scholarly journals Pixel-level statistical analyses of prescribed fire spread

2019 ◽  
Vol 49 (1) ◽  
pp. 18-26 ◽  
Author(s):  
M. Currie ◽  
K. Speer ◽  
J.K. Hiers ◽  
J.J. O’Brien ◽  
S. Goodrick ◽  
...  

Wildland fire dynamics are a complex three-dimensional turbulent process. Cellular automata (CA) is an efficient tool to predict fire dynamics, but the main parameters of the method are challenging to estimate. To overcome this challenge, we compute statistical distributions of the key parameters of a CA model using infrared images from controlled burns. Moreover, we apply this analysis to different spatial scales and compare the experimental results with a simple statistical model. By performing this analysis and making this comparison, several capabilities and limitations of CA are revealed.

Author(s):  
Hadj Miloua

Current study focuses to the application of an advanced physics-based (reaction–diffusion) fire behavior model to the fires spreading through surface vegetation such as grasslands and elevated vegetation such as trees present in forest stands. This model in three dimensions, called Wildland Fire Dynamics Simulator WFDS, is an extension, to vegetative fuels, of the structural FDS developed at NIST. For simplicity, the vegetation was assumed to be uniformly distributed in a tree crown represented by a well defined geometric shape. This work on will focus on predictions of thermal function such as the radiation heat transfer and and thermal function for diverse cases of spatial distribution of vegetation in forest stands. The influence of wind, climate characteristics and terrain topography will also be used to extend and validate the model. The results obtained provide a basis to carry out a risk analysis for fire spread in the studied vegetative fuels in the Mediterranean forest fires.


2017 ◽  
Vol 35 (5) ◽  
pp. 359-378 ◽  
Author(s):  
Albert Simeoni ◽  
Zachary C Owens ◽  
Erik W Christiansen ◽  
Abid Kemal ◽  
Michael Gallagher ◽  
...  

An experimental fire was conducted in 2016, in the Pinelands National Reserve of New Jersey, to assess the reliability of the fire pattern indicators used in wildland fire investigation. Objects were planted in the burn area to support the creation of the indicators. Fuel properties and environmental data were recorded. Video and infrared cameras were used to document the general fire behavior. This work represents the first step in the analysis by developing an experimental protocol suitable for field studies and describing how different fire indicators appeared in relation to fire behavior. Most of the micro- and macroscale indicators were assessed. The results show that some indicators are highly dependent on local fire conditions and may contradict the general fire spread. Overall, this study demonstrates that fire pattern indicators are a useful tool for fire investigators but that they must be interpreted through a general analysis of the fire behavior with a good understanding of fire dynamics.


2016 ◽  
Vol 25 (1) ◽  
pp. 114 ◽  
Author(s):  
Thomas J. Zajkowski ◽  
Matthew B. Dickinson ◽  
J. Kevin Hiers ◽  
William Holley ◽  
Brett W. Williams ◽  
...  

Small remotely piloted aircraft systems (RPAS), also known as unmanned aircraft systems (UAS), are expected to provide important contributions to wildland fire operations and research, but their evaluation and use have been limited. Our objectives were to leverage US Air Force-controlled airspace to (1) deploy RPAS in support of the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research (RxCADRE) project campaign objectives, including fire progression at multiple scales and (2) assess tactical deployment of multiple RPAS with manned flights in support of incident management. We report here on planning for the missions, including the logistics of integrating RPAS into a complex operations environment, specifications of the aircraft and their measurements, execution of the missions and considerations for future missions. Deployments of RPAS ranged both in time aloft and in size, from the Aeryon Scout quadcopter to the fixed-wing G2R and ScanEagle UAS. Real-time video feeds to incident command staff supported prescribed fire operations and a concept of operations (a planning exercise) was implemented and evaluated for fires in large and small burn blocks. RPAS measurements included visible and long-wave infrared (LWIR) imagery, black carbon, air temperature, relative humidity and three-dimensional wind speed and direction.


2016 ◽  
Vol 25 (2) ◽  
pp. 229 ◽  
Author(s):  
Anthony S. Bova ◽  
William E. Mell ◽  
Chad M. Hoffman

Simulating an advancing fire front may be achieved within a Lagrangian or Eulerian framework. In the former, independently moving markers are connected to form a fire front, whereas in the latter, values representing the moving front are calculated at points within a fixed grid. Despite a mathematical equivalence between the two methods, it is not clear that both will produce the same results when implemented numerically. Here, we describe simulations of fire spread created using a level set Eulerian approach (as implemented in the wildland–urban interface fire dynamics simulator, WFDS) and a marker method (as implemented in FARSITE). Simulations of surface fire spread, in two different fuels and over domains of increasing topographical complexity, are compared to evaluate the difference in outcomes between the two models. The differences between the results of the two models are minor, especially compared with the uncertainties inherent in the modelling of fire spread.


Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.


Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 139
Author(s):  
Rodman R. Linn ◽  
Judith L. Winterkamp ◽  
James H. Furman ◽  
Brett Williams ◽  
J. Kevin Hiers ◽  
...  

Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.


Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.


2016 ◽  
Vol 67 (5) ◽  
pp. 471-494 ◽  
Author(s):  
Matúš Hyžný

AbstractDecapod associations have been significant components of marine habitats throughout the Cenozoic when the major diversification of the group occurred. In this respect, the circum-Mediterranean area is of particular interest due to its complex palaeogeographic history. During the Oligo-Miocene, it was divided in two major areas, Mediterranean and Paratethys. Decapod crustaceans from the Paratethys Sea have been reported in the literature since the 19thcentury, but only recent research advances allow evaluation of the diversity and distribution patterns of the group. Altogether 176 species-level taxa have been identified from the Oligocene and Miocene of the Western and Central Paratethys. Using the three-dimensional NMDS analysis, the composition of decapod crustacean faunas of the Paratethys shows significant differences through time. The Ottnangian and Karpatian decapod associations were similar to each other both taxonomically and in the mode of preservation, and they differed taxonomically from the Badenian ones. The Early Badenian assemblages also differed taxonomically from the Late Badenian ones. The time factor, including speciation, immigration from other provinces and/or (local or global) extinction, can explain temporal differences among assemblages within the same environment. High decapod diversity during the Badenian was correlated with the presence of reefal settings. The Badenian was the time with the highest decapod diversity, which can, however, be a consequence of undersampling of other time slices. Whereas the Ottnangian and Karpatian decapod assemblages are preserved virtually exclusively in the siliciclastic “Schlier”-type facies that originated in non-reefal offshore environments, carbonate sedimentation and the presence of reefal environments during the Badenian in the Central Paratethys promoted thriving of more diverse reef-associated assemblages. In general, Paratethyan decapods exhibited homogeneous distribution during the Oligo-Miocene among the basins in the Paratethys. Based on the co-occurrence of certain decapod species, migration between the Paratethys and the North Sea during the Early Miocene probably occurred via the Rhine Graben. At larger spatial scales, our results suggest that the circum-Mediterranean marine decapod taxa migrated in an easterly direction during the Oligocene and/or Miocene, establishing present-day decapod communities in the Indo-West Pacific.


2020 ◽  
Vol 24 (4) ◽  
pp. 2061-2081 ◽  
Author(s):  
Xudong Zhou ◽  
Jan Polcher ◽  
Tao Yang ◽  
Ching-Sheng Huang

Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.


Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4999
Author(s):  
Matthew Craig ◽  
Taimoor Asim

In this study, advanced Computational Fluid Dynamics (CFD)-based numerical simulations have been performed in order to analyse fire propagation in a standard railway compartment. A Fire Dynamics Simulator (FDS) has been employed to mimic real world scenarios associated with fire propagation within railway carriages in order to develop safety guidelines for railway passengers. Comprehensive parametric investigations on the effects of ignition location, intensity and cabin upholstery have been carried out. It has been observed that a fire occurring near the exits of the carriage results in a lower smoke layer height, due to the local carriage geometry, than an identical fire igniting at the center of the carriage. This in turn causes the smoke density along the aisleway to vary by around 30%. Reducing the ignition energy by half has been found to restrict combustion, thus reducing smoke density and carbon exhaust gases, reducing the average temperature from 170 °C to 110 °C. Changing the material lining of the seating has been found to cause the most significant change in output parameters, despite its relative insignificance in bulk mass. A polyester sample produces a peak carbon monoxide concentration of 7500 ppm, which is 27× greater compared with nylon. This difference has been found to be due to the fire spread and propagation between fuels, signifying the polyester’s unsuitability for use in railway carriages.


Sign in / Sign up

Export Citation Format

Share Document