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14 results for “wildland fire”
FireCaster Wildland Fire Fuels Database for Corsican - Mediterranean Forest stand types
<p>This database includes wildland fuels data for Mediterranean basin vegetation stands types and in particular for those in Corsica, for fire/forest management, risk assessment and decision-support purposes. It gathers together some of the most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). It has been conceived by using a layering approach, this is, assuming that a vegetation stand type is constituted by one or more structurally distinct pseudo-homogenous layers of vegetation. Vegetation stand types considered are based on the fuel classification and mapping of the BDForêt® <em>2.0 – Corsica.</em> Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The building methods and the different data sources have been detailed in a paper which is under review.</p> <p> The attached dataset consists of two tables and one text document:</p> <p> - The first table (<em>FuelLayersData.csv</em>) contains fuel layers and fuel elements attributes for each vegetation stand type. The table has 15 columns. The first one (<em>CODE_TFV</em>) corresponds to the code assigned to each vegetation stand type following the BDForêt®<em> </em>nomenclature. Next columns, refer to the layer numbering, the stratum of the layer and the species scientific name. After that, next six columns correspond to the layer attributes and four columns correspond to the fuel element attributes. The last column is the diameter at breast height (DBH) for canopy layers. The empty cells in the table indicate that the corresponding attribute is not applicable for this particular layer.</p> <p> - The second table (<em>FuelParticlesData.csv</em>) contains the particle attributes, this is, the surface-to-volume ratio, particles density and low heat content.</p> <p> - The text document (<em>StandTypesDescription.docx</em>) is derived from BDForêt®<em> version 2.0 – Corsica</em> (https://geo.isula.corsica/wp-content/uploads/2021/01/descriptif-contenu-bd_foret-IGN.pdf) and contains a short description of the different stand types considered according to the CODE_TFV.</p> <p>This work was supported by the Agence Nationale de la Recherche, France (grant number ANR-16-CE04-0006 FIRECASTER) and by H2020-EU.3.5. Programme (FIRE-RES, Grant agreement ID: 101037419).</p> <p> </p> <p>Pérez-Ramirez Y, Ferrat L, Filippi JB. (2024) Wildland Fire Fuels Database for Corsican – Mediterranean Forest stand types. Forest Ecology and Management, 565, 122002.</p>
Can wildland fire management alter 21st-century subalpine fire and forests in Grand Teton National Park, Wyoming, USA
In subalpine forests of the western United States that historically experienced infrequent, high-severity fire, whether fire management can shape 21st-century fire regimes and forest dynamics to meet natural resource objectives is not known. Managed wildfire use (i.e., allowing lightning-ignited fires to burn when risk is low instead of suppressing them) is one approach for maintaining natural fire regimes and fostering mosaics of forest structure, stand age, and tree-species composition, while protecting people and property. However, little guidance exists for where and when this strategy may be effective with climate change. We simulated most of the contiguous forest in Grand Teton National Park, WY to ask: (1) How would subalpine fires and forest structure be different if fires had not been suppressed during the last three decades? (2) What is the relative influence of climate change versus fire management strategy on future fire and forests? We contrasted fire and forests from 1989-2098 under two fire management scenarios (managed wildfire use and fire suppression), two general circulation models (CNRM-CM5 and GFDL-ESM2M), and two representative concentration pathways (8.5 and 4.5). We found little difference between management scenarios in the number, size, or severity of fires during the last three decades. With 21st-century warming, fire activity increased rapidly, particularly after 2050, and followed nearly identical trajectories in both management scenarios. Area burned per year between 2018-2099 was 1,700% greater than in the last three decades (1989-2017). Large areas of forest were abruptly lost; only 65% of the original 40,178 ha of forest remained by 2098. However, forests stayed connected and fuels were abundant enough to support profound increases in burning through this century. Our results indicate that strategies emphasizing managed wildfire use, rather than suppression, will not alter climate-induced changes to fire and forests in subalpine land
Damage cadasters for wildland-urban interface fires in Chile, 2019-2022
<p><span>Wildland-urban interface (WUI) regions are particularly vulnerable to wildfires due to their proximity to both nature and urban developments, posing significant risks to lives and property. To enhance our understanding of the risk profiles in WUI areas, we analysed seven fire case studies in central Chile. </span></p> <p><span>In the paper <em>"</em><span><em>Modelling the vulnerability of urban settings to WUI fires in Chile",</em> </span>We developed a mixed-methods approach for conducting local-scale analyses which involved field surveys, remote-sensing through satellite and drone imagery, and GIS-based analysis of the collected data. The methodology led to the generation of a georeferenced dataset of damaged and undamaged dwellings, including 16 variables representing their physical characteristics, spatial arrangement, and the availability of fire suppression resources. A binary classification model was then used to assess the relative importance of these attributes as indicators of vulnerability. The analysis revealed that spatial arrangement factors have a greater impact on damage prediction than the structural conditions and fire preparedness of individual units. Specifically, factors such as dwelling proximity to neighbours, distance to vegetation, proximity to the border of dwelling groups, and distance from the origin of the fire substantially contribute to the prediction of fire damage. Other structural attributes associated with less affluent homes may also increase the likelihood of damage, although further data is required for confirmation. This study provides insights for the design, planning, and governance of WUI areas in Chile, aiding the development of risk mitigation strategies for both built structures and the broader territorial area.</span></p>
Wildland fire PM2.5 modeled estimates for the US from 2008-2018
<p>This dataset includes daily modeled wildland fire PM<sub>2.5 </sub>concentrations for 2008-2018 for the state of California at a 12-km grid spatial resolution, estimated using the U.S. EPA's Community Multiscale Air Quality (CMAQ) (v. 5.0.1- 5.3) modeling system. These wildland fire emissions estimates (which include wildfires and prescribed burns [but exclude agricultural burns]) incorporate multiple sources of fire activity. SMARTFIRE2 was used to reconcile the sources of fire activity data. Fuel consumption was calculated using the U.S. Forest Service's CONSUME ver. 3.0 fuel consumption model and the Fuel Characteristic Classification System (FCCS) fuel-loading database in the BlueSky Framework. Emission factors were taken from the Fire Emission Production Simulator (FEPS) model. Non-fire emissions sources are from the National Emissions Inventory (NEI). The model was run with all emissions (fire and non-fire sources) and again without fires. The calculated difference between these simulations ('all sources PM<sub>2.5</sub>'<sub> </sub>and 'non-fire PM<sub>2.5</sub>') isolates the fire contribution, or 'fire-only PM<sub>2.5</sub>', which is the dataset provided here. </p>
IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis. This paper will be presented at the IEEE World Forum on Internet of Things in November 2024 and available on IEEE Xplore after that.</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates various algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Utilizing geospatial data analysis and a validated wildland fire growth model burn maps were generated to guide sensor placement strategies across a defined region of interest. The algorithms evaluated include an even grid distribution, random distributions, and genetic algorithm-based methods. Each algorithm was tested against 50,000 selected burn maps to assess detection rates, with sensor counts ranging from 50 to 800 across 500 experimental runs. Results indicate that while the even grid distribution yielded the highest detection rates, the practicality of such a method in real-world applications is limited. Genetic algorithms showed promise, but require further exploration to more accurately simulate random distribution used in field deployment. Surprisingly, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, suggesting the need for additional research into the representativeness of selected burn maps.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</p>
Datasets for Communicating Wildland Urban Interface Fire Risk
<p>Wind and Fire Danger daily data for years 2007, 2012 and 2017 with relevant MATLAB codes to extract the data.</p>
Wildland fire PM2.5 modeled estimates for the US from 2008-2018
Open the record for dataset details and reuse information.
Wildland Fires in the U.S.
<p>In 2018, the United States experienced one of the most devastating year for wildland fire in recorded history. There were 58083 fires, which burn more than 8.7 million acres area and cost 3.1 billion dollars for suppression in total. Nowadays, the fire season are about 80 days longer than that in 1970, and many of the largest fires in record happened in the past decade. The wildland fire has clearly becoming a serious problem, especially for those area developing near the forest boundary. This project intends to use visualizations to better illustrate the trend and impacts of wildfire in the United States using statistic data between 2010 and 2018. The rest of this report is devided into objectives, requirements, system descriptions, development platforms, proposed visualizations, and experimental analyses.</p>
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
<p><span>Tree spatial patterns in dry coniferous forests of the western US, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre-existing forest structure shapes tree spatial patterns is limited. In this study, we (1) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed-conifer forest, (2) estimated tree mortality, and (3) examined tree spatial patterns of live trees before and after fire, and of fire-killed trees. Tree mortality in the historical period was clustered and density-dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, post-fire tree patterns were more uniform and devoid of moderately sized tree groups. Post-fire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western US. This divergence suggests that decades of forest dynamics without significant disturbances has altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.</span></p>
Characterizing the Dynamics of Wildland-Urban Interface and the Potential Impacts on Fire Activities in Alaska from 2000 to 2010
<p>The material contains the shapefiles of wildland-urban interface in Alaska in 2000 and 2010, which are derived from census data and National Land Cover Database.</p>
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
Open the record for dataset details and reuse information.
Annual wildland fire emissions (WFEIS v0.5) for Conterminous US and Alaska, 2001-2013
This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates.
ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015
This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra.
Characterizing the Dynamics of Wildland-Urban Interface and the Potential Impacts on Fire Activities in Alaska from 2000 to 2010
<p>The material contains the shapefiles of wildland-urban interface in Alaska in 2000 and 2010, which are derived from census data and National Land Cover Database.</p>
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Allen Brain Atlas
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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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