scholarly journals Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

Fire ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 6
Author(s):  
Gavin M. Schag ◽  
Douglas A. Stow ◽  
Philip J. Riggan ◽  
Robert G. Tissell ◽  
Janice L. Coen
Keyword(s):  
Multivariate Regression ◽  
Thermal Infrared ◽  
Growth Function ◽  
Fire Spread ◽  
Landscape Scale ◽  
Covariate Data ◽  
Rate Of Spread ◽  
Digital Elevation ◽  
Wildfire Spread ◽  
Statistical Relationships

The objectives of this study are to evaluate landscape-scale fuel and terrain controls on fire rate of spread (ROS) estimates derived from repetitive airborne thermal infrared (ATIR) imagery sequences collected during the 2017 Thomas and Detwiler extreme wildfire events in California. Environmental covariate data were derived from prefire National Agriculture Imagery Program (NAIP) orthoimagery and USGS digital elevation models (DEMs). Active fronts and spread vectors of the expanding fires were delineated from ATIR imagery. Then, statistical relationships between fire spread rates and landscape covariates were analyzed using bivariate and multivariate regression. Directional slope is found to be the most statistically significant covariate with ROS for the five fire imagery sequences that were analyzed and its relationship with ROS is best characterized as an exponential growth function (adj. R2 max = 0.548, min = 0.075). Imaged-derived fuel covariates alone are statistically weak predictors of ROS (adj. R2 max = 0.363, min = 0.002) but, when included in multivariate models, increased ROS predictability and variance explanation (+14%) compared to models with directional slope alone.

Instantaneous spatio-temporal rate of spread of fast spreading wildfires - a new approach from visible and thermal image processing

2021 ◽  
Author(s):  
Benjamin Schumacher ◽  
Katharine Melnik ◽  
Marwan Katurji ◽  
Veronica Clifford ◽  
Jiawei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
New Zealand ◽  
Stand Structure ◽  
Fire Severity ◽  
Thermal Infrared ◽  
Fire Spread ◽  
New Approach ◽  
Rate Of Spread ◽  
Fast Spreading ◽  
Fire Experiments

<p>The rate of spread (ROS) of wildfires is an important parameter for understanding fire-atmospheric interactions and developing fire-spread models, but it is also vital for firefighting operations to ensure the safety of firefighters (Plucinski 2017, Stow 2019). Spatial ROS observations are usually carried out by using visible and thermal satellite imagery of wildfires estimating the ROS on a time scale of hours to days for large fires (>100 ha) or repeated passing with an airborne thermal infrared imager for higher spatial and temporal resolution (Viedma et al. 2015, Stow 2014). For fire experiments in highly controlled conditions like laboratory fires or during light fuel prescribed burns, ROS estimation usually involves lag-correlation of temperature point measurements (Finney 2010, Johnston 2018). However, these methodologies are not applicable to fast-spreading grass or bush fires because of their temporal and spatial limitations. Instantaneous spatial ROS of these fires is needed to understand rapid changes in connection with the three major drivers of the fire: fuel, topography and atmospheric forcings.</p><p>We are presenting a new approach towards a spatial ROS product which includes newly developed image tracking methods based on thermal and visible imagery collected from unmanned aerial vehicles to estimate instantaneous, spatial ROS of fast spreading grass or bush fires. These techniques were developed using imagery from prescribed wheat-stubble burns carried out in Darfield, New Zealand in March 2018 (Finney 2018). Results show that both the visible and thermal tracking techniques produce similar mean ROS; however they differ in limitations and advantages. The visible-spectrum tracking method clearly identifies the flaming zone and provides accurate ROS measurements especially at the fire front. The thermal tracking technique is superior when resolving dynamics and ROS within the flaming zone because it resolves smaller scale structures within the imagery.</p><p> </p><p>References:</p><p>Finney, M. et al. 2010: An Examination of Fire Spread Thresholds in Discontinuous Fuel Beds.” International Journal of Wildland Fire, 163–170.</p><p>Finney, M. et al. 2018: New Zealand prescribed fire experiments to test convective heat transfer in wildland fires. In Advances in Forest Fire Research, Imprensa da Universidade de Coimbra: Coimbra, 2018.</p><p>Johnston, J. M., et al. 2018:  Flame-Front Rate of Spread Estimates for Moderate Scale Experimental Fires are Strongly Influenced by Measurement Approach. Fire 1: 16–17</p><p>Plucinski M., et al. 2017: Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1-12.</p><p>Stow, D., et al. 2014: Measuring Fire Spread Rates from Repeat PassAirborne Thermal Infrared Imagery. Remote Sensing Letters 5: 803–881.</p><p>Stow, D., et al. 2019: Assessing uncertainty and demonstrating potentialfor estimating fire rate of spread at landscape scales based on time sequential airbornethermal infrared imaging, International Journal of Remote Sensing, 40:13, 4876-4897</p><p>Viedma, O., et al. 2015:  Fire Severity in a Large Fire in a Pinus Pinaster Forest Is Highly Predictable from Burning Conditions, Stand Structure, and Topography. Ecosystems18: 237–250.</p>

The importance of fire - atmosphere coupling and boundary-layer turbulence to wildfire spread

2009 ◽  
Vol 18 (1) ◽  
pp. 50 ◽  
Author(s):  
Ruiyu Sun ◽  
Steven K. Krueger ◽  
Mary Ann Jenkins ◽  
Michael A. Zulauf ◽  
Joseph J. Charney
Keyword(s):  
Boundary Layer ◽  
Transient Behavior ◽  
Fire Spread ◽  
Behavior Prediction ◽  
Ambient Flow ◽  
Boundary Layer Turbulence ◽  
Rate Of Spread ◽  
Two Factors ◽  
Wildfire Spread ◽  
Induced Flow

The major source of uncertainty in wildfire behavior prediction is the transient behavior of wildfire due to changes in flow in the fire’s environment. The changes in flow are dominated by two factors. The first is the interaction or ‘coupling’ between the fire and the fire-induced flow. The second is the interaction or ‘coupling’ between the fire and the ambient flow driven by turbulence due to wind gustiness and eddies in the atmospheric boundary layer (ABL). In the present study, coupled wildfire–atmosphere large-eddy simulations of grassland fires are used to examine the differences in the rate of spread and area burnt by grass fires in two types of ABL, a buoyancy-dominated ABL and a roll-dominated ABL. The simulations show how a buoyancy-dominated ABL affects fire spread, how a roll-dominated ABL affects fire spread, and how fire lines interact with these two different ABL flow types. The simulations also show how important are fire–atmosphere couplings or fire-induced circulations to fire line spread compared with the direct impact of the turbulence in the two different ABLs. The results have implications for operational wildfire behavior prediction. Ultimately, it will be important to use techniques that include an estimate of uncertainty in wildfire behavior forecasts.

Fire Growth in Grassland Fuels

1995 ◽  
Vol 5 (4) ◽  
pp. 237 ◽  
Author(s):  
NP Cheney ◽  
JS Gould
Keyword(s):  
Wind Speed ◽  
Fire Spread ◽  
Small Scale ◽  
Fire Growth ◽  
Spread Rate ◽  
Wind Speeds ◽  
Rate Of Spread ◽  
Steady Rate ◽  
Scale Field ◽  
Wildfire Spread

The development of grass fires originating from both point and line ignitions and burning in both open grasslands and woodlands with a grassy understorey was studied using 487 periods of fire spread and associated fuel, weather and fire-shape observations. The largest fires travelled more than 1000 m from the origin and the fastest 2-minute spread rate was over 2 m s-1. Given continuous fuel of uniform moisture content, the rate of forward spread was related to both the wind speed and the width of the head fire measured normal to the direction of fire travel. The head fire width required to achieve the potential quasi-steady rate of forward spread for the prevailing conditions increased with increasing wind speeds. These findings have important implications for relating small-scale field or laboratory measurements of fire spread to predictions of wildfire spread. The time taken to reach the potential quasi-steady rate of spread at any wind speed was highly variable. This time was strongly influenced by the frequency of changes in wind direction and the rate of development of a wide head fire.

Fine-Scale Fire Spread in Pine Straw

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife
Keyword(s):  
Fire Spread ◽  
Small Scale ◽  
Natural Setting ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Fire Dynamics ◽  
Rate Of Spread ◽  
Scale Models ◽  
Fire Front ◽  
Visible Images

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.

scholarly journals Dimensional analysis on forest fuel bed fire spread

2018 ◽  
Vol 48 (1) ◽  
pp. 105-110
Author(s):  
Jiann C. Yang
Keyword(s):  
Dimensional Analysis ◽  
Fire Spread ◽  
Fuel Particle ◽  
Wind Condition ◽  
Spread Rate ◽  
Fuel Loading ◽  
Rate Of Spread ◽  
Dimensionless Groups ◽  
Dimensionless Correlations ◽  
Fuel Moisture Content

A dimensional analysis was performed to correlate the fuel bed fire rate of spread data previously reported in the literature. Under wind condition, six pertinent dimensionless groups were identified, namely dimensionless fire spread rate, dimensionless fuel particle size, fuel moisture content, dimensionless fuel bed depth or dimensionless fuel loading density, dimensionless wind speed, and angle of inclination of fuel bed. Under no-wind condition, five similar dimensionless groups resulted. Given the uncertainties associated with some of the parameters used to estimate the dimensionless groups, the dimensionless correlations using the resulting dimensionless groups correlate the fire rates of spread reasonably well under wind and no-wind conditions.

Laboratory fire spread analysis using visual and infrared images

2006 ◽  
Vol 15 (2) ◽  
pp. 179 ◽  
Author(s):  
J. Ramiro Martínez-de Dios ◽  
Jorge C. André ◽  
João C. Gonçalves ◽  
Begoña Ch. Arrue ◽  
Aníbal Ollero ◽  
...  
Keyword(s):  
Graphical Model ◽  
Fire Spread ◽  
Rate Of Spread ◽  
Virtual View ◽  
Fire Front ◽  
Computer Based ◽  
Front Shape ◽  
Processing Techniques ◽  
Spread Analysis ◽  
Base Width

This paper presents an experimental method using computer-based image processing techniques of visual and infrared movies of a propagating fire front, taken from one or more cameras, to supply the time evolutions of the fire front shape and position, flame inclination angle, height, and base width. As secondary outputs, it also provides the fire front rate of spread and a 3D graphical model of the fire front that can be rendered from any virtual view. The method is automatic and non-intrusive, has space–time resolution close to continuum and can be run in real-time or deferred modes. It is demonstrated in simple laboratory experiments in beds of pine needles set upon an inclinable burn table, with point and linear ignitions, but can be extended to open field situations.

scholarly journals Meteorological Conditions Conducive to the Rapid Spread of the Deadly Wildfire in Eastern Attica, Greece

2019 ◽  
Vol 100 (11) ◽  
pp. 2137-2145 ◽  
Author(s):  
K. Lagouvardos ◽  
V. Kotroni ◽  
T. M. Giannaros ◽  
S. Dafis
Keyword(s):  
Fire Behavior ◽  
Fire Spread ◽  
High Rate ◽  
Contributing Factors ◽  
Suburban Areas ◽  
Rate Of Spread ◽  
Rapid Spread ◽  
Westerly Flow ◽  
Downslope Flow ◽  
Extreme Fire

AbstractOn 23 July 2018, Attica, Greece, was impacted by a major wildfire that took place in a wildland–urban interface area and exhibited extreme fire behavior, characterized by a very high rate of spread. One-hundred civilian fatalities were registered, establishing this wildfire as the second-deadliest weather-related natural disaster in Greece, following the heat wave of July 1987. On the day of the deadly wildfire, a very strong westerly flow was blowing for more than 10 h over Attica. Wind gusts up to 30–34 m s−1 occurred over the mountainous areas of Attica, with 20–25 m s−1 in the city of Athens and surrounding suburban areas. This strong westerly flow interacted with the local topography and acted as downslope flow over the eastern part of Attica, with temperatures rising up to 39°C and relative humidity dropping to 19% prior to the onset of the wildfire. These weather elements are widely acknowledged as the major contributing factors to extreme fire behavior. WRF-SFIRE correctly predicted the spatiotemporal distribution of the fire spread and demonstrated its utility for fire spread warning purposes.

A cellular automata model to link surface fires to firebrand lift-off and dispersal

2013 ◽  
Vol 22 (4) ◽  
pp. 428 ◽  
Author(s):  
Holly A. Perryman ◽  
Christopher J. Dugaw ◽  
J. Morgan Varner ◽  
Diane L. Johnson
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fuel Loading ◽  
Cellular Automata Model ◽  
Rate Of Spread ◽  
Lift Off ◽  
Fuel Moisture Content ◽  
Spot Fires

In spite of considerable effort to predict wildland fire behaviour, the effects of firebrand lift-off, the ignition of resulting spot fires and their effects on fire spread, remain poorly understood. We developed a cellular automata model integrating key mathematical models governing current fire spread models with a recently developed model that estimates firebrand landing patterns. Using our model we simulated a wildfire in an idealised Pinus ponderosa ecosystem. Varying values of wind speed, surface fuel loading, surface fuel moisture content and canopy base height, we investigated two scenarios: (i) the probability of a spot fire igniting beyond fuelbreaks of various widths and (ii) how spot fires directly affect the overall surface fire’s rate of spread. Results were averages across 2500 stochastic simulations. In both scenarios, canopy base height and surface fuel loading had a greater influence than wind speed and surface fuel moisture content. The expected rate of spread with spot fires occurring approached a constant value over time, which ranged between 6 and 931% higher than the predicted surface fire rate of spread. Incorporation of the role of spot fires in wildland fire spread should be an important thrust of future decision-support technologies.

DEVS-FIRE: design and application of formal discrete event wildfire spread and suppression models

SIMULATION ◽  
2011 ◽  
Vol 88 (3) ◽  
pp. 259-279 ◽  
Author(s):  
Xiaolin Hu ◽  
Yi Sun ◽  
Lewis Ntaimo
Keyword(s):  
Fire Suppression ◽  
Discrete Event ◽  
Fire Spread ◽  
New Method ◽  
Weather Data ◽  
System Specification ◽  
Cellular Space ◽  
Scheduling Method ◽  
Wildfire Spread ◽  
Formal Design

DEVS-FIRE is a discrete event system specification (DEVS) model for simulating wildfire spread and suppression. It employs a cellular space model to simulate fire spread and agent models that interact with the cellular space to simulate fire suppression with realistic tactics. The complex interplay among forest cells and agents calls for formal treatment of the fire spread and fire suppression models to verify the correctness of DEVS-FIRE. This paper gives formal design specifications of fire spread and suppression agent models used in DEVS-FIRE and applies DEVS-FIRE to both artificially generated and real topography, fuels and weather data for a study area located in the US state of Texas. The paper also develops a new method, called pre_Schedule, for scheduling ignition events of forest cells more efficiently than the original onTime_Schedule event scheduling method used in DEVS-FIRE. Simulation results show the performance improvement of the new method, and demonstrate the utility of DEVS-FIRE as a viable discrete event model for wildfire simulations.

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