High-resolution observations of combustion in heterogeneous surface fuels

2014 ◽  
Vol 23 (7) ◽  
pp. 1016 ◽  
Author(s):  
E. Louise Loudermilk ◽  
Gary L. Achtemeier ◽  
Joseph J. O'Brien ◽  
J. Kevin Hiers ◽  
Benjamin S. Hornsby
Keyword(s):  
High Resolution ◽  
Regression Tree ◽  
Fire Spread ◽  
Recognition Algorithm ◽  
Fine Scale ◽  
Imaging Data ◽  
Fire Behaviour ◽  
Spread Model ◽  
Fuel Heterogeneity ◽  
Fuel Characteristics

In ecosystems with frequent surface fires, fire and fuel heterogeneity at relevant scales have been largely ignored. This could be because complete burns give an impression of homogeneity, or due to the difficulty in capturing fine-scale variation in fuel characteristics and fire behaviour. Fire movement between patches of fuel can have implications for modelling fire spread and understanding ecological effects. We collected high resolution (0.8×0.8-cm pixels) visual and thermal imaging data during fire passage over 4×4-m plots of mixed fuel beds consisting of pine litter and grass during two prescribed burns within the longleaf pine forests of Eglin Air Force Base, FL in February 2011. Fuel types were identified by passing multi-spectral digital images through a colour recognition algorithm in ‘Rabbit Rules,’ an experimental coupled fire-atmosphere fire spread model. Image fuel types were validated against field fuel types. Relationships between fuel characteristics and fire behaviour measurements at multiple resolutions (0.8×0.8cm to 33×33cm) were analysed using a regression tree approach. There were strong relationships between fire behaviour and fuels, especially at the 33×33-cm scale (R2=0.40–0.69), where image-to-image overlap error was reduced and fuels were well characterised. Distinct signatures were found for individual and coupled fuel types for determining fire behaviour, illustrating the importance of understanding fire-fuel heterogeneity at fine-scales. Simulating fire spread at this fine-scale may be critical for understanding fire effects, such as understorey plant community assembly.

Fire behaviour in wheat crops – effect of fuel structure on rate of fire spread

2020 ◽  
Vol 29 (3) ◽  
pp. 258 ◽  
Author(s):  
Miguel G. Cruz ◽  
Richard J. Hurley ◽  
Rachel Bessell ◽  
Andrew L. Sullivan
Keyword(s):  
Experimental Study ◽  
Prediction Models ◽  
Fire Spread ◽  
Propagation Rate ◽  
Forward Rate ◽  
Fire Behaviour ◽  
Natural Grass ◽  
Spread Model ◽  
Crop Condition ◽  
Fire Spread Model

A field-based experimental study was conducted in 50×50m square plots to investigate the behaviour of free-spreading fires in wheat to quantify the effect of crop condition (i.e. harvested, unharvested and harvested and baled) on the propagation rate of fires and their associated flame characteristics, and to evaluate the adequacy of existing operational prediction models used in these fuel types. The dataset of 45 fires ranged from 2.4 to 10.2kmh−1 in their forward rate of fire spread and 3860 and 28000 kWm−1 in fireline intensity. Rate of fire spread and flame heights differed significantly between crop conditions, with the unharvested condition yielding the fastest spreading fires and tallest flames and the baled condition having the slowest moving fires and lowest flames. Rate of fire spread in the three crop conditions corresponded directly with the outputs from the models of Cheney et al. (1998) for grass fires: unharvested wheat → natural grass; harvested wheat (~0.3m tall stubble) → grazed or cut grass; and baled wheat (<0.1m tall stubble) → eaten-out grass. These models produced mean absolute percent errors between 21% and 25% with reduced bias, a result on par with the most accurate published fire spread model evaluations.

Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture

2007 ◽  
Vol 16 (4) ◽  
pp. 503 ◽  
Author(s):  
W. Matt Jolly
Keyword(s):  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Surface Fire ◽  
Fuel Model ◽  
Spread Model ◽  
Fuel Models ◽  
Live Fuel Moisture ◽  
Behaviour Models ◽  
Fire Spread Model

Fire behaviour models are used to assess the potential characteristics of wildland fires such as rates of spread, fireline intensity and flame length. These calculations help support fire management strategies while keeping fireline personnel safe. Live fuel moisture is an important component of fire behaviour models but the sensitivity of existing models to live fuel moisture has not been thoroughly evaluated. The Rothermel surface fire spread model was used to estimate key surface fire behaviour values over a range of live fuel moistures for all 53 standard fuel models. Fire behaviour characteristics are shown to be highly sensitive to live fuel moisture but the response is fuel model dependent. In many cases, small changes in live fuel moisture elicit drastic changes in predicted fire behaviour. These large changes are a result of a combination of the model-calculated live fuel moisture of extinction, the effective wind speed limit and the dynamic load transfer function of some of the fuel models tested. Surface fire spread model sensitivity to live fuel moisture changes is discussed in the context of predicted fire fighter safety zone area because the area of a predicted safety zone may increase by an order of magnitude for a 10% decrease in live fuel moisture depending on the fuel model chosen.

Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbedsThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

2007 ◽  
Vol 37 (12) ◽  
pp. 2438-2455 ◽  
Author(s):  
David V. Sandberg ◽  
Cynthia L. Riccardi ◽  
Mark D. Schaaf
Keyword(s):  
Wind Speed ◽  
Environmental Conditions ◽  
Classification System ◽  
Fire Behavior ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Spread Model ◽  
Fuel Characteristic ◽  
Fire Spread Model

The Fuel Characteristic Classification System (FCCS) includes equations that calculate energy release and one-dimensional spread rate in quasi-steady state fires in heterogeneous but spatially-uniform wildland fuelbeds, using a reformulation of the widely used Rothermel fire spread model. This reformulation provides an automated means to predict fire behavior under any environmental conditions in any natural, modified, or simulated wildland fuelbed. The formulation may be used to compare potential fire behavior between fuelbeds that differ in time, space, or as a result of management, and provides a means to classify and map fuelbeds based on their expected surface fire behavior under any set of defined environmental conditions (i.e., effective wind speed and fuel moisture content). Model reformulation preserves the basic mathematical framework of the Rothermel fire spread model, reinterprets data from two of the original basic equations in his model, and offers a new conceptual formulation that allows the direct use of inventoried fuel properties instead of stylized fuel models. Alternative methods for calculating the effect of wind speed and fuel moisture, based on more recent literature, are also provided. This reformulation provides a framework for the incremental improvement in quantifying fire behaviour parameters in complex fuelbeds and for modeling fire spread.

scholarly journals Building Rothermel fire behaviour fuel models by genetic algorithm optimisation

2015 ◽  
Vol 24 (3) ◽  
pp. 317 ◽  
Author(s):  
Davide Ascoli ◽  
Giorgio Vacchiano ◽  
Renzo Motta ◽  
Giovanni Bovio
Keyword(s):  
Goodness Of Fit ◽  
Fire Spread ◽  
Validation Dataset ◽  
Model Parameters ◽  
Initial Population ◽  
Fire Behaviour ◽  
Continuous Space ◽  
Fuel Model ◽  
Spread Model ◽  
Fuel Models

A method to build and calibrate custom fuel models was developed by linking genetic algorithms (GA) to the Rothermel fire spread model. GA randomly generates solutions of fuel model parameters to form an initial population. Solutions are validated against observations of fire rate of spread via a goodness-of-fit metric. The population is selected for its best members, crossed over and mutated within a range of model parameter values, until a satisfactory fitness is reached. We showed that GA improved the performance of the Rothermel model in three published custom fuel models for litter, grass and shrub fuels (root mean square error decreased by 39, 19 and 26%). We applied GA to calibrate a mixed grass–shrub fuel model, using fuel and fire behaviour data from fire experiments in dry heathlands of Southern Europe. The new model had significantly lower prediction error against a validation dataset than either standard or custom fuel models built using average values of inventoried fuels, and predictions of the Fuel Characteristics Classification System. GA proved a useful tool to calibrate fuel models and improve Rothermel model predictions. GA allows exploration of a continuous space of fuel parameters, making fuel model calibration computational effective and easily reproducible, and does not require fuel sampling. We suggest GA as a viable method to calibrate custom fuel models in fire modelling systems based on the Rothermel model.

scholarly journals Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover

2021 ◽  
Vol 13 (16) ◽  
pp. 3270
Author(s):  
Yu Tao ◽  
Jan-Peter Muller ◽  
Susan J. Conway ◽  
Siting Xiong
Keyword(s):  
High Resolution ◽  
Landing Site ◽  
Input Image ◽  
3D Mapping ◽  
Fine Scale ◽  
Imaging Data ◽  
Large Area ◽  
Laser Altimeter ◽  
Visual Evaluation ◽  
Imaging Science

We demonstrate an end-to-end application of the in-house deep learning-based surface modelling system, called MADNet, to produce three large area 3D mapping products from single images taken from the ESA Mars Express’s High Resolution Stereo Camera (HRSC), the NASA Mars Reconnaissance Orbiter’s Context Camera (CTX), and the High Resolution Imaging Science Experiment (HiRISE) imaging data over the ExoMars 2022 Rosalind Franklin rover’s landing site at Oxia Planum on Mars. MADNet takes a single orbital optical image as input, provides pixelwise height predictions, and uses a separate coarse Digital Terrain Model (DTM) as reference, to produce a DTM product from the given input image. Initially, we demonstrate the resultant 25 m/pixel HRSC DTM mosaic covering an area of 197 km × 182 km, providing fine-scale details to the 50 m/pixel HRSC MC-11 level-5 DTM mosaic. Secondly, we demonstrate the resultant 12 m/pixel CTX MADNet DTM mosaic covering a 114 km × 117 km area, showing much more detail in comparison to photogrammetric DTMs produced using the open source in-house developed CASP-GO system. Finally, we demonstrate the resultant 50 cm/pixel HiRISE MADNet DTM mosaic, produced for the first time, covering a 74.3 km × 86.3 km area of the 3-sigma landing ellipse and partially the ExoMars team’s geological characterisation area. The resultant MADNet HiRISE DTM mosaic shows fine-scale details superior to existing Planetary Data System (PDS) HiRISE DTMs and covers a larger area that is considered difficult for existing photogrammetry and photoclinometry pipelines to achieve, especially given the current limitations of stereo HiRISE coverage. All of the resultant DTM mosaics are co-aligned with each other, and ultimately with the Mars Global Surveyor’s Mars Orbiter Laser Altimeter (MOLA) DTM, providing high spatial and vertical congruence. In this paper, technical details are presented, issues that arose are discussed, along with a visual evaluation and quantitative assessments of the resultant DTM mosaic products.

A two-dimensional model of fire spread across a fuel bed including wind combined with slope conditions

2002 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Frédéric Morandini ◽  
Paul A. Santoni ◽  
Jacques H. Balbi ◽  
João M. Ventura ◽  
José M. Mendes-Lopes
Keyword(s):  
Fire Management ◽  
Wildland Fire ◽  
Threshold Value ◽  
Fire Spread ◽  
Management Tool ◽  
Fire Behaviour ◽  
Dimensional Model ◽  
Proposed Model ◽  
Spread Model ◽  
Fire Spread Model

In a previous work (Santoni et al., Int. J. Wildland Fire, 2000, 9(4), 285–292), we proposed a twodimensional fire spread model including slope effects as another step towards our aim to elaborate a fire management tool. In the present study, we improve the model to include both wind conditions and wind combined with slope conditions. For this purpose the effect of wind and slope are considered similar, in the sense that they both force the flames to lean forward. However, this analogy remains acceptable only when flame tilt is below a threshold value. Simulation results are compared to experimental data under wind and no-slope conditions. The proposed model is able to describe the fire behaviour. Predictions of the model for wind and slope conditions are then considered and comparisons with observations are also provided.

Monte Carlo-based ensemble method for prediction of grassland fire spread

2010 ◽  
Vol 19 (4) ◽  
pp. 521 ◽  
Author(s):  
Miguel G. Cruz
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Fire Spread ◽  
Error Component ◽  
Ensemble Method ◽  
Fire Behaviour ◽  
Spread Model ◽  
Grassland Fire

The operational prediction of fire spread to support fire management operations relies on a deterministic approach where a single ‘best-guess’ forecast is produced from the best estimate of the environmental conditions driving the fire. Although fire can be considered a phenomenon of low predictability and the estimation of input conditions for fire behaviour models is fraught with uncertainty, no error component is associated with these forecasts. At best, users will derive an uncertainty bound to the model outputs based on their own personal experience. A simple ensemble method that considers the uncertainty in the estimation of model input values and Monte Carlo sampling was applied with a grassland fire-spread model to produce a probability density function of rate of spread. This probability density function was then used to describe the uncertainty in the fire behaviour prediction and to produce probability-based outputs. The method was applied to a grassland wildfire case study dataset. The ensemble method did not improve the general statistics describing model fit but provided complementary information describing the uncertainty associated with the predictions and a probabilistic output for the occurrence of threshold levels of fire behaviour.

High Resolution Stereography of Complementary Freeze-Fracture Replicas Reveals the Presence of Depressions Opposite Membrane Associated Particles of Split Membranes

1971 ◽  
Vol 29 ◽  
pp. 442-443
Author(s):  
Russell L. Steere
Keyword(s):  
High Resolution ◽  
Cardiac Muscle ◽  
Electron Micrographs ◽  
Chromic Acid ◽  
Freeze Fracture ◽  
Distilled Water ◽  
Fine Scale ◽  
Acid Cleaning ◽  
Cleaning Solution

Complementary replicas have revealed the fact that the two common faces observed in electron micrographs of freeze-fracture and freeze-etch specimens are complementary to each other and are thus the new faces of a split membrane rather than the original inner and outer surfaces (1, 2 and personal observations). The big question raised by published electron micrographs is why do we not see depressions in the complementary face opposite membrane-associated particles? Reports have appeared indicating that some depressions do appear but complementarity on such a fine scale has yet to be shown.Dog cardiac muscle was perfused with glutaraldehyde, washed in distilled water, then transferred to 30% glycerol (material furnished by Dr. Joaquim Sommer, Duke Univ., and VA Hospital, Durham, N.C.). Small strips were freeze-fractured in a Denton Vacuum DFE-2 Freeze-Etch Unit with complementary replica tooling. Replicas were cleaned in chromic acid cleaning solution, then washed in 4 changes of distilled water and mounted on opposite sides of the center wire of a Formvar-coated grid.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

Quantifying parametric uncertainty in the Rothermel model

2008 ◽  
Vol 17 (5) ◽  
pp. 638 ◽  
Author(s):  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Scott Goodrick
Keyword(s):  
Variance Reduction ◽  
Input Parameter ◽  
Parametric Uncertainty ◽  
Fire Spread ◽  
The United States ◽  
Linear System Of Equations ◽  
Spread Model ◽  
Non Linear System ◽  
The Impact ◽  
Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

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