The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period between 1984 and the current MTBS release. 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 of the location of all currently inventoried and mappable MTBS fires occurring between calendar year 1984 and the current MTBS release for the continental United States, Alaska, Hawaii and Puerto Rico. Map Service Feature Layer

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.

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.

Extreme fire spread events rapidly burn large areas with disproportionate impacts on people and ecosystems. Such events are associated with warmer and drier fire seasons and are expected to increase in the future. Our understanding of the landscape outcomes of extreme events is limited, particularly whether or not they burn more severely or produce spatial patterns less conducive to ecosystem recovery. To assess relationships between fire spread rates and landscape burn severity patterns, we used satellite fire detections to create day-of-burning (DOB) maps for 623 fires comprising 4,267 single-day events within forested ecoregions of the southwestern United States. We related satellite-measured burn severity and a suite of high-severity patch metrics to the daily area burned. Extreme fire spread events (defined here as burning >4900 ha/day) exhibited higher mean burn severity, a greater proportion of area burned severely, and increased like adjacencies between high-severity pixels. ..., , , # Manuscript title: Extreme fire spread events burn more severely and homogenize post-fire landscapes in the southwestern United States

Authors: Jessika R. McFarland, Jonathan D. Coop, Jared A. Balik, Kyle C. Rodman, Sean A. Parks, and Camille S. Stevens-Rumann

Dryad DOI:Â https://doi.org/10.5061/dryad.9kd51c5sr

OVERVIEW:

This folder includes the results, statistical models, raster data, and code used to produce our manuscript on extreme fire spread events and their burn severity outcomes on fires in the southwestern US. Descriptions of each facet of archived data are below.

Spatial data overview: We used a suite of publicly available, remotely sensed data products to do our analysis. Methods are documented in detail in McFarland et al. (2025)

1. Monitoring Trends in Burn Severity (MTBS):
MTBS data were utilized to select large (>404 ha) fire perimeters within our study area using the Burned Area Boundaries Dataset. 
These data can be found at: https://www...,

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.

Increasing area burned across western North America raises questions about the precedence and magnitude of changes in fire activity, relative to the historical range of variability (HRV) that ecosystems experienced over recent centuries and millennia. Paleoecological records of past fire occurrence provide context for contemporary changes in ecosystems characterized by infrequent, high-severity fire regimes. Here we present a network of 12 fire-history records derived from macroscopic charcoal preserved in sediments of small subalpine lakes within a c. 10,000 km2 landscape in the U.S. northern Rocky Mountains (Northern Rockies). We used this network to characterize landscape-scale burning over the past 2500 yr and evaluate the precedence of widespread regional burning experienced in the early 20th and 21st centuries. We further compare the Northern Rockies fire history to a previously published network of fire-history records in the Southern Rockies. In Northern Rockies subalpine forests, widespread fire activity was strongly linked to seasonal climate conditions, in contemporary, historical, and paleo records. The average estimated fire rotation period (FRP) over the past 2500 years was 164 yr (HRV: 127-225 yr), while the contemporary FRP from 1900-2021 CE was 215 yr. Thus, extensive regional burning in the early 20th century (e.g., 1910 CE) and in recent decades was within the HRV of recent millennia. Results from the Northern Rockies contrast with the Southern Rockies, which burned with less frequency on average over the past 2500 yr, and where 21st-century burning has exceeded the HRV. Our results support expectations that Northern Rockies fire activity will continue to increase with climatic warming, surpassing historical burning if more than one exceptional fire year akin to 1910 occurs within the next several decades. The societal and ecological consequences of climatic warming in subalpine forests will depend, in large part, on the magnitude of fire-regime changes relative to the past. Methods We provide paleoecological fire-history data used to support the analyses and results in Clark-Wolf et al. (2023). These data are from 12 lake-sediment records from within a c. 11,000-ha landscape in the northern Rocky Mountains, USA, spanning 46.6 to 47.5° N and 114.6 to 116° W. Sediment cores were collected from the deepest part of each lake in 2017 to 2019. Sediment cores were dated based on 210Pb activity in the upper sediments, as well as tephra layers from known volcanic eruptions and 14C dating of terrestrial macrofossils or concentrated charcoal. Sediment cores were sliced at 0.5-cm intervals and subsampled contiguously for macroscopic charcoal analysis. Charcoal pieces were counted under a stereomicroscope and used to calculate charcoal accumulation rate, which was analyzed using peak detection methods to identify peaks in charcoal accumulation inferred as local fire events. To produce a composite record of fire history across the study area, the percent of sites recording local fires per century was calculated. We also provide previously published data used in the analysis and figures presented in Clark-Wolf et al. (2023). These were obtained from publicly available datasets: MTBS [https://www.mtbs.gov], Northern Rockies Fire Atlas [https://doi.org/10.2737/RDS-2009-0006-2]), and gridMet [https://www.climatologylab.org/gridmet.html], as well as data obtained from archives associated with prior publications (Higuera et al. 2021 [https://doi.org/10.5061/dryad.rfj6q579n]).

The 2020 fire season punctuated a decades-long trend of increased fire activity across the western United States, nearly doubling the total area burned in the central Rocky Mountains since 1984. Understanding the causes and implications of such extreme fire seasons, particularly in subalpine forests that have historically burned infrequently, requires a long-term perspective not afforded by observational records. We place 21st century fire activity in subalpine forests in the context of climate and fire history spanning the past 2,000 y using a unique network of 20 paleofire records. Largely because of extensive burning in 2020, the 21st century fire rotation period is now 117 y, reflecting nearly double the average rate of burning over the past 2,000 y. More strikingly, contemporary rates of burning are now 22% higher than the maximum rate reconstructed over the past two millennia, during the early Medieval Climate Anomaly (MCA) (770 to 870 Common Era), when Northern Hemisphere temperatures were ∼0.3 °C above the 20th century average. The 2020 fire season thus exemplifies how extreme events are demarcating newly emerging fire regimes as climate warms. With 21st century temperatures now surpassing those during the MCA, fire activity in Rocky Mountain subalpine forests is exceeding the range of variability that shaped these ecosystems for millennia.

Methods Datasets used in this study are all publicly available through the links described in the manuscript (i.e., MTBS, NIFC, GridMET, LANDFIRE) or data repositories associated with the original publications (i.e., http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/fire-history for Calder et al. (2015), https://doi.org/10.5061/dryad.q2b8t for Higuera et al. (2014), https://doi.org/10.6084/m9.figshare.988687.v19 for Dunnette et al. (2014)). This Dryad repository includes all data used here, as well as all code use for analysis and to generate figures.

The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: A spatially explicit map of canopy base height supplies information used in fire behavior models such as FARSITE (Finney 1998) to determine the point at which a surface fire will transition to a crown fire. This critical canopy base height (CBH) describes the lowest point in a stand where there is sufficient available fuel (0.25 in dia.) to propagate fire vertically through the canopy. Specifically, CBH is defined as the lowest point at which the canopy bulk density is .012 kg m-3. It should be noted that LANDFIRE layers will not include canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen to reflect these conditions. This is because LANDFIRE assumes that the potential burnable biomass in the shorter tree canopies has been accounted for in the surface fuel model parameters. For example, maps of areas dominated by young or short conifer stands where the trees are represented by a shrub type fuel model will not include canopy characteristics. The CBH mapping process began by deriving field referenced estimates of canopy characteristics through LFRDB plot analysis. Approximately 70,000 plots were acquired throughout the U.S. for estimating CBH. Go to https://landfire.gov/participate_refdata_sub.php for more information regarding contributors of field plot data. Utilizing these plots, field referenced CBH values were calculated for each plot using the canopy fuel estimation software in Forest Vegetation Simulator (https://www.fs.usda.gov/fvs/). This process of deriving field referenced estimates for CBH was employed to create a training data set to model CBH values. Statistical analysis of plot variables indicated that Existing Vegetation Type (EVT) and Existing Vegetation Height (EVH) demonstrated some influence on CBH, with Existing Vegetation Cover (EVC) affecting CBH values within certain EVTs. To model these relationships, a regression tree analysis (RTA) approach was implemented in R for each EVT. (Similar to previous LANDFIRE versions, Juniper EVTs consistently showed similar CBH assignments and were given a value of 3.0m x 10). It was determined that there was not enough plot data to account for all EVC, EVH, and EVT combinations during the CBH RTA development. A filling approach was implemented to account for data gaps. The basic premise of this approach was to map assignments with the most detailed data available and fill in gaps with coarser aggregates to account for all combinations. Aggregate values for EVTs were derived at two coarser levels, existing vegetation groups (EVG) and existing vegetation systems (EVS). Each vegetation group was more generalized than the previous grouping. The resultant maps were analyzed, peer reviewed and tested to assess performance against previous LANDFIRE versions. For each vegetation grouping (or subset) a data threshold greater than or equal to thirty plots per EVT/EVG/EVS had to be reached before the RTA was implemented. All outliers greater than or equal to two standard deviations from the mean were removed prior to computing a CBH RTA value. The CBH data represented in the resultant layer are continuous from 0 to 9.9 meters (to the nearest 0.1 meter). Some stands dominated by broadleaf species which typically do not permit initiation of crown fire (e.g. Populus spp.) are coded with a CBH of 10 meters. Since crown fire is rarely observed in most hardwood stands, the highest CBH value possible was used to prevent false simulation of crown fire in these areas. Similarly, all non-forest values, including herbaceous, and shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0. Finally, certain types of agriculture and urban vegetation that are deemed burnable were assigned a constant value by LFTFC rule-sets based on region and vegetation type. LANDFIRE 2014 (lf_1.4.0) used LANDFIRE 2012 (lf_1.3.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape after 2012. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. Disturbance data used in the updating is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE events data call. Vegetation growth was modeled where disturbance occurred. Metadata and Downloads

The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.DATA SUMMARY: The existing vegetation cover (EVC) data layer depicts percent canopy cover by life form, and is an important input to other LANDFIRE mapping efforts. EVC is generated separately for tree, shrub and herbaceous life forms using training data and a series of geospatial predictor layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (http:fia.fs.fed.us) were used as the training data for tree canopy cover mapping, with canopy cover of the plots estimated from stem-mapped tree data and calibrated with line intercept field measurements of canopy cover (Toney and others 2009). Shrub and herbaceous canopy cover training data were also derived from plot-level, ground-based visual assessments. More information regarding contributors of field plot data can be found athttp:www.landfire.govparticipate_acknowledgements.php. Regression tree models were developed separately for each life form using the training data and a combination of multitemporal Landsat data, terrain data from a digital elevation model, and biophysical gradient data layers. Cubist software was used for modeling. The derived regression tree equations were then applied to the geospatial predictor data to create 30-m resolution, life form specific data layers (i.e., separate data layers are generated for tree, shrub and herbaceous vegetation cover).Each of the derived data layers (tree, shrub, herbaceous) has a potential range of 0-100 percent canopy cover. Tree, shrub and herbaceous values were binned into discrete classes (up to 10 bins at 10 percent intervals for tree, shrub and herbaceous canopy cover). The final EVC layer was evaluated and rectified through a series of QAQC measures to ensure that the life form of the canopy cover code matched the life form of the LANDFIRE existing vegetation type (EVT) layer.EVC is used in the development of subsequent LANDFIRE data layers. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone.REFRESH 2008 (lf_1.1.0):Refresh 2008 (lf_1.1.0) used Refrsesh 2001 (lf_1.0.5) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape after 2001. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in Refresh 2008 (lf_1.1.0) is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Refresh events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs.

Introduction: The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. Abstract: The existing vegetation height (EVH) data layer is an important input to LANDFIRE modeling efforts. Canopy height is generated separately for tree, shrub and herbaceous cover life forms using training data and a series of geospatial data layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (http:fia.fs.fed.us) were used as the training data for tree canopy height mapping. EVH is determined by the average height weighted by species cover and based on existing vegetation type (EVT) life-form assignments. Dominant life-form height of each plot is then binned as follows: (A) Tree classes; 0-5 m, 5-10 m, 10-25 m, 25-50 m, and greater than 50 m, (B) Shrub classes; 0-0.5 m, 0.5-1.0 m, 1.0-3.0 m, greater than 3.0 m, (C) Herbaceous vegetation classes; 0-0.5 m; 0.5-1.0 m, greater than 1 m. Go to http:www.landfire.govparticipate_acknowledgements.php for more information regarding contributors of field plot data. Decision tree models using field reference data and Land sat imagery, digital elevation model data, and biophysical gradient data, are then developed separately for each of the three life forms using C5 software. Life-form specific cross-validation error matrices are generated during this process to assess levels of accuracy of the models. Decision tree relationships are then used to generate life-form specific height class spatial data layers, which are later merged into a single composite height data layer. The final EVH layer is evaluated and rectified through a series of QAQC measures to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. EVH is used in many subsequent LANDFIRE data layers.LF 2012 (lf_1.3.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs.Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data.

Monitoring Trends in Burn Severity in the Conterminous U.S.This feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Forest Service (USFS), displays severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). Per the USFS, "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."Warren Grove FireData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Burned Area Boundaries (All Years)) and will support mapping, analysis, data exports and OGC API – Feature accessNGDAID: 116 (Monitoring Trends in Burn Severity Conterminous United States (Map Service))OGC API Features Link: (Monitoring Trends in Burn Severity in the Conterminous U.S. - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information: Monitoring Trends in Burn Severity (ver. 5.0, August 2023); MTBSSupport Documentation: Monitoring Trends in Burn Severity Burned Areas Boundaries - Metadata (07/23/23)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Land Use Land Cover Theme Community. Per the Federal Geospatial Data Committee (FGDC), Land Use-Land Cover "is a term referring collectively to natural and man-made surface features that cover the land (Land Cover) and to the primary ways in which land cover is used by humans (Land Use). Examples of Land Cover may be grass, asphalt, trees, bare ground, water, etc. Examples of Land Use may be urban, agricultural, ranges, and forest areas."For other NGDA Content: Esri Federal Datasets

The purpose of the Colorado All-Lands Quantitative Wildfire Risk Assessment (COAL) for the USFS Rocky Mountain Region (R2) is to provide foundational information about wildfire hazard and risk to highly valued resources and assets across all land ownerships in the state of Colorado. Such information supports fuel management planning decisions, as well as revisions to land and resource management plans. A wildfire risk assessment is a quantitative analysis of assets and resources and how they would be potentially impacted by wildfire. The COAL analysis considers several different components, each resolved spatially across the project area, including:likelihood of a fire burning;the intensity of a fire if one should occur;the exposure of assets and resources based on their locations;the susceptibility of those assets and resources to wildfire. This data is part of the COAL 'fuelscape', which is a data stack or collection of raster files that describe the fuel and physical environment for a given spatial extent used in fire behavior modeling. The fuelscape is also referred to as the Farsite/FlamMap data sandwich, Farsite/FlamMap landscape files, (LCP) and similar notations.In addition to canopy base height, the COAL 2021 fuelscape was updated to include wildfire and other disturbances from the year 2020 and is intended for use in the 2021 fire season. Data may be applicable beyond 2021 or may be updated to reflect disturbances occurring after 2020. The COAL 2021 fuelscape consists of geospatial data layers representing surface fuel model, canopy cover, canopy height, canopy base height, canopy bulk density, and topography characteristics (slope, aspect, elevation). The original COAL fuelscape was developed from Landfire Remap 2016 30-m raster data and edited based on feedback from interagency fuels and fire staff across Colorado. The original COAL fuelscape was updated using RAVG, MTBS, and fire perimeter datasets where available to account for wildfire disturbances that occurred between 2017 and 2019. The fuelscape was also updated with Forest Service Activity Tracking System (FACTS) and National Fire Plan Operations and Reporting System (NFPORS) treatment data. Landfire disturbance records reported a large fire perimeter (approximately 30,000 acres) in the Breckenridge, CO area from 2015. During the fuelscape calibration workshop, this was identified as an error in the FACTS database carried forward in Landfire Remap. This record was removed from the fuel disturbance grid to prevent misrepresentation of fuel reduction within the original treatment polygon.For more information regarding quantitative wildfire risk assessment, please refer to GTR-315: https://www.fs.usda.gov/rm/pubs/rmrs_gtr315.pdf. For information about the COAL QWRA, please refer to the COAL report: https://pyrologix.com/download. For details about Landfire treated map logic, refer to Landfire Rulesets Database: https://www.landfire.gov/fuel_rulesets_db.php.Data was developed for the Rocky Mountain Region of the USDA Forest Service by Pyrologix LLC.

Introduction: The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.Abstract: The existing vegetation height (EVH) data layer is an important input to LANDFIRE modeling efforts. Canopy height is generated separately for tree, shrub and herbaceous cover life forms using training data and a series of geospatial data layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (http://fia.fs.usda.gov/) were used as the training data for tree canopy height mapping. EVH is determined by the average height weighted by species cover and based on existing vegetation type (EVT) life-form assignments. Dominant life-form height of each plot is then binned as follows: (A) Tree classes; 0-5 m, 5-10 m, 10-25 m, 25-50 m, and greater than 50 m, (B) Shrub classes; 0-0.5 m, 0.5-1.0 m, 1.0-3.0 m, greater than 3.0 m, (C) Herbaceous vegetation classes; 0-0.5 m; 0.5-1.0 m, greater than 1 m. Go to https://www.landfire.gov/participate_refdata_sub.php for more information regarding contributors of field plot data. Decision tree models using field reference data and Landsat imagery, digital elevation model data, and biophysical gradient data, are then developed separately for each of the three life forms using C5 software. Life-form specific cross-validation error matrices are generated during this process to assess levels of accuracy of the models. Decision tree relationships are then used to generate life-form specific height class spatial data layers, which are later merged into a single composite height data layer. The final EVH layer is evaluated and rectified through a series of QA/QC measures to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. EVH is used in many subsequent LANDFIRE data layers.LF 2012 (lf_1.3.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs.Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data. Metadata and Downloads