Oregon saw the largest area burned by wildfires across the United States in 2024. That year, about 2,232 individual wildfires burned in the northwestern state, ravishing almost 1.89 million acres. Texas followed second, with roughly 1.3 million acres burned due to wildfires that year. Fire season 2021 and California’s wildfire suppression costs As one of the most wildfire-prone states in the country, California spends a significant amount of money on their suppression. Estimates suggest wildfire suppression expenditure in California climbed to 1.2 billion U.S. dollars in the fiscal year ending June 2022. The fiscal year, which includes the summer and fall months of 2021, was among the most devastating fire seasons on record, with that year’s Dixie fire becoming the second-largest California wildfire by acres burned. The Dixie fire was responsible for over 963,000 acres burned across the state that year. Wildfire causes Wildfires are uncontrolled fires burning across any type of combustible vegetation such as grass- and brushland, forests, and agricultural fields. They are also referred to as wildland fires, forest fires, or bushfires, with the latter term particularly common in Australia. Wildfires regularly occur on all continents of the world, except for Antarctica, but are particularly common in dry regions with dense vegetation. As the rise in average global temperatures is changing weather patterns and resulting in more and more countries being affected by dry, hot weather conditions, the severity and rapid spread of wildfires have increased in recent years. The most common causes of wildfires are natural phenomena such as lightning strikes as well as human activity. The area burned due to human-caused wildfires in the U.S. surpassed 1.5 million acres in 2023.

In 2024, there were a total of 64,897 wildland fires recorded in the United States. This represents an increase of roughly 14 percent from the previous year. That year, California was the state with the highest number of wildfires in the United States.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview-National Wildfires provides a summary of wildland fires in the U.S. and related weather and climate conditions. Statistical summaries such as the number of fires and acres burned are provided as are reports from the U.S. Drought Monitor and fire danger maps. Monthly reports for the summer "fire season" and annual summaries begin in July 2002. Depending on conditions, reporting was extended beyond the summer and fall seasons, and beginning in 2009 a summary was generated for each month. Following the July 2013 report, and until further notice, NCEI will no longer issue the Wildfire component of its Monthly Climate report. All previous Wildfire reports will be maintained online. Updated statistics will be updated on our Wildfire Societal Impacts webpage.

Based on the FireTracks Scientific Dataset, the global wildfire exposure dataset implements space-time alignment between fire clusters and climate as well as socioeconomic data across the globe from 2002 to 2020. The global wildfire exposure dataset is produced to meet the demand for wildfire exposure and its analysis of global inequity.

The data structure is shown in the following:

• fire_clusters.csv:
  • Contains detailed records of global wildfire clusters, including fire characteristics, geographic distribution, and population exposure.

  • Column	Description	Units	Valid Range	Data Type
    cp	fire component label	-	>=0	int64
    duration	fire duration	days	>= 1	uint16
    maxFRP_sum	sum of maximum fire radiative powers	MW*10	>= 0	float64
    area	total burned area	km^2	>= 0.86 (1 MODIS pixel)	float64
    country	country of occurrence	-	-	string
    continent	continent of occurrence	-	-	string
    dtime_min	ignition date (YYYY-MM-DD)	-	>= 2002-01-01	datetime64
    lat_mean	mean location latitude	degrees	[-180, 180]	float64
    lon_mean	mean location longitude	degrees	[-90, 90]	float64
    exposure	primary population exposure to wildfire	persontime	>=0	int64
    5km_band_pop	secondary population exposure to wildfire	persontime	>=0	int64
    year	year of occurrence	-	[2002, 2020]	int64
    month	month of occurrence	-	[1, 12]	int64

• exposure_four_covariate.csv:
  • Aggregates population exposure and covariates for equity-focused analyses, linking wildfire impacts to socioeconomic and climatic drivers.

  • Column	Description	Units	Valid Range	Data Type
    exposure	population exposed to wildfire	persontime	>=0	int64
    year	year of occurrence	-	[2002, 2020]	int64
    country	country of occurrence	-	-	string
    continent	continent of occurrence	-	-	string
    gdp	National Gross Domestic Product for the corresponding year	million U.S. dollars	>=0	float64
    country_pop	national population of the corresponding year	persontime	>=0	int64
    fwi	Fire Weather Index	-	>=0	float32
    vpd	Vapor Pressure Deficit	kPa	[0, 10]	float32
    ndvi	Normalized Difference Vegetation Index	-	[0, 1]	float64
    dnbr	Delta Normalized Burn Ratio	-	[-2, 2]	float64
    iso3	standardized country code for cross-dataset integration	-	-	string
    cluster	results from K-means clustering	-	[0, 2]	int64

Wildfire activity in the United States saw a significant increase in 2024, with approximately *** million acres burned. This marks a more than ********* increase from the previous year. Such development boosts the concerning upward trend in wildfire damage across the country that has developed in the past half a century. Humans or lightning? A wildfire can start by natural causes. In 2024, Oregon and Arizona were the states most affected, each with more than *** cases recorded. Nevertheless, human-caused wildfires continue to play a significant role in the overall landscape. In 2024, over ****** wildfires in the U.S. were attributed to human activity, resulting in more than *** million acres burned. Wildfire suppression The financial burden of wildfire suppression remains substantial. The estimated costs of wildfire suppression in the U.S. stood at almost *** million U.S. dollars in 2023, a 13-fold increase in comparison to 1985. As climate change continues to alter weather patterns and create more favorable conditions for wildfires, the need for effective prevention, management, and suppression strategies is becoming increasingly critical.

National burn probability (BP) and conditional fire intensity level (FIL) data were generated for the conterminous United States (US) using a geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. [2011]). The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FIL for the conterminous US at a 270-meter grid spatial resolution. The six FILs correspond to flame-length classes as follows: FIL1 = < 2 feet (ft); FIL2 = 2 < 4 ft.; FIL3 = 4 < 6 ft.; FIL4 = 6 < 8 ft.; FIL5 = 8 < 12 ft.; FIL6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FIL*_20160830 data must be used in conjunction with the BP_20160830 data for risk assessment.

Get data on forest fires, compiled annually for the National Forestry Database

The National Forestry Database includes national forest data and forest management statistics to seve as a credible, accurate and reliable source of information on forest management and its impact on the forest resource.

Forest fire data is grouped into eight categories, which are further broken down by geographic location. These include:

• number of fires by cause class and response category
• area burned by cause class and response category
• number of fires by month and response category
• area burned by month and response category
• number of fires by fire size class and response category
• area burned by fire size class and response category
• area burned by productivity class, stocking class, maturity class and response category
• other fire statistics, such as property losses

The increase in wildfires, particularly in the western U.S., represents one of the greatest threats to multiple native ecosystems. Despite this threat, there is currently no central repository to store both past and current wildfire perimeter data. Currently, wildfire boundaries can only be found in disparate local or national datasets. These datasets are generally restricted to specific locations, fire sizes, or time periods. Our objective was to create a comprehensive national wildfire perimeter dataset by combining all freely available wildfire datasets that we could download. We combined and dissolved individual wildfire polygons from multiple datasets if they were in the same year and overlapped each other or were within 1km of the fire boundary. This combined dataset includes spatial summary statistics such as number of times burned, earliest fire of record, and most recent fire of record.

Portugal has been the European country most affected by wildfires over the past decade. Between 2009 and 2023, an average area of over 93,731 hectares was burned every year. Furthermore, 2017 saw the greatest area lost to wildfires in a single year, with the rapidly spreading wildfires from June 17 to 24 in Central Portugal among the most deadly in recent history – 66 people were confirmed to have been killed. As of December 2024, the size of wildfire-burnt area that year was around 143,313 hectares, a considerable increase from the previous year.

Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world.

DATA SOURCE: National Institute for Water and Atmospheric Research (NIWA) [Technical report available at https://www.mfe.govt.nz/publications/environmental-reporting/fire-risk-assessment-measure-quantify-fire-risk-new-zealand and https://www.mfe.govt.nz/publications/environmental-reporting/ministry-environment-atmosphere-and-climate-report-2020-updated]

Adapted by Ministry for the Environment and Statistics New Zealand to provide for environmental reporting transparency

Dataset used to develop the "Wildfire indicator [available at https://www.stats.govt.nz/indicators/wildfire-risk]

This indicator measures fire danger using the New Zealand Fire Danger Rating at 30 sites around New Zealand from 1997 to 2019, although not all sites start at 1997. We report on the number of days per year with ‘very high and extreme’ (VH+E) fire danger for each of these sites, and trends over time.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

This web map shows the wildfire hazard potential (WHP) for the conterminous United States aggregated from states to block groups and 50 km hex bins. The data is from the USDA Forest Service Fire Modeling Institute providing an index of WHP at a 270 meter resolution. Wildfire hazard potential provides information on the relative potential for wildfire that would be difficult for fire crews to contain. Areas with higher wildfire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior. A score of 5 is very high risk and a score between 0-1 is non-burnable area such as water or asphalt. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as communities, structures, or powerlines, it can approximate relative wildfire risk to those resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic planning and fuels management.Each layer has been enriched with 2020 Esri demographic attributes to better approximate wildfire hazard risk. A hosted imagery layer of this data is available in ArcGIS Living Atlas for additional analysis.Data notes:Zonal Statistics as Table were run against a local copy of the WHP data using US standard geographies as the feature zone input for the analysis. Geographies included are: State, County, Congressional District, ZIP Code, Tract, and Block Group. Statistical tables were joined to geographies. To learn more about zonal statistics, view the documentation here. 50 km hex bins were created using Generate Tessellation and then joined to zonal statistics as described above (step 1).Data was enriched with 2020 Esri Demographics. Attributes include population, households & housing units, growth rate, and calculated variables such as population change over time. To create the population-weighted attributes on the state, congressional district, and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average WHP were multiplied.The hex bins were converted into centroids and summarized within the state, congressional district, and county boundaries.The summation of these values were then divided by the total population of each respective geography.

The FinalFirePerimeter polygon layer represents final mapped wildland fire perimeters. This feature class is a subset of the FirePerimeters feature class. Incidents of 10 acres or greater in size are expected. Incidents smaller than 10 acres in size may also be included. Data are maintained at the Forest/District level, or their equivalent, to track the area affected by wildland fire. Records in FirePerimeter include perimeters for wildland fires that have corresponding records in FIRESTAT, which is the authoritative data source for all wildland fire reports. FIRESTAT, the Fire Statistics System computer application, required by the USFS for all wildland fire occurrences on National Forest System Lands or National Forest-protected lands, is used to enter and maintain information from the Individual Fire Report (FS-5100-29).National USFS fire occurrence final fire perimeters where wildland fires have historically occurred on National Forest System Lands and/or where protection is the responsibility of the US Forest Service. Knowing where wildland fire events have happened in the past is critical to land management efforts in the future.This data is utilized by fire & aviation staffs, land managers, land planners, and resource specialists on and around National Forest System Lands.*This data has been updated to match 2021 National GIS Data Dictionary Standards.Metadata and Downloads

Globally, ************ hectares of tree cover were lost to wildfires in 2023. During the same year, the total area of tree cover loss caused by fires in general (wildfires and other fire events like clearing for agriculture) amounted to ************ hectares.

Wildland fire has a major impact on the sustainability of many Canadian forests. Fire policies attempt to balance suppression costs with values at risk while recognizing the natural role of fire in managing the landscape. There are three aspects of wildland fire in Canada: fire regimes, fire management, and fire research.

Data during wildfire seasons (May 1 - October 31) over the years 2008 - 2012 in the contiguous U.S. used for spatial causal analysis of wildland fire-contributed PM2.5. The two sources of PM2.5 data are monitor data from the EPA’s Air Quality System (AQS) and simulated PM2.5 from the CMAQ model. This dataset is associated with the following publication: Larsen, A., S. Yang, B. Reich, and A. Rappold. A spatial causal analysis of wildland fire-contributed PM2:5 using numerical model output. Annals of Applied Statistics. Institute of Mathematical Statistics, Beachwood, OH, USA, 16(4): 2714-2731, (2022).

As of November 2024, Turkey reported nearly 120,000 hectares lost to forest fires that year, more than four times the figure recorded one year earlier. During the period in consideration, Turkey saw the largest wildfire-affected area in 2021, with fires having burnt more than 200,000 hectares across the country.

Upload Tb and NEXRAD data for 2020 Creek FIre, CA

🇪🇸 스페인