Representa la información estadística de los incendios forestales en España ocurridos entre los años 1983-2015 y su información espacial con sus coordenadas de origen.

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.

It represents the statistical information of forest fires in Spain that occurred between 1983-2015 and their spatial information with their coordinates of origin.

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.

The DNRC Interactive Wildland Fire Map provides up-to-date resources and information related to present and past wildfire incidents in the State of Montana. Leveraging the Esri Web AppBuilder platform, a variety of tools/widgets allow the user to interact with application to better understand forest fires and their impact to the landscape and residents of Montana.

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.

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

CAL FIRE's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, the National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data. This map highlights the recent large fires (≥5,000 acres) on a backdrop of all of the dataset's documented fire perimeters dating back to 1878. This map includes perimeters symbolized by decade, county boundaries, California vegetation, and NAIP imagery back to 2005. Popups provide a narrative of known details pertaining to each incident, including alarm and containment dates, GIS calculated acreage, and responding agency, among other attribution.This is the landing page map in the California Historical Wildland Fire Perimeters Exploratory App.For any questions, please contact the data steward:Kim Wallin, GIS SpecialistCAL FIRE, Fire & Resource Assessment Program (FRAP)kimberly.wallin@fire.ca.gov

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.

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

This archive contains the data and code used to produce the Western United States Large Forest-Fire Stochastic Simulator (WULFFSS), version 1.0, which is a monthly gridded forest-fire model using interpretable statistics. The WULFFSS operates at 12-km resolution and calculates monthly probabilities of forest fires ≥100 ha as well as the area burned per fire. The model is forced by variables related to vegetation, topographic, anthropogenic, and climate factors, organized into three indices representing spatial, annual-cycle, and lower frequency temporal domains. These indices can interact, so variables promoting fire in one domain amplify fire-promoting effects in another. The fire probability and size modules use multiple logistic and linear regression, respectively, and can be easily updated as new data or ideas emerge. During its training period of 1985–2024, WULFFSS captures >70% and >80% of observed interannual variability in western US forest-fire frequency and area, respect..., , # The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics

Dataset DOI: 10.5061/dryad.63xsj3vdb

Description of the data and file structure

This repository contains the data and code used to produce version 1.0 of the Western United States Large Forest-Fire Stochastic Simulator (WULFFSS), as well as the equations that comprise the model and code to run the model. The WULFFSS simulates the probabilities and sizes of forest fires at least 1 km2 in size every month across forested areas of the western US on a 12-km resolution grid. The model is forced by variables related to vegetation, topographic, anthropogenic, and climate factors, organized into three indices representing spatial, annual-cycle, and lower frequency temporal domains. These indices can interact, so variables promoting fire in one domain amplify fire-promoting effects in another. Fire probability and si...,

Forest fires are an important part of the Canadian landscape. The number of fires and area burned can vary dramatically from year to year, but there are more than 8000 reported wildfires in Canada during a typical year, burning an average of 2.5 million hectares or 25 000 square kilometres. Only 3 percent of fires in Canada reach a final size greater than 200 hectares, but these fires are responsible for 97 percent of the total area burned. This map shows the forest fire ignition causes for fires greater than 200 hectares. The data represent a compilation of all fire point location and perimeters for fires greater than 200 hectares, as provided by fire management agencies of provinces, territories and Parks Canada.

In 2024, a total of *** forest fires were recorded in Germany. This was a decrease compared to ***** forest fires in 2023. Figures fluctuated during the specified time period, though they peaked significantly in certain years, namely 1992 and 2003.

This is a regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data.

Data Set Information:

In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function. Then, several Data Mining methods were applied. After fitting the models, the outputs were post-processed with the inverse of the ln(x+1) transform. Four different input setups were used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: 12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The best RMSE was attained by the naive mean predictor. An analysis to the regression error curve (REC) shows that the SVM model predicts more examples within a lower admitted error. In effect, the SVM model predicts better small fires, which are the majority.

Attribute Information:

For more information, read [Cortez and Morais, 2007].

1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9
2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9
3. month - month of the year: 'jan' to 'dec'
4. day - day of the week: 'mon' to 'sun'
5. FFMC - FFMC index from the FWI system: 18.7 to 96.20
6. DMC - DMC index from the FWI system: 1.1 to 291.3
7. DC - DC index from the FWI system: 7.9 to 860.6
8. ISI - ISI index from the FWI system: 0.0 to 56.10
9. temp - temperature in Celsius degrees: 2.2 to 33.30
10. RH - relative humidity in %: 15.0 to 100
11. wind - wind speed in km/h: 0.40 to 9.40
12. rain - outside rain in mm/m2 : 0.0 to 6.4
13. area - the burned area of the forest (in ha): 0.00 to 1090.84 (this output variable is very skewed towards 0.0, thus it may make sense to model with the logarithm transform).

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('forest_fires', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This map shows the burn areas of wildfires between 1887 to present in California. The perimeters are colored by decade to highlight the size and quantity of more recent years. The Living Atlas layer used in this map contains the fire perimeters from the previous calendar year, and those dating back to 1878, for California. Perimeters are sourced from the Fire and Resource Assessment Program (FRAP) and are updated shortly after the end of each calendar year. Information below is from the FRAP web site.About the Perimeters in this LayerThe California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data. For detailed and current metadata and update cycle about this data and the source, visit this resource.Source: Fire and Resource Assessment Program (FRAP)For any questions, please contact the data steward:Kim Wallin, GIS SpecialistCAL FIRE, Fire & Resource Assessment Program (FRAP)kimberly.wallin@fire.ca.gov

Forest fires are an important part of the Canadian landscape. The number of fires and area burned can vary dramatically from year to year, but there are more than 8000 reported wildfires in Canada during a typical year, burning an average of 2.5 million hectares or 25 000 square kilometres. Only 3 percent of fires in Canada reach a final size greater than 200 hectares, but these fires are responsible for 97 percent of the total area burned. This map shows fires greater than 1000 hectares. The data represent a compilation of all fire point location and areas for fires greater than 1000 hectares, as provided by fire management agencies of provinces, territories and Parks Canada.

The risk classification at the municipal level for the summer and winter macro-season is derived from the raster cartography of forest fire risk for the year 2015. This raster cartography was approved with DGR 1540 of 29 December 2015 relating to the revision of the Regional Forecast Plan , prevention and active fight against forest fires, prepared with the technical-scientific collaboration of the International Environmental Monitoring Center - CIMA of Savona. For further information, consult "Areas at Risk of Forest Fire - Year 2015" - Coverage: Entire Regional Territory - Origin: Vector data processing, grid and digital terrain model - DTM

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.

Our interactive map visually shows active fires, current fire danger across the province and restricted fire zones in place due to high fire danger.

The map now shows perimeters for some fires over 40 hectares in size. Please note that not all fires are mapped and perimeters are not updated every day (may differ from the size reported in the table).

Additional Documentation

Forest Fire Information Map

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

Not stated

Contact

Northeast Region: Isabelle Chenard, Fire Information Officer, Aviation, Forest Fire and Emergency Services, isabelle.chenard@ontario.ca

Northwest Region: Jonathan Scott, Fire Information Officer, Aviation, Forest Fire and Emergency Services, jonathan.scott@ontario.ca

This layer represents forest fire perimeters, for information purposes. It is complementary to the official statistical information on forest fires published on the SIGIF website (Integrated Forest Fire Management System) where you can consult the statistics on forest fires in the Valencian Community, both the definitive ones, whose data cover from 1968 to 2016, and the provisional ones, of which the data include the period from 2017 to the present. Not all forest fires of the period are mapped. In case of discrepancy between the cartography and statistics, the part of fires shall prevail, except for error or omission. Certificates shall be processed in accordance with Decree 66/2007. The encoding of the attributes follows the "Notes for Office Encoding of the Forest Fire Party". Forest Fire Fighting Committee. The mapping of areas affected by forest fires is only informative and non-binding.