N: 90 S: -90 E: 180 W: -180
Description
The Global Fire Atlas is a global dataset that tracks the day-to-day dynamics of individual fires to determine the timing and location of ignitions, fire size, duration, daily expansion, fire line length, speed, and direction of spread. These individual fire characteristics were derived based on the Global Fire Atlas algorithm and estimated day of burn information at 500-m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 MCD64A1 burned area product. The algorithm identified 13.3 million individual fires (>=21 ha or 0.21 km2; the size of one MODIS pixel) over the 2003-2016 study period.
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
Copy Citation
Get Help
QUESTIONS & SUPPORT
Get help with NASA Earth science data, datasets, tools, services, and metadata on the Earthdata Forum. Experienced staff in the data user community review and answer questions.
Get help on Earthdata ForumForum
Documents
USER'S GUIDE
GENERAL DOCUMENTATION
Get Help
QUESTIONS & SUPPORT
Get help with NASA Earth science data, datasets, tools, services, and metadata on the Earthdata Forum. Experienced staff in the data user community review and answer questions.
Get help on Earthdata ForumForum
Publications Citing This Dataset
| Title | Authors | Year Sort ascending | Referenced Datasets |
|---|---|---|---|
| Estimation of distribution of vegetation fire zones in the Russian Far East |
|
||
| Scenario-based mapping reveals soil organic carbon responses to land-use change in the Amazon Basin |
|
||
| Fuel availability versus flammability: divergent fire controls across Eurasian drylands |
|
||
| Assessing fire danger classes and extreme thresholds of the Canadian |
|
||
| Non-parametric spatiotemporal trends in fire: An approach to identify |
|
||
| State of Wildfires 2024-2025 |
|
||
| Detecting Burned Vegetation Areas by Merging Spectral and Texture Features in a ResNet Deep Learning Architecture |
|
||
| Using hydrological modelling to improve the Fire Weather Index system |
|
||
| A Hybrid Index for Monitoring Burned Vegetation by Combining Image |
|
||
| Holocene fire dynamics and their climatic controls on the southern Cape |
|
||
| Improving the fire weather index system for peatlands using peat-specific hydrological input data |
|
||
| The Canadian Fire Spread Dataset |
|
|
|
| The effect of fire-induced forest-degradation on rainfall: a causal inference analysis of the case of the Brazilian Amazon |
|
||
| Characterization of land cover-specific fire regimes in the Brazilian |
|
||
| Global scale coupling of pyromes and fire regimes |
|
|
|
| Stakeholder perceptions of wildfire management strategies as nature-based solutions in two Iberian biosphere reserves |
|
||
| Forecasting the anthropogenic fire hazard of vegetation according to satellite images |
|
||
| Assessing Wildfire Vulnerability of Vegetated Serpentine Soils in the Balkan Peninsula |
|
||
| A review of carbon monitoring in wet carbon systems using remote sensing |
|
|
|
| Re-examining the assumption of dominant regional wind and fire spread directions |
|
||
| Mining the drivers of forest cover change in the upper Indus Valley |
|
||
| Declining severe fire activity on managed lands in Equatorial Asia |
|
||
| Wildfires in the Arctic and tropical biomes: what is the relative role of climate? |
|
||
| Assessing expected economic losses from wildfires in eucalypt plantations of western Brazil |
|
||
| Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin |
|