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304 results for “burned area”
Castel Fusano preliminary burned area delineation through Sentinel 2A and Landsat 8
<p>Castel Fusano urban park has been affected by a series of wildfire that have destroyed a vast area of Mediterranean pine forest . The first huge fire started on 17 July 2017.Followed by a new one on 21 July 2017.</p> <p>Sentinel 2A and Landsat 8 data have been used to map burned areas at 20m and 30m respectively by testing NBR and dNBR with available pre and post images. Sentinel 2A - MSI data are dated 20 June 2017 (pre)and 20 July 2017(post). Landsat 8 - OLI data are dated 6 July 2017 (pre) and 22 July 2017 (post). By using both satellites it is possible to map the effects of the two fires in terms of burned areas. At this preliminary stage the perimeters (here available) have been derived manually as further analysis is in progress.</p> <p>Contains modified Copernicus Sentinel data [2017]; Landsat 8-OLI courtesy of the U.S. Geological Survey</p> <p>SNAP and HARRIS- ENVI </p>
Data for fitting a statistical global burned area model for seamless integration into Dynamic Global Vegetation Models
<p>The dataset is a large R data.table object saved in RDS format. It contains global, monthly data spanning the period from 2002 to 2018, with a 0.5 degrees spatial resolution. The dataset is utilized to develop and validate statistical models for predicting global burnt areas resulting from wildfires.</p>
Data associated with the publication "Multi-decadal increase of forest burned area in Australia is linked to climate change"
<p>Data from various sources related to fires in Australian forests, associated with the publication "Multi-decadal increase of forest burned area in Australia is linked to climate change"</p>
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. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data
<p>This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. </p> <div>Not all variables were used in the final analysis<br> <table> <tbody> <tr> <td><strong> NetCDF File</strong></td> <td> <p><strong> Variable</strong></p> </td> <td> <p><strong>Used/not Used</strong></p> </td> <td> <p><strong>Source/Reference</strong></p> </td> </tr> <tr> <td> <p>burned_area.nc</p> </td> <td> <p>Burned area</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> <p>burned_area_nat_veg.nc</p> </td> <td> <p>Burned area in natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p>burned_area_non_nat_veg.nc</p> </td> <td> <p>Burned area in non natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p> tas_max.nc</p> </td> <td> <p> Maximum Temperature</p> </td> <td> <p>Used</p> </td> <td><br><br> <p>ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>precip.nc</p> </td> <td> <p>Precipitation</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>vpd.nc</p> </td> <td> <p>Vapor pressure deficit</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>rhumid.nc</p> </td> <td> <p> Relative Humidity </p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td><br>consec_dry_days.nc</td> <td><br> <p>Consecutive number of dry days </p> </td> <td>Not Used</td> </tr> <tr> <td> <p>soilM.nc</p> </td> <td> <p>Soil Moisture</p> </td> <td> <p>Not Used</p> </td> <td> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>lightn.nc </p> </td> <td> <p> Lightning</p> </td> <td> <p>Not Used</p> </td> <td><br> <p> ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>popDen.nc</p> </td> <td> <p> Population density</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>road_density.nc</p> </td> <td> <p>Road density</p> </td> <td>Used</td> <td> <p> GRIP global</p> <p>(MEIJER et al., 2018)</p> </td> </tr> <tr> <td> <p>cveg.nc</p> </td> <td> <p>Vegetation carbon</p> </td> <td> <p>Not Used</p> </td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>csoil.nc</p> </td> <td> <p>Carbon in dead vegetation</p> </td> <td>Used</td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>forest.nc</p> </td> <td> <p> Forest</p> </td> <td> <p>Used</p> </td> <td><br><br><br> <p> MAPBIOMAS, 2022</p> </td> </tr> <tr> <td> <p>grassland.nc</p> </td> <td> <p> Grassland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>savanna.nc</p> </td> <td> <p> Savanna</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>cropland.nc</p> </td> <td> <p> Cropland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>pasture.nc</p> </td> <td> <p> Pasture</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>np.nc</p> </td> <td> <p>Number of patches </p> </td> <td> <p>Not Used</p> </td> <td><br><br> <p>Calculated from MAPBIOMAS,<br>2022</p> <br><br></td> </tr> <tr> <td> <p>ed.nc </p> </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div> <p> </p>
BAMS map: 2023 Canadian forest burned area dataset
<p><span>The BAMS map is a 2023 Canadian forest burned area dataset with a 10-meter resolution. It uses an unsigned integer data type, where a pixel value of 1 represents burned forest, while 0 indicates unburned areas, non-forest, or no data.</span> <span>The map is provided in the WGS 1984 coordinate system (EPSG:4326), covering the region from 145°W to 50°W longitude and from 40°N to 85°N latitude.</span></p>
GloCAB: Global Cropland Area Burned
<p>Please note this dataset is undergoing peer review and may be subject to changes. </p> <p>The Global Cropland Area Burned (GloCAB) dataset represents the first attempt at a global cropland-focused burned area product. The dataset provides monthly, cropland burned area files at 0.25-degree spatial resolution from July 2002 - December 2020 for our upper (high) and lower (low) estimates. The data is provided in annual GeoTIFF stacks, where the 2002 tiff file only contains 6 layers (July - December), compared to 12 layers for 2003 - 2020 (January - December). </p> <p>Please refer to the readme file for more details about the dataset. </p> <p> </p>
Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
<p>This is the R code and shapefiles of the coniferous HUC-12 watersheds and their western US boundary for the publication, "Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds" </p>
Moderate Spatial Resolution Burned Area Atlas for the Mau Forest Reserve in Kenya (2012 - 2017)
<p>This dataset contains moderate spatial resolution annual burned area records over the Mau Forest Reserve in Kenya. The dataset was derived from VIIRS Active Fire Detections (AFDs) and Landsat 7 & 8 Atmospherically Corrected Surface Reflectance, applying the ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) algorithm and the differenced Normalized Burn Ratio (dNBR) for burned area identification.</p> <p>Please consider that the dataset is still subject to revision to address inaccuracies associated with the spatial extent of derived burned areas.</p> <p>Further documentation and related resources can be found at: https://github.com/StefMeh/Burned-Area-Estimation.git</p>
Estimation of biomass combustion carbon emissions data for 2016 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2016, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2015 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2015, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
Open the record for dataset details and reuse information.
Soil Respiration in burned and unburned areas in and around watershed C4 within the Caribou-Poker Creeks Research Watershed from 1998-2004
This database reports soil respiration before and after the 1999 Frostfire experimental burn in watershed C4 of the Caribou-Poker Creeks Research Watershed. We measured soil respiration in three replicate pairs of sites (planned burned, control) in two vegetation types (black spruce, mixed hardwoods) using a LiCor 6262 infrared gas analyzer attached to semi-automated chambers (3 replicate chambers per site). Measurements began in summer 1998 and continued each growing season through 2004; in addition, we measured winter respiration during winter 1998-1999 using a different chamber design on top of the snow pack. All of the black spruce sites were located on Helmer's Ridge; the mixed hardwood sites initially were located on the east side of the C4 watershed or just outside its eastern boundary. Following the 1999 fire, all of the mixed hardwood sites were moved burned and unburned areas near the main trail ascending Helmer's Ridge.
Tree growth (height and average basal area increment) and herbivory measurements 20 years after the 1994 Hajdukovich Creek burn
This dataset contains height, diameter at breast height, and average basal area increment of aspen and black spruce from historical sites established in the Hajdukovich Creek burn. 12 pre-established sites were used, 6 from both low and high fire severity. Sites were sampled in 2014, 20 years after the fire. Furthermore, for each individual the distance from sampling point (plotless density), levels of past herbivory, health, basal age (number of tree rings visible from basal disk), age of the tree at 3 meters height, and age and height of some collected twig clusters (evidence of past herbivory) is included.
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
Reconstructed Global Monthly Burned Area Maps from 1901 to 2020
<p>Satellites provide direct observations of fire activities (e.g., burned area, fire radiative power) (Andela et al., 2017; Giglio et al., 2006; Luo et al., 2024), but their temporal coverages are limited because most satellite data were available only after 1980s (Chuvieco et al., 2019). Fire modules in dynamic global vegetation models (DGVMs) are able to simulate long-term burned area and the interactions with vegetation dynamics based on climate conditions and soil properties (Sitch et al., 2015; Sitch et al., 2024), but the spatial resolution at the global scale is usually coarse due to the coarse resolution of the input meteorological forcing data, and most models failed in capturing the global trends of burned area (Andela et al., 2017; Hantson et al., 2020). The processes included and the parameterizations of fire processes are widely different across fire models, resulting in a large range of simulated burned area at both the regional and global scales (Hantson et al., 2020). Considering the limitations of satellite observations and fire models, a spatiotemporally consistent burned area dataset over the 20th century trained from present-day observations, is essential for fire modelling and can serve as publicly available benchmark for fire ecology and carbon cycle studies.</p> <p>This study produced a global monthly 0.5°×0.5° burned area fraction (BAF) dataset from1901 to 2020 using machine learning models based on climate conditions, vegetation states, population density and land use data. We first divided the globe into 14 regions following the Global Fire Emission Dataset (GFED regions) (Giglio et al., 2006; van Der Werf et al., 2017) and conducted all steps described below in each GFED region individually. To better capture extreme fires, we first developed a classification model to distinguish grid cells with extreme and regular fires, using the 90th percentile of all burned area fractions within a region as the threshold to define extreme fires. We then trained separate regression models for grid cells categorized as having extreme or regular fires. The models were trained against the satellite-based burned area product (FireCCI51) during 2003-2020 excluding cropland fires, and then used to reconstruct the burned area from 1901 to 2020. In addition to validation against satellite observations that were not used for model training, we also compared our burned area predictions with charcoal records and other independent global and regional burned area datasets. In addition to the historical reconstructed burned area dataset based on the FireCCI51 mentioned above, we also produced two additional products of historical burned area with the same spatiotemporal resolution as the FireCCI51-based burned area reconstruction: 1) the GFED5-based data version, which is based on machine-learning models trained by the burned area from GFED5 which has much more fires than GFED4 (Chen et al., 2023) instead of FireCCI51, and 2) the FireCCI51-based data with burned area further calibrated using the relationship between statistic-based burned area (Mouillot and Field, 2005) and GDP (Bolt and Van Zanden, 2024) at the regional scale before 2000 (named as FireCCI51-GDP version).</p> <p>This dataset can be used to benchmark historical simulations from fire modules in DGVMs, re-calculate historical fire emissions and estimate legacy effects of vegetation recovery after fires on terrestrial carbon sink. Though the temporal coverage of our product is long enough to support studies related to fire disturbance, carbon dynamics and climate change, more reliable explanatory data for model training and burned area data for validation would help further improve the accuracy of the reconstructed burned area product.</p> <p>Description of variable names in netcdf files:<br>'lat' -- latitude of the center of grid cells<br>'lon' -- longitude of the center of grid cells<br>'ba' -- burned area fraction in 0.5°×0.5°grid cells<br>'ba_type' -- 0=no fire; 1=regular burned area; 2=extreme burned area. Note that regular & extreme BA is defined in each GFED region individually. Extreme BA refers to that burned area within grid cells surpassing the 90th percentile of all grid cells with burned area in each GFED region.</p> <p>This dataset is currently for manuscript submission to the scientifc journal <Earth System Science Data>.</p>
Burned areas dataset for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil)
<p><span>The present dataset includes annual mapping of fire scars for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil), at 30-meter spatial resolution, for the period 2000-2023. The enclave areas occupy a total of 241,000 hectares.</span></p> <p><span>The detection of burned areas was carried out using Burned Area Mapping (BAMS) algorithm (Bastarrika et al, 2014), followed by the performance of visual supervision processes and the use of active fire products to optimize the detection date of each fire event. The algorithm is applied to the Surface Reflectance series of Landsat (TM, ETM+, OLI and OLI-2) – Collection II, accessed using Google Earth Engine (Gorelick et al, 2017). Data from active fire products MCD14DL V006, VIIRS-NPP, VIIRS-NOA20 and GOES16, as well as burned area data from product MCD64A1 v006, were used to optimize the detection date of each fire scar.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>July 08, 2023 – The version 1.0 includes the period 2000-2023, with a total of 356,688.5 hectares of fire affected areas, distributed across 453 fire scars.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>The files available include: </span></p> <p><span>i) “fire_scars_dataset.rar”: annual burned areas vector files, in shapefile format (*.shp), projected at WGS84 UTM 20S. Each observation is an individual fire scar, with his attribute table indicating:</span></p> <p><span>- ID: Identification number for each;</span></p> <p><span>- area_ha - area of each fire scar, calculated in hectares.</span></p> <p><span>- date - detection date of the fire scar</span></p> <p><span>- date_preci - precision flag of the fire detection date:</span></p> <p><span>0 - fire date detected using only Landsat data</span></p> <p><span>1 - fire date optimized based on active fires dataset (MCD14DL V006; VIIRS-NPP; VIIRS-NOA20; or GOES16)</span></p> <p><span>ii) “enclaves_Mapinguari_National_Park.rar”: vector file of study area location, in shapefile format (*.shp), projected at WGS84 UTM 20S. Includes 8 enclaves of grasslands and savannah situated inside Mapinguari National Park.</span></p> <p><span>iii) “dataset_description.pdf”: description of the dataset.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>We thank the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) (process number 126772/2022-3) for the grant conceded to the second and third authors.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p> </p> <p><span>References</span></p> <p> </p> <p><span>Bastarrika, Aitor, Maite Alvarado, Karmele Artano, Maria Pilar Martinez, Amaia Mesanza, Leyre Torre, Rubén Ramo, and Emilio Chuvieco. 2014. “BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data.” Remote Sensing 6: 12360–80. https://doi.org/10.3390/rs61212360.</span></p> <p><span>Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031.</span></p>
Figure 1. Pantanal burned area per year until 9 in The Pantanal is on fire and only a sustainable agenda can save the largest wetland in the world
Figure 1. Pantanal burned area per year until 9/25/2020.
Updated 30 m resolution global annual burned area map
<p>Updated 30 m resolution global annual burned area maps (GABAM) of 2014-2021 are released for free download.The annual burned area map is defined as <strong>spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years.</strong>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° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10°×10° tiles spanning the range 180°W–180°E and 80°N–60°S.<br>contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn</p>
Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests
<p><span>The global burned area declined by nearly one-quarter between 1998 and 2015. Drylands contain a large proportion of these global fires but there are important differences within the drylands, e.g., savannas and tropical dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have reduced the burned area, while the fire in the TDF is one of the most critical factors impacting biodiversity and carbon emissions. Moreover, under climate change scenarios TDF is expected to increase its current extent and raise the risk of fires. Despite regional and global scale effects, and the influence of this ecosystem on the global carbon cycle, little effort has been dedicated to studying the influence of climate (seasonality and extreme events) and socioeconomic conditions of fire regimen in TDF. Here we use the Global Fire Emissions Database and, climate and socioeconomic metrics to better understand long-term factors explaining the variation in burned area and biomass in TDF at the Pantropical scale. On average, fires affected 1.4% of the total TDF' area (60,208 km<sup>2</sup>) and burned 24.4% (259.6 Tg) of the global burned biomass annually at Pantropical scales. Climate modulators largely influence local and regional fire regimes. Inter-annual variation in fire regime is shaped by El Niño and La Niña. During El Niño and the forthcoming year of La Niña, there is an increment in extension (35.2 and 10.3%) and carbon emissions (42.9 and 10.6%). Socioeconomic indicators such as land management and population were modulators of the size of both, burned area and carbon emissions. Moreover, fires may reduce the capability to reach the target of "half protected species" in the globe, i.e., high-severity fires are recorded in ecoregions classified as nature could reach half protected. These observations may contribute to improving fire management.</span></p>
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