The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (including wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector polygon shapefile of the location of all currently inventoried fires occurring between calendar year 1984 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 were not discernable from available imagery.

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Map ServicesThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This map layer is a vector polygon shapefile of the perimeters of all currently inventoried fires occurring between calendar year 1984 and 2022 that do not meet standard MTBS size criteria. These data are published to augment the data that are available from the MTBS program. This product was produced using the methods of the Monitoring Trends in Burn Severity Program (MTBS); however, these fires do not meet the size criteria for a standard MTBS assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically maps fires using an initial assessment (immediately after the fire) or an extended assessment (peak of green the season after the fire) for low-biomass and high-biomass fires respectively. Refer to MTBS.gov for more information on MTBS methods and criteria. Standard MTBS mappings must meet the size criteria of at least 500 acres for the eastern states and territories and 1,000 acres for the western states and territories to be eligible for mapping. Undersized MTBS fires are those fires that do not meet the standard MTBS size criteria but are otherwise mapped using standard MTBS methodologies.

This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2020 that do not meet standard MTBS size criteria. These data are published to augment the data that are available from the MTBS program. This product was produced using the methods of the Monitoring Trends in Burn Severity Program (MTBS), however these fires do not meet the size criteria for a standard MTBS assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically maps fires using an initial assessment (immediately after the fire) or an extended assessment (peak of green the season after the fire) for low-biomass and high-biomass fires respectively. Refer to MTBS.gov for more information on MTBS methods and criteria. Standard MTBS mappings must meet the size criteria of at least 500 acres for the eastern states and territories and 1,000 acres for the western states and territories to be eligible for mapping. Undersized MTBS fires are those fires that do not meet the standard MTBS size criteria but are otherwise mapped using standard MTBS methodologies.

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Wildland fires, commonly called wildfires, originate from many sources and can have a wide range of impacts. Collecting consistent information on when a fire starts, the cause, and how large of an area they impact can be difficult as many different agencies (local, state, federal) are involved in responding. This was especially true before coordinated multi-agency efforts were established in the late 2000s. In an effort to create a temporally consistent analysis of wildland fires in the USA, the US Geological Survey and Forest Service have teamed up to create the Monitoring Trends in Burn Severity program (MTBS). Using Landsat's 40+ year archive of satellite observations of burn scars, MTBS is able to consistently assess and document the effects of fire at a national scale. With the establishment of IRWIN, MTBS has been able to integrate their database of fires into its products. IRWIN data will be replaced by NIFC in subsequent versions of the data. Fire Type definitionsWildfire - An unplanned, unwanted wildland fire including unauthorized human-caused fires, escaped wildland fire use events, escaped prescribed fire projects, and all other wildland fires where the objective is to put the fire out (definition currently under NWCG review).Prescribed Fire - Any fire intentionally ignited by management actions in accordance with applicable laws, policies, and regulations to meet specific objectives.Wildland Fire Use - An event dealing with the management of a naturally ignited wildland fire to accomplish specific prestated resource management objectives in predefined geographic areas outlined in the Fire Management Plans.Unknown - A fire event whose incident type was not reported from the original reporting agency.Complex - Two or more individual incidents located in the same general area which are assigned to a single incident commander or unified command.Out of Area Response - Multiple resource response to a wildfire incident. This term applies exclusively to wildfire incidents.TimeA time field is included for animating or querying. To animate the map, select the map Options and "Enable Time Animation."UpdatesThis layer will be updated once a year with the regular releases of data by MTBS.

These data were generated to map spatial burn severity and emissions of each historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California in the US. Event-based assessments were conducted at 30-m resolution for all fires and daily emissions were calculated at 500-m resolution for fires burned since 2002. A total of 1697 wildfires were assessed using the Wildfire Burn Severity and Emissions Inventory(WBSE) framework developed by Xu et al 2022. The comprehensive, long-term event and daily emissions records described here could be used to study health effects of wildfire smoke, either by combining them with transport modeling to model air quality and estimate exposures, or by incorporating them into statistical models predicting health impacts as a direct function of estimated emissions. These data will also facilitate analyses of changing emissions impacts on the carbon cycle over the last three decades. High resolution severity and emissions raster maps are generated for each fire event to support further spatial analysis. While the emissions calculated for California with WBSE are not a substitute for real-time daily emissions estimates, it is designed to extend the estimated emissions record back to 1984 with a finer spatial resolution and provide more up-to-date estimates on emissions factors reflecting information from California's recent extreme fires. Methods This dataset provides estimates of 30 m resolution burn severity, and emissions of CO2, CO, CH4, non-methane organic compounds (NMOC), SO2, NH3, NO, NO2, nitrogen oxides (NOx = NO + NO2), PM2.5, OC, and BC. WBSE was implemented for California large wildfires on a per-fire event scale since 1984 and also a daily scale since 2002. The inventory implementation steps, input datasets, and output data are summarized in figure 1 in Xu et al, 2022. Burn severity calculation Fire records for California from 1984 to 2019 were retrieved from MTBS (https://mtbs.gov/viewer/index.html) via interactive viewer on 8 May 2021, resulting in a dataset with a total of 1623 wildfires. We also acquired fire perimeters for 74 large wildfires in 2020 from CAL FIRE (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) and calculated dNBR for each 2020 fire using the dNBR calculation tool with Google Earth Engine (GEE). This process first selects either initial assessment or extended assessment for each fire. The initial assessment utilizes Landsat images acquired immediately after a fire to capture first-order fire effects. The extended assessment uses images obtained during the growing season following the fire to identify delayed first-order effects and dominant second-order effects (Eidenshink et al 2007). We utilized LANDFIRE Biophysical Settings (BPS) to determine which assessment type to apply for each fire burned in 2020. After Picotte et al (2021), we used extended assessment if the majority of general vegetation groups within the fire perimeter are forests, while initial assessment is used when the majority of general vegetation groups are grassland/shrubland. By contrast, MTBS uses extended assessment for forest and shrubland types. We did not delineate grasslands into burn severity categories. Instead, we classified them as burned ('grass burn') because of difficulties in assessing vegetation change. Post-fire images for extended assessment were selected during the next peak of the green season (June–September) using the mean compositing approach suggested by Parks et al (2018). Composite post-fire images acquired immediately within two months after the fire containment dates were used for the initial assessment. Composite pre-fire images for extended and initial assessments were acquired with the matching periods from the preceding year. The dNBR images were produced by quantifying the spectral difference between composite pre-fire and post-fire Landsat scenes. We calculated the unitless, continuous CBI variable from dNBR/NBR values using the linear and Sigmoid B regression models developed for the CONUS by Picotte et al (2021). CBI values were then classified following thresholds modified based on Crotteau et al(2014) into six severity classes: unburned, low severity, moderate severity, high severity, grass burn, and non-processing area. Emissions calculation Emissions of all species are calculated as a function of area burned, fuel loading, the fraction of vegetation burned based on burn severity, and an emissions factor specific to each vegetation type using the following equation modified from the FINN model (Wiedinmyer et al 2011). Fuel categories were assigned from LANDFIRE EVT products. For emissions calculations, EVT data were then categorized into five general vegetation categories: grass, shrub, forest under 5500 feet (1676 m), forest between 5500–7500 feet (1676–2286 m), and forest above 7500 feet (2286 m), updated for California ecosystems. Fuel consumption was determined following Hurteau et el 2014 assigning fuel loading and consumption values for each severity class for the five general vegetation categories based on the First Order Fire Effects Model v5 (Reinhardt et al 1997). Emission factors for greenhouse gases, particulate matter, and reactive trace gases were updated with recent data for each general vegetation class using results from recent field campaigns and studies specific for California ecosystems and Western U.S. ecosystems. Day of burning and daily emissions To assign the day of burning for individual pixels, NASA fire information for resource management system (FIRMS) active fire products from MODIS (Collection 6) within 750 m of the fire perimeter shapefiles supplied by MTBS or CAL FIRE were selected for interpolation to account for detections that might be outside the boundary due to detection radius. VIIRS 375 m data, when available since 2012, was added to complement MODIS data with improved performance to assign burn dates using the fire progression raster tool (figure 4). We filtered the MODIS/VIIRS detection points to the date range of interest and created a 500 m buffer around each point. Points were then converted to circle polygons to represent each point's detection extent properly. The average date was selected as the proper date in regions of overlapping buffers. We then calculated daily emissions and assigned them to the centroids of the aggregated daily progression polygons.

LANDFIRE's Annual Disturbance products track how landscapes change across space and time on an annual basis. The Annual Disturbance (Dist) product identifies satellite-detected areas larger than 4.5 hectares (11 acres) that underwent natural or human-caused changes within a specific year (for Dist23, October 1, 2022 – September 30, 2023), or represent fire activity/field treatments as small as 80 square meters. While creating the Annual Disturbance product a variety of data sources are leveraged. 1) National fire mapping programs: This includes information from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), which offer severity information for fire-caused disturbances. 2) Agency-reported events: There are 18 designated classes for contributed polygon "Event" types such as disease, insects, development, harvest, etc. that are reported by government agencies for inclusion into the disturbance product. 3) Remotely sensed imagery: Harmonized Landsat Sentinel (HLS) satellite images offer a comprehensive-uninterrupted view of the landscape covering all lands, public and private, to fill in the gaps inherent in the previous data sources. These data are reviewed and edited by a team of image analysts to ensure and maintain high quality standards. To create the LF Annual Disturbance product, individual Landsat scenes are stacked and made into composites representing the 15th, 50th, and 90th percentiles of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year and the two prior years serves as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are most commonly caused by differences in annual or seasonal phenology, artifacts in the image composites, or difficult to map classes such as wetlands and grasses. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from modeling are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed as fire by using Burned Area (BA) Level-3 science products derived from Landsat 8 and 9. BA data is only available in the lower 48 states (CONUS). Causality information assigned to annual disturbance products are prioritized by source, with the highest priorities reserved for fire mapping program data (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, satellite image-based change. Severity is assigned directly from fire program data. For events and satellite-detected change, severity is derived from pre- and post-burn standard deviation values of the differenced Normalized Burn Ratio (dNBR). When mapping the LF Annual Disturbance product, the start date is utilized for disturbances from fire program data whereas all other disturbances utilize the end date.

This product ("Prairie fires") presents burned area boundaries for The Flint Hills Ecoregion (KS and OK), one of the most fire prone ecosystems in the United States where hundreds of thousands of acres burn annually as prescribed fire and wildfire. The prairie fire products provide the extent of larger prairie fires in the Flint Hills to record the occurrence of fire and can be used to identify individual burned areas within the perimeters. This product is published to provide fire information of the most fire prone ecosystems to individuals and land management communities for assessing burn extent and impacts on a time sensitive basis. The methods used to produce the prairie fire products from 2019 to present are different than Monitoring Trends in Burn Severity Program (MTBS) methods. The product is developed by running a classification tree model on Landsat and Sentinel imagery for all available image dates with visible fires and without greater than 80 percent cloud cover in the spring of each year. The model takes each image, uses all Landsat bands 2-7 or Sentinel 2b bands 2-4, 8, 11, and 12, and finds thresholds between burnt and unburnt areas to create perimeters. Fire perimeters are created by the model and no manual editing is performed. Thus, these data are 100 percent (model based) auto-generated, however, analysts do review and remove small polygons less than 3 acres. The Prairie Fire dataset will include multi-part polygons and have one record for each source image date. These new methods are optimized to efficiently map and characterize the large number of fires that occur in this region on an annual basis. Prior to 2019, the standard MTBS fire mapping methods were used. Because of the unique frequency and extent of fire in this prairie biome, these fire products are now delivered through the Burn Severity Portal and are no longer included as part of the MTBS products unless a fire is identified in IRWIN, NFPORS or a legacy federal fire occurrence database. The provided data products will vary slightly based on the mapping methodology applied at the time of fire occurrence (pre-2019 or 2019 and later). This map layer is a vector point shapefile of fires occurring three acres and greater in size between calendar year 2009 and 2024 for the Flint Hills Ecoregion.

Area burned is an important variable for measuring wildfire activity. In the western United States (US), the timing and magnitude of area burned can be associated with meteorological and human activity to find the drivers of wildfire activity, but this type of research is dependent on the spatial and temporal resolution of available wildfire datasets. The Western US MTBS-Interagency (WUMI2) database is a dataset of wildfire events in the western United States (US) larger than 1 km2 for 1984 to 2020. WUMI2 includes the important Monitoring Trends in Burned Severity (MTBS) project (Eidenshink et al., 2007)—a Landsat satellite-based dataset of large fires (>4.04 km2)—and adds small (>1 to 4.04 km2) and large fires from government agency databases, including from the Fire Program Analysis (FPA) fire-occurrence database (Short et al., 2022). We performed extensive quality control to merge the datasets together and remove errors. The result is a western US-wide dataset with accurate fir..., Version WUMI2 Updated August 1, 2024: Our WUMI2 fire database consists of 21,693 western US fire events from 1984 through 2020. A text file (west_US_fires_1984-2020_WUMI2.txt) provides a list of each fire event, including the fire’s name, discovery date, point location, total area burned, and forested area burned (see the corresponding readme.txt file for column labels). We also include NetCDF files of the 1-km map of forest fractional coverage (forest_type_frac.nc) and the 1-km maps of monthly burned area over 1984–2020 (burnarea_1984-2020_WUMI2.nc). Fires included in this database are from the Monitoring Trends in Burned Severity Product (MTBS) (Eidenshink et al., 2007), the Fire Program Analysis fire-occurrence database (FPA FOD 6th edition) of interagency fires (Short, 2022), and interagency fires from local databases (CalFire, ST/C&L, TRIBE), and interagency fires from government agency databases (BIA, BLM, BOR, DOD, DOE, NPS, FWS, FS, NPS). More information on methodology can ..., The name of this version of the database is WUMI2. When using this database, please cite the following databases: Juang, C. S., Williams, A. P., Abatzoglou, J. T., Balch, J. K., Hurteau, M. D., & Moritz, M. A. (2022). Rapid growth of large forest fires drives the exponential response of annual forest-fire area to aridity in the western United States. Geophysical Research Letters, 49, e2021GL097131. https://doi.org/10.1029/2021GL097131. Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z. L., Quayle, B., & Howard, S. (2007). A project for monitoring trends in burn severity. Fire ecology, 3, 3-21. https://doi.org/10.4996/fireecology.0301003 Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6, # Western US MTBS-Interagency (WUMI) wildfire dataset

Dataset: Western US MTBS-Interagency (WUMI) Wildfire database

Version: WUMI2

Authors: Caroline S. Juang, A. Park Williams

Format: TXT

Last updated: 08/01/2024

DOI: https://doi.org/10.5061/dryad.sf7m0cg72

Description of the data

Our WUMI2 fire database consists of 21,693 western US fire events from 1984 through 2020. A text file (west_US_fires_1984-2020_WUMI2.txt) provides a list of each fire event, including the fire’s name, discovery date, point location, total area burned, and forested area burned (see the corresponding** readme.txt file for column labels). We also include NetCDF files of the 1-km map of forest fractional coverage (forest_type_frac.nc) and the 1-km maps of monthly burned area over 1984–2020 (burnarea_1984-2020_WUMI2.nc).** Fires included in this database are from the Monitoring Trends in Burned Severity Product (MTBS) ([Eidens...

These data show all the postfire erosion results affiliated with this data release summed by wildfire and attached to a polygon of each fire perimeter, as defined by Monitoring Trends in Burn Severity (MTBS). The results are shown as attributes for each polygon of wildfire perimeter. Some of the original MTBS data (name, ignition date, and ID) were preserved to allow for joining to other MTBS data. Results include WEPP modeling results of hillslope and channel erosion, a sum of postfire debris flow modeling results and field-based measurements, and a few derived results such as total sediment and total yield (mass per area).

Introduction Climate change is predicted to increase the frequency of extreme single-day fire spread events, with major ecological and social implications. In contrast with well-documented spatio-temporal patterns of wildfire ignitions and perimeters, daily progression remains poorly understood across continental spatial scales, particularly for extreme single-day events (“blow ups†). Here, we characterize daily wildfire spread across North America, including occurrence of extreme single-day events, duration and seasonality of fire and extremes, and ecoregional climatic niches of fire in terms of Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD) annual climate normals. Methods Remotely sensed daily progression of 9,636 wildfires ≥400 ha was used to characterize ecoregional patterns of fire growth, extreme single-day events, duration, and seasonality. To explore occurrence, extent, and impacts of single-day extremes among ecoregions, we considered complementary ecoregional..., Daily Fire Progression and Identification of Extreme Events We measured daily area burned (ha d-1) for individual wildfires by interpolating spatially continuous daily progression maps following methods developed by Parks (2014; Fig. S1). This technique interpolates VIIRS and MODIS hotspot detections to map the most likely day of burning at 30-m resolution within final wildfire perimeters obtained from national repositories. Previous studies have successfully utilized this technique to study various aspects of fire activity, including daily area burned (Hart and Preston 2020), spread (Holsinger et al. 2016, Wang et al. 2017), and refugia (Meigs et al. 2020, Downing et al. 2021). We constrained all daily progression interpolations to final fire perimeters obtained from the Monitoring Trends in Burn Severity (MTBS, USA; USDA Forest Service and USGS (2023)) and National Burned Area Composite (NBAC, Canada; Hall et al. (2020)) national repositories. Centroids of final fire perimeters were u..., , # Day of burning dataset: Biogeography of daily wildfire progression

https://doi.org/10.5061/dryad.2jm63xswg

Daily fire progression dataset of daily areas burned.

Description of the data and file structure

Variable Descriptions: Ecoregion_10: US EPA level 1 ecoregion fire occurred in. combined.ID: concatenation of MTBS or NBAC fire.id and fire.year (year of fire's occurrence) fire.year: year of fire's occurrence fire.id: MTBS or NBAC event ID of final fire perimeter DOB: day of burning as day of year, numeric (1-366) pixel.count: n 30x30m pixels per DOB region area.ha: DOB region area in hectares Within.Ecoregion.Areal.Ex.Threshold: area burned threshold for classifying days as extreme events; defined as ecoregional mean + 2 SD extreme.day: binary variable, FALSE = daily area burned did not exceed Within.Ecoregion.Areal.Ex.Threshold, TRUE = daily area burned exceeded Within.Ecoregion.Areal.Ex.Threshold Country: Country fire occurred in; ...

LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.

This product is published on a provisional basis to provide necessary information to individuals assessing burn severity impacts on a time sensitive basis. This product was produced using the methods of the Monitoring Trends in Burn Severity (MTBS) Program; however, this fire may not meet the criteria for an MTBS initial assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically maps fires using an initial assessment (immediately after the fire) or an extended assessment (peak of green the season after the fire) for low-biomass and high-biomass fires respectively. Refer to MTBS.gov for more information on MTBS methods and criteria. Fires reported as greater than 40,000 acres in burned area are mapped on a provisional basis, using an initial assessment strategy regardless of vegetation type or density, provided suitable imagery is available. Once imagery for an extended assessment is available, this fire will be assessed under the MTBS program and an official MTBS initial or extended assessment product will be published under that program. This map layer is a vector point shapefile of the location of all currently inventoried Provisional Initial Assessment fires occurring between calendar year 2020 and 2024 for CONUS, Alaska, Hawaii, and Puerto Rico.

The data generated by Monitoring Trends in Burn Severity (MTBS) will be used to identify national trends in burn severity, providing information necessary to monitor the effectiveness of the National Fire Plan and now, the National Cohesive Wildfire Management Strategy. MTBS is sponsored by the Wildland Fire Leadership Council (WFLC), a multi-agency oversight group responsible for implementing and coordinating the National Fire Plan and Federal Wildland Fire Management Policies. The MTBS project objective is to provide consistent, 30 meter resolution burn severity data and burned area delineations that will serve four primary user groups: 1. National policies and policy makers such as the National Fire Plan and WFLC which require information about long-term trends in burn severity and recent burn severity impacts within vegetation types, fuel models, condition classes, and land management activities. 2. Field management units that benefit from mid to broad scale GIS-ready maps and data for pre- and post-fire assessment and monitoring. Field units that require finer scale burn severity data will also benefit from increased efficiency, reduced costs, and data consistency by starting with MTBS data. 3. Existing databases from other comparably scaled programs, such as Fire Regime and Condition Class (FRCC) within LANDFIRE, that will benefit from MTBS data for validation and updating of geospatial data sets. 4. Academic and agency research entities interested in fire severity data over significant geographic and temporal extents.

The U. S. Fish and Wildlife Service (FWS) requests burn severity assessments through an agreement with the U.S. Geological Survey (USGS) to be completed by analysts with the Monitoring Trends in Burn Severity (MTBS) Program. These data products are burned area boundary shapefiles derived from post-fire sensor data (including Landsat TM, Landsat ETM+, Landsat OLI). The pre-fire and post-fire subsets included were used to create Normalized Burn Ratio (NBR) and then a differenced Normalized Burn Ratio (dNBR) image. The objective of this assessment was to generate burned area boundaries for each fire. Data bundles also include post-fire subset, pre-fire subset, NBR, and dNBR images. This map layer is a thematic raster image of burn severity classes for all inventoried fires occurring in CONUS during calendar year 1996. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.