Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Parsons, Russell A.; Mell, William E.; McCauley, Peter

doi:10.1016/j.ecolmodel.2010.10.023

Cited by 99 publications

(79 citation statements)

References 62 publications

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“…While this issue is a matter of scale, subsequent research (e.g., Frandsen andAndrews 1979, Catchpole et al 1989) and other innovations (Fujioka 1985, Finney 2003 such as the two-fuel model concept (Rothermel 1983, Martin 1988, as well as geographic information system (GIS)-based fire growth models (Beck 2000, Finney 2004, Tymstra et al 2010 have not substantially reduced this problem. It thus remains a continuing research challenge (Parsons et al 2011) and involves both the physical fuel characteristics as well as fuel moistures, including differences due to topographic features such as slope exposure (Cheney 1981). 2.…”

Section: Model Applicabilitymentioning

confidence: 99%

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

AlexanderMartin

Cruz

2013

The Forestry Chronicle

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The degree of accuracy in model predictions of wildland fire behaviour characteristics are dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data. While much progress has been made by fire behaviour research in the past 35 years or so in addressing these three sources of model error, the accuracy in model predictions are still very much at the mercy of our present understanding of the natural phenomena exhibited by free-burning wildland fires and the inherent temporal and spatial variability in the fire environment. This paper will serve as a state-of-the-art primer on the subject of error sources in model predictions of wildland fire behaviour and includes a short historical overview of wildland fire behaviour research as it relates to model development.

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Section: Model Applicabilitymentioning

confidence: 99%

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

AlexanderMartin

Cruz

2013

The Forestry Chronicle

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“…Understanding the spatial and temporal dynamics of fuels may provide a better grasp of the impact of various wildland fuel management activities on fuel properties [17] and it also might help explain unexpected fire behaviors and effects (e.g., [18]). It may also aid in developing effective fuel applications that integrate spatial variability in their design such as new fuel classifications, sampling methods, and geospatial data [9].…”

Section: Introductionmentioning

confidence: 99%

“…It may also aid in developing effective fuel applications that integrate spatial variability in their design such as new fuel classifications, sampling methods, and geospatial data [9]. Patterns of fuel characteristics will be important inputs to the fire effects and behavior models of the future [18,19].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

Keane

2016

Forests

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Abstract:Fire regimes are ultimately controlled by wildland fuel dynamics over space and time; spatial distributions of fuel influence the size, spread, and intensity of individual fires, while the temporal distribution of fuel deposition influences fire's frequency and controls fire size. These "shifting fuel mosaics" are both a cause and a consequence of fire regimes. This paper synthesizes results from two major fuel dynamics studies that described the spatial and temporal variability of canopy and surface wildland fuel characteristics found in US northern Rocky Mountain forests. Eight major surface fuel components-four downed dead woody fuel size classes (1, 10, 100, 1000 h), duff, litter, shrub, and herb-and three canopy fuel characteristics-loading, bulk density and cover-were studied. Properties of these fuel types were sampled on nested plots located within sampling grids to describe their variability across spatiotemporal scales. Important findings were that fuel component loadings were highly variable (two to three times the mean), and this variability increased with the size of fuel particles. The spatial variability of loadings also varied by spatial scale with fine fuels (duff, litter, 1 h, 10 h) varying at scales of 1 to 5 m; coarse fuels at 10 to 150 m, and canopy fuels at 100 to 600 m. Fine fuels are more uniformly distributed over both time and space and decayed quickly, while large fuels are rare on the landscape but have a high residence time.

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“…Emerging interests in wildland fire behavior and risk (Hiers et al 2009;Parsons et al 2011;Ottmar et al 2012), bioenergy utilization (Dassot et al 2012;Fernandez-Sarria et al 2013), carbon sequestration , and wildlife conservation (Lesak et al 2011;Palminteri et al 2012) among others, increasingly rely on accurate assessments of the amount and location of biomass, often at finer scales than traditional methods have provided. As field measurements are not always viable, scientists and managers employ models to infer biomass from tree lists, stand tables, and maps of vegetation composition and structure (Parresol 1999;.…”

Section: Introductionmentioning

confidence: 99%

“…The primary motivation driving the study of canopy architecture comes from the radiative transfer domain (Oker-blom and Kellomaki 1983;Duursma and Mäkelä 2007;Da Silva et al 2008;Parveaud et al 2008), but the arrangement of crown material is also important across a diverse array of interests ranging from long-established forest growth and competition modeling (Vanclay 1994) to novel approaches in wildland fire modeling that attempt to understand fire behavior and effects at very fine grains (Parsons et al 2011;Hoffman 2012).…”

Section: Introductionmentioning

confidence: 99%

Conifer crown profile models from terrestrial laser scanning

Ferrarese¹,

Affleck²,

Seielstad³

2015

Silva Fenn.

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• Crown models are derived from terrestrial laser data for 3 NW USA conifer species.• Crown models require only crown length for implementation.• Beta and Weibull curves fit to 95th percentile widths describe crown extent.• Crown profile curves are species-specific and not interchangeable.• Crown shape is not strongly conditioned by tree size or site. AbstractRegional crown profile models were derived for three conifer species of the interior northwestern USA from terrestrial laser scans of eighty-six trees across a range of sizes and growing conditions. Equations were developed to predict crown shape from crown length for Pseudotsuga menziesii, Pinus ponderosa, and Abies lasiocarpa from parametric curves applied to crown-length normalized laser point clouds. The 95th width percentile adequately described each crown's outer limit; alternate width percentiles produced little profile shape variation. For P. menziesii and P. ponderosa, a scaling parameter-modified beta curve gave the most accurate fit (using cross-validated Mean Absolute Error) to aggregated 95th width percentile points. For A. lasiocarpa, beta and Weibull curves (equivalently modified) produced similar results. For all species, modified beta and Weibull curves fit crown points with less error than conic or cylindrical profiles. Crown profile curves were species-specific; interchanging among species increased error significantly. Laser-derived crown base metrics provided objectivity and consistency, but underestimated field-derived base heights through inclusion of dead branches. Profile curve parameters were not correlated with tree or stand characteristics suggesting that crown shape is not strongly conditioned by size and site factors. However, laser sampling necessarily favored more open growing conditions, potentially under-representing variations in crown shape associated with social position. Overall, Terrestrial Laser Scanning (TLS) lends itself to detailed measurements of external crown architecture with occlusion-imposed limits to characterization of internal features. Yet, the time and cost of collecting and processing individual tree data precludes use of TLS as a common field sampling tool.

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Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Cited by 99 publications

References 62 publications

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

Conifer crown profile models from terrestrial laser scanning

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