21 datasets found
  1. California Vegetation Burn Severity Data Online Viewer Web App

    • data.cnra.ca.gov
    • data.ca.gov
    • +1more
    Updated Aug 26, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    California Department of Forestry and Fire Protection (2025). California Vegetation Burn Severity Data Online Viewer Web App [Dataset]. https://data.cnra.ca.gov/dataset/california-vegetation-burn-severity-data-online-viewer-web-app
    Explore at:
    html, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Aug 26, 2025
    Dataset authored and provided by
    California Department of Forestry and Fire Protectionhttp://calfire.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Description

    RdNBR is a remotely sensed index of the pre- to post-fire change in vegetation greenness, in this case the growing seasons in the year prior to and the year after the year in which the fire occurred. The mean composite scene selection method utilizes all valid pixels in all Landsat scenes over a specified date range to calculate the fire severity index. The CBI is a standardized field measure of vegetation burn severity (Key and Benson 2006), which here is predicted from a remotely sensed fire severity index using regression equations developed between CBI field plot data and the remote index, RBR (Parks et al 2019). The dataset featured provides an estimation of fire severity of past fires, with fire severity defined here as fire-induced change to vegetation. The dataset is limited to fires included in CAL FIRE’s Historic Wildland Fire Perimeters database and therefore is subject to the same limitations in terms of missing or erroneous data.


    This web app was developed to satisfy the requirements of Senate Bill No. 1101: An act to amend Sections 10295 and 10340 of the Public Contract Code, and to add Section 4114.4 to the Public Resources Code, relating to fire prevention.

    Methods:
    To develop these datasets, a feature service for fire perimeters was created from the CAL FIRE Fire and Resource Assessment Program’s Historic Wildland Fire Perimeters database (firep23_1) for fires or fires that were a part of complexes >= 1,000 acres from 2015 to 2023. This feature service is viewable on the California Vegetation Burn Severity Viewer and used to discover the RdNBR and CBI vegetation burn severity datasets. The feature service is titled Burn Severity Fire Perimeters (firep23_1_2015_2023_Fires_Complex_1000ac). After this feature service was uploaded to Google Earth Engine (GEE) as an asset, the Parks et al. 2018 script was used to generate RdNBR values with offset (rdnbr_w_offset) data for each individual fire and the Parks et al. 2019 script was used to generate bias corrected Composite Burn Index values (cbi_bc) data for each individual fire using 30m resolution Landsat Collection 2 data. To specify the date range of Landsat satellite images to be queried to create the one-year pre-fire and one-year post-fire mean composite image scenes in both scripts, the variable 'startday' was set to 152 (June 1st) and the variable 'endday' was set to 258 (September 15th) for all fires, as specified in Parks et al. (2019). These variables were used to define the ranges of Landsat scenes that were queried to create the one-year-pre-fire and one-year-post-fire mean composite Landsat scenes. These values were used, as they were detailed as the leaf-on period for the State of California in Parks et al. 2019.

    Once the RdNBR raster data for each fire had been produced using Parks et al. 2018's GEE script and the CBI raster data for each fire had been produced using Parks et al. 2019's GEE script, a Python script (run in a Jupyter Notebook embedded in the ArcGIS Pro software) was used to clip each fire-specific, continuous feature class to the extent of its fire perimeter. Each CBI feature class was additionally clipped to the extent of Conifer Forest and Hardwood Forest classes (defined in FVEG15's WHR13 Lifeform class for fires from 2015 to 2021 and defined in FVEG22's WHR13 Lifeform class for fires from 2022 to 2023).Once each continuous feature class had been clipped, values were reclassified to create a discrete RdNBR and CBI feature classes. Classes for RdNBR were arbitrarily chosen and do not correspond to meaningful categories of burn severity. Higher RdNBR values do indicate greater loss of vegetation greenness and negative values indicate an increase in greenness, but there is not necessarily a direct or linear correlation between RdNBR values and impacts to vegetation or ecological effects. Remotely sensed fire severity indices are translated into CBI using regression equations developed between CBI field plot data and the remote indices. Very few CBI plots exist in California or elsewhere in the U.S. for vegetation types other than forest. We therefore chose to include only forest vegetation in our CBI dataset.

    Classes for RdNBR were as follows:
    Code | Lower Limit (RdNBR) | Upper Limit (RdNBR)

  2. a

    Vegetation Burn Severity Unchanged

    • win-snc.opendata.arcgis.com
    Updated May 7, 2018
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Sierra Nevada Conservancy (2018). Vegetation Burn Severity Unchanged [Dataset]. https://win-snc.opendata.arcgis.com/datasets/vegetation-burn-severity-unchanged/geoservice
    Explore at:
    Dataset updated
    May 7, 2018
    Dataset authored and provided by
    Sierra Nevada Conservancy
    Area covered
    Description

    These data were created by the USDA Forest Service fire and fuels monitoring project to support monitoring of wildland fire and fire regimes. These data will allow better understanding of current fire regimes, improve the accuracy of fire perimeter data, and add spatial data on fire severity and complexity.

    These data are derived from Landsat Thematic Mapper imagery. The pre-fire and post-fire subscenes were used to create a Relative Differenced Normalized Burn Ratio (RdNBR). The RdNBR is correlated to the variation of burn severity within a fire. The RdNBR data are calibrated with the Composite Burn Index (CBI). The severity ratings provided by the derived products listed below are based on the severity to vegetation.

    For a description of RdNBR see:

    Miller, J.D. and Thode, A.E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio(dNBR). Remote Sensing of Environment 109(1):66-80.

    For a description of the calibration of RdNBR to severity measures see:

    Miller, J.D., Knapp, E.E., Key, C.H., Skinner, C.N., Isbell, C.J., Creasy, R.M. and Sherlock, J.W. (2009). Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sensing of Environment 113(3): 645-656.

    Miller, J.D. and Quayle, B. (2015). Calibration and validation of immediate post-fire satellite derived data to three severity metrics. Fire Ecology 11(2): 12-30.

    This database may contain more than one assessment for each fire: initial and/or extended. Initial assessment data can differ from extended assessment data because: 1.) The calibrations used to derive the severity data are based on one year post-fire field data. Calibrations for Initial assessments were derived through regression modeling of satellite reflectance values from imagery collected one year post-fire to imagery acquired immediate post-fire. 2.) Imagery used to make initial assessments are acquired immediately after containment which can be late in the year when sun angles are low due to fire containment dates and therefore fire effects on north facing slopes can be hidden due to topographic shadows caused by low sun angles causing high severity fire effects in these data to be under represented. Low to moderate severity in dense stands on east, west or south aspects may also be under-represented due to a low sun illumination angle. 3.) Imagery used for extended assessments are acquired the year after the fire occurred and can therefore include effects due to resprouting vegetation, delayed mortality of trees, and any management actions that have taken place between the fire containment date and image acquisition date.

  3. Data from: MOSEV: A global burn severity database from MODIS (2000-2020)

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Nov 10, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Esteban Alonso-González; Esteban Alonso-González; Víctor Fernández-García; Víctor Fernández-García (2020). MOSEV: A global burn severity database from MODIS (2000-2020) [Dataset]. http://doi.org/10.5281/zenodo.4265209
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 10, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Esteban Alonso-González; Esteban Alonso-González; Víctor Fernández-García; Víctor Fernández-García
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    To advance in the fire discipline as well as in the study of CO2 emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products scenes since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning.

  4. Data from: Severe fire weather and intensive forest management increase fire...

    • zenodo.org
    • datadryad.org
    bin
    Updated May 29, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Harold S.J. Zald; Christopher J. Dunn; Harold S. J. Zald; Harold S.J. Zald; Christopher J. Dunn; Harold S. J. Zald (2022). Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape [Dataset]. http://doi.org/10.5061/dryad.3gv5c78
    Explore at:
    binAvailable download formats
    Dataset updated
    May 29, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Harold S.J. Zald; Christopher J. Dunn; Harold S. J. Zald; Harold S.J. Zald; Christopher J. Dunn; Harold S. J. Zald
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

  5. d

    Data from: Data for use in poscrptR post-fire conifer regeneration...

    • catalog.data.gov
    • data.usgs.gov
    Updated Sep 30, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Geological Survey (2025). Data for use in poscrptR post-fire conifer regeneration prediction model [Dataset]. https://catalog.data.gov/dataset/data-for-use-in-poscrptr-post-fire-conifer-regeneration-prediction-model
    Explore at:
    Dataset updated
    Sep 30, 2025
    Dataset provided by
    U.S. Geological Survey
    Description

    These data support poscrptR (wright et al. 2021). poscrptR is a shiny app that predicts the probability of post-fire conifer regeneration for fire data supplied by the user. The predictive model was fit using presence/absence data collected in 4.4m radius plots (60 square meters). Please refer to Stewart et al. (2020) for more details concerning field data collection, the model fitting process, and limitations. Learn more about shiny apps at https://shiny.rstudio.com. The app is designed to simplify the process of predicting post-fire conifer regeneration under different precipitation and seed production scenarios. The app requires the user to upload two input data sets: 1. a raster of Relativized differenced Normalized Burn Ratio (RdNBR), and 2. a .zip folder containing a fire perimeter shapefile. The app was designed to use Rapid Assessment of Vegetative Condition (RAVG) data inputs. The RAVG website (https://fsapps.nwcg.gov/ravg) has both RdNBR and fire perimeter data sets available for all fires with at least 1,000 acres of National Forest land from 2007 to the present. The fire perimeter must be a zipped shapefile (.zip file, include all shapefile components: .cpg, .dbf, .prj, .sbn, .sbx, .shp, and .shx). RdNBR must be 30m resolution, and both the RdNBR and fire perimeter must use the USA Contiguous Albers Equal Area Conic coordinate reference system (USGS version). RDNBR must be alligned (same origin) as RAVG raster data. References: Stewart, J., van Mantgem, P., Young, D., Shive, K., Preisler, H., Das, A., Stephenson, N., Keeley, J., Safford, H., Welch, K., Thorne, J., 2020. Effects of postfire climate and seed availability on postfire conifer regeneration. Ecological Applications. Wright, M.C., Stewart, J.E., van Mantgem, P.J., Young, D.J., Shive, K.L., Preisler, H.K., Das, A.J., Stephenson, N.L., Keeley, J.E., Safford, H.D., Welch, K.R., and Thorne, J.H. 2021. poscrptR. R package version 0.1.3.

  6. Composite Burn Index (CBI) data and field photos collected for the FIRESEV...

    • agdatacommons.nal.usda.gov
    bin
    Updated Jan 22, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pamela G. Sikkink; Gregory K. Dillon; Robert E. Keane; Penelope Morgan; Eva C. Karau; Zachary A. Holden; Robin P. Silverstein (2025). Composite Burn Index (CBI) data and field photos collected for the FIRESEV project, western United States [Dataset]. http://doi.org/10.2737/RDS-2013-0017
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    Pamela G. Sikkink; Gregory K. Dillon; Robert E. Keane; Penelope Morgan; Eva C. Karau; Zachary A. Holden; Robin P. Silverstein
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Western United States, United States
    Description

    This set of Composite Burn Index (CBI) data was collected from 2009 to 2011 and supports several products created during the FIRESEV project, which was funded by the Joint Fire Sciences Program. FIRESEV (FIRE SEVerity mapping tools) is a comprehensive set of tools and protocols to deliver, create, and evaluate fire severity maps for all phases of fire management. This CBI data describes fire effects for the western U.S. for five vegetation strata after burning in 2008 to 2010 (Key and Benson 1999). The strata include substrates (litter, duff, fuel, and soil); herbs, low shrubs, and small trees; tall shrubs and sapling trees; intermediate trees; and big trees. The field assessments were conducted in deciduous and coniferous forests, shrublands, and grasslands. The dataset includes information on the fires that burned each area, plot locations and sample protocols, topographic characteristics, canopy characteristics, substrate and ground covers, pre- and post-burn estimates of vegetation in each stratum, estimates of the percentage of plot altered by stratum, CBI values calculated for each of the five strata, and a composite CBI value for the entire plot. Field photos at each location are included for perspective on the field conditions related to the CBI assessments.These data were collected to support the following major FIRESEV products: (1) a Severe Fire Potential Map (SFPM), which quantified the potential for fires to burn with high severity, should they occur, for any 30 meter (m) x 30 m piece of ground across the western United States (not including Alaska or Hawaii); (2) a fire severity mapping algorithm in the Wildland Fire Assessment Tool (WFAT), which was used to map predicted fire severity explicitly from fire effects simulation models (e.g., the First Order Fire Effects Model, the CONSUME model, and others) for real-time and planning wildfire applications; and (3) a suite of research studies, synthesis papers, and popular articles, which improved the description, interpretation, and mapping of fire severity for wildland fire managers. Our primary purpose for this sampling effort was to collect field data that could be used to assess the accuracy of the maps produced to quantify the probability of severe fires for the western US. In addition, data were collected to analyze the degree to which various measures of burn severity interpreted from satellite imagery (NBR, dNBR, RdNBR) correlated with field indicators of burn severity collected one year post-fire. We sought to assess measures in the field and remotely that related to three different axes of burn severity used in this project. These included 1) soil heating, 2) surface fuel consumption, and 3) change in vegetation cover and mortality. The CBI values comprising this collection were used in each of these products, either directly or indirectly, to compare on-site changes in vegetation, canopy structure, and soil characteristics with fire severity interpretations and assessments derived from satellite imagery. All of the products were based either directly or indirectly on the CBI dataset in this archive.Original metadata date was 11/19/2013. Minor metadata updates on 12/15/2016.

  7. Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984 -2022)

    • zenodo.org
    bin
    Updated Apr 10, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dina Jahanianfard; Joana Parente; Oscar Gonzalez-Pelayo; Akli Benali; Dina Jahanianfard; Joana Parente; Oscar Gonzalez-Pelayo; Akli Benali (2025). Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984 -2022) [Dataset]. http://doi.org/10.5281/zenodo.15188051
    Explore at:
    binAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Dina Jahanianfard; Joana Parente; Oscar Gonzalez-Pelayo; Akli Benali; Dina Jahanianfard; Joana Parente; Oscar Gonzalez-Pelayo; Akli Benali
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Portugal
    Description

    The Portuguese burn severity atlas (1984 -2022) provides satellite-derived burn severity estimates of each historical fire with its start and end dates recorded and equal to or larger than100 ha for fires from 1984 to 2022 in Portugal. The fire perimeters are provided by the Instituto da Conservação da Natureza e das Florestas (ICNF) (https://sig.icnf.pt/portal/home/item.html?id=983c4e6c4d5b4666b258a3ad5f3ea5af). Landsat imagery is applied for the creation of this atlas. The burn severity indices applied within this atlas are differenced Normalized Burn Ratio (dNBR), Relative differenced Normalized Burn Ratio (RdNBR), Relative Burn Ratio (RBR), and index combining dNBR with enhanced vegetation index (dNBR-EVI).

    Since 1984 to 2022, the total burned area recorded in Portugal is 4.85 million ha. Only valid fires, which are fires equal to or larger than 100 ha with known dates, were considered for the burn severity estimates. The total area of valid fires is 3.29 million ha. The Portuguese Burn Severity Atlas provides estimates for 3.17 million ha, accounting for 65% of all fires and 96% of valid fires.

    The Portuguese burn severity atlas is organized in subfolders, each entitled as the corresponding year and containing shapefile, maps and a table with details on pairs of images used for burn severity estimates. Within each subfolder, the following data are stored:

    • the annual fires’ perimeters shapefile (.dbf, .prj, .shp, .shx)
    • dNBR map (.tiff)
    • RdNBR map (.tiff)
    • RBR map (.tiff)
    • dNBR-EVI map (.tiff)
    • confidence map: average “SUITABILITY in each pixel within the area of the fire (.tiff)
    • comma separated value (.csv) file containing details on the pair of images used for burn severity estimates (year, ID, iteration number, pre-fire time lag(day), pre-fire cloud%, pre-fire suitability (%), post-fire time lag (day), post-fire cloud%, post-fire suitability(%), confidence in the iteration(%), area with dNBR estimation(ha), area of fire (ha), fire i number (to refer in GEE code), dNBR offset value, RdNBR offset value, RBR offset value, and dNBR-EVI offset value).

    To the best of our knowledge, Portuguese Burn Severity Atlas is the first open access atlas providing burn severity estimates of historical fires for an entire European country, in this case, Portugal, with 38 years of coverage. Target audience who can benefit from this atlas can be policymakers at national levels, field managers, project managers, and agency and academic researchers.

    *** The second version of this atlas provides updated fire data and burn severity estimates of fires from 1984 to 2000 with corrected dates area equal or larger than 100 ha. Moreover, for 2012, aside from burn severity estimates provided via imagery from Terra abroad Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat7-derived maps are included.

  8. d

    Rapid Assessment of Vegetation Condition after Wildfire Burned Areas...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Oct 1, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Geological Survey (2025). Rapid Assessment of Vegetation Condition after Wildfire Burned Areas Boundaries for 2007-2024 [Dataset]. https://catalog.data.gov/dataset/rapid-assessment-of-vegetation-condition-after-wildfire-burned-areas-boundaries-for-2007-2
    Explore at:
    Dataset updated
    Oct 1, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    The Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program provides assessments of vegetation conditions following large fires on forested lands. Fire effects are represented by three metrics: percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized Composite Burn Index (CBI). These data are derived from moderate resolution multi-spectral imagery (e.g., Landsat 8 Operational Land Imager or Sentinel-2 Multispectral Instrument). The Relative Differenced Normalized Burn Ratio (RdNBR), which is correlated to the variation of burn severity within a fire, is calculated from a pair of images (pre- and postfire), judiciously selected to capture fire effects. The three-severity metrics are in turn calculated from RdNBR using regression equations developed from and calibrated with historical field data. This map layer is a vector polygon shapefile of the location of all currently inventoried fires occurring between calendar year 2007 and 2024 for CONUS, Alaska, Hawaii, and Puerto Rico. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires which were not discernable from available imagery.

  9. n

    Fire Severity Classification - Dataset - CKAN

    • nationaldataplatform.org
    Updated Jul 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2025). Fire Severity Classification - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/oper-fire-severity-classification
    Explore at:
    Dataset updated
    Jul 11, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Fire severity classification (low, moderate, high) that burned within the last 10 years (2012-2021). The difference-adjusted relativized difference normalized burn ratio (RDNBR) was calculated using methods modified from Parks et al (2018). Fire perimeters were obtained from CAL FIRE's April 2021 fire perimeter database. A function for estimating basal area loss from RDNBR values was fit to data from Miller et al (2009) using quasibinomial logistic regression and applied to the 2022-2021 fires. Estimated basal area loss was thresholded to represent low (75% loss) burn severity. For areas where multiple sequential fires burned from 2022-2021 the maximum burn severity is reported. Updated April 2023 to incorporate CAL FIRE's October-2022 revisions to fire perimeters and to minimize data loss resulting from spatial reprojection.

  10. f

    Data from: Comparison of contrasting optical and LiDAR fire severity remote...

    • tandf.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated Jun 2, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Matthew G. Gale; Geoffrey J. Cary; Marta Yebra; Adam J. Leavesley; Albert I. J. M. Van Dijk (2023). Comparison of contrasting optical and LiDAR fire severity remote sensing methods in a heterogeneous forested landscape in south-eastern Australia [Dataset]. http://doi.org/10.6084/m9.figshare.19690873.v1
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Matthew G. Gale; Geoffrey J. Cary; Marta Yebra; Adam J. Leavesley; Albert I. J. M. Van Dijk
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Eastern states of Australia, Australia
    Description

    Spectral indices derived from satellite optical remote sensing data have typically been used for fire severity estimation, although other remote sensing systems such as Light Detection and Ranging (LiDAR) are increasingly applied. Despite a multitude of remotely sensed fire severity estimation methods, comparisons of method performance are few. Insights into the merits and limitations of remotely sensed fire severity methods help develop appropriate spatial tools for the management of fire-affected areas. We evaluated the performance of seven passive (optical) and active (LiDAR) remotely sensed fire severity estimation methods in classifying and explaining variation in a field-estimated modified Composite Burn Index (MCBI) for a recent large wildfire in south-eastern Australia. Our evaluation included three commonly applied indices; the differenced Normalized Burn Ratio (dNBR), Relative dNBR (RdNBR) and Relative Burn Ratio (RBR). We compared these NBR indices against two recently proposed fire severity estimation methods that have not previously been evaluated with CBI field data – the Vegetation Structure Perpendicular Index (VSPI) spectral index and the LiDAR point cloud-derived Profile Area Change (PAC), along with experimental relativized forms of these indices (RVSPI and RPAC, respectively). The RVSPI (κ = 0.47) demonstrated similar overall classification accuracy (N classes = 4) to the PAC (κ = 0.48), however both indices had lower classification accuracy than the dNBR (κ = 0.59), RdNBR (κ = 0.59) and RBR (κ = 0.61). The VSPI and PAC were unable to accurately represent non-structural changes caused by lower severity fire. Application of these optical and LiDAR indices should consider their discussed limitations in relation to the objectives of their application.

  11. d

    Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Thematic Burn...

    • catalog.data.gov
    • s.cnmilf.com
    Updated Oct 1, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Geological Survey (2025). Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Thematic Burn Severity Mosaic for CONUS in 2022 [Dataset]. https://catalog.data.gov/dataset/rapid-assessment-of-vegetation-condition-after-wildfire-ravg-thematic-burn-severity-mosaic
    Explore at:
    Dataset updated
    Oct 1, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    The RAVG (Rapid Assessment of Vegetation Condition after Wildfire) program provides assessments of vegetation conditions following large fires on forested lands. Fire effects are represented by three metrics: percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized Composite Burn Index (CBI). These data are derived from moderate resolution multi-spectral imagery (e.g., Landsat 8 Operational Land Imager or Sentinel-2 Multispectral Instrument). The Relative Differenced Normalized Burn Ratio (RdNBR), which is correlated to the variation of burn severity within a fire, is calculated from a pair of images (pre- and postfire), judiciously selected to capture fire effects. The three-severity metrics are in turn calculated from RdNBR using regression equations developed from and calibrated with historical field data. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2022. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.

  12. f

    DataSheet1_Characterizing spatial burn severity patterns of 2016 Chimney...

    • frontiersin.figshare.com
    bin
    Updated Jun 2, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Taejin Park; Sunhui Sim (2023). DataSheet1_Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data.docx [Dataset]. http://doi.org/10.3389/frsen.2023.1096000.s001
    Explore at:
    binAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Frontiers
    Authors
    Taejin Park; Sunhui Sim
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Chimney Tops
    Description

    The Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems.

  13. Data for: Fuel treatment effectiveness in the context of landform,...

    • agdatacommons.nal.usda.gov
    bin
    Updated Jan 22, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Susan J. Prichard; Nicholas A. Povak; Maureen C. Kennedy; David W. Peterson (2025). Data for: Fuel treatment effectiveness in the context of landform, vegetation and large, wind-driven wildfires [Dataset]. http://doi.org/10.2737/RDS-2020-0003
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    Susan J. Prichard; Nicholas A. Povak; Maureen C. Kennedy; David W. Peterson
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This data publication represents source data and R scripts for a manuscript entitled "Fuel treatment effectiveness in the context of landform, vegetation and large, wind-driven wildfires" (Prichard et al. 2020). This study was conducted on the 2014 Carlton Complex study area in north-central Washington State. The R scripts include code needed to run the random forest analysis (RF), simultaneous autoregression (SAR) and final plots for the publication. The source data tables include 30-meter (m) resolution point datasets with location x and y representing UTM northing and easting locations, respectively, of every 30 m pixel within the Carlton Complex study areas (A = South, B = North), fire severity indices (dNBR, RBR, RdNBR) as response variables, and predictor variables including past fuel treatments, vegetation type, landform, and weather. Source predictor variables were taken from datasets that preceded the 2014 wildfires.Large wildfires (>50,000 hectares) are becoming increasingly common in semi-arid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State.These data were published on 02/19/2020. On 03/22/2024, minor metadata updates were made.

  14. Burn Severity Datasets for British Columbia 1985-2019

    • zenodo.org
    zip
    Updated Feb 6, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Carole Mahood; Carole Mahood (2025). Burn Severity Datasets for British Columbia 1985-2019 [Dataset]. http://doi.org/10.5281/zenodo.14811612
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 6, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Carole Mahood; Carole Mahood
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    British Columbia
    Description

    Burn severity datasets for wildfires in British Columbia (BC) that occurred between 1985 and 2019 were created based on methodology and JavaScript code developed by Parks et al. (2018) with minor modifications to account for BC's growing season: pre- and post-fire scene selection dates were changed to June 1-Sept 30. Floating point geoTIFFs were created in Google Earth Engine’s code development environment using JavaScript; all other processing was completed using ArcGIS 10.6 & Python 2.7.

    Only wildfires >25ha are included in this repository. Fire perimeters from BC's Wildfire Perimeters - Historical spatial dataset were used to determine wildfire extents. Fire numbers correspond to the [FIRE_NUMBER] field within this dataset.

    Please be aware that no field verification has been performed on this data.

    Burn Severity Metrics

    • dnbr – differenced normalized burn ratio
    • rbr – relativized burn ratio
    • rdnbr – relativized differenced normalized burn ratio

    Each metric has also been run with an offset (see “_w_offset” directories), which includes pixels from a 180m buffer outside the mapped fire perimeter. This offset may help assess unburned areas within the fire perimeter as long as the cover types outside the perimeter are similar to the cover types within the perimeter.

    Available Datasets

    There are 5 raster and/or vector datasets available for each fire:

    1. Floating point geoTIF as exported from Google Earth Engine (GEE)

    o

    o ex. C10006_1985_dnbr.tif

    2. Integer geoTIF

    o Created from the floating point geoTIF

    o

    o ex. C10006_1985_dnbr_int.tif

    3. Integer geoTIF clipped to mapped fire perimeter

    o Fire perimeter from the BC Wildfire Perimeter - Historical spatial dataset , with any internal holes removed, is used to clip the integer geoTIF

    o

    o ex. C10006_1985_dnbr_masked.tif

    4. Reclassed clipped geoTIF

    o Clipped geoTIF is reclassed using the thresholds in Table 1 below (from Parks et al. 2018).

    o

    o ex. C10006_1985_dnbr_reclass.tif

    Table 1: Burn severity thresholds from Parks et al.

    Low

    Moderate

    High

    dNBR

    <=185

    186-417

    >=418

    dNBR with offset

    <=159

    160-392

    >=393

    RdNBR

    <=248

    249-544

    >=545

    RdNBR with offset

    <=212

    213-511

    >=512

    RBR

    <=135

    136-300

    >=301

    RBR with offset

    <=115

    116-282

    >=383

    5. Reclassed burn severity shapefile

    o Reclassed geoTIF is converted into a polygon shapefile and the classification is converted to text (Low, Moderate, High); no smoothing is applied

    o

    o ex. C10006_1985_dnbr_reclass_poly.shp

    Known Issues

    A small number of fires failed during the Google Earth Engine processing (see Table 2). These fires have no burn severity classification available.

    Table 2. Fires without burn severity information available

    Year

    Fire Number

    1985

    N50175

    N70035

    V70088

    1988

    V50004

    1990

    C50050

    K10013

    R50117

    V50055

    VB0002

    1992

    K50034

    1993

    VB0007

  15. Geospatial data for 2017-2018 wildland fires in the southwestern United...

    • agdatacommons.nal.usda.gov
    bin
    Updated Jan 22, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Alicia L. Reiner; Craig R. Baker; Maximillian M. Wahlberg (2025). Geospatial data for 2017-2018 wildland fires in the southwestern United States used for region-specific Rapid Assessment of Vegetation Condition after Wildfire (RAVG) models: burned area boundaries and burn indices derived from Landsat and Sentinel-2 satellite imagery [Dataset]. http://doi.org/10.2737/RDS-2022-0019
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    Alicia L. Reiner; Craig R. Baker; Maximillian M. Wahlberg
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Southwestern United States, United States
    Description

    These data were derived to develop fire effects models tailored to the southwest U.S. for use in the Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program at the USDA Forest Service Geospatial Technology and Applications Center (GTAC). They include a vector dataset comprising boundaries for the 23 fires in Arizona and New Mexico that were sampled for this project and raster datasets containing burn-related indices for each fire. The raster data were derived from satellite imagery (Landsat-8 Optical Line Imager (OLI) or Landsat-7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-2 Multispectral Imager (MSI)) and include six indices derived from each of four pairs of images for a total of 24 raster datasets for each fire or cluster of adjacent fires. The indices are the dNBR (delta normalized burn ratio), the RdNBR (relativized dNBR), and the relative burn ratio (RBR), each calculated with and without a scene-pair-specific offset value used to account for non-fire differences between the two scenes. The four image pairs consist of two Landsat pairs and two Sentinel-2 pairs. Each pair includes one pre-fire scene and one post-fire scene. For each sensor (Landsat and Sentinel-2), one pair captures change visible within a few weeks after fire containment and the other captures change visible approximately one year after the fire. All fires occurred in 2017 or 2018. Imagery acquisition dates are from 2015 to 2019.These data were collected to develop fire effects models tuned to the southwest United States to supplement or replace models developed from data collected in the Sierra Nevada, northern California and southern Oregon.For more information about these data, see Reiner et al. (2022)

  16. a

    Eaton Final trimmed

    • hub.arcgis.com
    Updated Jan 17, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    NASA ArcGIS Online (2025). Eaton Final trimmed [Dataset]. https://hub.arcgis.com/datasets/a34dfda63675404795f01fb8ae054514
    Explore at:
    Dataset updated
    Jan 17, 2025
    Dataset authored and provided by
    NASA ArcGIS Online
    Area covered
    Description

    Date of Image(s):Pre-fire images taken on 12/01/24, 12/06/24, 12/18/24, and 12/26/24. Post-fire images taken on 1/10/25 and 1/12/25.Date of Next Image:Another iteration of this product can be made available following additional suitable Sentinel-2 overpasses of the area.Summary:This is a binary burned area product that identifies areas that burned during a fire and has a spatial resolution of 20m. The product was developed using data from Sentinel-2 and VIIRS. For Sentinel-2, we compute the 75th percentile of the relative difference normalized burn ratio index (RdNBR) using Sentinel-2 NIR (8a) and SWIR (12) bands following Miller and Thode, 2006. Burn severity is classified into high, moderate, low, and unchanged based on the computed RdNBR. For VIIRS, we aggregate all active fire detections within the area of interest and apply a 375m buffer around the center of each active fire detection. To be counted as burned in our binary burned area product, a 20-m grid cell must have either an RdNBR of moderate or high, or it must have an RdNBR of low and fall inside of a VIIRS active fire detection.Suggested Usage:The product is a given as a shapefile where all points inside the polygons are considered burned and all points outside the polygons are considered unburned.Satellite/Sensor:Sentinel-2 and VIIRSResolution:20 metersCredits:E.B. Wiggins, E.M. Gargulinski, and N. RosenthalNASA LaRC and Kettle Reinsurance CompanyContact: Elizabeth.b.wiggins@nasa.govEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/california_wildfires_202501/Palisades_Binary_Burn/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2025/california_wildfires_202501/viirs_sentinel2_binaryburn/

  17. d

    Data from: Drought then wildfire reveals a compound disturbance in a...

    • datadryad.org
    • search.dataone.org
    zip
    Updated Feb 23, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Lewis Walden (2022). Drought then wildfire reveals a compound disturbance in a resprouting forest [Dataset]. http://doi.org/10.5061/dryad.s7h44j166
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 23, 2022
    Dataset provided by
    Dryad
    Authors
    Lewis Walden
    Time period covered
    Apr 16, 2021
    Description

    The frequency and intensity of forest disturbances such as drought and fire are increasing globally, with an increased likelihood of multiple disturbance events occurring in short succession. Disturbances layered over one another may influence the likelihood or intensity of subsequent events (a linked disturbance) or impact response and recovery trajectories (a compound disturbance), with substantial implications for ecological spatiotemporal vulnerability. This study evaluates evidence for disturbance interactions of drought followed by wildfire in a resprouting eucalypt-dominated forest (the Northern Jarrah Forest) in southwestern Australia. Sites were stratified by drought (high, low) from previous modelling and ground validation, and fire severity (high, moderate, unburnt) via remote sensing using the relative difference normalised burn ratio (RdNBR). Evidence of a linked disturbance was assessed via fine fuel consumption and remotely sensed fire severity (RdNBR). Compound dis...

  18. f

    Data_Sheet_1_Comparing Remote Sensing and Field-Based Approaches to Estimate...

    • frontiersin.figshare.com
    zip
    Updated Jun 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Brieanne Forbes; Sean Reilly; Matthew Clark; Ryan Ferrell; Allison Kelly; Paris Krause; Corbin Matley; Michael O’Neil; Michelle Villasenor; Mathias Disney; Phil Wilkes; Lisa Patrick Bentley (2023). Data_Sheet_1_Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity.zip [Dataset]. http://doi.org/10.3389/ffgc.2022.818713.s001
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Brieanne Forbes; Sean Reilly; Matthew Clark; Ryan Ferrell; Allison Kelly; Paris Krause; Corbin Matley; Michael O’Neil; Michelle Villasenor; Mathias Disney; Phil Wilkes; Lisa Patrick Bentley
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales.

  19. d

    Post-fire conifer regeneration observations for National Forest land in...

    • catalog.data.gov
    • data.usgs.gov
    • +2more
    Updated Oct 1, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Geological Survey (2025). Post-fire conifer regeneration observations for National Forest land in California (2009 - 2017) [Dataset]. https://catalog.data.gov/dataset/post-fire-conifer-regeneration-observations-for-national-forest-land-in-california-2009-20
    Explore at:
    Dataset updated
    Oct 1, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    California
    Description

    This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

  20. n

    ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana,...

    • access.uat.earthdata.nasa.gov
    • cmr.earthdata.nasa.gov
    • +2more
    zip
    Updated Feb 26, 2019
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2019). ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 [Dataset]. http://doi.org/10.3334/ORNLDAAC/1595
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 26, 2019
    Time period covered
    Jul 26, 2017 - Jul 28, 2017
    Area covered
    Description

    This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
California Department of Forestry and Fire Protection (2025). California Vegetation Burn Severity Data Online Viewer Web App [Dataset]. https://data.cnra.ca.gov/dataset/california-vegetation-burn-severity-data-online-viewer-web-app
Organization logo

California Vegetation Burn Severity Data Online Viewer Web App

Explore at:
html, arcgis geoservices rest apiAvailable download formats
Dataset updated
Aug 26, 2025
Dataset authored and provided by
California Department of Forestry and Fire Protectionhttp://calfire.ca.gov/
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
California
Description

RdNBR is a remotely sensed index of the pre- to post-fire change in vegetation greenness, in this case the growing seasons in the year prior to and the year after the year in which the fire occurred. The mean composite scene selection method utilizes all valid pixels in all Landsat scenes over a specified date range to calculate the fire severity index. The CBI is a standardized field measure of vegetation burn severity (Key and Benson 2006), which here is predicted from a remotely sensed fire severity index using regression equations developed between CBI field plot data and the remote index, RBR (Parks et al 2019). The dataset featured provides an estimation of fire severity of past fires, with fire severity defined here as fire-induced change to vegetation. The dataset is limited to fires included in CAL FIRE’s Historic Wildland Fire Perimeters database and therefore is subject to the same limitations in terms of missing or erroneous data.


This web app was developed to satisfy the requirements of Senate Bill No. 1101: An act to amend Sections 10295 and 10340 of the Public Contract Code, and to add Section 4114.4 to the Public Resources Code, relating to fire prevention.

Methods:
To develop these datasets, a feature service for fire perimeters was created from the CAL FIRE Fire and Resource Assessment Program’s Historic Wildland Fire Perimeters database (firep23_1) for fires or fires that were a part of complexes >= 1,000 acres from 2015 to 2023. This feature service is viewable on the California Vegetation Burn Severity Viewer and used to discover the RdNBR and CBI vegetation burn severity datasets. The feature service is titled Burn Severity Fire Perimeters (firep23_1_2015_2023_Fires_Complex_1000ac). After this feature service was uploaded to Google Earth Engine (GEE) as an asset, the Parks et al. 2018 script was used to generate RdNBR values with offset (rdnbr_w_offset) data for each individual fire and the Parks et al. 2019 script was used to generate bias corrected Composite Burn Index values (cbi_bc) data for each individual fire using 30m resolution Landsat Collection 2 data. To specify the date range of Landsat satellite images to be queried to create the one-year pre-fire and one-year post-fire mean composite image scenes in both scripts, the variable 'startday' was set to 152 (June 1st) and the variable 'endday' was set to 258 (September 15th) for all fires, as specified in Parks et al. (2019). These variables were used to define the ranges of Landsat scenes that were queried to create the one-year-pre-fire and one-year-post-fire mean composite Landsat scenes. These values were used, as they were detailed as the leaf-on period for the State of California in Parks et al. 2019.

Once the RdNBR raster data for each fire had been produced using Parks et al. 2018's GEE script and the CBI raster data for each fire had been produced using Parks et al. 2019's GEE script, a Python script (run in a Jupyter Notebook embedded in the ArcGIS Pro software) was used to clip each fire-specific, continuous feature class to the extent of its fire perimeter. Each CBI feature class was additionally clipped to the extent of Conifer Forest and Hardwood Forest classes (defined in FVEG15's WHR13 Lifeform class for fires from 2015 to 2021 and defined in FVEG22's WHR13 Lifeform class for fires from 2022 to 2023).Once each continuous feature class had been clipped, values were reclassified to create a discrete RdNBR and CBI feature classes. Classes for RdNBR were arbitrarily chosen and do not correspond to meaningful categories of burn severity. Higher RdNBR values do indicate greater loss of vegetation greenness and negative values indicate an increase in greenness, but there is not necessarily a direct or linear correlation between RdNBR values and impacts to vegetation or ecological effects. Remotely sensed fire severity indices are translated into CBI using regression equations developed between CBI field plot data and the remote indices. Very few CBI plots exist in California or elsewhere in the U.S. for vegetation types other than forest. We therefore chose to include only forest vegetation in our CBI dataset.

Classes for RdNBR were as follows:
Code | Lower Limit (RdNBR) | Upper Limit (RdNBR)

Search
Clear search
Close search
Google apps
Main menu