INVESTIGATIVE WILDFIRE DATA FOR TURKEY/ NASA

BE CAREFUL OF THE FILE NAMES.

IT CONTAINS THE DATA NEEDED TO RESEARCH LATEST FOREST FIRES IN TURKEY.

PAY ATTENTION TO THE DATE INTERVALS. THESE ARE 7-11 DAILY DATA OF LAST TIMES.

• fire _ nrt _ M _ C61 _ 212465 _ all _ countries.csv

This file is important for all countries becuase it contains fire data of last 11 days for all around the world

Content

Data on recent forest fires in Turkey, published with permission from NASA Portal. The data was created based on the hotspots and obtained from the satellite.

3 SEPARATE SATELLITE DATA:

• MODIS C6.1
• SUOMI VIIRS C2
• J1 VIIRS C1

GENERAL ATTRIBUTES

• Latitude Center of nominal 375 m fire pixel

• Longitude Center of nominal 375 m fire pixel

• Bright_ti4 (Brightness temperature I-4) VIIRS I-4: channel brightness temperature of the fire pixel measured in Kelvin.

• Scan (Along Scan pixel size) The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.

• Track (Along Track pixel size) The algorithm produces approximately 375 m pixels at nadir. Scan and track reflect actual pixel size.

• Acq_Date (Acquisition Date) Date of VIIRS acquisition.

• Acq_Time (Acquisition Time) Time of acquisition/overpass of the satellite (in UTC).

• Satellite N Suomi National Polar-orbiting Partnership (Suomi NPP)

• Confidence This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.

Please note:

• Low confidence nighttime pixels occur only over the geographic area extending from 11° E to 110° W and 7° N to 55° S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.

• Version Version identifies the collection (e.g. VIIRS Collection 1) and source of data processing: Near Real-Time (NRT suffix added to collection) or Standard Processing (collection only).

"1.0NRT" - Collection 1 NRT processing.

"1.0" - Collection 1 Standard processing.

• Bright_ti5 (Brightness temperature I-5) I-5 Channel brightness temperature of the fire pixel measured in Kelvin.

• FRP (Fire Radiative Power) FRP depicts the pixel-integrated fire radiative power in MW (megawatts). Given the unique spatial and spectral resolution of the data, the VIIRS 375 m fire detection algorithm was customized and tuned in order to optimize its response over small fires while balancing the occurrence of false alarms. Frequent saturation of the mid-infrared I4 channel (3.55-3.93 µm) driving the detection of active fires requires additional tests and procedures to avoid pixel classification errors. As a result, sub-pixel fire characterization (e.g., fire radiative power [FRP] retrieval) is only viable across small and/or low-intensity fires. Systematic FRP retrievals are based on a hybrid approach combining 375 and 750 m data. In fact, starting in 2015 the algorithm incorporated additional VIIRS channel M13 (3.973-4.128 µm) 750 m data in both aggregated and unaggregated format.

Satellite measurements of fire radiative power (FRP) are increasingly used to estimate the contribution of biomass burning to local and global carbon budgets. Without an associated uncertainty, however, FRP-based biomass burning estimates cannot be confidently compared across space and time, or against estimates derived from alternative methodologies. Differences in the per-pixel FRP measured near-simultaneously in consecutive MODIS scans are approximately normally distributed with a standard deviation (ση) of 26.6%. Simulations demonstrate that this uncertainty decreases to less than ~5% (at ±1 ση) for aggregations larger than ~50 MODIS active fire pixels. Although FRP uncertainties limit the confidence in flux estimates on a per-pixel basis, the sensitivity of biomass burning estimates to FRP uncertainties can be mitigated by conducting inventories at coarser spatiotemporal resolutions.

http://cedadocs.ceda.ac.uk/770/1/SEVIRI_FRP_documentdesc.pdf

• Type (Inferred hot spot type) 0 = presumed vegetation fire

1 = active volcano

2 = other static land source

3 = offshore detection (includes all detections over water)

• DayNight (Day or Night)

D= Daytime fire

N= Nighttime fire

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available fire datasets that identify wildfire and prescribed fire burned areas across the United States. However, these datasets are all limited in some way. Their time periods could cover only a couple of decades or they may have stopped collecting data many years ago. Their spatial footprints may be limited to a specific geographic area or agency. Their attribute data may be limited to nothing more than a polygon and a year. None of the existing datasets provides a comprehensive picture of fires that have burned throughout the last few centuries. Our dataset uses these existing layers and utilizes a series of both manual processes and ArcGIS Python (arcpy) scripts to merge these existing datasets into a single dataset that encompasses the known wildfires and prescribed fires within the United States a ...

Description:

The dataset reflects the damage sustained by structures across various fire incidents, categorized by damage percentage—ranging from minor damage (1-10%) to complete destruction (50-100%) and collected by field inspectors who evaluate structures impacted by wildland fires.

This dataset is invaluable for fire prevention, emergency response, and disaster management efforts.

Attribute Statement:

This dataset is provided by the California Department of Forestry and Fire Protection (CAL FIRE) in collaboration with the National Interagency Fire Center (NIFC) and the Fire Integrated Real-Time Intelligence System (FIRIS).

No Endorsement Statement:

The publication of this dataset does not imply endorsement by CAL FIRE, NIFC, or any associated entities. The data is provided "as-is" without any guarantees regarding accuracy or completeness.

Citation:

CAL FIRE, "California Wildfire Perimeter Data: Real-Time Insights from FIRIS & NIFC," California Open Data Portal. Available at: https://gis.data.cnra.ca.gov/datasets/CALFIRE-Forestry::ca-perimeters-nifc-firis-public-view

License:

This dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Provenance:

This dataset is sourced from the California Open Data Portal and is maintained by CAL FIRE and associated agencies.
- Original Source: California Open Data Portal - FIRIS Dataset
- Data Collection Methods:
- Data transmitted from satellites and aerial infrared platforms.
- Near-real-time processing for fire perimeter modeling by WIFIRE.
- Decision support integration through Intterra's software.

DISCLAIMER: Please independently verify the data and it's derivation(s) before applying it to research or decision-making.

This dataset contains a comprehensive collection of environmental and meteorological data, comprising 17 columns and 118,858 entries. All features are numerical (float64), and there are no missing values, making it a clean dataset suitable for direct use.

The frp (Fire Radiative Power) data is sourced from NASA FIRMS VIIRS SCC (Visible Infrared Imaging Radiometer Suite Suomi NPP). Other meteorological features, such as pressure_mean, wind_direction_mean, wind_direction_std, solar_radiation_mean, dewpoint_mean, cloud_cover_mean, evapotranspiration_total, humidity_min, temp_mean, temp_range, and wind_speed_max, are derived from Open-Meteo. The dataset also includes geographical coordinates (lat, lon), a fire_weather_index, and daynight_N (likely indicating day or night), along with occured (a binary flag possibly indicating the occurrence of a fire).

This dataset is ideal for tasks such as:

Predicting fire occurrences (occurred).

Estimating fire intensity (frp).

Analysing the relationships between meteorological conditions and fire events.

Developing fire risk assessment models.

It provides a rich foundation for environmental and climate-related research, particularly in the domain of wildfire prediction and management.

Historic Fire Perimeter Dataset Feedback (arcgis.com)

Current criteria for data collection are as follows:

CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.

All cooperating agencies submit perimeters ≥10 acres.

Version update:

Firep24_1 was released in April 2025. Five hundred forty-eight fires from the 2024 fire season were added to the database (2 from BIA, 56 from BLM, 197 from CAL FIRE, 193 from Contract Counties, 27 from LRA, 8 from NPS, 55 from USFS and 8 from USFW). Six perimeters were added from the 2025 fire season (as a special case due to an unusual January fire siege). Five duplicate fires were removed, and the 2023 Sage was replaced with a more accurate perimeter. There were 900 perimeters that received updated attribution (705 removed “FIRE” from the end of Fire Name field and 148 replaced Complex IRWIN ID with Complex local incident number for COMPLEX_ID field). The following fires were identified as meeting our collection criteria but are not included in this version and will hopefully be added in a future update: Addie (2024-CACND-002119), Alpaugh (2024-CACND-001715), South (2024-CATIA-001375). One perimeter is missing containment date that will be updated in the next release.

Cross checking CALFIRS reporting for new CAL FIRE submissions to ensure accuracy with cause class was added to the compilation process. The cause class domain description for “Powerline” was updated to “Electrical Power” to be more inclusive of cause reports.

Includes separate layers filtered by criteria as follows:

California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale.

Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2020-January 2025), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.

California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-January 2025. Symbolized by decade, and display starting at country level scale.

Detailed metadata is included in the following documents:

Wildland Fire Perimeters (Firep24_1) Metadata

See more information on our Living Atlas data release here:

CAL FIRE Historical Fire Perimeters Available in ArcGIS Living Atlas

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

This dataset comprises information related to forest fires and is intended for training algorithms designed for forest fire detection, alongside data for object detection. The section dedicated to fire classification consists of 2974 images, divided into two categories: the first category includes images depicting forest fires, while the second category contains images of intact forests without fires. As for the object detection data, it encompasses 1690 images, suitable for object detection purposes. These data have been distributed across training, validation, and test sets with proportions of 80%, 15%, and 5%, respectively. [1] A. Khan and B. Hassan, “Dataset for forest fire detection,” Mendeley Data, vol. 1, p. 2020, 2020, Accessed: Nov. 18, 2023. [Online]. Available: https://data.mendeley.com/datasets/gjmr63rz2r/1 [2] https://www.kaggle.com/datasets/phylake1337/fire-dataset

These data were gathered from various sources on the internet and were manually filtered to ensure data integrity. Additionally, a portion of this data was generated manually by simulating forest fires after obtaining the necessary approvals from relevant authorities.

This project is part of a master's thesis titled "Development of a Deep Learning-Based Surveillance System for Forest Fire Detection and Monitoring using UAV (İHA KULLANILARAK ORMAN YANGINLARININ TESPİTİ VE GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI GÖZETLEME SİSTEMİNİN GELİŞTİRİLMESİ)" at Karabuk University in Turkey, conducted by the student Ibrahim Shmata and supervised by Dr. Batıkan Erdem Demir.

Context

This Dataset contains the details about the forest fire that happens a othen due to several conditions try to predict the Areas got affected so the futher forest fire can be cotrolled due to the Machine learning .

Content

It contains the Factors that induce the Fire also the Factors that makes useful like Wind and Rain respectively.

Acknowledgements

This contents are specially taken from the uci repository and edit by us.thanks a lot for the dataset @uci_repository.

Inspiration

Try to predict the area which is going to be get affected so that the Area can be saved in prior before the fauna and flora species got to ash by the fire.

This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2020. It is the fifth update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as a Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG), including an updated wildfire-cause standard (approved August 2020). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 2.3 million geo-referenced wildfire records, representing a total of 180 million acres burned during the 29-year period. Identifiers necessary to link the point-based, final-fire-reporting information to published large-fire-perimeter and operational-situation-reporting datasets are included. View MetadataAdditional InformationThis 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 OGC WMS CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrence6thEdition_01/MapServer/29 Research Data Archive Geodatabase Download (Coming Soon) Shapefile Download (Coming Soon) For complete information, please visit https://data.gov.

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at https://github.com/river-corridors-sfa/rc_sfa-rc-3-wenas-meta . This data package was originally published in March 2024. It was updated in April 2025 (v2; new and modified files). See the change history section below for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.

Field Description:

A wildfire is an uncontrolled burn of vegetation, which includes the burning of forests, shrublands and grasslands, savannas, and croplands.

Wildfires can be caused by human activity — such as arson, unattended fires, or the loss of control of planned burns — and natural causes, such as lightning.

The spread of wildfires, once ignited, is determined by a range of factors, such as the amount and types of dry vegetation in the surrounding area, wind direction and speed, moisture levels, and heat. The amount of area burned by wildfires — and the impacts on ecosystems — is driven by a combination of weather patterns, human activity, the management of vegetation and landscapes, and responses to suppress their spread.

Data Description:

There are 5 csv files in this dataset.

1- cumulative area burnt by wildfires by week:

Weekly burned area

A useful way of tracking the evolution of wildfires across the year is to look at their week-by-week progression. This allows us to compare the extent of wildfires at a given time this year to the same period in a previous year.

It lets us see whether wildfires have started earlier or later than in previous years and whether they’re tracking above or below what we might expect from historical records.

2- annual area burnt by wildfires

Burned area by year

How much area is burned by wildfires each year? The data for the current year is also included to allow you to put this year’s current burn figures into context.

3- share of the total land area burnt by wildfires each year

Share of land area burned by wildfires

It’s useful to put the total extent of wildfires in the context of total land area: what share of land is burned each year?

Africa tends to be the region with the largest share of area burned — typically ranging from 6% to 8% each year. Figures tend to be lower in other regions but can vary a lot from year to year.

It’s important to note that the same land area can burn multiple times over successive years. So, a rate of 5% burn per year doesn’t mean that 50% of a country is burned over a decade. Some areas will burn multiple times over that period, while others will never be exposed.

4- annual area burnt per wildfire

Land area burned per wildfire

The spread of a wildfire, once ignited, will be influenced by many factors. Natural phenomena such as the type of vegetation, weather conditions, and the intensity of heat will influence how well the fire will be contained. Certain types of vegetation, for example, burn more easily than others. The effectiveness of the local fire containment management strategies will also play a role. This means some wildfires end up being significantly larger than others, and the impact can vary across different regions and countries.

5- annual burned area by landcover

Burned area by land type

When “wildfires” are mentioned, people often picture forest fires. But lots of other ecosystem types can burn. Much larger areas of grasslands and savannas are burned each year than forests.

Source of data:

Ourworldindata.org

By Veronika Samborska and Hannah Ritchie

The FirePerimeter polygon layer represents daily and final mapped wildland fire perimeters. 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 DownloadsThis 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 CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

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.

The attached code and csv files accompany the manuscript titled: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires by Duncan et al. accepted for publication in the journal GeoHealth in September 2023.

Project wildfire data.csv contains a subset wildfire data obtained from:

Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6

Modeled PM2.5 data_clean.csv includes modeled PM2.5 concentrations used to make Figure 2. Model results can be obtained from EPA's Fused Air Quality Surfaces Using Downscaling Tool. plot_Modeled.R contains the R code to generate this figure.

ORs all Counties.csv contains the odds ratios, confidence intervals, and p-values used to make Figures 3, 4, and 5.PM2.5 35ug-m3 ORs.csv contains the odds ratios, confidence intervals, and p-values used to make Figure S1. Wildfire_Forest.R contains the R code to generate these figures.

This ArcGIS Online hosted feature service displays perimeters from the National Incident Feature Service (NIFS) that meet ALL of the following criteria:

Dataset

This dataset was created by Syed Shayan Shahid

Released under CC0: Public Domain

Contents

This data package is associated with the publication “Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning” submitted to Remote Sensing of Environment (Zahura et al. 2023). In this research, a machine learning algorithm, random forest (RF), was utilized to examine the impact of climate and topography on post-fire vegetation recovery. We used enhanced vegetation index (EVI) to examine varying burn severity and land cover types. The data package includes the input files for RF model training, outputs from model predictions and analysis, and python scripts to run the model, analyze the results to understand model performance and interpretability, and plot manuscript figures. This data package contains three folders (Data, Scripts, and Figures), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Postfire_recovery_flmd.csv for a list of all files contained in this data package and descriptions for each. The data dictionary (Postfire_recovery_dd.csv) describes the csv column headers. The “Data” folder provides all the inputs and outputs to train the RF model, evaluate performance, and interpret predictions. The “Scripts” folder contains python scripts and jupyter notebooks for model training and result analysis. The “Figures” folder includes the figures used in the manuscript in “.png” and “.jpg” format.

This data release contains numerous comma-separated text files with data summarizing observations in the within and adjacent to the Woodbury Fire, which burned from 8 June to 15 July 2019. In particular, this monitoring data was focused on debris flows in burned and unburned areas. Rainfall data (Wdby_Rainfall.zip) are contained in csv files called Wdby_Rainfall for 3 rain gages named: B2, B6, and Reavis. This is time-series data where the total rainfall is recorded at each timestamp. The location of each rain gage is listed as a latitude/longitude in each file. Data from absolute (i.e. not vented) pressure transducers (Wdby_Pressure.zip), which can be used to constrain the time of passage of a flood or debris flow, are available in csv files called Wdby_Pressure for four drainages (B1, B6, Reavis 1, and Reavis 2). This is time-series data where the measured pressure in kilopascals is recorded at each timestamp. The location of each pressure transducer is listed as a latitude/longitude in each file. Infiltration data are located in the csv file called WoodburyInfiltration.csv. The location of the measurement is listed as a latitude/longitude. Three measurement values are reported at each location: Saturated Hydraulic Conductivity (Ks) [mm/hr], Sorptivity (S) [mm/h^(1/2)], and pressure head (hf) [m]. The date of each measurement and soil burn severity class are also reported at each location, as well as a table explaining the burn-severity numerical class conversion. Particle size analyses using laser diffraction (WoodburyLaserDiffractionSummary.zip) are located in the files called WoodburyLaserDiffractionSummary for the fine fraction (< 2 mm) of hillslope and debris flow Deposits. The diameter of each particle size class is listed in the first column. All subsequent columns begin with the sample name. The value in each row is the percentage of the grain sizes in the size class. Location data for each of these samples is listed in the accompanying data table titled: WoodburyParticleSizeSummary.csv. The particle size data are summarized in the csv files (WoodburyParticleSizeSummary.zip) called WoodburyParticleSizeSummary by debris flow deposits and hillslope samples. These files group the raw data into more useable information. The sample name (Lab ID) is used to identify the Laser Diffraction data. The data columns (Lat) and (Lon) show the latitude and longitude of the sample locations. The total fraction of all the grain sizes, determined by sieving, are listed in three classes (Fraction < 16 mm, Fraction < 4 mm, Fraction < 2 mm). The fine fractions (< 2 mm) are also summarized in the columns (%Sand, %Silt, %Clay), as determined by laser diffraction. The data are identfied as in the burn area using entries of Yes, whereas unburned areas are shown as No, indicating no burn. The median particle size (D50) is listed if the sample collected in the field was representative of the deposit. In some cases, large cobbles and boulders had to be removed from the sample because were much too large to be included in sample bags that were brought back to the lab for analysis. The last column label (Description) contains notes about each sample. Pebble count data (WoodburyPebbleCountsSummary.zip) are available in csv files called WoodburyPebbleCountsSummary for six drainages (U10 Fan, U10 Channel, U22 Channel, B1 Channel, B7 Fan, and U42 Fan). Here U represents unburned, and B represents burned. The data name indicates whether the data come from a deposit located in a channel or a fan. In each file the particle is numbered (Num) and the B-axis measurement of the particle is reported in centimeters. The location of each pebble count is listed as a latitude/longitude in each file. Channel width measurements for 23 channels are saved in unique shapefiles within the file called Channel_Width_Transects.zip. These width measurements were made using Digital Globe imagery from 19 October 2019. The study basins used for the entire study can be found in the shapefile: Woodbury_StudyBasins.shp. The attribute table along with many morphometric and fire related statistics for each basin is also available in the file Woodbury_StudyBasins_Table.csv. A description of each column name in the table is available in the file Woodbury_StudyBasins_Table_descriptions.csv. Debris flow volumes were available in eleven drainage basins. The volume data is contained in the file Wdby_FlowVolume.csv in a column named (Volume). The volume units are cubic meters. The other column is the Basin ID, which can be found in the shapefile: Woodbury_StudyBasins.shp.

IMPORTANT NOTICE This item has moved to a new organization. It entered Mature Support in May 2025 and will retire in December 2026. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here The reported active fire locations are updated daily as provided by fire management agencies (provinces, territories and Parks Canada). The wildfires 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. This initiative focuses on the 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 page: https://ciffc.ca/publications/general-publications. The active fires data includes attributes for agency, fire name, latitude, longitude, start date, fire burn area (ha), time zone and stage of control (fire status). The 4 stages of control include:Out of Control (OC)Being Held (BH)Under Control (UC)Other (Various percentages of control)The fire burn areas in hectares are calculated or estimated by the agencies using a variety of methods from simple visual estimation and satellite hotspot buffering to more advanced methods such as helicopter GPS flight, air photography, and Landsat image classification. Additional Resources: The metadata for the active fires service can be accessed through the Canadian Wildland Fire Information System (CWFIS) Datamart page and by this interactive map: https://cwfis.cfs.nrcan.gc.ca/interactive-map. Update Frequency:The feature service is created from the active fires csv provided on NRCan's metadata page and is updated every 3 hours using a Notebook only during wildfire season. It should be noted that the active fires csv includes fire sizes of less than 1 hectare whereas as WFS version does not. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Les emplacements des incendies actifs signalés sont mis à jour quotidiennement et fournis par les organismes de gestion des incendies (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. Cette initiative vise principalement le développement et la mise en œuvre de normes de données et cherche à rendre accessibles et partageables les données nationales sur les incendies. Plus de détails sont offerts dans le document traitant de la stratégie de gestion de l’information et de la technologie du Centre interservices des feux de forêt du Canada (CIFFC) : https://ciffc.ca/publications/general-publications.Les données sur les incendies actifs comprennent des attributs relatifs à l’organisme, le nom de l’incendie, la latitude, la longitude, la date de début, la superficie brûlée (ha), le fuseau horaire et l’étape de contrôle (état de l’incendie). Les quatre étapes du contrôle sont les suivantes:Hors contrôle (OC pour Out of Control)Contenu (BH pour Being Held )Maîtrisé (UC pour Under Control)Autres (divers pourcentages de contrôle) Les superficies brûlées en hectares sont calculées ou estimées par les organismes à l’aide d’une variété de méthodes, qui vont de la simple estimation visuelle et à l’établissement par satellite de zones tampons autour des points chauds, pour passer à des méthodes plus avancées telles que le vol GPS par hélicoptère, la photographie aérienne et la classification d’images Landsat. Autres ressources: Les métadonnées du service des feux actifs sont accessibles par l’intermédiaire du mini-entrepôt de données du Système canadien d’information sur les feux de végétation (SCIFV), ainsi que par cette carte interactive: https://cwfis.cfs.nrcan.gc.ca/carte-interactive. Fréquence de mise à jour:Le service d’entité est créé à partir des fichiers CSV des feux actifs fournis sur la page de métadonnées de RNCan. Il est mis à jour toutes les trois heures à l’aide d’un bloc-notes, uniquement pendant la saison des feux de forêt (avril-octobre). Il convient de noter que le fichier CSV des incendies actifs comprend les incendies de moins d’un hectare, ce qui n’est pas le cas pour la version WFS.