Overview of Forest Fire Propagation Research

Viegas, Domingos X.

doi:10.3801/iafss.fss.10-95

Cited by 13 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During a long, dry heat-wave period, an original wildfire can occur naturally and may become uncontrolled when the weather conditions, topography, and fuels, the drivers of fire behavior, are suitable for fire spread or propagation [7,8]. From the viewpoint of ecosystem succession, a burnt forestland will recover over a long-term period of succession and consequently be suitable for the development of new communities of vegetation and wildlife [9].…”

Section: Introductionmentioning

confidence: 99%

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Lin

Huang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

show abstract

Section: Introductionmentioning

confidence: 99%

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Lin

Huang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Computer-based wildfire spread modeling has emerged during the past two decades as a powerful tool for applications in both fire risk management and fire emergency response. However, because the underlying wildfire dynamics feature complex multi-physics occurring at multiple scales, our ability to accurately simulate the behavior of wildfires remains limited [1]. The dynamics of wildfires are determined by interactions between pyrolysis, combustion, flow dynamics as well as atmospheric dynamics.…”

mentioning

confidence: 99%

Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position

Rochoux¹,

Emery²,

Ricci³

et al. 2014

Fire Saf. Sci.

View full text Add to dashboard Cite

The objective of this study is to develop a prototype data-driven wildfire simulator capable of forecasting the fire spread dynamics. The prototype simulation capability features the following main components: a level-set-based fire propagation solver that adopts a regional scale viewpoint, treats wildfires as propagating fronts, and uses a description of the local rate of spread (ROS) of the fire as a function of vegetation properties and wind conditions based on Rothermel's model; a series of observations of the fire front position; and a data assimilation algorithm based on an Ensemble Kalman Filter (EnKF). Members of the EnKF ensemble are generated through variations in estimates of the fire ignition location and/or variations in the ROS model parameters; the data assimilation algorithm also features a state estimation approach in which the estimation targets (the control variables) are the two-dimensional coordinates of the discretized fire front. The prototype simulation capability is first evaluated in a series of verification tests using syntheticallygenerated observations; the tests include representative cases with spatially-varying vegetation properties and temporally-varying wind conditions. The prototype simulation capability is then evaluated in a validation test corresponding to a controlled grassland fire experiment. The results indicate that data-driven simulations are capable of correcting inaccurate predictions of the fire front position and of subsequently providing an optimized forecast of the wildfire behavior.

show abstract

“…An alternative approach to wildfire modelling is computational fluid dynamics (CFD)-based models such as FIRETEC (Linn 1997), FOREFIRE (Filippi et al 2009;Filippi et al 2014a) or Wildland-Urban Interface Fire Dynamics Simulator (WFDS) (Mell et al 2007). However, these are restricted to research use, and small-and particular-scale applications owing to the high computational costs and initialising data required (Viegas 2011). A simplified version of a CFD model that couples weather modelling with Rothermel's fire-spread model (Rothermel 1972) named WRF-SFIRE (Mandel et al 2014(Mandel et al , 2011 was recently reported to have been applied operationally, as a 6-h forecast could be launched with a 30-min run in a multicore processor.…”

Section: Introductionmentioning

confidence: 99%

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

Rios

Pastor

Valero

et al. 2016

Int. J. Wildland Fire

View full text Add to dashboard Cite

Abstract.A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.

show abstract

Overview of Forest Fire Propagation Research

Cited by 13 publications

References 4 publications

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position

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

Contact Info

Product

Resources

About