The downloadable ZIP file contains an Esri ArcInfo Coverage. This data set reflects fire intensity as measured by canopy scorch for the 1989 Lowman Fire along the South Fork of the Payette River within Boise National Forest, Idaho. The data are intended to assist efforts surrounding broad-scale forest planning for analysis in fire-recovery: soil stabilization, reestablishing vegetation, and protection of other resources such as wildlife, riparian habitat, water quality, fisheries and timber. These data were contributed to INSIDE Idaho at the University of Idaho Library in 2000.

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).

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.

Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed th..., Initial Communities Data The initial communities layer is the base vegetation layer that sets the starting conditions for the exchange of carbon, water, energy, species interactions, disturbance effects, and other landscape processes. The initial treatment optimization study in this landscape (Krofcheck et al., 2019) used 68 Forest Inventory and Analysis (FIA) plots from within the Santa Fe National Forest that had been inventoried in 2010 or later and had not burned since 2005. Forest types represented by the FIA plots were piñon-juniper, ponderosa pine, Douglas-fir (Pseudotsuga menziesii), Engelman spruce (Picea engelmannii) and limber pine (Pinus flexilis). The latter three were grouped into a general mixed-conifer forest type. The authors then used elevation, transformed aspect using Topographic Radiation Aspect Index, TRASP (Roberts & Cooper, 1989), and a tasseled cap transformation of spectral data from Landsat 8 (available at https://www.usgs.gov/landsat-missions/landsat-8) a..., R, These data are outputs of LADIS-II simulations using the photosynthesis and evapotranspiration (PnET) succession, Dynamic Fuels and Fire, and Biomass Harvest extensions to simulation forest growth and disturbance using a 100m resolution.

We ran 25 simulations for each of 5 different climate models (CCSM, CNRM, FGOALS, GFDL and MIROC5) for a total of 125 replicates,both for the management and no management scenarios. Each simulation lasted 50 years. The objective of the study was to examine the effects of additional plot data on the initial communites layer and the subsequent model outputs. We compared results from this study and Krofcheck et al., 2019 in the no management scenario, and results from this study between the management and no management scenarios.

Files with the word 'new' refer to files from this study derived from an initial communities layer made using Forest Inventory and Analysis (FIA) + Common Stand Exam (CSE) plots. Files with the word 'old' refer to files from Kro...

We provide data on ecological community responses to wildfire, collected three years post-fire, across three burn conditions (unburned, moderate severity and high severity) in the Eldorado National Forest, California. The data were collected with 19 sampling methods deployed across 27 sites (nine in each burn condition) used to estimate richness, body size, abundance and biomass density for 849 species (including 107 primary producers, 634 invertebrates, 94 vertebrates). The sampling methods are detailed in a companion data paper. To maximize transparency and ease of use we have made our data available in four formats: Raw, tidy, temporary and summary. Raw data is as close as possible to the form in which it was collected. As such, raw data is not ready for analysis. We have provided tidy versions of each raw data set that are ready for analysis. Temporary data files are included for transparency but are used to create summary data files, and not intended to be informative as stand alone products. Summary data files provide estimated biomass densities and network structure for each of our three burn categories. All tidy and summary data files are accompanied by metadata files describing their contents. We provide the R code to transform raw data files into tidy, temporary and summary files. We have included the code necessary to reproduce our work. We have broken the code into three R scripts: Biomass density estimation, resource filters assignment and network assembly.

Methods The methods used to collect the data are described in an companion data paper. The methods used to tidy and summarize the data are in the included R scripts.

The downloadable ZIP file contains an Esri ArcInfo Coverage. The data are intended to assist efforts surrounding broad-scale forest planning for analysis in fire-recovery: soil stabilization, reestablishing vegetation, and protection of other resources such as wildlife, riparian habitat, water quality, fisheries and timber. These data were contributed to INSIDE Idaho at the University of Idaho Library in 2000.

This data publication contains a geospatial file in raster format of wildfires and fuels treatments that occurred between 1995 and 2013 on Stanislaus National Forest and Yosemite National Park in California within the area burned by the 2013 Rim Fire, excluding the outer 500 meters of the fire perimeter. Tabular data are provided for three sets of circular sample windows of size 500 acres (ac), 2500 ac and 5000 ac within the same geospatial extent. Variables included for the sample windows are proportion burned at high severity in the Rim Fire; proportion treated/burned prior to the Rim Fire; mean values for actual evapotranspiration, water deficit, energy release component, and burning index; and proportion in shrubland, riparian, hardwood, conifer, and grassland LandFire vegetation classes. Tabular data are also provided for a set of transects within the same geographic extent that are placed along radial lines centered on the Rim Fire's origin point.These data were used to gain insight into the influence of fuels treatments, weather, vegetation, and water balance on fire severity in a large wildfire occurring in Sierra Nevada mixed-conifer forest at three different landscape scales, and to assess the impact of fuels treatments and previous fires on fire severity along the general direction of fire spread.These data were published on 04/27/2017. On 10/17/23 minor metadata updates were made, including reference to article that is now published.

This data release contains data summarizing observations within and adjacent to the Tadpole Fire, which burned from 6 June to 4 July 2020. In particular, this monitoring data were focused on debris flows triggered on 8 September 2020 in four drainage basins (TAD1, TAD2, TAD3, and TAD4). Rainfall data (1a_rain_geophones.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Index, GaugeID (name of rain gauge), StormID (the storm number starting at the first record, where a new storm is defined by 8 hours with no rainfall), TimeStamp (local time), Bin Accum (mm) (The total accumulated rainfall between timesteps in units of millimeters), TotalAccum (mm) (the cumulative rainfall starting from the beginning of the record), 5-minute Intensity (mm/h) (the 5-minute rainfall intensity), 10-minute Intensity (mm/h) (the 10-minute rainfall intensity), 15-minute Intensity (mm/h) (the 15-minute rainfall intensity), 30-minute Intensity (mm/h) (the 30-minute rainfall intensity), and 60-minute Intensity (mm/h) (the 60-minute rainfall intensity). The location of the rain gage is: 32.955, -108.232. Rainfall data (1b_rain_only.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Index, GaugeID (name of rain gauge), StormID (the storm number starting at the first record, where a new storm is defined by 8 hours with no rainfall), TimeStamp (local time), Bin Accum (mm) (The total accumulated rainfall between timesteps in units of millimeters), TotalAccum (mm) (the cumulative rainfall starting from the beginning of the record), 5-minute Intensity (mm/h) (the 5-minute rainfall intensity), 10-minute Intensity (mm/h) (the 10-minute rainfall intensity), 15-minute Intensity (mm/h) (the 15-minute rainfall intensity), 30-minute Intensity (mm/h) (the 30-minute rainfall intensity), and 60-minute Intensity (mm/h) (the 60-minute rainfall intensity).The location of each rain gage station is: 32.956, -108.241. Geophone data (2_geophone.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: TimeStamp (local time), GeophoneUp_mV (the upstream geophone data in millivolts), GeophoneDn_mV (the downstream geophone data in millivolts). The geophones are co-located with a rain gage at: 32.955, -108.232. Field measurement data (3_combined_data.csv) are provided in a comma-separated value (CSV) file. This dataset describes pieces of wood found within different debris flow deposits in four drainages TAD1-TAD4, and there were multiple debris flow deposits in each drainage. The columns in the csv file are: ID (a unique identifier for each wood piece). For example, if there is one piece of wood at a location in the channel TAD1, the wood piece was mapped as TAD1-1. However, in the case of a single debris flow deposit with multiple pieces of wood, a letter is appended for each additional wood piece, such as TAD1-1a, TAD1-1b, TAD1-1c, etc.), ID_base (a unique identifier for each deposit, which may contain multiple wood pieces), Latitude (the Latitude expressed in Decimal Degrees), Longitude the Longitude expressed in Decimal Degrees), Elevation (the elevation expressed in meters), Length (m) (the length of a wood piece in meters), Diameter (cm) (the diameter of the approximate middle of a wood piece in centimeters), Class (a description of the wood piece), Charred (%) (the percent of the wood piece that was charred by fire), Trapped Sediment (m3) (the total volume of sediment in a debris flow deposition cubic meters), Timing (this is a description of when the wood was deposited with respect to the debris flow. The options are Before, During, or After), Pinned (this indicates wood was pinned against an obstacle or not. If it is pinned, the item is named, otherwise it is labeled as no), Roots/Branches (here indicate either if the roots or branches where still attached to the wood, otherwise it is labeled as no), Orientation (in some locations, the qualitative orientation of the wood with respect to the flow direction is noted), Channel Width (m) (measurements of channel width in meters), Flow Depth (m) (measurements of flow depth in meters), Slope (deg) (the slope value in degrees obtained by selecting the raster slope value from a 1 m lidar underneath the observation point), Lidar Width (m) (the channel width in meters measured 1 meter above the lowest point in the channel), Drainage Area (m2) (the upstream contributing drainage area for at each measurement point), Notes (any notes from the site). Photographic data (4_GameCameraPhotos.zip) are presented from a camera located approximately 25 m from the geophones, focusing on the channel monitored by the geophones. The photos are in a .jpg format and are catalogued by date using the date format (ddMMMYYYY).

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published CBI-4 datasets for fires that burned during calendar years 2012 through 2023, excluding 2020 extended assessments. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php).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.

The downloadable ZIP file contains an Esri ArcInfo Coverage. This data set reflects fire intensity as measured by canopy scorch for 1994 for Boise National Forest, Idaho.These data were contributed to INSIDE Idaho at the University of Idaho Library in 2000.

Silvicultural treatments, fire, and insect outbreaks are the primary disturbance events currently affecting forests in the Sierra Nevada Mountains of California, a region where plants and wildlife are highly adapted to a frequent-fire disturbance regime that has been suppressed for decades. Although the effects of both fire and silviculture on wildlife have been studied by many, there are few studies that directly compare their long-term effects on wildlife communities. We conducted avian point counts from 2010 to 2019 at 1987 in situ field survey locations across eight national forests and collected fire and silvicultural treatment data from 1987 to 2016, resulting in a 20-year post-disturbance chronosequence. We evaluated two categories of fire severity in comparison to silvicultural management (largely pre-commercial and commercial thinning treatments) as well as undisturbed locations to model their influences on abundances of 71 breeding bird species. More species (48% of the community) reached peak abundance at moderate-high-severity-fire locations than at low-severity fire (8%), silvicultural management (16%), or undisturbed (13%) locations. Total community abundance was highest in undisturbed dense forests as well as in the first few years after silvicultural management and lowest in the first few years after moderate-high-severity fire, then abundance in all types of disturbed habitats was similar by 10 years after disturbance. Even though the total community abundance was relatively low in moderate-high-severity-fire habitats, species diversity was the highest. Moderate-high-severity fire supported a unique portion of the avian community, while low-severity fire and silvicultural management were relatively similar. We conclude that a significant portion of the bird community in the Sierra Nevada region is dependent on moderate-high-severity fire and thus recommend that a prescribed and managed wildfire program that incorporates a variety of fire effects will best maintain biodiversity in this region.

https://www.mdpi.com/1999-4907/12/2/150/htm

This study aimed to determine the effects of climate change on forest fire trends in Canada by measuring correlations between weather conditions and the frequency and size of forest fires. Upon identifying the correlations, a model was created to understand future forest fire trends. The purpose of this study was to prevent the increasing trend of forest fires and devise solutions to reduce their damages. The data obtained from the Canadian National Fire Database underwent a linear regression and a machine learning algorithm to respectively predict and correlate weather conditions with future forest fire trends. It was concluded that temperature and wind speed experienced a positive correlation with forest fire frequency and size and precipitation experienced a negative correlation. To reduce the harmful effects of forest fires, cloud seeding can be used to create more precipitation and wind farms can be built to lower wind speed and attract lightning. However, more research and stricter policies directly targeting climate change are necessary for long term stability or decrease in forest fire trends.

The downloadable ZIP file contains an Esri ArcInfo Coverage. This data set reflects fire intensity as measured by canopy scorch for the 1994 Star Gulch Fire within Boise National Forest, Idaho. The data are intended to assist efforts surrounding broad-scale forest planning for analysis in fire-recovery: soil stabilization, reestablishing vegetation, and protection of other resources such as wildlife, riparian habitat, water quality, fisheries and timber. These data were contributed to INSIDE Idaho at the University of Idaho Library in 2000.

This web map shows the current wildfires and fire danger zones within Canada. The layers used within this web map are Esri Canada's wildfire live feature services that are updated daily along with NRCan's current fire danger WMS. A description of each layer can be found below along with the link to their respective items on ArcGIS Online.***The live feature services within this webmap are now paused and will not receive data updates until next fire season. April 1st, 2025 is the predicated date for this service to resume***Active Wildfires in CanadaReported active fire locations are updated daily as provided by fire management agencies (provinces, territories and Parks Canada). The fires data is managed through a national Data Integration Project (DIP) coordinated by the Canadian Interagency Forest Fire Centre (CIFFC) and Natural Resources Canada with participation from all partner agencies. The active fires data includes attributes for agency, fire name, latitude, longitude, start date, fire size (ha) and stage of control (fire status). Possible values for stage of control include: OC (Out of Control), BH (Being Held), UC (Under Control), EX (Out).Supplemental InformationThe national Data Integration Project (DIP) is coordinated by the Canadian Interagency Forest Fire Centre (CIFFC) and Natural Resources Canada with participation from all partner agencies. This initiative focuses on development and implementation of data standards and enabling the exchange and access of national fire data. More details are available in the CIFFC IM/IT Strategy, available at: https://ciffc.ca/publications/general-publications.Feux de végétation actifs au CanadaLes positions rapportées des feux de végétation actifs sont mises à jour quotidiennement d'après les données fournies par les agences de gestion des feux (provinces, territoires et Parcs Canada). Les données sur les feux sont gérées dans le cadre d'un Projet d'intégration de données national coordonné par le Centre interservices des feux de forêt du Canada (CIFFC) et par Ressources naturelles Canada, avec la participation de tous les organismes partenaires. Les données sur les feux actifs comprennent les champs d'attributs des agences, le nom du feu, la latitude, la longitude, le début du feu, la taille du feu (ha) et le stade de contrôle (état du feu). Les valeurs possibles pour le stade de contrôle sont les suivantes : OC (out of control/hors de contrôle), BH (being held/contenu), UC (under control/maîtrisé) et EX (out/éteint).Renseignements complémentairesLe Projet d'intégration de données national est coordonné par le CIFFC et par Ressources naturelles Canada, avec la participation de tous les organismes partenaires. Cette initiative a pour but d'élaborer et de mettre en œuvre des normes de données, ainsi que de rendre possible l'accès aux données nationales sur les feux et l'échange de ces données. On trouvera plus de détails à ce sujet dans la Stratégie de GI/TI du CIFFC, à l'adresse suivante : https://ciffc.ca/publications/general-publicationsActive Wildfire Perimeters in CanadaThis dataset displays active wildfire perimeters derived from hotspots identified in satellite imagery provided by the Canadian Wildland Fire Information System (CWFIS) and Natural Resources Canada (NRCan) updated every 3 hours. || Ce jeu de données, mis à jour toutes les trois heures, affiche les périmètres de feux de forêt actifs dérivés des points chauds relevés dans l’imagerie satellite fournie par le Système canadien d’information sur les feux de végétation (SCIFV) et Ressources naturelles Canada (RNCan).Wildfire Smoke Forecast in CanadaThis layer displays forecasted wildfire smoke across Canada sourced from BlueSky Canada's FireSmoke Canada app, updated every 6 hours. The wildfire smoke layer consists of hourly concentrations of particulate matter 2.5 microns and smaller (PM2.5) in units of micrograms per meter cubed (µg/m3) observed at ground level from wildfires. It is an approximation of when and where wildfire smoke events may occur over the next two days. This layer is sourced from BlueSky Canada's FireSmoke Canada app.Current Fire DangerFire Danger is a relative index of how easy it is to ignite vegetation, how difficult a fire may be to control, and how much damage a fire may do. Fire Danger is a reclassification of the CFFDRS fire weather index (FWI) which is a numeric rating of fire intensity.These general fire descriptions apply to most coniferous forests. The national fire danger maps show conditions as classified by the provincial and territorial fire management agencies. Choice and interpretation of classes may vary between provinces. For fuel-specific fire behavior, consult the Fire Behavior Prediction maps.• Low: Fires likely to be self-extinguishing and new ignitions unlikely. Any existing fires limited to smoldering in deep, drier layers.• Moderate: Creeping or gentle surface fires. Fires easily contained by ground crews with pumps and hand tools.• High: Moderate to vigorous surface fire with intermittent crown involvement. Challenging for ground crews to handle; heavy equipment (bulldozers, tanker trucks, aircraft) often required to contain fire.• Very High: High-intensity fire with partial to full crown involvement. Head fire conditions beyond the ability of ground crews; air attack with retardant required to effectively attack fire's head.• Extreme: Fast-spreading, high-intensity crown fire. Very difficult to control. Suppression actions limited to flanks, with only indirect actions possible against the fire's head.Forecasted weather data provided by Environment Canada. More information about forecasted weather is available at https://cwfis.cfs.nrcan.gc.ca/background/dsm/fwiMore information about the Canadian Forest Fire Weather Index (FWI) System is available at https://cwfis.cfs.nrcan.gc.ca/background/summary/fwiMaps are produced using Spatial Fire Management System and are updated multiple times per day.Maps updated daily, year-round.Supplemental InformationThe Canadian Forest Fire Danger Rating System (CFFDRS). is a national system for rating the risk of forest fires in Canada.Forest fire danger is a general term used to express a variety of factors in the fire environment, such as ease of ignition and difficulty of control. Fire danger rating systems produce qualitative and/or numeric indices of fire potential, which are used as guides in a wide variety of fire management activities.The CFFDRS has been under development since 1968. Currently, two subsystems–the Canadian Forest Fire Weather Index (FWI) System and the Canadian Forest Fire Behavior Prediction (FBP) System–are being used extensively in Canada and internationally.Risque d'incendie actuelLe risque d'incendie est un indice relatif indiquant le niveau de facilité pour allumer un incendie de végétation, le niveau de difficulté qu'un incendie peut demander pour être contrôlé ainsi que la quantité de dommages qu'un incendie peut causer.Ces descriptions générales des incendies s'appliquent à la plupart des forêts de conifères. Les cartes nationales sur le danger d'incendie illustrent les conditions telles qu'elles sont classées par les agences provinciales et territoriales de gestion des feux. Le choix et l'interprétation des classes peuvent varier entre les provinces. En ce qui a trait au comportement des incendies en regard du combustible spécifique, veuillez consulter les cartes de prédiction du comportement des incendies.• Faible: Incendie possiblement auto-extincteur; de nouveaux allumages sont invraisemblables. Tout incendie existant est limité à couver dans des couches profondes plus sèches.• Modéré: Incendie de surface rampant modéré. Il est facilement circonscrit par les équipes au sol munies de pompes et d'outils manuels.• Élevé: Incendie de surface modéré à vigoureux avec implication intermittente des cimes. Pose des défis aux équipes chargées de le combattre sur le terrain; les équipements lourds (bouteurs, camions-citernes à eau et avions) sont souvent requis pour contenir l'incendie.• Très élevé: Incendie de forte intensité avec implication partielle ou complète des cimes. Les conditions au front de l'incendie sont au-delà de la capacité des équipes sur le terrain à y faire face; les attaques aériennes avec largage de produits ignifugeants sont requises pour combattre effectivement le front de l'incendie.• Extrême: Feu de cimes à forte intensité et à propagation rapide. Très difficile à contrôler. Les actions de suppression sont limitées aux flancs alors que seules des actions indirectes sont possibles au front de l'incendie.Prévisions météorologiques fournies par Environnement Canada. Pour de plus amples renseignements sur les prévisions, consultez la section Renseignements généraux.De plus amples informations sur la Méthode canadienne de l'indice Forêt-Météo (IFM) sont disponibles à la rubrique Renseignements généraux.Les cartes sont produites à l'aide du Système de gestion spatiale des feux de forêt et sont mises à jour plusieurs fois par jour.Les cartes sont mises à jour quotidiennement, tout au long de l'année l'année.Renseignements complémentairesLa Méthode canadienne d'évaluation des dangers d'incendie de forêt (MCEDIF) est une méthode nationale pour classer le risque d'incendie de forêt au Canada.Le danger d'incendie de forêt est un terme général employé pour exprimer une diversité de facteurs dans les conditions de brûlage tels que la facilité d'allumage et la difficulté de contrôle. Les méthodes d'évaluation du danger d'incendie génèrent des indices qualitatifs ou numériques du potentiel d'incendie qui sont utilisés comme guides dans une grande variété d'activités de gestion des incendies de forêt.La MCEDIF est en cours d'élaboration depuis 1968. En ce moment, deux sous-systèmes – la Méthode

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.

The data are from the 1999 Megram wildfire that burned into an area of the Six Rivers National Forest that had been affected by a blowdown event in the winter of 1995-96. Surface fuels reduction in a portion of the blowdown area was accomplished via yarding and burning in 1997. Eleven plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot.

The downloadable ZIP file contains an Esri ArcInfo Coverage. This data set reflects fire intensity as measured by canopy scorch for the 1994 Thunderbolt Fire, Cascade Ranger District, Boise National Forest, Idaho. The data are intended to assist efforts surrounding broad-scale forest planning for analysis in fire-recovery: soil stabilization, reestablishing vegetation, and protection of other resources such as wildlife, riparian habitat, water quality, fisheries and timber. These data were contributed to INSIDE Idaho at the University of Idaho Library in 2000.

The Study’s subject: Forestry is besides the Agriculture one of the most important form of land use by area. Wood harvest indicates the intensity of use of forestal products. Data of wood harvest are the basis for the analysis of forestry profitability. The lumber industry is the forestry’s most important income source, therefore the harvested lumber is sorted in different intended use groups and assessed by a statistical classification. Another important task of forestry is the maintenance of the forest. In this way a fundamental contribution to the care and conservation of the agricultural landscape is made by forestry. These requirements for the forestry should be described by selected statistical indexes and parameters, supplying information about - the forestry entrepreneurs and forestry area,- the development of wood harvest and therefore the intensity of forest use,- the usage of wood as lumber for industry ,- damages of the forest by forest fires,- finally, the total balance-sheet of forestry and foreign trade balance of wood. The description of forest enterprises, forestry areas, and logging is made according to the type of tenure in forestry. There are the following three types of tenure: (a) State Forest (Forest owned by the single federal German States, Forest Trust and the National Forest owned by the German federal government) (b) corporate forest (c) private forest (a) State Forest: This forests are designated as state-owned National Forest. The forest owned by the Federal Republic of Germany, although the state forest, usually referred to as the Federal Forestry. The forests owned by the state are supervised by the Federal Forest Service. The National Forest covers 3.7 percent of the forested area and is located mainly in military used areas and along federal waterways and highways. Federal forests therefore are usually subject of a special purpose.The forests owned by the German federal states predominantly stems from former properties of landlords or sovereigns that in the context of the Enlightenment was transformed from private possession of the former ruling families into state ownership. Another case is ecclesiastical possessions which became state ownership by expropriation under the secularization beginning of the 19th century. (b) Corporate Forest: This forest areas are according to §3 paragraph 3 of the National Forest Act owned by public corporations as for example communities or towns (often called as ‘Kommunalwald’ (= corporate forests), Stadtwald (= urban woodland or city forest), ‘Gemeindewald’ (= communal forests) ). (c) Private forest: Private forests are possessions of natural, legal persons, or business partnerships. In Germany, the area of private woods is around 47% of the total forest area, and therefore this form of forest ownership has the highest proportion of all forms of ownership in Germany. The study’s aim: The aim was the compilation of long time series on the basis of the publications of official statistics. An attempts has been made to cover a period from the start of official statistics from 1871 until to the present in 2010 with statistical parameters of German forestry. For the period of the German Empire (1871 – 1938/39) especially for forest enterprises and for forest areas time series date could be collected from the issues of the statistical yearbook of the German Empire. In the case of the former German Democratic Republic (between 1945-1989) which is since 1990 the area of the new Länder (Brandenburg, Mecklenburg-Western Pomerania, Saxony, Saxony-Anhalt, Thuringia), no information for forestry companies and their forest areas could be found in the publications of official statistics former GDR. In this case the reporting period starts in 1990, the period of time after the German reunification. In the case of logging statistics and usage of wood, however, statistical information for the former GDR was available and could be included into this compilation. The values for total wood balance, and foreign trade balance for timber again refers to the territory of the former Federal Republic of Germany and within the borders of 3rd October 1990. The following topics are covered by the data:A) farms and forest land in total and by ownership (state forest, corporate and community forestry, private forestry);B) logging (logging =) by types of trees and forms of ownership;C) damage caused by forest fires;D) total wood balance and foreign trade balance for wood.

National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. 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 FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FLPs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 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 FLP data must be used in conjunction with the BP data for risk assessment.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.

This data publication contains a set of files in which different variables related to fire burned severity (Canada Landsat Burned Severity, CanLaBS) were computed for all events in Canada between 1985 and 2015 as detected by the Canada Landsat Disturbance (CanLaD (Guindon et al. 2017 and 2018) product. Details on the creation of this product are available in Guindon et al. 2020 (https://doi.org/10.1139/cjfr-2020-0353) and in supplementary materials accompanying the publication. The current document is therefore a complement to the article and supplementary materials. The supplementary materials are referenced in the publication (cjfr-2020-0353suppla, cjfr-2020-0353supplb etc.). This is the first Canada-wide product that aims to promote nationwide research on fire severity by making available the data used in the article. The data is in the form of grids composed of pixels at a resolution of 30m. To simplify the distribution and manipulation of the data and considering that two or three fire occurrences within a given location is rare (respectively 2.3% and less than 0.01%), only the most recent fire data are considered in the final product. For these very rare cases, from 2015 to 1985, the most recent burned areas overlap the older data. Overlapping fire count can be found in layer “CanLaBS_Nbdisturb_v0”, multiple fire events in same areas have values equal to or greater than two. Landsat radiometric values for calculating the NBR index were derived from summer Landsat mosaics (July and August), for years 1984 to 2015 (Guindon et al. 2018). These mosaics were developed from individual USGS Landsat scenes with surface reflectance correction (Masek et al., 2006; Vermote et al., 2006). For each annual compound, the pixel with the less atmospheric opacity was selected. An algorithm was also developed to remove clouds that were not detected by the cloud masks provided with the USGS data. Here is a general description of the layers provided and a more technical description can be found in Table 1 (see "Ressources" section below): 1. NBR and dNBR. All these values are multiplied by 1000. The value of dNBR represents the value obtained for NBRpre - NBRpost. It is calculated for each pixel that was classified as a fire in CanLaD, according to the corrected year (see cjfr-2020-0353suppla). 2. Year of fire. The fire years detected in CanLaD (Guindon et al. 2018) was corrected using different fire databases, this layer contains the correct year. (see cjfr-2020-0353suppla) 3. Julian Days of the Fire, based on various high-resolution products. However, this variable is only available from 1989 onwards. 4. Presence of salvage logging one year after the fire. Classification of satellite images detecting scarified soils (see cjfr-2020-0353suppld). 5. Pre-fire forest attributes: Pre-fire forest attributes values were calculated for median mosaics, from 1985 to 2000. These attributes values were derived from NFI (national forest inventory) photo-plot attributes and were spatialized. Pre-fire attribute values were created to stratify the analyses (see cjfr-2020-0353supplc). The predicted variables are as follows: • Canopy density in percent. • Predicted living biomass in tonnes per hectare. • Percentage coniferous biomass proportion of total biomass. • Percentage hardwood biomass proportion of total biomass. • Percentage unknown species biomass proportion of total biomass. Note, as unknown species are found especially in northern areas, they are considered coniferous for the purpose of the article. 6. Missing remote sensing data, one year after the fire. The estimation of burned severity needs NBR data (NBRpost) in the next year after fire occurrences. NBRpost is available for 91% of the cases, but for the remaining 9%, no data were available due to the presence of clouds. For these cases, satellite data from the years following the fire were used with a regression radiometry correction. This gives values to missing data for year following the fire. This layer flags the areas that have derived data. The values of 1= one year after the fire (no regression), 2= two years after the fire (regression), 3= three years after the fire (regression) and 4= four years after the fire (no regression, set as missing data). (see cjfr-2020-0353supplb). 7. Areas with more than one fire disturbance between 1985 and 2015 (1=one single disturbance, 2=two or more, 3=three or more). ## Data citation: 1. Guindon, L., Villemaire P., Manka F., Dorion H. , Skakun R., St-Amant R., Gauthier S. : Canada Landsat Burned Severity (CanLaBS): a Canada-wide Landsat-based 30-m resolution product of burned severity since 1985 https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd 2. The creation, the validation and the limits of the CanLaBS product are describe in the text and supplementary material: Guindon, L., Gauthier, S., Manka, F., Parisien, MA, Whitman, E., Bernier, P., Beaudoin, A., Villemaire P., Skakun R. Trends in wildfire burn severity across Canada, 1985 to 2015 https://doi.org/10.1139/cjfr-2020-0353 ## References cited: 1. Guindon, L., Villemaire, P., St-Amant, R., Bernier, P.Y., Beaudoin, A., Caron, F., Bonucelli, M., and Dorion, H. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad 2. Guindon, L., Bernier, P., Gauthier, S., Stinson, G., Villemaire, P., & Beaudoin, A. (2018). Missing forest cover gains in boreal forests explained. Ecosphere, 9(1), e02094. https://doi.org//10.1002/ecs2.2094 3. Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. 4. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.