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1,826 results for “burn”
Beltie Burn restored reach cross section survey data 2020-2024
<p>Annual survey cross section data from 2020, 2021, 2022, 2023 and 2024. Surveyed with a Leica GS16 dGPS. Data in csv file format. Coordinates based on Ordnance Survey datum. </p> <p>Data collection funded by the Scottish Government Rural & Environment Science & Analytical Services (RESAS) Division Research Programme.</p> <p> </p>
Database of clustered vOTUs recovered from a viromics prescribed burn study of forest soil
<p>Database of dereplicated viral operational taxonomic units (vOTUs) recovered from a viromics (viral-size fraction metagenomics) prescribed burn study of forest soil</p>
Fig. 3 in Assessment of post-burn removal methods for Acacia saligna in Cape Flats Sand Fynbos, with consideration of indigenous plant recovery
Fig. 3. The rank-abundance curve represents the post-treatment plant species composition in the experimental plots at the BNR restoration project. Curves are plotted for each removal treatment and the control. Only indigenous species were included in this analysis.
Fig. 2 in Assessment of post-burn removal methods for Acacia saligna in Cape Flats Sand Fynbos, with consideration of indigenous plant recovery
Fig. 2. The percentage cover of vegetative growth forms in the A. saligna removal plots at the BNR restoration project four months after treatment implementation.
Fig. 1 in Assessment of post-burn removal methods for Acacia saligna in Cape Flats Sand Fynbos, with consideration of indigenous plant recovery
Fig. 1. An example of a treatment block layout at the BNR study site for the different post-burn A.saligna removal methods. The block is sub-divided into four 5 × 5 m plots, each with their corresponding treatment or control.
Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars"
<p>This dataset contains the data used in the burned area classificaiton and statistical analysis in:</p> <p><em>Rietze et al. (<strong>in prep.</strong>) - Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars</em></p> <p>All code to preprocess, analyze and visualize this data can be found under <a href="https://github.com/nrietze/SiberiaFires" target="_blank" rel="noopener">https://github.com/nrietze/SiberiaFires</a>.</p> <p><strong>Folder structure:</strong></p> <p>This dataset contains three major components:</p> <ol> <li>The feature layers like training polygons used for the burned area classification and areas of interest.</li> <li>The burned area maps produced from 3 m PlanetScope imagery.</li> <li>The raster layers used in the statistical analysis, e.g., pre-processed digital elevation models and burned area products.</li> </ol> <pre><code>└───geodata ├───feature_layers │ ├───aoi_wv │ ├───burn_polygons │ │ └───planet │ └───training_polygons └───raster ├───arcticDEM ├───burned_area │ └───planet ├───landsat ├───predictors └───water_area └───planet</code></pre> <p><strong>Detailed description of content:</strong></p> <p> </p> <p><em><strong>Feature layers</strong></em></p> <table> <tbody> <tr> <td>planet_masks.shp</td> <td>Manually delineated polygons to mask out undetected clouds in the Berelech and Lapcha sites.</td> </tr> <tr> <td>aoi_wv/aois_analysis.geojson</td> <td>Square areas of interest for data cropping and selection.</td> </tr> <tr> <td>burn_polygons/planet/rough_burn_perimeter_{<em>fire scar name</em>}.shp</td> <td>6 shapefiles (1 per fire scar) that contain the fire perimeter generated in ArcGIS.</td> </tr> <tr> <td>training_polygons/training_polygons_burn_area.shp</td> <td>Training polygons for all fire scars used for the burned area classification.</td> </tr> </tbody> </table> <p> </p> <p><strong><em>Raster data</em><br></strong></p> <table> <tbody> <tr> <td>arcticDEM/aoi_{<em>fire scar name</em>}_dem_v3_utm.tif</td> <td>Elevation data (<a href="https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics/v3.0/2m.json">ArcticDEM v3</a>) reprojected to UTM 55N and cropped to the square areas of interest of each fire scar. </td> </tr> <tr> <td>burned_area/ba_descals_landsat_2020_utm_shifted.tif</td> <td>Landsat-based burned area from <a href="https://doi.org/10.1126/science.abn9768" target="_blank" rel="noopener">Descals et al. (2022)</a>, contains data from "Tile 17" only. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class (2019) = 29, burned class (2020) = 30, unburned = 0)</td> </tr> <tr> <td>burned_area/N75E145_burn_class_UTM_55N.tif</td> <td>Landsat-based burned area from <a href="https://vapd.gitlab.io/post/gabam/" target="_blank" rel="noopener">Wei et al. (2022)</a>. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class = 255, unburned = 0)</td> </tr> <tr> <td>burned_area/planet/{<em>fire scar name</em>}_burned_area_top5TD.tif</td> <td>Binary PlanetScope-based burned area (this study) for each fire scar based on random forest classifiers using the top 5 predictors ranked by transformed divergence (TD). (burned class = 2, unburned = 1)</td> </tr> <tr> <td>landsat/LC08_L2SP_115010_20200608_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 8 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>landsat/LC08_L2SP_116010_20200615_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 15 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>predictors/{<em>fire scar name</em>}_predictors_30m.tif</td> <td>Raster maps of model predictors & the response <em>burned_fraction</em> for the ZOIB model (exported in "ZOIB_model.R" before running the model). Raster bands are named.</td> </tr> <tr> <td>water_area/{<em>fire scar name</em>}_Landsat_mask.tif</td> <td>Landsat-8 binary (water = 2, clear pixels = 1) water mask for each fire scar based on the Quality assessment rasters.</td> </tr> <tr> <td>water_area/planet/{<em>fire scar name</em>}__water_area_top5TD.tif</td> <td>[deprecated] 3 m resolution water areas classified from PlanetScope imagery. Not used for the analysis.</td> </tr> </tbody> </table> <p> </p> <p><strong>Using this data:</strong></p> <p>Clone the Github repository before downloading this data and insert the contents of this dataset into the empty "data" folder from the Github repo. </p> <p>Important: Please move the entire "geodata" folder into the "data" folder from the code repo.</p> <p>If you use this data, please cite as follows:</p> <p>Rietze, N., Heim, R. J., Troeva, E., Schaepman-Strub, G. & Assmann, J. J. (in prep.). Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12650945" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12650945</a></p> <p><strong>Abstract (from manuscript):</strong></p> <p>The fire season of 2020 in Siberia set a precedent for extreme wildfires in the Arctic tundra. Large fires burned in the carbon-rich permafrost landscape, releasing vast amounts of carbon, and changing land surface processes by burning vegetation and organic soils. However, little is known about the mosaics of burned and unburned patches formed by tundra fires and the underlying processes that generate them. In this study, we investigated six fire scars in the northeastern Siberian tundra using high-resolution PlanetScope imagery (3 m) to map burned fraction within the scars. We then used Bayesian mixed models to identify which biotic and abiotic predictors influenced the burned fraction. We observed high spatial variation in burned fraction across all tundra landforms common to the region. Current medium-resolution fire products could not capture this heterogeneity, thereby underestimating the burned area of fire scars by a factor of 1.1 to 4.4. The heterogeneity of the burn mosaic indicates a mix of burned and unburned patches, with median unburned patch sizes being smaller than 180 to 324 m². Pre-fire land surface temperature, vegetation heterogeneity and topography predicted burn fraction in our analysis, matching factors previously shown to influence large-scale fire occurrence in the Arctic. Future studies need to consider the fine-scale heterogeneity within tundra landscapes to improve our understanding and predictions of fire spread, carbon emissions, post-fire recovery and ecosystem functioning.</p> <p><strong>Acknowledgements (from manuscript):</strong></p> <p>N.R. was supported through the TRISHNA Science and Electronics Contribution (T-SEC), ESA PRODEX Trishna T-SEC project (PEA C4000133711). Field work and vegetation sample processing were conducted in the scope of State Assignment of the Ministry of Science and Higher Education of the Russian Federation (Project AAAA-A21-121012190038-0), using the equipment of the Centre for collective use of Federal Research Centre "Yakut Scientific Centre" (grant no. 13.TsKP.21.0016). We would like to thank Planet Labs fo free access to PlanetScope imagery. We would like to thank Tim Gyger for helpful discussions regarding our statistical analysis. The authors declare no competing interests.</p>
Abundance of lungworm parasites in invasive cane toads from burned and unburned sites
<p>The frequency and severity of wildfires are increasing due to anthropogenic modifications to habitats and to climate. Post-fire landscapes may advantage invasive species via multiple mechanisms, including changes to host-parasite interactions. We surveyed the incidence of endoparasitic lungworms (<i>Rhabdias pseudosphaerocephala</i>) in invasive cane toads (<i>Rhinella marina</i>) in near-coastal sites of eastern Australia, a year after extensive fires in this region. Both the prevalence of infection, and number of worms in infected toads, increased with toad body size in unburned areas. In contrast, parasite load decreased rather than increased with toad body size in burned areas. By killing moisture-dependent free-living lungworm larvae, the intense fires may have liberated adult cane toads from a parasite that can substantially reduce the viability of its host. Smaller toads, which are restricted to moist environments, did not receive this benefit from fires.</p>
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>
Evolution of light absorption enhancement of black carbon aerosols from biomass burning in atmospheric photooxidation aging
<p>The Dataset of the evolution of light absorption enhancement of black carbon aerosols from biomass burning in atmospheric photooxidation aging.</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>
Grazing intensity effects on herbaceous community composition in burned sagebrush-steppe
<p>There is limited knowledge on grazing impacts to long-term plant community dynamics following fire in sagebrush steppe, This study evaluated vegetation response to different intensities of deferred rotation cattle grazing over 16 years (2007–2022) on burned Wyoming big sagebrush steppe in eastern Oregon. Treatments were applied in a randomized complete block, including on grazing on burned (Non-use, n=5) and unburned (Control, n=5) steppe; and cattle grazing at low (Low, n=4), moderate (Moderate, n=4), and high (High, n=4) intensities on burned steppe. Vegetation dynamics were evaluated by repeated measures analysis of canopy cover and density of shrub and herbaceous species and functional groups. Herbaceosus functional groups were an early season bunchgrass (one species, Sandberg bluegrass), tall perennial bunchgrass, perennial forbs, annual grass (one species, cheatgrass) and annual forbs. Tall perennial bunchgrass, Sandberg bluegrass, and perennial forb cover and density did not differ among the treatments but did decrease over time in all treatments. Cover of several tall bunchgrass species were generally less in the High treatment, mainly, Idaho fescue, and Thurber's needlegrass. Cover of cheatgrass and annual forbs varied across years but were greater among the burned grazed and Non-use treatments than the Control. Native plant cover in the burned treatments (grazed and Non-use) represented 77 to 85 % of total herbaceous cover versus the Control where natives comprised 91% of the total. Annual weather variability appears to account for most of the compositional dynamics measured in the various grazed and ungrazed treatments.</p>
An Investigation of the Effect of Types of Catheters on Bloodstream Infection in Patients With Major Burns: Prediction With Procalcitonin and Prognosis
ClinicalTrials.gov study NCT05581316. IPD Sharing: NO. Countries: 1. Publications: 1.
Measuring Hand Edema in Burn Patients
ClinicalTrials.gov study NCT00137228. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of a Clinical Decision Algorithm on Length of Hospital Stay and Costs of Care of Burned Patients
ClinicalTrials.gov study NCT03876340. IPD Sharing: NO. Countries: 1. Publications: 3.
Evaluation of Safety and Efficacy of realSKIN® (Skin Xenotransplant) for Complete Closure of Severe Burn Wounds
ClinicalTrials.gov study NCT03695939. IPD Sharing: NO. Countries: 1. Publications: 28.
Organ Protective Effect of Cetirizine In Patients With Severe Burns
ClinicalTrials.gov study NCT06126991. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effects of a Nurse-led Transitional Burns Rehabilitation Programme
ClinicalTrials.gov study NCT04517721. IPD Sharing: NO. Countries: 1. Publications: 45.
Use of Progenitor Biological Bandages in Burn Care (Bru_PBB)
ClinicalTrials.gov study NCT05339490. IPD Sharing: NO. Countries: 1. Publications: 5.
Effect of Task Oriented Training and Xbox KinectTM on Hand Function in Pediatric Burn
ClinicalTrials.gov study NCT04497103. IPD Sharing: YES. Countries: 1. Publications: 3.
Psychosocial Factors of Burn Related Injuries and Perceived Gaps in Service Delivery
ClinicalTrials.gov study NCT03094013. IPD Sharing: NO. Countries: 1. Publications: 10.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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