Short-term fire front spread prediction using inverse modelling and airborne infrared images

Rios, Oriol; Pastor, Elsa; Valero, Miguel Ángel; Planas, Eulàlia

doi:10.1071/wf16031

Cited by 29 publications

(22 citation statements)

References 35 publications

(41 reference statements)

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“…0, which corresponds to a positive term on the right-hand-side of Eq. (7) and as seen in Fig. 2a, to outward propagation of the simulated fireline.…”

Section: Illustration Of the Effect Of The Luenberger Observer Correcmentioning

confidence: 76%

“…at scales ranging from tens of meters up to several kilometers), wildland fires are usually described as fronts that self-propagate normal to themselves into unburnt vegetation; the local speed of the propa-gating front is referred to as the rate of spread (ROS). Current operational fire spread simulators adopt this regional-scale perspective using Eulerian [1,2] or Lagrangian [3,4] A possible approach to overcome these limitations is data assimilation (DA) [1,[5][6][7][8][9][10]. DA offers a valuable framework to integrate fire sensor observations into a computer model, with the goal to find optimal estimates of the targets or "control variables" (e.g.…”

mentioning

confidence: 99%

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Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment

Zhang

Collin

Moireau

et al. 2019

Proceedings of the Combustion Institute

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Data-driven wildfire spread modeling is emerging as a cornerstone for forecasting real-time fire behavior using thermal-infrared imaging data. One key challenge in data assimilation lies in the design of an adequate measure to represent the discrepancies between observed and simulated firelines (or "fronts"). A first approach consists in adopting a Lagrangian description of the flame front and in computing a Euclidean distance between simulated and observed fronts by pairing each observed marker with its closest neighbor along the simulated front. However, this front marker registration approach is difficult to generalize to complex front topology that can occur when fire propagation conditions are highly heterogeneous due to topography, biomass

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“…0, which corresponds to a positive term on the right-hand-side of Eq. (7) and as seen in Fig. 2a, to outward propagation of the simulated fireline.…”

Section: Illustration Of the Effect Of The Luenberger Observer Correcmentioning

confidence: 76%

mentioning

confidence: 99%

Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment

Zhang

Collin

Moireau

et al. 2019

Proceedings of the Combustion Institute

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show abstract

“…In parallel to those developments, we explored a data-driven system based on marker-tracking implementation of Rothermel model (Rios et al, 2014b(Rios et al, , 2016. This tool integrates data assimilation techniques in order to calibrate a semi-empirical fire behavior model on-line.…”

Section: Introductionmentioning

confidence: 99%

“…After the correct validation of the concept with flat experiments (Rios et al, 2016), this data-driven modeling system needs validation against a case as close as possible to real wildfire conditions. In order to be applied in a real fire scenario, our simulator should ideally be coupled with a real-time fire monitoring system.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire

Rios¹,

Valero²,

Pastor³

et al. 2019

Front. Mech. Eng.

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While full-physics fire models continue to be unsuitable for wildfire emergency situations, the so-called operational fire spread simulators are incapable of providing accurate estimations of the macroscopic fire behavior while quickly reacting to a change of governing spread mechanisms. A promising approach to overcome these limitations are data-driven simulators, which assimilate observed data with the aim of improving their forecast with affordable computation times. Although preliminary results obtained by several data-driven simulators are promising, this scheme needs intensive validation. Detailed studies of the particular aspects related to data assimilation are essential to gain insight about the applicability of this approach to operational wildfire simulation. This paper presents the validation of the simulator presented in Rios et al. (2014b, 2016, 2018) with a large scenario of real complexity with intricate terrain. The study case corresponds to a wildfire of significant repercussions occurred in Catalonia in March 2014. We employed as reference data the event reconstruction performed by the Catalan Fire Service and validated with operational observations. Detailed information about fuel and meteorology was collected by the fire brigades and allowed reconstructing the fire development with Farsite, a widely employed simulator. Subsequently, our simulator was tested without a detailed description of the fuel and wind parameters, i.e., imitating its intended deployment conditions. It proved capable of automatically estimating them and correctly simulating the fire spread. Additionally, the effect of the assimilation window on the forecast accuracy was analyzed. These results showed that the simulator is able to correctly handle complex terrain and wind situations to successfully deliver a short-term fire-front forecast in those real and complex scenarios.

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“…Furthermore, being able to accurately determine the geographical location of the fuel burning interface and its rate of spread could be helpful to calibrate a number of data-driven fire spread simulators. [7][8][9][10][11] If the fire evolution can be tracked in real time, these simulators may be able to emit quick forecasts of the subsequent fire development based on the observed dynamics.…”

Section: Introductionmentioning

confidence: 99%

Flame filtering and perimeter localization of wildfires using aerial thermal imagery

Valero

Verstockt

Rios

et al. 2017

Thermosense: Thermal Infrared Applications XXXIX

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Airborne thermal infrared (TIR) imaging systems are being increasingly used for wildfire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.

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Short-term fire front spread prediction using inverse modelling and airborne infrared images

Cited by 29 publications

References 35 publications

Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment

Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment

A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire

Flame filtering and perimeter localization of wildfires using aerial thermal imagery

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