Online ISSN: Comparative energy analysis from fire resistance tests on combustible versus noncombustible slabs Alastair I. Schartel T. An overview of flame retardancy of polymeric materials: application, technology, and future directions Alexander B. Morgan Jeffrey W. Recent issues. Tools Submit an Article Browse free sample issue Get content alerts.
Subscribe to this journal. Fire here is a social-ecological system, influenced by specific LULC and with implications from landscape to regional scales. Understanding how LULC changes interact with fire is powerful for improving landscape and regional planning. Values in the tables are the percentages of the area that remain in the same vegetation class or change from one period to the next.
Fire | An Open Access Journal from MDPI
Note that the individual columns for each LULC represent each of the three time periods —, —, and — The color code is the same for A, B and C. Abstract Fire is one of the main disturbance factors shaping the landscape, and landscape is a key driver of fire behavior. Considering the role played by land use and land cover LULC changes as the main driver of landscape dynamics, the aim of this [ Fire is one of the main disturbance factors shaping the landscape, and landscape is a key driver of fire behavior.
Considering the role played by land use and land cover LULC changes as the main driver of landscape dynamics, the aim of this study was to calculate and analyze i the real impact of fire on LULC changes and ii how these LULC changes were influencing the fire regime. We used methods of historical geography and socio-spatial systemic analysis for reconstructing and assessing the LULC change and fire history in six case studies in the Central Mountain System Spain from archival documentary sources and historical cartography.
The main result is an accurate dataset of fire records from to and a set of LULC maps for three time points s—s, —, and the s. We have shown the nonlinear evolution of the fire regime and the importance of the local scale when assessing the interaction of landscape dynamics and fire regime variation. Our findings suggest that LULC trends have been the main influencing factor of fire regime variation in Central Spain since the midth century. Province NUTS3 level limits in light gray.
Central Mountain System limits in purple. Fitted trend dashed line for the number of fires vs. Source: Fire Statistics Database. Source: Fire Statistical Database.
Abstract Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas.
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The increasing availability of [ Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system GPS polygons with data collected from the moderate resolution imaging spectroradiometer MODIS thermal anomalies product between and for Amol County, northern Iran.
The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors topographic, meteorological, vegetation, anthropological, and hydrological factors , was used to evaluate the potential of different machine learning ML approaches for the spatial prediction of wildfire susceptibility.
The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics ROC curve. The input layer consists of input data conditioning factors , and the output layer is a probability map which shows the wildfire susceptibility.
The classification is done based on the wildfire inventory data that is introduced to the model as a layer consisting of the value of one for wildfire pixels and zero for the other areas.
In our case, trees and five variables were selected after testing different settings which could not improve the results any more. Roos , Grant J. Williamson and David M. Abstract Paleofire studies frequently discount the impact of human activities in past fire regimes. Globally, we know that a common pattern of anthropogenic burning regimes is to burn many small patches at high frequency, thereby generating landscape heterogeneity. Is this type of anthropogenic pyrodiversity [ Paleofire studies frequently discount the impact of human activities in past fire regimes.
Is this type of anthropogenic pyrodiversity necessarily obscured in paleofire records because of fundamental limitations of those records? We evaluate this with a cellular automata model designed to replicate different fire regimes with identical fire rotations but different fire frequencies and patchiness. Our results indicate that high frequency patch burning can be identified in tree-ring records at relatively modest sampling intensities. However, standard methods that filter out fires represented by few trees systematically biases the records against patch burning.
In simulated fire regime shifts, fading records, sample size, and the contrast between the shifted fire regimes all interact to make statistical identification of regime shifts challenging without other information. Recent studies indicate that integration of information from history, archaeology, or anthropology and paleofire data generate the most reliable inferences of anthropogenic patch burning and fire regime changes associated with cultural changes.
The shaded region is the range of actual fire-intervals experienced across all simulations for that fire regime. Note that the four fire regimes are clearly differentiated at 25 samples 0. Columns are for the date before the end of the model run when the forced regime shift happened. Corresponding centuries in the real world are shown for comparison to fire-scar studies. Rows are for different sampling intensities. Note that recent regime shifts are accurately predicted at all sampling intensities. Open Access Technical Note.
Ziegler , Chad M. Hoffman and William Mell.
Abstract Wildland fire and ecological researchers use empirical and semi-empirical modeling systems to assess fire behavior and danger. This technical note describes the firebehavioR package, a porting of two fire behavior modeling systems, Crown Fire Initiation and Spread and a Rothermel-based framework, to the [ Wildland fire and ecological researchers use empirical and semi-empirical modeling systems to assess fire behavior and danger.
This technical note describes the firebehavioR package, a porting of two fire behavior modeling systems, Crown Fire Initiation and Spread and a Rothermel-based framework, to the R programming language.
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We also highlight supporting data objects and functions to predict inputs required for fire behavior estimation. Campbell , Wesley G. Page , Philip E. Dennison and Bret W. Abstract For wildland firefighters, the ability to efficiently evacuate the fireline is limited by terrain, vegetation, and fire conditions. The impacts of terrain and vegetation on evacuation time to a safety zone may not be apparent when considering potential control locations either at the [ For wildland firefighters, the ability to efficiently evacuate the fireline is limited by terrain, vegetation, and fire conditions.
The impacts of terrain and vegetation on evacuation time to a safety zone may not be apparent when considering potential control locations either at the time of a wildfire or during pre-suppression planning.
To address the need for a spatially-explicit measure of egress capacity, this paper introduces the Escape Route Index ERI. Ranging from 0 to 1, ERI is a normalized ratio of the distance traveled within a time frame, accounting for impedance by slope and vegetation, to the optimal distance traveled in the absence of these impediments. An ERI approaching 1 indicates that terrain and vegetation conditions should have little impact on firefighter mobility while an ERI approaching 0 is representative of limited cross-country travel mobility.
A previously published, crowd-sourced relationship between slope and travel rate was used to account for terrain, while vegetation was accounted for by using land cover to adjust travel rates based on factors from the Wildland Fire Decision Support System WFDSS.
Land cover was found to have a stronger impact on ERI values than slope. We conclude that mapping ERI prior to engaging a fire could help inform overall firefighter risk for a given location and aid in identifying locations with greater egress capacity in which to focus wildland fire suppression, thus potentially reducing risk of entrapment. Continued improvements in accuracy of vegetation density mapping and increased availability of light detection and ranging lidar will greatly benefit future implementations of ERI.
T1 represents the time required for the fire to reach a safety zone and T2 is the time required for firefighter s FF to reach the same safety zone. In order to create a Margin of Safety T1 should exceed T2. This paper presents findings from a fire management workshop where experiences and perspectives were shared among 60 academic, government, [ This paper presents findings from a fire management workshop where experiences and perspectives were shared among 60 academic, government, and Indigenous representatives from 27 organizations from Venezuela, Brazil, and Guyana.
The data, in the form of small group discussions, participatory drawings, whole group reflections, and videos, showed that although there was general acceptance about the central role of fire in traditional Indigenous livelihoods and its importance for protecting the biological and cultural diversity of ecosystems, there were also tensions around the past imposition of a dominant fire exclusion discourse of governmental institutions in Indigenous territories.
Green areas indicate woodland ecosystems and brown ones savannas. White circles indicate Indigenous communities, yellow circles indicate localities of reference e.
Three Things to Know About the Fires Blazing Across the Amazon Rainforest
Abstract Simulations of wildland fire risk are dependent on the accuracy and relevance of spatial data inputs describing drivers of wildland fire, including canopy fuels. Spatial data are freely available at national and regional levels. However, the spatial resolution and accuracy of these types [ Simulations of wildland fire risk are dependent on the accuracy and relevance of spatial data inputs describing drivers of wildland fire, including canopy fuels.
However, the spatial resolution and accuracy of these types of products often are insufficient for modeling local conditions. Fortunately, active remote sensing techniques can produce accurate, high-resolution estimates of forest structure. RF-kNN models produced strong relationships between estimated canopy fuel attributes and field-based data for stand age Adj. These results suggest that low-density LiDAR can help estimate canopy fuel attributes in mixed forests, with robust model accuracies and high spatial resolutions compared to currently utilized fire behavior model inputs.
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