Describing the Brushfire Hazard in Southern California

Yool, Stephen R.; Eckhardt, David W.; Estes, J. E.; Cosentino, Michael J.

doi:10.1111/j.1467-8306.1985.tb00076.x

Cited by 34 publications

(13 citation statements)

References 15 publications

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“…For instance, relations between fire occurrence and distance to roads, to settlements or to specific land uses have been documented in several studies (Chuvieco and Congalton, 1989;Vega-García et al, 1995;Vasconcelos et al, 2001;Prestemon and Butry, 2005;Mollicone et al, 2006;Maingi and Henry, 2007;Vasilakos et al, 2007;Yang et al, 2007). Most commonly, these studies are based on cartographic and statistical information managed through Geographical Information Systems (GIS), which serve also as framework for the integration of human factors into synthetic fire risk indices (Yool et al, 1985;Sebastiá n-Ló pez et al, 2002;Chuvieco et al, 2003;San Miguel-Ayanz et al, 2003;Morehouse et al, 2006). Recently, Maingi and Henry (2007) tested the relationship between monthly unemployment rate and arson fires but found no correlation between them.…”

Section: Introductionmentioning

confidence: 97%

Human-caused wildfire risk rating for prevention planning in Spain

Martínez

Vega‐García

Chuvieco

2009

Journal of Environmental Management

382

292

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Section: Introductionmentioning

confidence: 97%

Human-caused wildfire risk rating for prevention planning in Spain

Martínez

Vega‐García

Chuvieco

2009

Journal of Environmental Management

382

292

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“…The hot, dry climate, the warm, seasonal Santa Ana winds, and the fact that the predominant vegetation is highly flammable chaparral combine to make Southern California one of the most fire-prone areas in the world (Yool, Eckhardt, and Cosentino, 1985;Keeley and Keeley, 1988 Schoenberg et al, 2002b), but for this example we focus exclusively on wildfire sizes as marks. Point process models for such data sets, as well as other forestry data, are typically separable (Stoyan and Pettinen, 2000), though this assumption is generally not checked.…”

Section: Application: Los Angeles County Wildfiresmentioning

confidence: 99%

Testing Separability in Spatial‐Temporal Marked Point Processes

Schoenberg

2004

Biometrics

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Summary. Nonparametric tests for investigating the separability of a spatial-temporal marked point process are described and compared. It is shown that a Cramer-von Mises-type test is very powerful at detecting gradual departures from separability, and that a residual test based on randomly rescaling the process is powerful at detecting nonseparable clustering or inhibition of the marks. An application to Los Angeles County wildfire data is given, in which it is shown that the separability hypothesis is invalidated largely due to clustering of fires of similar sizes within periods of up to 3.9 years.

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“…This capacity has been widely used to generate locally orientated GIS-based fire danger rating systems which cover a small area at a high resolution (typically 50±100 m grid size). Examples of this approach are provided by Cosentino et al (1981), Burgan and Shasby (1984), Yool et al (1985), Palmer (1987), McKinsey (1988), Salazar and Power (1988), Hamilton et al (1989) and Chuvieco and Salas (1996). However, there have been some attempts to create global, low-resolution fire danger models (e.g.…”

Section: Spatial Information Systems and ®Re Modellingmentioning

confidence: 94%

Current approaches to modelling the spread of wildland fire: a review

Perry

1998

Progress in Physical Geography: Earth and Environment

125

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This review considers the development of some of the models and modelling approaches designed to predict the spread and spatial behaviour of wildland fire events. Such events and their accurate prediction are of great importance to those seeking to understand and manage fire-prone ecosystems. The key problem which fire modelling seeks to address is outlined. Models predicting the rate of fire spread may be classified as physical, semi-physical or empirical according to the nature of their construction. The benefits and shortcomings of each type of model are considered with reference to specific examples of each type. It is shown that there are problems with current operational models which restrict their effective use. However, the development of rigorous physical models as replacements is impeded by conceptual and practical difficulties. Accurate estimation of the rate of spread and the intensity of a fire allows prediction of the final shape and area of a fire event. The modelling techniques used to estimate the shape and area of a fire are considered including the development of sophisticated computerbased simulations of fire spread. Spatial information technologies such as remote sensing and geographic information systems (GIS) offer great potential for the effective modelling of wildland fire behaviour. While such spatial information technologies have been frequently used in the evaluation of fire danger risk, their use for the simulation of the spatiotemporal behaviour of wildland fire is not common. The way in which spatial information technologies and decisionsupport systems are used for fire risk evaluation and fire spread simulation is discussed. Two research areas of great importance if fire modelling techniques are to improve are a better understanding of fire-dependent phenomena and the development of a`new generation' of fire spread models; current trends in these areas of research are evaluated.

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Describing the Brushfire Hazard in Southern California

Cited by 34 publications

References 15 publications

Human-caused wildfire risk rating for prevention planning in Spain

Human-caused wildfire risk rating for prevention planning in Spain

Testing Separability in Spatial‐Temporal Marked Point Processes

Current approaches to modelling the spread of wildland fire: a review

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