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304 results for “burned area”
30m resolution global forest burned area products, 2014-2021
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts: zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
FireSR: A Dataset for Super-Resolution and Segmentation of Burned Areas
<p><br># FireSR Dataset</p> <p>## Overview</p> <p>**FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques.</p> <p>## Dataset Structure</p> <p>The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring:</p> <p>- **S2**: Contains Sentinel-2 images.<br> - **pre**: Pre-fire Sentinel-2 images (high resolution).<br> - **post**: Post-fire Sentinel-2 images (high resolution).</p> <p>- **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.<br> - **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.<br> - **post**: Burned area labels corresponding to post-fire conditions.</p> <p>- **MODIS**: Contains post-fire MODIS images (lower resolution).</p> <p>- **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023).</p> <p>- **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries.</p> <p>### File Naming Convention</p> <p>Each GeoTIFF (.tif) file is named according to the format: `CA_<year>_<province>_<id>.tif`, where:<br>- `CA` stands for Canada.<br>- `<year>` is the year of the wildfire event.<br>- `<province>` is the province code (e.g., AB for Alberta, BC for British Columbia).<br>- `<id>` is a unique identifier for the wildfire event.</p> <p>### Directory Structure</p> <p>The dataset is organized as follows:</p> <p>```<br>FireSR/<br>│<br>├── dataset/<br>│ ├── S2/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── mask/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── MODIS/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── LULC/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── Daymet/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>```</p> <p>### Spatial Resolution and Channels</p> <p>- **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4)<br>- **MODIS Images**: 250 meters (Bands: B7, B2, B1)<br>- **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned)<br>- **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp)<br>- **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland)</p> <p>**Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions:</p> <p>| Name | Units | Min | Max | Description |</p> <p>|------|-------|-----|-----|-------------|</p> <p>| dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. |</p> <p>| prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. |</p> <p>| srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. |</p> <p>| swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. |</p> <p>| tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. |</p> <p>| tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. |</p> <p>| vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. |</p> <p>**ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year.</p> <p>| Class Value | Land Cover Class |</p> <p>|-------------|------------------|</p> <p>| 1 | Water |</p> <p>| 2 | Trees |</p> <p>| 4 | Flooded Vegetation |</p> <p>| 5 | Crops |</p> <p>| 7 | Built Area |</p> <p>| 8 | Bare Ground |</p> <p>| 9 | Snow/Ice |</p> <p>| 10 | Clouds |</p> <p>| 11 | Rangeland |</p> <p><br>## Usage Tutorial</p> <p>To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow:</p> <p>### Step 1: Extract FireSR.tar.gz</p> <p>```bash<br>tar -xvf FireSR.tar.gz<br>```</p> <p>### Step 2: Tiling the GeoTIFF Files</p> <p>The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images:</p> <p>```python<br>import rasterio<br>from rasterio.windows import Window<br>import os</p> <p>def tile_image(image_path, output_dir, tile_size=128):<br> with rasterio.open(image_path) as src:<br> for i in range(0, src.height, tile_size):<br> for j in range(0, src.width, tile_size):<br> window = Window(j, i, tile_size, tile_size)<br> transform = src.window_transform(window)<br> outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")<br> with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:<br> dst.write(src.read(window=window))</p> <p># Example usage<br>tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/')<br>```</p> <p>### Step 3: Loading Data into a Machine Learning Model</p> <p>After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch:</p> <p>```python<br>import torch<br>from torch.utils.data import Dataset<br>from torchvision import transforms<br>import rasterio</p> <p>class FireSRDataset(Dataset):<br> def __init__(self, image_dir, transform=None):<br> self.image_dir = image_dir<br> self.transform = transform<br> self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]</p> <p> def __len__(self):<br> return len(self.image_paths)</p> <p> def __getitem__(self, idx):<br> image_path = self.image_paths[idx]<br> with rasterio.open(image_path) as src:<br> image = src.read()<br> if self.transform:<br> image = self.transform(image)<br> return image</p> <p># Example usage<br>dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor())<br>dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)<br>```</p> <p>## License</p> <p>This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.</p> <p>## Contact</p> <p>For any questions or further information, please contact:<br>- Name: Eric Brune<br>- Email: ebrune@kth.se</p>
Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests
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Cross-correlation between burned area and climate teleconnections in the Iberian Peninsula
<p>Set of figures and html app showing the cross-correlation results between burned area and climate teleconnections in the Iberian Peninsula.</p>
Arid environmental manuscript for wildfire human opinion and burned area data
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Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill protected area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. </p>
Figure 6. Burned area per month detected between 1985 and 2020 in Breeding biology of the Ochre-breasted pipit (Anthus nattereri), with notes on its conservation in the Upper Rio Grande Grasslands, south-eastern Brazil
Figure 6. Burned area per month detected between 1985 and 2020 in São João del-Rei, south-eastern Brazil. Data from the MapBiomas (2022). The dotted line indicates the average across months.
Climate data, burned areas and active fires predited in Guinea-Savannah and Forest-savannah mosaic zones in Ghana
<p>Climate data, burned areas and active fires predited in Guinea-Savannah zone and Forest-savannah mosaic zone in Ghana</p>
A burned area database from Sentinel-2 imagery (2016-2022) for Madagascar, southern Mozambique, Eswatini and eastern South Africa
<p>This database includes georeferenced burned area at 20 m and fire dates covering the period 2016-2022 for Madagascar, southern Mozambique (Maputo, Maputo City, Gaza, Inhambane), Eswatini, and eastern South Africa (Limpopo, Mpumalanga, KwaZulu-Natal, Eastern Cape). The classification of burned areas has been done based on 165,833 Sentinel-2 scenes (2A and 2B), by applying a two-phased algorithm on the probability output of a random forest model. The product has been validated in Madagascar with long temporal reference burned area units distributed into two fire activity strata. The accuracy analysis performed for the years 2019 and 2021 revealed a Dice coefficient of ≥79%, commission errors ≤18% and omission error ≤24% with a relative bias of about -8%. Intercomparisons with other available burned area products (FireCCISFD11, FireCCISFD20, GABAM, FireCCI51, C3SBA11, MCD64) indicated a consistent performance throughout the entire period. The product is provided in shapefiles, divided into four-month periods. Each shapefile contains a field named “BurnDate” indicating the date when the burned area was detected in format YYYYMMDD. Missing values indicate areas that were not burned, while zero values represent areas that were not considered in the mapping process due to persistent pixel low quality conditions.</p>
Data from: New methods provide a 300–year perspective on modern area burned in two wilderness areas of the southwest United States
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Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa’s rangelands and fill protected area funding gaps
Open the record for dataset details and reuse information.
VIIRS/NPP Burned Area Monthly L4 Global 500m SIN Grid V001
The VNP64A1 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP64A1](https://doi.org/10.5067/VIIRS/VNP64A1.002) Version 2 data product.The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1-366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this [provisional](https://viirsland.gsfc.nasa.gov/Val_overview.html) data in research. The Version 2 burned area product generated with an improved cloud mask was released on October 22, 2024. Users are encouraged to use the improved V002 burned area product.The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS). Additional information on falsely identified burn areas related to cloud mask artifacts and cropland burning is provided in section 6 of the User Guide.
Data from: Warmer and drier fire seasons contribute to increases in area burned at high severity in western US forests from 1985-2017
<p>Increases in burned area across the western US since the mid-1980's have been widely documented and linked partially to climate factors, yet evaluations of trends in <i>fire severity</i> are lacking. Here, we evaluate fire severity trends and their interannual relationships to climate for western US forests from 1985-2017. Significant increases in annual area burned at high severity (AAB<sub>hs</sub>) were observed across most ecoregions, with an overall eight-fold increase in AAB<sub>hs</sub> across all western US forests. The relationships we identified between the annual fire severity metrics and climate, as well as the observed and projected trend toward warmer and drier fire seasons, suggest that climate change will contribute to increased fire severity in future decades where fuels remain abundant. The growing prevalence of high-severity fire in western US forests has important implications to forest ecosystems, including an increased probability of fire-catalyzed conversions from forest to alternative vegetation types.</p>
2021 Global Annual Burned Area Maps (GABAM)
<p>30 m resolution global annual burned area maps (GABAM) of 2021 are released for free download. The annual burned area map is defined as spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years. GABAM was generated via an automated pipeline based on Google Earth Engine (GEE), using all the available Landsat images on GEE platform. The product was projected in a Geographic (Lat/Long) projection at 0.00025 degree (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10 degree × 10 degree tiles spanning the range 180W–180E and 80N–60S.</p>
Figure 99 from: Fernandez-Triana J, Whitfield J, Rodriguez J, Smith M, Janzen D, Hajibabaei M, Burns J, Solis A, Brown J, Cardinal S, Goulet H, Hebert P (2014) Review of Apanteles sensu stricto (Hymenoptera, Braconidae, Microgastrinae) from Area de Conservación Guanacaste, northwestern Costa Rica, with keys to all described species from Mesoamerica. ZooKeys 383: 1-565. https://doi.org/10.3897/zookeys.383.6418
Figure 99 - Apanteles carloszunigai. A Habitus, lateral view B Fore wing C Hypopygium and ovipositor sheats D Posterior half of antenna E Anterior half of antenna F Head, frontal view G Head, meso- and metasoma (partially), dorsal view.
Figure 98 from: Fernandez-Triana J, Whitfield J, Rodriguez J, Smith M, Janzen D, Hajibabaei M, Burns J, Solis A, Brown J, Cardinal S, Goulet H, Hebert P (2014) Review of Apanteles sensu stricto (Hymenoptera, Braconidae, Microgastrinae) from Area de Conservación Guanacaste, northwestern Costa Rica, with keys to all described species from Mesoamerica. ZooKeys 383: 1-565. https://doi.org/10.3897/zookeys.383.6418
Figure 98 - Apanteles robertoespinozai. A Habitus, lateral view B Fore wing C Hypopygium and ovipositor sheats D Head, frontal view E Head, dorsal view F Meso- and metasoma (partially), dorsal view.
Figure 96 from: Fernandez-Triana J, Whitfield J, Rodriguez J, Smith M, Janzen D, Hajibabaei M, Burns J, Solis A, Brown J, Cardinal S, Goulet H, Hebert P (2014) Review of Apanteles sensu stricto (Hymenoptera, Braconidae, Microgastrinae) from Area de Conservación Guanacaste, northwestern Costa Rica, with keys to all described species from Mesoamerica. ZooKeys 383: 1-565. https://doi.org/10.3897/zookeys.383.6418
Figure 96 - Apanteles carlosrodriguezi. A Habitus, lateral view B Fore wing C Hypopygium and ovipositor sheats D Anterior half of antenna E Posterior half of antenna F Head, frontal view G Head, meso- and metasoma, dorsal view.
Figure 97 from: Fernandez-Triana J, Whitfield J, Rodriguez J, Smith M, Janzen D, Hajibabaei M, Burns J, Solis A, Brown J, Cardinal S, Goulet H, Hebert P (2014) Review of Apanteles sensu stricto (Hymenoptera, Braconidae, Microgastrinae) from Area de Conservación Guanacaste, northwestern Costa Rica, with keys to all described species from Mesoamerica. ZooKeys 383: 1-565. https://doi.org/10.3897/zookeys.383.6418
Figure 97 - Apanteles gloriasihezarae. A Habitus, lateral view B Fore wing C Hypopygium and ovipositor sheats D Antenna E Head, frontal view F Head, meso- and metasoma, dorsal view.
Figure 94 from: Fernandez-Triana J, Whitfield J, Rodriguez J, Smith M, Janzen D, Hajibabaei M, Burns J, Solis A, Brown J, Cardinal S, Goulet H, Hebert P (2014) Review of Apanteles sensu stricto (Hymenoptera, Braconidae, Microgastrinae) from Area de Conservación Guanacaste, northwestern Costa Rica, with keys to all described species from Mesoamerica. ZooKeys 383: 1-565. https://doi.org/10.3897/zookeys.383.6418
Figure 94 - Apanteles jesusbrenesi. A Habitus, lateral view B Fore wing C Hypopygium and ovipositor sheats D Antenna E Head, frontal view F Head, meso- and metasoma, dorsal view.
Figure 95 from: Fernandez-Triana J, Whitfield J, Rodriguez J, Smith M, Janzen D, Hajibabaei M, Burns J, Solis A, Brown J, Cardinal S, Goulet H, Hebert P (2014) Review of Apanteles sensu stricto (Hymenoptera, Braconidae, Microgastrinae) from Area de Conservación Guanacaste, northwestern Costa Rica, with keys to all described species from Mesoamerica. ZooKeys 383: 1-565. https://doi.org/10.3897/zookeys.383.6418
Figure 95 - Apanteles williamcamposi. A Habitus, lateral view B Fore wing C Hypopygium and ovipositor sheats, with details of metatibia D Antenna E Head, frontal view F Head, dorsal view G Meso- and metasoma (partially), dorsal view H Propodeum.
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