Burn probability (BP) models the likelihood that a location could burn. However, predicting BP is extremely challenging, because fire behavior varies strongly among landscapes and with changing weather conditions and wildfire spread simulations are computationally intensive and require integration of data with large spatial and temporal variability. In this data release we include the monthly BP estimation for the state of California, USA for the 2015-2019 period produced using a machine learning model and two different sets of input features. For the first case, the baseline, the model used all available input features to predict BP. The second output set corresponds to the BP predictions when the model used only the set of optimal features as determined in the cited paper.

In the face of recent wildfires across the Western United States, it is essential that we understand both the dynamics that drive the spatial distribution of wildfire, and the major obstacles to modeling the probability of wildfire over space and time. However, it is well documented that the precise relationships of local vegetation, climate, and ignitions, and how they influence fire dynamics, may vary over space and among local climate, vegetation, and land use regimes. This raises questions not only as to the nature of the potentially nonlinear relationships between local conditions and the fire, but also the possibility that the scale at which such models are developed may be critical to their predictive power and to the apparent relationship of local conditions to wildfire. In this study we demonstrate that both local climate – through limitations posed by fuel dryness (CWD) and availability (AET) – and human activity – through housing density, roads, electrical infrastructure, and agriculture, play important roles in determining the annual probabilities of fire throughout California. We also document the importance of previous burn events as potential barriers to fire in some environments, until enough time has passed for vegetation to regenerate sufficiently to sustain subsequent wildfires. We also demonstrate that long-term and short-term climate variations exhibit different effects on annual fire probability, with short-term climate variations primarily impacting fire probability during periods of extreme climate anomaly. Further, we show that, when using nonlinear modeling techniques, broad-scale fire probability models can outperform localized models at predicting annual fire probability. Finally, this study represents a powerful tool for mapping local fire probability across the state of California under a variety of historical climate regimes, which is essential to avoided emissions modelling, carbon accounting, and hazard severity mapping for the application of fire-resistant building codes across the state of California.

Methods Climate data used in this study was drawn from the California Basin Characterization Model v8, and consists of monthly estimates of cumulative water deficit (CWD) and actual evapotranspiration (AET) from 1951 – 2016. This dataset represents a 270-m grid-based model of water balance calculations that incorporates climate inputs through PRISM data in addition to solar radiation, topographic shading, cloudiness, and soil properties to estimate evapotranspiration. Using these monthly values, we calculated the 1980 – 2009 mean CWD and AET normals, as well as mean deviations from those normals over a three-year period preceding each year of interest.

Cultivated and agricultural areas were identified using the 2016 National Land Cover Database data, which estimated dominant land cover throughout North America at 30-m resolution. The proportion of cultivated area and of water features that covered each 1-km pixel were then calculated by resampling to 1-km scale. Mean housing density data was drawn from the Integrated Climate and Land-Use Scenarios (ICLUS) dataset, which provides decadal estimates of housing density throughout the United states from 1970 - 2020. As precise continuous estimates of housing density were not available, housing density within each pixel was set to the mean of its class. Annual values were estimated from decadal data using linear interpolation. Ecoregions within California (hereafter referred to as “regions”) were delineated using CalVeg ecosystem provinces data.

Road data were drawn from 2018 TIGER layer data, and consisted of all primary and secondary roads across California. Electrical infrastructure data was drawn from 2020 transmission lines data. In both cases, the distance of nearest roads or transmission lines to each pixel were then calculated. Pixels which contained roads or electrical infrastructure were assigned distances of 0 km.

Fire history data was drawn from FRAP fire perimeter data, which incorporates perimeters of all known timber fires >10 acres (>0.04 km2), brush fires >30 acres (>0.12 km2), and grass fires >300 acres (>1.21 km2) from 1878 – 2017. Using this data, the presence of fire in each 1-km pixel was classified in a binary fashion (e.g. 1 for burned, 0 for unburned) for each year of interest. Due to computational limits and the quantity of data involved in this study, we did not calculate the burned area within each pixel, or distinguish pixels in which a single fire occurred in a given year from those in which multiple fires occurred. This data was also used to calculate the number of years since the most recent fire within any pixel, prior to each year in which fire probability was projected. Thus, locations in which no fire was observed throughout the fire record were treated as having gone a maximum of 100 years without a fire event for the purposes of model construction. These pixels comprised 29% - 33% of data annually (depending on year), and included both locations in which fire would not be expected (such as highly xeric regions) as well as locations in fire-prone areas in which no fire had been documented within the FRAP fire perimeter data used in this study.

This project was initiated to provide land managers with spatial information on the potential for recreation destinations to be closed or disrupted due to wildfire. Wildfires are a common occurrence in fire prone landscapes including much of southern California. Wildfires of any size can close national forests to the public for short durations due to safety concerns for forest visitors while the fire is active. However, larger, more destructive wildfires can lead to long-term recreation displacement by removing trail and campsite infrastructure, killing surrounding vegetation leading to safety concerns from falling trees, and increase the time to restore recreation opportunities. In this project, we create spatial data to show where the greatest risk of long-term recreation displacement due to wildfire is likely. We developed these recreation displacement likelihood datasets using two weather scenarios "dry" and "moderate". Each recreation displacement likelihood map was created using the following five spatial data inputs: canopy height, flame length probability, potential heat per unit area, burn probability, and potential fire severity. Canopy height was used as a measurement of vegetation type most likely to cause long-term disruption to recreation, that is fire-killed tall trees are more likely to disrupt recreation than shrubs or grass fuel types. Flame length probability and potential heat per unit area were used to measure fire intensity and amount of energy released from a fire. Burn probability conveys the likelihood of a fire occurring at a given location across the landscape. Potenial Fire severity indicates how damaging a fire would be if an ignition occurred. This data publication includes a separate geodatabase for dry and moderate weather conditions. Both of these geodatabases include 5 rasters: potential for fire to impact recreation, potential fire severity, burn probability, potential heat per unit area, and flame length probability. A geodatabase containing priority and non-priority trail, road, and place of interest vector data, which show where highly frequented locations overlap with the above-mentioned datasets, is also provided.The main goals of this project were to determine where wildfires are most likely to occur within the Angeles National Forest. Then, if a wildfire occurs what are the potential long-term impacts of burning to places of interest that are important to recreation.The recreation displacement likelihood datasets were developed using two weather scenarios "dry" and "moderate" following Scott and Burgan (2005).

These metrics represent the probability of low, moderate, or high severity fire, respectively, as constructed by Pyrologix LLC. Operational-control probability rasters indicate the probability that the headfire flame length in each pixel will exceed a defined threshold for certain types of operational controls, manual and mechanical.

Low severity fire represents fire with flame lengths of less than 4 feet and can be controlled using manual control treatments. Moderate severity fire represents fire with flame lengths between 4 and 8 feet and can be controlled using mechanical control treatments. High severity fire represents fire with flame lengths exceeding 8 feet and are generally considered beyond mechanical control thresholds.

Annual Burn Probability represents the likelihood of a wildfire of any intensity occurring at a given location (pixel) in a single fire season. In a complete assessment of wildfire hazard, wildfire occurrence and spread are simulated in order to characterize how temporal variability in weather and spatial variability in fuel, topography, and ignition density influence wildfire likelihood across a landscape. In such cases, the hazard assessment includes modeling of burn probability, which quantifies the likelihood that a wildfire will burn a given point (a single grid cell or pixel) during a specified period of time. Burn probability for fire management planning applications in this case is reported on an annual basis - the probability of burning during a single fire season.

These metrics represent the probability of low, moderate, or high severity fire, respectively, as constructed by Pyrologix LLC. Operational-control probability rasters indicate the probability that the headfire flame length in each pixel will exceed a defined threshold for certain types of operational controls, manual and mechanical.

Low severity fire represents fire with flame lengths of less than 4 feet and can be controlled using manual control treatments. Moderate severity fire represents fire with flame lengths between 4 and 8 feet and can be controlled using mechanical control treatments. High severity fire represents fire with flame lengths exceeding 8 feet and are generally considered beyond mechanical control thresholds.

These data show model estimates of debris flow likelihood and volume that may be produced by a storm in a recently burned landscape. The scientific methods used by the U.S. Geological Survey Emergency Assessment of Post-Fire Debris-Flow Hazards were changed following 2015, and these shapefiles are a re-release of ten fires that occurred between 1997 and 2015 fires, using the updated methods. These ten fires were re-run to provide estimates of debris flow volumes as post-fire debris flows were documented but no field measurements were published.

This data release contains gridded estimates of postfire debris flow probability and magnitude for six different rainfall and wildfire scenarios in southern California. The scenarios represent the present and hypothetical future precipitation and fire regimes for the region. The results are provided for 1 km^2 cells across the study area.

Fire Threat is a combination of two factors: 1) fire probability, or the likelihood of a given area burning, and 2) potential fire behavior (hazard). These two factors are combined to create 5 threat classes ranging from low to extreme.This version (fthrt14_2) is an update created from fthrt14_1 (created for the FRAP 2017 Forest and Rangeland Assessment). Fire Rotation data in fthrt14_1 was replaced with Annual Fire Probability data developed for California by Pyrologix Inc.

This project was initiated to provide land managers with spatial information on the potential for recreation destinations to be closed or disrupted due to wildfire. Wildfires are a common occurrence in fire prone landscapes including much of southern California. Wildfires of any size can close national forests to the public for short durations due to safety concerns for forest visitors while the fire is active. However, larger, more destructive wildfires can lead to long-term recreation displacement by removing trail and campsite infrastructure, killing surrounding vegetation leading to safety concerns from falling trees, and increase the time to restore recreation opportunities. In this project, we create spatial data to show where the greatest risk of long-term recreation displacement due to wildfire is likely. We developed these recreation displacement likelihood datasets using two weather scenarios "dry" and "moderate", for the San Bernardino National Forest, in California using data from 2016-2023. Each recreation displacement likelihood map was created using the following five spatial data inputs: canopy height, flame length probability, potential heat per unit area, burn probability, and potential fire severity. Canopy height was used as a measurement of vegetation type most likely to cause long-term disruption to recreation, that is fire-killed tall trees are more likely to disrupt recreation than shrubs or grass fuel types. Flame length probability and potential heat per unit area were used to measure fire intensity and amount of energy released from a fire. Burn probability conveys the likelihood of a fire occurring at a given location across the landscape. Potential fire severity indicates how damaging a fire would be if an ignition occurred. This data publication includes a separate geodatabase for dry and moderate weather conditions. Both of these geodatabases include 5 rasters: potential for fire to impact recreation, potential fire severity, burn probability, potential heat per unit area, and flame length probability. A geodatabase containing priority and non priority trail, priority and non priority places of interest, as well as road vector data with extracted dry and moderate condition raster values is also provided. Vector data show where highly frequented locations overlap with the above-mentioned datasets. Priority locations represent highly visited locations or locations of high importance, whereas non priority locations are visited less frequently.The main goals of this project were to determine where wildfires are most likely to occur within the San Bernardino. Then, if a wildfire occurs what are the potential long-term impacts of burning to places of interest that are important to recreation.The recreation displacement likelihood datasets were developed using two weather scenarios "dry" and "moderate" following Scott and Burgan (2005).

These metrics represent the probability of low, moderate, or high severity fire, respectively, as constructed by Pyrologix LLC. Operational-control probability rasters indicate the probability that the headfire flame length in each pixel will exceed a defined threshold for certain types of operational controls, manual and mechanical.

Low severity fire represents fire with flame lengths of less than 4 feet and can be controlled using manual control treatments. Moderate severity fire represents fire with flame lengths between 4 and 8 feet and can be controlled using mechanical control treatments. High severity fire represents fire with flame lengths exceeding 8 feet and are generally considered beyond mechanical control thresholds.

Fire Threat is a combination of two factors: 1) fire probability, or the likelihood of a given area burning, and 2) potential fire behavior (hazard). These two factors are combined to create 5 threat classes ranging from low to extreme.This version (fthrt14_2) is an update created from fthrt14_1 (created for the FRAP 2017 Forest and Rangeland Assessment). Fire Rotation data in fthrt14_1 was replaced with Annual Fire Probability data developed for California by Pyrologix Inc.

Wildfire - Fire Risk and Fire Responsibility Areas (CAL FIRE) for development of the Parcel Inventory dataset for the Housing Element Site Selection (HESS) Pre-Screening Tool.

• This data set represents Moderate, High, and Very High Fire Hazard Severity Zones in State Responsibility Areas (SRA) and Very High Fire Hazard Severity Zones in Local Responsibility Areas (LRA) for the San Francisco Bay Region and some of its surrounding counties. The data was assembled by the Metropolitan Transportation Commission from multiple shapefiles provided by the California Department of Forestry and Fire Protection. The SRA data was extracted from a statewide shapefile and the LRA data is a combination of county shapefiles. All source data was downloaded from the Office of the State Fire Marshal's Fire Hazard Severity Zones Maps page (https://osfm.fire.ca.gov/divisions/community-wildfire-preparedness-and-mitigation/wildland-hazards-building-codes/fire-hazard-severity-zones-maps/). *

State Responsibility Areas

PRC 4201 - 4204 and Govt. Code 51175-89 direct CAL FIRE to map areas of significant fire hazards based on fuels, terrain, weather, and other relevant factors. These zones, referred to as Fire Hazard Severity Zones (FHSZ), define the application of various mitigation strategies to reduce risk associated with wildland fires.

CAL FIRE is remapping FHSZ for SRA and Very High Fire Hazard Severity Zones (VHFHSZ) recommendations in LRA to provide updated map zones, based on new data, science, and technology.

Local Responsibility Areas

Government Code 51175-89 directs the CAL FIRE to identify areas of very high fire hazard severity zones within LRA. Mapping of the areas, referred to as VHFHSZ, is based on data and models of, potential fuels over a 30-50 year time horizon and their associated expected fire behavior, and expected burn probabilities to quantify the likelihood and nature of vegetation fire exposure (including firebrands) to buildings. Details on the project and specific modeling methodology can be found at https://frap.cdf.ca.gov/projects/hazard/methods.html. Local Responsibility Area VHFHSZ maps were initially developed in the mid-1990s and are now being updated based on improved science, mapping techniques, and data.

Local government had 120 days to designate, by ordinance, very high fire hazard severity zones within their jurisdiction after receiving the CAL FIRE recommendations. Local governments were able to add additional VHFHSZs. There was no requirement for local government to report their final action to CAL FIRE when the recommended zones are adopted. Consequently, users are directed to the appropriate local entity (county, city, fire department, or Fire Protection District) to determine the status of the local fire hazard severity zone ordinance.

In late 2005, to be effective in 2008, the California Building Commission adopted California Building Code Chapter 7A requiring new buildings in VHFHSZs to use ignition resistant construction methods and materials. These new codes include provisions to improve the ignition resistance of buildings, especially from firebrands. The updated very high fire hazard severity zones will be used by building officials for new building permits in LRA. The updated zones will also be used to identify property whose owners must comply with natural hazards disclosure requirements at time of property sale and 100 foot defensible space clearance. It is likely that the fire hazard severity zones will be used for updates to the safety element of general plans.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This project was initiated to provide land managers with spatial information on the potential for recreation destinations to be closed or disrupted due to wildfire. Wildfires are a common occurrence in fire prone landscapes including much of southern California. Wildfires of any size can close national forests to the public for short durations due to safety concerns for forest visitors while the fire is active. However, larger, more destructive wildfires can lead to long-term recreation displacement by removing trail and campsite infrastructure, killing surrounding vegetation leading to safety concerns from falling trees, and increase the time to restore recreation opportunities. In this project, we create spatial data to show where the greatest risk of long-term recreation displacement due to wildfire is likely. We developed these recreation displacement likelihood datasets using two weather scenarios "dry" and "moderate", for the Cleveland National Forest, in California using data from 2016-2023. Each recreation displacement likelihood map was created using the following five spatial data inputs: canopy height, flame length probability, potential heat per unit area, burn probability, and potential fire severity. Canopy height was used as a measurement of vegetation type most likely to cause long-term disruption to recreation, that is fire-killed tall trees are more likely to disrupt recreation than shrubs or grass fuel types. Flame length probability and potential heat per unit area were used to measure fire intensity and amount of energy released from a fire. Burn probability conveys the likelihood of a fire occurring at a given location across the landscape. Potential fire severity indicates how damaging a fire would be if an ignition occurred. This data publication includes a separate geodatabase for dry and moderate weather conditions. Both of these geodatabases include 5 rasters: potential for fire to impact recreation, potential fire severity, burn probability, potential heat per unit area, and flame length probability. A geodatabase containing priority and non priority trail, priority and non priority road, as well as priority and non priority places of interest vector data is also provided. Vector data show where highly frequented locations overlap with the above-mentioned datasets. Priority locations represent highly visited locations or locations of high importance, whereas non priority locations are visited less frequently.The main goals of this project were to determine where wildfires are most likely to occur within the Cleveland National Forest. Then, if a wildfire occurs what are the potential long-term impacts of burning to places of interest that are important to recreation.The recreation displacement likelihood datasets were developed using two weather scenarios "dry" and "moderate" following Scott and Burgan (2005).

These rasters depict the predicted human- and lightning-caused ignition probability for the state of California. Ignition is regulated by complex interactions among climate, fuel, topography, and humans. Considerable studies have advanced our knowledge on patterns and drivers of total areas burned and fire frequency, but much is less known about wildfire ignition. To better design effective fire prevention and management strategies, it is critical to understand contemporary ignition patterns and predict the probability of wildfire ignitions from different sources. UC Davis researchers modeled and analyzed human- and lightning-caused ignition probability across the whole state and sub-ecoregions of California, USA.

Findings reinforce the importance of varying humans vs biophysical controls in different fire regimes, highlighting the need for locally optimized land management to reduce ignition probability. Based on the most complete ignition database available, researchers developed maximum entropy models to predict the spatial distribution of long-term human- and lightning-caused ignition probability at 1 km and investigated how a set of biophysical and anthropogenic variables controlled their spatial variation in California and across its sub-ecoregions. Results showed that the integrated models with both biophysical and anthropogenic drivers predicted well the spatial patterns of both human- and lightning-caused ignitions in statewide and sub-ecoregions of California. Model diagnostics of the relative contribution and marginalized response curves showed that precipitation, slope, human settlement, and road network were the most important variables for shaping human-caused ignition probability, while snow water equivalent, lightning density, and fuel amount were the most important variables controlling the spatial patterns of lightning-caused ignition probability. The relative importance of biophysical and anthropogenic predictors differed across various sub-ecoregions of California.

Prescribed fires play a critical role in reducing the intensity and severity of future wildfires by systematically and widely consuming accumulated vegetation fuel. While the current probability of prescribed fire escape in the United States stands very low, its consequential impact, particularly the large wildfires it causes, raises substantial concerns. The most direct way of understanding this trade-off between wildfire risk reduction and prescribed fire escapes is to explore patterns in the historical prescribed fire records. This study investigates the spatiotemporal patterns of escaped prescribed fires in California from 1991 to 2020, offering insights for resource managers in developing effective forest management and fuel treatment strategies. The results reveal that the months close to the beginning and end of the wildfire season, namely May, June, September, and November, have the highest frequency of escaped fires. Under similar environmental conditions, areas with more recor..., , # Temporal and spatial pattern analysis of escaped prescribed fires in California from 1991 to 2020

Dataset DOI: 10.5061/dryad 2v6wwq019

Description of the data and file structure

Data information

Records of escaped prescribed fires in California from 1991 to 2020 are provided in spreadsheet format (.xlsx), with related environmental variables available as geospatial raster data in GeoTIFF (.tiff) format.Â

Note: This dataset does not include any personally identifiable information. Coordinates have been generalized to 3 decimal precision to protect privacy. Incident names referring to private property or individuals were masked or removed.

ExcapedRxfires_CA_1991-2020.xlsx

Escaped prescribed fire records

• Spreadsheet list
  • "compiled": the cleaned escaped fire records in CA from 1991 to 2020
  • "CALSTATS": records obtained and extracted from the California Incident Data and Statistics (CalStats) Program, CAL FIRE. Data were colle...,

These rasters depict the predicted human- and lightning-caused ignition probability for the state of California. Ignition is regulated by complex interactions among climate, fuel, topography, and humans. Considerable studies have advanced our knowledge on patterns and drivers of total areas burned and fire frequency, but much is less known about wildfire ignition. To better design effective fire prevention and management strategies, it is critical to understand contemporary ignition patterns and predict the probability of wildfire ignitions from different sources. UC Davis researchers modeled and analyzed human- and lightning-caused ignition probability across the whole state and sub-ecoregions of California, USA.

Findings reinforce the importance of varying humans vs biophysical controls in different fire regimes, highlighting the need for locally optimized land management to reduce ignition probability. Based on the most complete ignition database available, researchers developed maximum entropy models to predict the spatial distribution of long-term human- and lightning-caused ignition probability at 1 km and investigated how a set of biophysical and anthropogenic variables controlled their spatial variation in California and across its sub-ecoregions. Results showed that the integrated models with both biophysical and anthropogenic drivers predicted well the spatial patterns of both human- and lightning-caused ignitions in statewide and sub-ecoregions of California. Model diagnostics of the relative contribution and marginalized response curves showed that precipitation, slope, human settlement, and road network were the most important variables for shaping human-caused ignition probability, while snow water equivalent, lightning density, and fuel amount were the most important variables controlling the spatial patterns of lightning-caused ignition probability. The relative importance of biophysical and anthropogenic predictors differed across various sub-ecoregions of California.

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire – also termed the fire severity – are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate "wall-to-wall" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.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.

Simulation modeling was used to examine tradeoffs and synergies between hypothetical post-treatment conditions generated according to distinct treatment prioritization schemes (Housing Protection, Federal Risk Transmission, Random) and variable treatment extents. We used stochastic wildfire simulation and computations of exposure to wildfire to compare strategy performance across two very large landscapes - the southern Sierra in California (approximately 28 million acres) and northern New Mexico (approximately 21 million acres). This data publication represents the model results for the two study areas analyzed as well as all input data required to reproduce our analysis. All input data and simulation model parameters were calibrated to represent conditions within the two study areas in 2015.Despite the recent progress represented by advances in fire simulation, quantitative estimates of risk informing fuels management planning, and risk analysis being used to inform planning that supports operational fire management decisions, a need remains for guidance for designing and prospectively evaluating landscape-scale fuel treatments with protection objectives, resource management objectives, and wildfire response in mind. This project looks to illustrate an approach for examining whether, and how, fuels management can foster the expansion of beneficial wildfire.For more information about this study and these data, see Thompson et al. (2022).

These data were published on 04/15/2022. On 10/24/2024, minor metadata updates were made.

Successive catastrophic wildfire seasons in western North America have escalated the urgency around reducing fire risk to communities and ecosystems. In historically frequent-fire forests, fuel buildup as a result of fire exclusion is contributing to increased fire severity, but the probability of high severity fire can be reduced by active forest management that reduces fuels, prompting federal and state agencies have committed significant resources to increase the pace and scale of fuel reduction treatments. However, wildfires also have the potential to act as “treatments” in areas that burn at lower severity, but even catastrophic fires with large areas of high severity can still have substantial area of lower severity fire that may be improving forest conditions. We quantified active management and wildfire severity across yellow pine and mixed conifer forests (YPMC) in the Sierra Nevada of California over a 22-year period (2001-2022). We did not detect clear increases in the area treated through time but the area of beneficial wildfire (low to moderate severity) increased substantially, exceeding active treatment area in 9 of 22 years. Overall, beneficial wildfire treated ~20% more area than all treatments combined, and nearly seven times more area than fire-related treatments alone. We then used disturbance history to evaluate resistance to high severity wildfire and forest loss across the YPMC range. Of the 2.3 million ha that were YPMC forests in 2001, 19% lost mature forests due to high severity fire by 2022, nearly half of all YPMC area burned. Most of the landscape (47%) remains at risk of high severity fire because it had no restorative disturbances, but 33% of the study area has some level of resistance to high severity wildfire. In these areas, resistance will need to be enhanced and maintained over time via active management or managed wildfire. These treatment needs will likely outpace capacity even under optimistic implementation scenarios. Given limited resources for implementing active management and the likelihood of a more fiery future, incorporating beneficial wildfire into landscape-level treatment planning has the potential to amplify active management treatments, expanding the forest area that is resistant to high severity wildfire. Methods As detailed in the manuscript, we acquried publicly available data from a variety of sources and created fire severity maps for 2018-2022 via Google Earth Engine.