS_20180723_1200_en.tar<br> (ensemble forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723_1200_hr.tar<br> (deterministic forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_hr_e5.tar<br> (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_en_e5.tar<br> (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_e5.tar<br> (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)</p> </li> <li> <p>bbox.tar, containing 1 index (FWI) for the bounding box:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> </ul> </li> <li> <p>lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Data from: Savannas after afforestation: assessment of herbaceous community responses to wildfire versus native tree planting
<p>Afforestation and fire exclusion are pervasive threats to tropical savannas. In Brazil, laws limiting prescribed burning hinder the study of fire in the restoration of Cerrado plant communities. We took advantage of a 2017 wildfire to evaluate the potential for tree cutting and fire to promote the passive restoration of savanna herbaceous plant communities after destruction by exotic tree plantations. We sampled a burned pine plantation (Burned Plantation); a former plantation that was harvested and burned (Harvested & Burned); an unburned former plantation that was harvested, planted with native trees, and treated with herbicide to control invasive grasses (Native Tree Planting); and two old-growth savannas which served as reference communities. Our results confirm that herbaceous plant communities on post-afforestation sites are very different from old-growth savannas. Among post-afforestation sites, Harvested & Burned herbaceous communities were modestly more similar in composition to old-growth savannas, had slightly higher richness of savanna plants (3.8 species per 50-m2), and supported the greatest cover of native herbaceous plants (56%). These positive trends in herbaceous community recovery would be missed in assessments of tree cover: whereas canopy cover in the Harvested & Burned site was 6% (less than typical of savannas of the Cerrado), the Burned Plantation and Native Tree Planting, supported 34% and 19% cover, respectively. By focusing on savanna herbaceous plants, these results highlight that tree cutting and fire, not simply tree planting and fire exclusion, should receive greater attention in efforts to restore savannas of the Cerrado.</p>
ECMWF data for analysing smoked-charged vortices after the 2019-2020 Australian wildfires
<p>The dataset contains GRIB2 files produced from the operational IFS model and assimilation system of the European Centre for Medium Range Weather Forecast (ECMWF).</p> <p>OPZLWDA2020mmdd-SH.grd files contain log of surface pressure, zonal wind, meridional wind, temperature, relative vorticity and ozone mixing ratio for the 137 levels of the model on a 1°x1° grid in the southern hemisphere for the long window analysis at 6UTC and 18UTC every day from 1st January 2020 to 31 March 2020.</p> <p>OPZFCST2020mmdd-SH.grd files contain log of surface pressure, temperature, relative vorticity and ozone mixing ratio for the 137 levels of the model on a 1°x1° grid in the southern hemisphere every day for the 10-day forecast run starting at 00UTC every four days from 7 January 2020 to 31 March 2020.</p> <p>For basic access, these files are readable using python tools. The package pygrib available under conda-forge is recommended. The eccode library that allows read from C or Fortran is freely available from ECMWF and is installed along pygrib. Reading with eccode library is also possible in C and Fortran.</p> <p>A simple reader using pygrib is provided. </p> <p>Dedicated packages for the project are available on github/bernard-legras/STC/STC-Australia with dependencies in github/bernard-legras/STC/STC/pylib. The package that reads and process ECMWF data is github/bernard-legras/STC/STC/pylib/ECMWF_N.py. This package needs setup modification to discover the files where they have been copied.</p> <p>All requirements should be made to bernard.legras@lmd.ipsl.fr</p>
Tropical riparian forests in danger from large savanna wildfires
<p>1. Tropical savannas are known for the fire-prone ecosystems, yet, riparian evergreen forests are another important landscape feature. These forests usually remain safe from wildfires in the wet riparian zones. With global changes, large wildfires are now more frequent in savanna landscapes, exposing riparian forests to unprecedented impact.</p> <p>2. In 2017, a large wildfire spread across the Chapada dos Veadeiros National Park, an iconic UNESCO site in central Brazil, raising concerns about its impact on the fire-sensitive ecosystems. By combining remote sensing analysis of Google Earth images (2003-2019) with detailed field information from 36 sites, we assessed wildfire impacts on riparian forests. For this, we measured the structure of trees, saplings and herbaceous plants, as well as topsoil variables.</p> <p>3. Since 2003, all riparian forests had canopy cover above 90 %, but after 2017, canopy cover dropped to 20 % in some forests, indicating large variation in wildfire damage. A closer look in the field revealed that, on average, the wildfire killed 52 % of adult trees and 87 % of tree saplings in flooded forests. In non-flooded forests, impacts on adult trees were negligible, but fire killed 75 % of tree saplings. Opportunistic vines and the invasive grass Melinis minutiflora were already present in severely disturbed flooded forests. In all forests, impacts on many ecosystem variables were related to canopy damage, a variable measurable from satellite. Overall, seasonally flooded riparian forests were the most severely impacted, possibly due to the relatively thinner barks of their trees.</p> <p>4. Synthesis and applications. Our findings reveal how riparian forests embedded in tropical savanna landscapes are in danger from large wildfires. The destruction of some forests has opened space for new plant species that may propel a shift to an alternative ecosystem state. Riparian forests are habitat of large savanna animals and their loss could affect entire trophic networks. Managing wildfires and invasive grasses locally is probably the best strategy to maintain riparian forests resilient. As wildfire regimes intensify in tropical savanna landscapes, our findings stress the need for an integrated management that considers riparian forests as a vulnerable element of the system.</p>
Spatial heterogeneity in soil pyrogenic carbon mediates tree growth and physiology following wildfire
<table> <tbody> <tr> <td>Pyrogenic carbon (PyC) is a ubiquitous legacy of wildfire in terrestrial soils, yet how it affects the growth and function of regenerating plants has received little research attention.<br> <br> We examined responses to a natural gradient of PyC deposition five years following a severe fire in a northern boreal forest, based on measurements of growth (height, basal area increment, and leader extension), physiological performance (Fv/Fm), and foliar nutrition (foliar C, N, P, K, Mg) of Pinus banksiana Lamb. We determined the concentration of PyC, expressed as a dosage (t·ha-1), in mineral soils collected from the rhizospheres of each sapling and used it as an independent factor to model trait responses to increasing PyC levels, in conjunction with measurements of soil physio-chemical properties (pH, EC, VOC, Ash, N, P, K, Ca, and Mg).<br> <br> Quantification and spatial analysis of PyC reveals heterogeneous deposition across the landscape with fine-grained patchiness at scales <0.5 m. In response to this heterogeneity, phenotypic and nutritional adjustments followed dose-dependent response patterns. Beneficial effects of PyC on sapling growth occurred to an optimum point of ~30-60 t·ha-1, while declining patterns were found for trees in dosages exceeding 100 t·ha-1. Some traits were positively and negatively related to soil K and N, respectively, and shared strong negative associations with soil pH and volatile matter.<br> <br> Synthesis. This study supports the longstanding hypothesis that soil PyC enhances growth and physiological function of fire-adapted plants, but indicates that responses are highly dosage-dependent, with natural levels of PyC deposition commonly exceeding an optimum point. These results also suggest that the main mechanisms for observed responses to PyC include: i) enhanced supply of base cations, ii) immobilization of N, and iii) pronounced liming. Future changes in climate are expected to increase fire frequency, particularly in circumpolar boreal forests. We predict shifts in PyC to frequently exceed the threshold resulting in reduced plant growth and ultimately ecosystem productivity.</td> <td> </td> <td> </td> </tr> </tbody> </table>
Data from: Resilience of lake biogeochemistry to boreal-forest wildfires during the late Holocene
Novel fire regimes are expected in many boreal regions, and it is unclear how biogeochemical cycles will respond. We leverage fire and vegetation records from a highly flammable ecoregion in Alaska and present new lake-sediment analyses to examine biogeochemical responses to fire over the past 5300 years. No significant difference exists in δ13C, %C, %N, C:N, or magnetic susceptibility between pre-fire, post-fire, and fire samples. However, δ15N is related to the timing relative to fire (Χ2=19.73, p<0.0001), with higher values for fire-decade samples (3.2±0.3‰) than pre-fire (2.4±0.2‰) and post-fire (2.2±0.1‰) samples. Sediment δ15N increased gradually from 1.8±0.6‰ to 3.2±0.2‰ over the late Holocene, probably as a result of terrestrial-ecosystem development. Elevated δ15N in fire decades likely reflects enhanced terrestrial nitrification and/or deeper permafrost-thaw depths immediately following fire. Similar δ15N values before and after fire decades suggest that N cycling in this lowland-boreal watershed was resilient to fire disturbance. However, this resilience may diminish as boreal ecosystems approach climate-driven thresholds of vegetation structure, permafrost thaw, and fire.
Fire-D: Analysis and ML-Ready NASA-Centric Remote Sensing of Wildfire and Smoke
<p>Earth science remote sensing imagery is rich in structural and spectral information, making such data an ideal platform for benchmarking for a broad range of machine learning (ML) tasks, from pattern retrieval to physics-informed classification to anomaly detection to transfer learning. Nevertheless, the utility of Earth science remote sensing data remains largely unexplored by the broader ML community. Our goal is to bridge this gap and bring a rich variety of multisource multi-resolution Earth image data to a wider range of ML researchers who are non-experts in remote sensing, thereby increasing the utility and societal impact of such data products. In particular, motivated by the emerging wildfire crisis, we present radiometrically and geometrically calibrated radiance data from airborne and orbital instruments from the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the Korean Meteorological Administration (KMA).</p> <p>Given the scarce occurrence of wildfires and complex spatio-temporal dependencies in radiance data, these datasets are especially well suited for benchmarking unsupervised and self-supervised learning tasks both on images and non-Euclidean objects. Our experiments on these datasets indicate that contrastive learning and transfer learning algorithms can capture the structures of views and scenes, map pixel space of multi-sensor imagery to a high-level embedding space for further downstream tasks, and facilitate more cohesive integration of the state-of-the-art ML approaches into wildfire risk analytics.</p> <p>All NASA-based observations are freely usable under the <a href="https://science.data.nasa.gov/license/">Creative Commons Zero License</a>.There are also no restrictions on the use of <a href="https://registry.opendata.aws/noaa-goes/">GOES Data</a>. <a href="https://registry.opendata.aws/noaa-gk2a-pds/">GK2A data</a> are also open data without any restrictions on its use.<br><br>For the Planet data, we cannot not share the Radiances, but all masks within this dataset are freely usable with no restrictions.</p> <p> </p> <p>Use:</p> <p>On the data input, input geometrically and radiometrically calibrated radiance data has been pulled from various NASA, NOAA, Planet, and KMA archives. For instruments that have multiple different spatial resolutions within their spectral bands (GOES and GK2A), all bands have been resampled to the lowest collective spatial resolution.</p> <p>Geometric and radiometric calibration has been done by the science data processing pipelines of the various missions, and would not need to be done by anyone else looking to curate the same data. Further information for each instrument can be found in each of the publicly available Level-1 algorithm theoretical basis documents (ATBDs)</p> <p>All input and label data have been put in GeoTiff format. Each band is in a separate raster band and each scene is in a separate GeoTiff file. Label files and input files are in separate tar files, labeled respectively, and the file names match for input and labels, with the exception of an additional .fire and .smoke in the respective label filenames and subfolders.<br><br>The <a href="https://www.earthdata.nasa.gov/about/esdis/esco/standards-practices/geotiff">GeoTiff</a> data format natively contains geolocation metadata internally, and can be interfaced with via C/C++/Python <a href="https://gdal.org/en/stable">GDAL</a> packages, or other python packages that wrap GDAL, like <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> and <a href="https://corteva.github.io/rioxarray/stable/">rioxarray</a> . The documentation for <a href="https://nicks-personal-organization-2.gitbook.io/sit-fuse">SIT-FUSE</a> , the package with which the labels were generated, also has examples on how to read and interface with various data formats, including GeoTiffs. Lastly, this data can be interfaced with using Geographic Information Systems (GIS), like the free and open-source <a href="https://qgis.org/">QGIS</a>.</p> <p>An example of programmatic data access and usage can be found in the dataset's associated <a href="https://github.com/Fire-D-Dataset/FIRE-D">GitHub repository</a>. </p> <p>A working example using data from this repository for ML tasks is available <a href="https://drive.google.com/drive/folders/16aJO6LhrxJ3gsWoTU9BNN3hsb8W0refG?usp=sharing">here</a>.</p> <p>Timing information can be found in the file names, which all use the standard formats from the various instruments' L1B datasets.</p> <p>V2 includes additional GOES-18 radiance data and associated smoke and fire labels for the recent LA fires (Palisades and Eaton fires in January of 2025).</p> <p>V3 provides a reorganization of all data, and an inclusion of improved and additional data from airborne and satellite platforms in 2019, associated with this study: https://arxiv.org/pdf/2501.15343 . </p> <p>V4 provides additional AVIRIS-C Radiances and fixes the spatial range of the GOES-17 radiances to match that of the associated labels. The AVIRIS-C radiances are split across 5 tar files, ordered temporally - all associated labels are in a single tar file.</p> <p><br>Current fire coverage includes:</p> <ul> <li>2019: Williams Flats, Sheridan, Horsefly, and Mosquito (US)</li> <li>2022: Uljin Forest Fire (S. Korea; largest fire on record in S. Korea)</li> <li>2025: Palisades and Eaton Fires (US)</li> </ul> <p>Additional data for the 2025 Palisades and Eaton fires from the TEMPO instrument is currently being validated and will be released in a V4 shortly.</p> <p>Croissant file for dataset metadata specification is also included</p> <p>Validation:</p> <p>These labels have been extensively validated and further information can be referenced in associated publications:<br><a href="https://doi.org/10.3390/rs13122364">https://doi.org/10.3390/rs13122364</a><br><a href="https://doi.org/10.3390/rs17071267">https://doi.org/10.3390/rs17071267</a></p> <p> </p> <p> </p>
Dataset for paper "Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity"
<p>Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity . New collected data</p>
The Portuguese Large Wildfire Spread Database (PT-FireSprd)
<p>The <strong>Portuguese Large Wildfire Spread Database (PT-FireSprd v2.0 ) </strong>includes the reconstruction of the spread of 155 large wildfires that occurred in Portugal between 2015 and 2024. It includes a detailed set of fire behaviour descriptors, such as rate-of-spread (ROS), fire spread direction, fire growth rate (FGR) and fireline intensity.</p> <p>The wildfires were reconstructed by converging evidence from complementary data sources, such as satellite imagery/products, airborne and ground data collected by fire personnel, official fire data and information in external reports. We then implemented a digraph-based algorithm to estimate the fire behaviour descriptors. Fireline intensity was estimated using Byram's equation assuming full fuel load consumption as provided by national level fuel maps.</p> <p><strong>PT-WFireSprd database is organized in 3 levels: </strong></p> <ul> <li> <p><strong>L1: Wildfire Progression, </strong>representing the spatial and temporal evolution of the wildfire spread (i.e. where and when).</p> </li> <li> <p><strong>L2: Wildfire Behavior,</strong> including quantitative behavior descriptors of how a wildfire burned, such as the rate-of-spread (ROS), fire growth rate (FGR) and fire line intensity (FLI)</p> </li> <li> <p><strong>L3: Simplified Wildfire Behavior,</strong> averaging fire behavior over longer periods that represent relatively homogenous fire runs.</p> </li> </ul> <p>The data from the different levels is composed by a large set of maps that can be useful for several applications and target users.</p> <p>The PT-FireSprd is the first open access fire progression and behaviour database in Mediterranean Europe, dramatically expanding the extant information. Updating the PT-FireSprd database will require a continuous joint effort by researchers and fire personnel.</p>
Wildfire Data
<p>USDA data for 2109 wildfire occurrences over 20 years, representing 35 million acres burned, NASA MODIS remote sensing data consisting of 1.3 billion satellite observations, ERA5 atmospheric reanalysis data, Justice 40 initiative highlighting which communities are disadvantaged communities</p>
Trout and invertebrate assemblages in stream pools through wildfire and drought
<p>Climate change is increasing the frequency, severity, and extent of wildfires and drought in many parts of the world, with numerous repercussions for the physical, chemical, and biological characteristics of streams. Yet information on how these perturbations affect top predators and their impacts on lower trophic levels in streams is limited.</p> <p>The top aquatic predator in southern California streams is native <em>Oncorhynchus mykiss</em>, the endangered southern California steelhead trout (trout). To examine relationships among the distribution of trout, environmental factors, and stream invertebrate resources and assemblages, we sampled pools in 25 stream reaches that differed in the presence (9 reaches) or absence (16 reaches) of trout over 12 years, including 8 reaches where trout were extirpated during the study period by drought or post-fire flood disturbances.</p> <p>Trout were present in deep pools with high water and habitat quality. Invertebrate communities in trout pools were dominated by a variety of medium-sized collector-gatherer and shredder invertebrate taxa with non-seasonal life cycles, whereas tadpoles and large, predatory invertebrates (Odonata, Coleoptera, Hemiptera (OCH)), often with atmospheric breather traits, were more abundant in troutless than trout pools.</p> <p>Structural equation modeling (SEM) of the algal-based food web indicated a trophic cascade from trout to predatory invertebrates to collector-gatherer taxa and weaker direct negative trout effects on grazers; however, both grazers and collector-gatherers also were positively related to macroalgal biomass. SEM also suggested that bottom-up interactions and abiotic factors drove the detritus-based food web, with shredder abundance being positively related to leaf litter (CPOM) levels, which, in turn, were positively related to canopy cover and negatively related to flow. When compared to the literature, these results emphasize the context dependency of trout effects on prey communities and the relative importance of top-down versus bottom-up interactions on food webs, contingent on environmental conditions (flow, light, nutrients, disturbances) and the abundances and traits of component taxa.</p> <p>Invertebrate assemblage structure changed from a trout to a troutless configuration within a year or two after trout were lost owing to post-fire scouring flows or drought. Increases in OCH abundance after trout were lost were much more variable after drought than after fire. The reappearance of trout in one stream where they were lost resulted in quick, severe reductions in OCH abundance.</p> <p>These results indicate that climate-change induced disturbances can result in the extirpation of a top predator, with cascading repercussions for stream communities and food webs. This study also emphasizes the importance of preserving or restoring refuge habitats, such as deep, shaded, perennial, cool stream pools with high habitat and water quality, to prevent the extirpation of sensitive species and preserve native biodiversity during a time of climate change. </p>
Data for Wildfire-induced Increases in Photosynthesis in Boreal Forest Ecosystems of North America
<div> <p>Jupyter notebooks and datasets for Kim et al. (2024), Global Change Biology<br><br><span>Kim, J. E.</span>, <span>Wang, J. A.</span>, <span>Li, Y.</span>, <span>Czimczik, C. I.</span>, & <span>Randerson, J. T.</span> (<span>2024</span>). <span>Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America</span>. <em>Global Change Biology</em>, <span>30</span>, e17151. <a href="https://doi.org/10.1111/gcb.17151">https://doi.org/10.1111/gcb.17151</a></p> </div>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.