California Wildfires Analysis (2020–2025)

Goal: Analyze how wildfire activity has changed across California counties between 2020 and 2025, focusing on total acres burned and the frequency of wildfire events.

Data & Process: The dataset was cleaned using Google Sheets and visualized in Tableau to reveal patterns and trends in wildfire activity over time.

Key Insights:

Certain counties consistently experience higher wildfire activity.

Users can explore how both acres burned and fire frequency vary by county and year.

Tableau Dashboard: View Dashboard

This is a simple, structured dataset for analyzing California wildfires from 2020 to 2025. It includes county-level data on the number of wildfires and total acres burned, making it suitable for time-series analysis, geospatial visualization, and frequency trend exploration.

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Source: The original wildfire data was collected from publicly available records provided by CAL FIRE and related California wildfire reporting resources.
The dataset has been cleaned and compiled for easier analysis and visualization.

This application was developed to provide public information about CAL FIRE Fuel Reduction Projects with work within the current and previous fiscal years. This item is the main application, which makes use of several supporting items in AGOL.-------------------------- The Department of Forestry and Fire Protection (CAL FIRE) collects, manages, and distributes information systematically across its Wildland Fuels Reduction programs. Projects are funded through state and federal funding mechanisms and administered by several departmental programs and collaborating partner agencies statewide. The CAL FIRE Management Activity Project Planning & Event Reporter (CalMAPPER) is the Department’s mechanism to capture map based data about project activities. This information can then be distributed to internal or external stakeholders for purposes of planning, accountability, management, and emergency response.What's Included in this Application? The data in this application represents a static view of the CalMAPPER data as of the date indicated in the lower right corner of the application. The application data is updated monthly. This application only includes data for the current and previous fiscal years (as indicated on the Overview tab's headers). Fiscal Year refers to the State fiscal calendar and runs from July 1 - June 30. As such, the current fiscal year represents an incomplete reporting period, and caution should be exercised when comparing to the previous fiscal year. The first reports of the new fiscal year begin in August, which is when there is a full month of data (July) available for the new fiscal year. What is included in this application:Active and completed activities in the period of interest.Activities with a treatment objective of Broadcast Burn, Fuel Reduction, Fuel Break or Right of Way Clearance.California Forest Improvement Program (CFIP) projects with fuel reduction activities (ex: Thinning, Pruning, Site Prep, Release, Follow up, etc). These are all categorized as Fuel Reduction in this viewer.Fire Plan, Vegetation Management Program (VMP), and California Vegetation Treatment Program (CalVTP) activities where CAL FIRE or Contract County resources are committed.Wildfire Prevention and Forest Health grant funded projects, including work performed by grantees and contractors.What's not included in this application:Planned activities.Pest management/control, reforestation, ecological restoration, fire prevention education, and other objectives not listed above.Projects planned, managed, and implemented by the Federal Government on Federal property without CAL FIRE involvement.* Wildfire Prevention and Forest Health Grant Program and CFIP accomplishments are based on invoices received & entered in CalMAPPER. Invoices are often received a month or more after the activity occurred, resulting in a potential delay before projects and acres are represented in this application.

This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices. Reflectance measurements for the spectroscopic AVIRIS and MASTER sensors are distributed as multi-band geotiffs for each megafire and acquisition date. Derived operational metric products for each sensor are provided in individual GeoTIFFs. GeoTIFFs produced from LiDAR point data depict first order topographic indices and summary statistics of vertical vegetation structure.

Wildfires are increasing in frequency and severity with changes in the climate. Increases in fire activity may lower soil carbon (C) stocks if photosynthetic C inputs are outpaced by microbial decomposition in burned ecosystems. However, fires may preferentially deplete particulate organic carbon (POC, which is more susceptible to microbial decomposition) over mineral-associated organic carbon (MAOC, which is protected from decomposers), potentially slowing microbial C losses after wildfires. Yet it remains unclear how plants, microorganisms, and soil organic matter pools interact to control the fate of C after wildfires. To assess how wildfires affect the persistence of soil C, we measured POC, MAOC, pyrogenic organic C, plant cover, extracellular enzyme activity (EEA), soil microbial abundance, and microbial community composition 17 days, 1, 3, and 4 years after the Holy Fire burned 94 km2 of a fire-adapted chaparral. We found that the fire immediately decreased POC by 50% (from 50.9 ..., Site description This study was conducted within the Holy Fire burn scar in the Cleveland National Forest. The Holy Fire burned 94 km2 of manzanita-dominated chaparral shrubland between August 6 and September 13, 2018. Seventeen days after the fire was extinguished, we established 9 plots; 6 of these plots were within the burn scar of the Holy Fire, while the other 3 served as control unburned plots (Figure S1; Pulido-Chavez et al. 2022). Each plot (~25 m2) consisted of four 1 m2 subplots located 5 m from the center of the plot in each cardinal direction (i.e., N, S, E, W; Figure S1). All nine plots had similar aspects, slope, elevation, and pre-fire vegetation dominated by manzanita (Arctostaphylos glandulosa) and chamise (Adenostoma fasciculatum) shrubs. The climate at the site is Mediterranean with hot, dry summers (average 9.2 mm precipitation per month since 1990) and cool, wet winters (average 101 mm precipitation per month since 1990). Annual temperature averages 17 °C, and total..., , # Data from: Wildfire-induced losses of soil particulate and mineral-associated organic carbon persist for over four years in a chaparral ecosystem

Authors: Alexander H. Krichels, Elizah Z. Stephens, Chloe Reid, M. Fabiola Pulido-Chavez, Maria Ordonez, Jennie R. McLaren, Meg Kargul, Loralee Larios, Sydney I. Glassman, Peter M. Homyak

Corresponding author: Alexander H. Krichels, Rocky Mountain Research Station, alexander.krichels@usda.gov, ahkrichels@gmail.com

Data collected between 2018 and 2024

Geographic location of data collection: Riverside County, California and Orange County, California.

Empty cells indicate missing data.

DATA & FILE OVERVIEW

File list (file names and brief description of all data files):

Compiled_Open_Data.csv:

| Column name | Description | Units | Data format | Missing data code | | :-...,

This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.

National Risk Index Version: March 2023 (1.19.0)A Wildfire is an unplanned fire burning in natural or wildland areas such as forests, shrub lands, grasslands, or prairies. Annualized frequency values for Wildfire are in units of events per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021.

This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format.

This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates.

This dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing. Tree mortality is expressed as the amount of AGC stored in trees killed by disturbance (Mg carbon per km2). The dataset also includes annual uncertainty maps that were generated using a Monte Carlo approach in which tree biomass, biomass carbon content, and disturbance severity were iteratively varied by their uncertainty.

This data set provides active fire locations and estimates of annual fire frequencies for South America from 2000-2007. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra (2000-??2007) and Aqua (2003-2007) satellite platforms were analyzed to determine spatial and temporal patterns in satellite fire detections.

The analysis considered a high-confidence subset of all MODIS fire detections to reduce the influence of false fire detections over small forest clearings in Amazonia (Schroeder et al., 2008). The number of unique days on which the active fire detections were recorded within a 1 km radius was estimated from the subset of active fire detections and the ArcGIS neighborhood variety algorithm.

There are 14 data files with this data set: 7 GeoTIFF (.tif) files of fire frequency at MODIS 250 m resolution, where each grid cell value represents the number of days in that year on which active fires were detected, and 7 shape files of active fire locations for the years 2001-2007.

This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of "young" stands (less than 60 years old at time of fire) was estimated.

This data set provides hot pixel data, as an indicator of fires that were detected by the GOES-8 satellite for the state of Acre, Brazil. Image data were collected for extended periods over the course of 3 years (1998, 2000 and 2001). Data were filtered to select only pixels identified and processed by the GOES-8 Automated Biomass Burning Algorithm (ABBA), where estimates of sub-pixel fire characteristics including size and temperature were able to be determined. There are three comma-delimited ASCII data files with this data set.

This data set provides products characterizing immediate and longer-term ecosystem changes from fires in the circumpolar boreal forests of Northern Eurasia and North America. The data include fire intensity (fire radiative power; FRP), increase in spring albedo, decrease in tree cover, normalized burn ratio, normalized difference vegetation index, and land surface temperature, as well as three derived fire metrics: crown scorch, vegetation destruction, and fire-induced tree mortality. Longer-term changes are indicated by mean albedo determined 5-12 years after fires, mean percent decrease in tree cover 5-7 years after fires, and mean annual burned percentage. The data cover the period 2001-2013 and are provided at quarter, half, and one degree resolutions for boreal forests within the 40 to 80 degree North circumpolar region. The data were derived from a variety of sources including MODIS products, climate reanalysis data, and forest inventories. A data file with identified boreal forest area (pixels), as defined by climate and vegetation type, and a file with the defined North American and Eurasian boreal forest study regions are included.

This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format.

This data set contains modeled estimates of carbon flux, biomass, and annual burning emissions across the Brazilian state of Mato Grosso from 2000-2006. The model, DEforestation CArbon Flux (DECAF), was used to provide annual carbon fluxes from large deforestation events (>25 ha) based on post-deforestation land use, and the frequency and duration of active fires during the deforestation process. Carbon fluxes associated with the conversion of Cerrado to mechanized crop production, fires in Cerrado, and managed pasture cover types were also estimated.

Model data outputs provided include:
* Estimated aboveground live biomass from DECAF in 2000 and 2004. * Annual biomass burning emissions estimates for 2001-2005 from low, middle, and high emissions scenarios with DECAF. There are 15 GeoTIFF files for annual emissions which represent the carbon emissions per pixel in grams of carbon per m2 (g C m-2).

Model data inputs provided include: * Annual burn trajectories for 2001 - 2005, including deforestation, Cerrado land cover conversion, and fires in pasture and Cerrado ecosystems unrelated to agricultural expansion. These data were assembled from three sources: MODIS 500-m burned area maps, annual deforestation based on data from the INPE PRODES program, and the conversion of Cerrado savannah/woodland to cropland estimated from land cover information from MODIS phenology metrics. * Annual land cover data 2001-2004 for the portion of Mato Grosso covered by MODIS phenology metrics, tile h12v10, updated based on annual land cover changes in Amazon forest and Cerrado cover types. * Monthly Normalized Difference Vegetation Index (NDVI) for MODIS tile h12v10 from 10/2000 - 09/2006, based on cloud and gap-filled 16-day NDVI data from MODIS Collection 4 16-day NDVI composites MOD13 product (Huete et al., 2002).

There are six compressed (*.gz) files with this data set.

This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file.

This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017.

This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada. Burned sites included peatland and upland. These field data include assessment of burn severity, vegetation inventories, ground cover, diameter and height for trees and shrubs, seedling and sprouting cover, soil moisture, and depth of unfrozen soil. Plot sizes were 10 m x 10 m with smaller subplots for selected measurements. Similar data were collected for these sites in the years 2015-2019 and are available in related separate datasets. Field data are provided in CSV format. The dataset includes digital photographs (in JPEG format) of vegetation conditions at sampling sites.

This data set contains estimates of understory fuel loads (forest litter) at six locations near Paragominas in Northeastern Amazonia. Samples were collected from three different forest conditions: primary forest, logged forest, and burned forest. Volumes and weights are provided by size and condition class based on the planar transect method of estimating understory fuel loads (Brown 1971). Means and standard errors are reported from 3 transects in each forest x condition class.

There is one comma-delimited data file (.csv) with this data set. DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.