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1,989 results for “fires”
Global Fire Weather Indices - ISI using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - DC using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - BUI using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - FWI using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - DMC using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - DC using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - DSR using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Fire Skink (Lepidothyris fernandi) lizard brain illustration
<p>3D model of the Fire Skink lizard brain highlighting the anatomy and the spatial arrangement of its major subdivisions.</p> <p>The brain reconstruction was obtained from a microCT scan of a iodine-stained specimen through manual segmentation using the software Amira 5.5.0.</p> <p>Other illustrations can be found <strong><a href="https://zenodo.org/search?page=1&size=20&q=keywords:%22squamate%20brain%22">here</a></strong>.</p> <p>If you are interested in reptile brain evolution and behavior, please, have a look to our recent publication:</p> <p><a href="https://www.nature.com/articles/s41467-019-13405-w"><em><strong>"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</strong></em></a></p> <p><strong>Simone Macrì, Yoland Savriama, Imran Khan & Nicolas Di-Poï</strong></p> <p><em>Nature Communications</em> <strong>10, </strong>5560 (2019)</p> <p> </p> <p><em>Check out also our *4K* video collection of various snake and lizard 3D brains:</em></p> <p><strong><a href="https://www.youtube.com/playlist?list=PLgx4vtT32C8hqxG_icKiuXGtZVLVX-oG1">Snake and Lizard brain reconstructions video collection</a></strong></p> <p> </p> <p>For any inquiries or additional information, please, refer to the contacts provided in the <strong><a href="https://www.nature.com/articles/s41467-019-13405-w">article</a></strong>.</p>
Build-in-Wood Regulation Analysis – Fire Safety
<p>This dataset contains an analysis of selected EU Member State building regulations covering fire safety in residential multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
NDSI values in a region affected by a forest fire
<p>This dataset includes maps with NDSI values that come from Landsat 5 Surface Reflectance collection images available in Google Eath Engine. TIFF images cropped to a region of Lanjaron where different post-fire treatments were established are presented. This area was affected by a forest fire in September 2005. That is why NDSI values and binary layers are presented for two periods, before and after the fire (hese binary layers are generated based on a threshold of 0.35). Comparisons are made in three forest management treatments after the fire. These treatments were No Intervention (NI), Partial Court (PCL) and Salvage Logging (SL).</p><p>This dataset also includes a grid with the sizes of the Landsat pixels that includes as information the treatment to which it belongs (column Trat_1).</p><p>And a folder of outputs where each cell has an associated value of ancillary variables (such as elevation, slope, shadows) and another where they present the NDSI values extracted from the images of the dataset</p><p> </p>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
Copernicus EMS fire activations delimitations (2012 - 2020) rasterised at 30m and aggregated per year and season
<p>This dataset was created as part of the <a href="https://opendatascience.eu/">Geo-harmonizer project</a>, with the scope of making open data easier to access. It contains all the fire activations (forest fire, wild fire, wildfire) mapped by the<a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid"> Copernicus Emergency Rapid Mapping Service</a> between 2012 and 2020. To obtain these GeoTIFFs, the vector data packages from CEMS were individually downloaded, rasterized and mosaicked per year and season, resampled at 30-m and reprojected to <a href="https://epsg.io/3035">EPSG 3035: ETRS89-extended / LAEA Europe</a>. If no CEMS fire activation was identified in a specific year and season, the raster was not created. The rasters are provided as COG files, type=16Int, nodata value is 255.</p> <p>To allow an easier and faster search through all 2012 - 2020 CEMS fire activations, we have prepared a point vector layer (geojson) containing one point for each fire activation area of interest with the following attributes attached: CEMS identification number <ems_id>, area of interest defined by CEMS <ems_aoi>, URL link to the CEMS activation <ems_link>, year of the event <year_start>, <year_end> , <season> and the name of the <geo_harmonizer_raster> where the 30m rasterised delimitations of the burned areas of the corresponding fire activation can be found. </p> <p>For any additional questions regarding the data please contact the author at codrina.ilie[at]terrasigna.com.</p> <p>The Copernicus Emergency Rapid Mapping Service data access policy is available <a href="https://emergency.copernicus.eu/mapping/sites/default/files/files/CopernicusEMS-Data_and_Dissemination_Policy.pdf">here</a>.</p>
Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas
<p><strong>Related article:</strong> Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>In this dataset:</strong></p> <p>This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning.</p> <p><strong>Please cite this dataset as:</strong></p> <p>Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>Column names</strong></p> <p>NAME: Name of protected area</p> <p>Fires_sum: Number of observed fires (VIIRS)</p> <p>Month: Month</p> <p>Year: Year</p> <p>Precipitation: Precipitation (mm)</p> <p>Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago</p> <p>YEAR_CREAT: Year of establishment of protected area</p> <p>Biome: Biome</p> <p>REP_AREA: Area of protected area (km<sup>2</sup>)</p> <p>Fires_per_km2: Fires per km<sup>2</sup></p> <p>Prec_acc_12m: Accumulated precipitation during the last 12 months</p> <p>fBiome: Biome as factor</p> <p>fNAME: Name as factor</p> <p>sPrecipitation: Precipitation (scaled; see Methods section of article)</p> <p>sPlag_1: Precipitation in previous month (scaled; see Methods section of article)</p> <p>sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article)</p> <p>Pred_Zinb_1a: Predicted fires (see Methods section of article)</p> <p>Diff_Zinb_1a: Difference: Observed fires - predicted fires</p> <p>Year_pred: Year for prediction</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p>
PM2.5 emissions from Siberian forest fires 2004-2021
<p>The dataset contains Supplementary Materials for the article <em>''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis''</em> in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis. <br> <br> <strong>Supplementary Figures</strong>:<br> - Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line); </p> <p>- Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha. </p> <p>- Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map);</p> <p>- Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region;</p> <p>- Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic;</p> <p>- Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region; </p> <p>- Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory; </p> <p>- Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory. <br> </p> <p>We share Copernicus Atmosphere Monytoring Service <strong>PM2.5 emissions maps</strong> (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594. </p> <p><br> To determine emissions from the territory of Russia, we provide <strong>shapefiles</strong> with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334). </p> <p>Also, there are<strong> initial dataset</strong> for analysis (Initital data_SARIMAX archive) and <strong>SARIMAX model settings</strong> (doc.). </p>
Fire data cube for 2020
<p>This is a 50 km x 50 km collocated dataset for fire analysis and verification. It contains </p> <p>1) Burned areas from ESA-CCI</p> <p>2) Actiive fires from MODIS</p> <p>3) ignition points </p> <p>4) probabilistic forecast for ignitions pil_2020.nc</p> <p>5) deterministic forecast for ignitions WEL2_LIHM_2020_det.nc</p> <p> </p> <p>This new version has been updated with</p> <p>FWI, DC,DMC,FFMC,ISI and BUI</p> <p>also vegetation indices VOD and LAI</p>
Fuel loads monitoring dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fuel loads monitoring data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_____________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 30 monitored experimental plots of 1 hectare each (100×100 m). Three experimental treatments were then established: 12 plots were burned in May (Early-Dry Season – EDS), further 12 plots were burned in August (Mid-Dry Season – MDS), and 6 plots were kept as control by ensuring fire exclusion throughout the duration of the experiment. Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th.</p> <p>Measurements of fuel load were obtained from eight subplots of 0.5 × 0.5 m randomly distributed within each plot. Samples included graminoids, leaves and branches near the ground. Biomass was dried at 70°C for 48 hours, and weighed to determine total fuel load (kg. m<sup>-2</sup>) for each sample. Samples were taken before fire for all control, EDS and MDS burn plots during both field campaigns, and repeated sampling was carried out after fire for the burned plots. In 2020, all 30 monitored plots were sampled again in May and August.</p> <p>The files available include: i) a table (Fuel_load_measures.csv) that contains the measures of fuel loads for each plot; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_____________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5).</p>
Fire behavior dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fire behavior data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_________________________________________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 24 experimental fires carried out in 2019 (12 in Early-Dry Season - EDS; 12 in Middle-Dry Season - MDS), each corresponding to a plot of 1 hectare (100x100 meters). Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th. The files available include: i) a table (Fire_behavior_dataset.csv) that contains the fire parameters calculated for each experimental fire performed; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_________________________________________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5)</p>
video_fire_polishing_puntil
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_fire_polishing_blowpipe
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"
<p>Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment.</p> <p>Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).</p>
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.