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1,826 results for “burn”
CSA02 Soil Macroarthropod densities and biomass on annually burned and unburned watersheds
Belowground densities and biomass of macroarthropods on annually were measured by hand-sorting techniques. Total herbivore biomass was greater in soils of annually burned sites, and was composed largely of white grubs (Scarabaeidae).
PBG01 Plant species composition in the Patch Burning-grazing Experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1(1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis.
PTN01 Aboveground net primary productivity along transects spanning topographic gradients on an annually burned and unburned watershed at Konza Prairie
In 1989, single transects spanning upland-lowland-upland topographic positions were established in a long-term unburned (0020B) and an annually burned (001D) watershed. Standing crop biomass data were collected in late season at 11 sites along each transect and sorted into live graminoids, forbs and woody plants, current year's dead, and previous years dead vegetation. Four 0.1 m2 quadrats were harvested at each of the 11 sites per watershed and all data except previous years' dead were combined to provide an estimate of aboveground NPP. In 1993, soil moisture measurements began along each transect at 15 and 30 cm depths (where possible) with a Time Domain Reflectomerty (TDR) system. Measurements were made twice a month from March - October and intermittently during the winter months.
PAB03 Aboveground primary productivity of tallgrass prairie based on accumulated plant biomass on LTER watersheds burned at different seasons
Data set contains estimates of standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) and seasonal burning treatments (spring, summer, fall, winter).
PAB02 Biweekly measurement of aboveground net primary productivity on an unburned and annually burned watershed at Konza Prairie
Data set contains estimates of standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, current year's dead, and previous year's dead vegetation. Twenty quadrats (0.1 square meters) are harvested for each watershed (001a and 020a) on each sample date.
CSM08 Small mammal host-parasite sampling data for 16 linear trapping transects located in 8 LTER burn treatment watersheds at Konza Prairie
Data set contains summaries (summer) of the number of individuals of each species of small mammal captured (relative abundance) on each transect. Each record contains date, treatment, transect, trap station, species, specimen number, recapture status, specimen disposition, external body measurements (where applicable), reproductive information, and miscellaneous associated comments. These sampling records are based on nightly captures during one 4-night trapping period in summer (June through August) for each of 16 permanent transects established on eight fire treatments (two transects per treatment). These treatments include two seasonal burn watersheds (SpB, SuB), two reversal burn watersheds (R1A, R20A), one annual burn watershed (1D), two 4-year burn watersheds (4B, 4F, and one 20-year burn watershed (20B). None of these treatments implement bison grazing.
Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020
<p>The dataset "Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020" contains the emissions analysed in the manuscript "Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates", published in Fire 2023, 6(11), 426; <a href="https://doi.org/10.3390/fire6110426">https://doi.org/10.3390/fire6110426</a>.</p>
Data, plotting scripts, and figures for "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "A physics-based ignition model with detailed chemical<br>kinetics for live fuel burning studies" by Diba Behnoudfar and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes
<p>This dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS.</p><p>We also provided this data for an additional 16 large fires across the western US ("Extra Test" folder, including Dataset – X file – and Label – Y file).</p><p>Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.</p>
Extra data to accompany code in GitHub burntfields_punjab, both used in Walker et. al. 2022, Detecting crop burning in India using satellite data
<p>Supplementary data files to accompany GitHub code 'burntfields_punjab' supporting Walker et. al. (2022) Detecting crop burning in India using satellite data [<a href="https://arxiv.org/abs/2209.10148">available here</a>] and Jack et. al. (2024) Money (not) to burn: Payments for ecosystem services to reduce crop residue burning).</p> <p>Includes custom Sentinel-2 cloud masks and data from Sentinel-2 Spectral Mixture Analysis to highlight Char (burning) based on general concept and methods from Daldegan et. al (2019). Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment 232, 111340. </p> <p>Note: Bands in weekly BASMA layers are: 0 = green vegetation, 1 = Non-productive vegetation and bare soil, 2 = Char (burned).</p> <p>further details are provided at: <a href="https://github.com/klwalker-sb/burntfields_punjab">https://github.com/klwalker-sb/burntfields_punjab</a> (archived at: <a href="https://doi.org/10.5281/zenodo.11225292" target="_blank" rel="noopener">DOI: 10.5281/zenodo.11225292</a>)</p>
Supplementary Material for "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems"
<p>This Zenodo repository contains all data, scripts, and supplementary materials for the manuscript entitled, "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems".</p>
Data supporting "Burn Period: A use-inspired metric to track wildfire risk across the southwest U.S."
<p>Comma delimited data file of derived daily meteorological metrics from hourly, gap filled and quality controlled Remote Automated Weather Station (RAWS) data for Arizona and New Mexico (southwest U.S.) provided by the Climate, Ecosystems, and Fire Applications (CEFA) program at the Desert Research Institute (Brown, 2022, unpublished data). Data file contains daily average dewpoint temperature, air temperature, maximum Hot-Dry-Windy Index, maximum Fosberg Fire Weather Index, maximum vapor pressure deficit, and total number of hours/day with relative humidity below 20% for 124 RAWS from 2000-2022.</p>
The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf
<p>This dataset contains the measured hourly mean microclimate and surface energy flux data from a field experiment. The experiment consisted of three treatments: unirrigated artificial turf, unirrigated natural turf, and irrigated natural turf (4 mm/day, 13:00-13:23 local time). The experiment was conducted from 2024-01-28 to 2024-03-18 in Burnley, Melbourne, Australia.<br><br>For each treatment, the measured hourly mean data included albedo, soil moisture content, air temperature, vapour pressure of water, wind speed, black globe temperature, mean radiant temperature, universal theraml climate index, wet-bulb globe temperature, soil temperature, turf surface temperature, incoming and outgoing longwave and shortwave radiant fluxes, sensible heat flux, latent heat flux, and ground heat flux. <br><br>Turf surface temperature, and incoming and outgoing longwave and shortwave radiant fluxes were measured at 1.5 m above ground surface.<br>Air temperature and vapour pressure of water were measured at 0.6 and 1.1 m above ground surface.<br>Wind speed, black globe temperature, mean radiant temperature, universal thermal climate index, and wet-bulb globe temperature were measured at 1.1 m above ground surface.<br>Soil moisture content, soil temperature and ground heat flux were measured at 0.1 m below ground surface.<br>Sensible heat flux and latent heat flux were calculated using the Bowen ratio-energy balance method.<br><br>Additionally, the hourly mean background weather conditions (air temperature and cloud amount) from the nearest public climate station in the study period were included in 'ReferenceClimateStation.csv'. Hourly total rainfall data measured at the study site was also included. <br><br>The aims of this study was to:<br>1. Compare the microclimate and human heat stress among the three treatments.<br>2. Assess and compare the human skin burn risks of the three treaments from their turf surface temperatures.<br>3. Analyse the surface energy fluxes of the three treatments to identify the mechanisms by which artificial turf develops any microclimate, human heat stress and turf surface temperature differences.<br><br>This study was published in:</p> <p><span>Cheung, P. K., & Livesley, S. J. (2025). The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf. <em>Building and Environment</em>, 112679. https://doi.org/10.1016/j.buildenv.2025.112679<br></span><br>Contact person: Dr Paul Cheung (cheung.p@unimelb.edu.au)</p>
Dry matter databases derived by crossing burned areas databases with above ground estimations from SMOS sensor
<p>Estimates of the the fuel consumed during a fire (dry-matter, DM) is derived by combining burned areas (from two different fire inventories) with above ground biomass (derived from SMOS L Band Vegetation Optical Depth). Six new datasets are provided on a 25 km grid for the years 2010-2017.</p> <p>The portion of vegetation that is consumed during biomass burning is expressed as:<br> <strong><span class="math-tex">\(DM= AGB * BA*\beta\)</span></strong></p> <p>where BA is the burned area; AGB is the fuel load, the amount of biomass or organic matter an ecosystem contains per unit area; <span class="math-tex">\(\beta\)</span> is the combustion completeness or burning efficiency, which is the fraction of fuel actually consumed during the fire.</p> <p>Monthly maps of AGB are converted from SMOS L-band vegetation optical depth (VOD) through an algorithm described in [1] and obtained looking at the relationship between three static AGB benchmark maps from the works of [2],[3] and [4]. Each static map provide three L-VOD to AGB conversion curves fitting the 5th, 50th and 95th percentiles of the data using a logistic regression([1]) In the following, the three different databases are named AGB-BA (Baccini), AGB-SA (Saatchi) and AGB-AV (Avitabile) </p> <p>Monthly total of burned areas are available from two sources. The first one, called hereinafter BA-GFED, is available though GEFD4.1s [5] and is based on MODIS MCD64A1 product. It has a 500m pixel resolution and is also available on a regular grid of 0.25 deg. The second BA product, named hereinafter BA-CCI, is a multi-sensors product provided by the European Space Agency Climate Change Initiative (ESA-CCI) [6] We use version FireCCI5.1 which is calculated using a two-phase algorithm, where MODIS active fire locations are used to identify seed pixels corresponding to high confidence burned areas. These areas are then grown using Medium Resolution Imaging Spectrometer (MERIS) vegetation input data.</p> <p>Combustion completeness, is a taken from table 4 of [7]. </p> <p>By selectively combining any AGB estimations with the two available BA datasets, a total of 6 products are generated.</p> <p> </p> <p><strong>References </strong></p> <p>[1] https://doi.org/10.5194/bg-15-4627-2018</p> <p>[2] <a href="https://doi.org/10.1126/science.aam5962">DOI: 10.1126/science.aam5962</a></p> <p>[3]<a href="https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf">https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf</a></p> <p>[4] <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139">https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139</a></p> <p>[5] <a href="https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html">https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html</a></p> <p>[6] <a href="https://geogra.uah.es/fire_cci/firecci51.php">https://geogra.uah.es/fire_cci/firecci51.php</a></p> <p>[7] <a href="https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf">https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf</a></p> <p> </p>
Global Fire Emissions Database (GFED5) Burned Area
<p>The monthly GFED5 burned area data produced in this study are available in netCDF files. For years between 2001 and 2020, five layers of burned area (Norm: normal type, Crop: cropland burning, Defo: deforestation burning, Peat: peatland burning, Total: the sum of all burning) are provided at 0.25°×0.25° resolution. The ‘Norm’ layer contains burned areas in each grid cell (in km<sup>2</sup>) separated by 17 major land cover types. For the pre-MODIS era (1997-2000), only the ‘Total’ burned area layer with reduced spatial resolution (1°×1°) is provided. We also provide two global maps of burnable area (with water and snow/ice cover excluded) in each grid cell (0.25°×0.25° for the MODIS era and 1°×1° for the pre-MODIS era). Please refer to readme.html or readme.pdf for more detail about the dataset.</p>
Emission of volatile organic compounds from residential biomass burning and their rapid chemical transformations.
<p>Volatile Organic Compounds (VOCs) were monitored during the Ioannina 2022/23 winter campaign, in north-east Greece. The campaign was carried out between December 6th, 2021, and January 10th, 2022. Nitrogen oxides, carbon monoxide, carbon dioxide, methane, PM10 and Black carbon were also monitored, as well as meteorological variables. Ioannina is nested within the Dinaric mountains and suffers from intense winter pollution events, due to the topology which traps the pollution over the city. The instruments deployed included a Proton Transfer Time-of-Flight Mass Spectrometry (PTR-ToF-MS 4000 – Ionicon GmbH, Austria), a greenhouse gas monitor (G2301 – Picarro Inc., USA), a suite of carbon monoxide, ozone, nitrogen oxide analyzers (APMA-360, APNA-360 and APOA-360 – Horiba Ltd., Japan), a PM10 monitor (F-701-20 – DURAG, Germany), an aethalometer (AE33 – Magee Scientific, USA) and a weather station. Radiation, historical temperature data from the University of Ioannina, and PMF analysis results are also submitted.</p>
ONFIRE Dataset: Monthly Gridded Burned Area data
<p>The ONFIRE Dataset presents a 1° x 1° lat-long gridded database detailing monthly burned areas (BA) stemming from national fire data across five regions: Australia, Canada, Chile, Europe, and the United States. Each grid cell encapsulates the center's latitude and longitude, coupled with the total square meters burned within the month. The dataset spans varying periods per region, starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the US, extending up to 2021.The ONFIRE DATASET is available in netCDF4, RData, and ASCII formats for accessibility and ease of integration.</p>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
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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)
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DANDI Archive for NWB datasets
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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.