Evaluation of fire danger rating indexes using logistic regression and percentile analysis

Andrews, Patricia L.; Loftsgaarden, Don O.; Bradshaw, Larry

doi:10.1071/wf02059

Cited by 153 publications

(148 citation statements)

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“…Fire danger is 'a general term used to express an assessment of both fixed and variable factors of the fire environment that determine the ease of ignition, rate of spread, difficulty of control, and fire impact; often expressed as an index' (FAO 2005 Keetch et al 1968). Of these, we found a very frequent use of FFMC (Cunningham and Martell 1973;Martell et al 1989;Vega-Garcia et al 1995;Wotton et al 2010;Lee et al 2012;Boulanger et al 2014;Li et al 2014;Beccari et al 2015), DC (Wotton et al 2003(Wotton et al , 2010Carvalho et al 2008;Drever et al 2009;Bedia et al 2014) and DMC (Martell et al 1987;Todd and Kourtz 1991;Wotton et al 2003;Magnussen and Taylor 2012), FWI (Haines et al 1983;Martell et al 1987;Vega-Garcia et al 1996;Carvalho et al 2010;Reineking et al 2010;Ager et al 2014;Beccari et al 2015;Papakosta and Straub 2016), ISI (Haines et al 1970(Haines et al , 1983Vega-Garcia et al 1995;Li et al 2014), FFDI (Bradstock et al 2009;Penman et al 2013;Plucinski 2014;Plucinski et al 2014), KBDI (Prestemon and Butry 2005) and ERC (Andrews et al 2003).…”

Section: Predictors For Short-term Studiesmentioning

confidence: 79%

“…Accepting that fires are rare events and that rarely more than one fire takes place in the temporal and spatial unit under study allows a binary dependent variable to be used. Fire occurrence can be modelled as absence or presence of fire (coded 0 or 1), and most research papers have focused on this binary prediction of wildfires (Andrews et al 2003;Reineking et al 2010;Zhang et al 2010Zhang et al , 2016Arndt et al 2013;Pan et al 2016). Many HCF occurrence models are probabilistic; their output is the probability that 'at least one fire occurs', ranging from 0 to 1.…”

Section: Spatialising Ignition Datamentioning

confidence: 99%

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Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

133

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Abstract. The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.

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Section: Predictors For Short-term Studiesmentioning

confidence: 79%

Section: Spatialising Ignition Datamentioning

confidence: 99%

Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

133

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“…Mandallaz and Ye (1997a,b) and Viegas et al (1999) assessed the performance of various European and Canadian fire danger indices in predicting wildfire activity in southern Europe and the former found that using weather variables and other covariates in addition to a fire danger index could produce substantial improvement in prediction of wildfires. Andrews et al (2003) evaluated the US NFDRS indices and fire activity in the Tonto National Forest in Arizona, finding that a logistic function of ERC predicts wildfire incidence better than a logistic function of BI.…”

Section: Examined the Nfdrs Ignition Component And Spreadmentioning

confidence: 99%

A critical assessment of the Burning Index in Los Angeles County, California

Schoenberg

Chang

Keeley

et al. 2007

Int. J. Wildland Fire

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The Burning Index (BI) is commonly used as a predictor of wildfire activity. An examination of data on the BI and wildfires in Los Angeles County, California from January 1976 to December 2000 reveals that although the BI is positively associated with wildfire occurrence, its predictive value is quite limited. Wind speed alone has a higher correlation with burn area than BI, for instance, and a simple alternative point process model using wind speed, relative humidity, precipitation and temperature well outperforms the BI in terms of predictive power. The BI is generally far too high in Winter and too low in Fall, and may exaggerate the impact of individual variables such as wind speed or temperature during times when other variables, such as precipitation or relative humidity, render the environment ill-suited for wildfires.

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“…Logistic regression is one of the most frequently used methods (Andrews et al 2003, Vasconcelos et al 2001, Chuvieco et al 2003, Amatulli et al 2007, Brosofske et al 2007, Zhang et al 2010. It has been used to develop regional models with a large spatial extent (Chuvieco et al 1999, Martinez et al 2009) as well as for developing models on a local scale (Vega-Garcia et al 1995, Vasconcelos et al 2001.…”

Section: Parameter and Model Selectionmentioning

confidence: 99%

Modeling human-caused forest fire ignition for assessing forest fire danger in Austria

Arndt¹,

Vacik²,

Koch³

et al. 2013

iForest

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© iForest -Biogeosciences and Forestry IntroductionFire danger is generally understood as the likelihood of a fire to occur (Chuvieco & Congalton 1989). In fire danger assessments the evaluation of the chances of fire ignition is generally done by identifying the contributing factors and their integration into an index quantifying the level of danger (Chuvieco et al. 2003, Sebastián-López et al. 2008. For the probability of a fire to occur, two agents are identified: natural (predominantly lightning) and anthropogenic causes, which are mainly related to human activities. In this context, the probability of human-caused fire ignition is the result of the direct or indirect presence of human activity in the landscape (Martinez et al. 2009). International studies indicate that roughly 90% of forest fires are human-caused, whereas only a small percentage of forest fires have natural causes, i.e., lightning (Cardille et al. 2001, Grissino-Mayer et al. 2004, Mollicone et al. 2006, Vacik et al. 2011. In Europe, human activities account for the majority of fire ignition (Leone et al. 2002, Catry et al. 2009, Martinez et al. 2009).Various goods and services provided by forests such as water supply, carbon sinks, recreation and protection services are most likely to be impacted by wildfires (Wotton et al. 2003, Grissino-Mayer et al. 2004, Brown et al. 2004, Fried et al. 2004, Catry et al. 2009, Dumas et al. 2008, Weibel et al. 2009). Especially in the densely populated European mountain forests the danger of fire ignition is of high significance for the maintenance of its ecosystem goods and services as these ecosystems are very sensitive to environmental changes (Steininger & WeckHannemann 2002, Lindner et al. 2010. Additionally the economic value of goods and services of the alpine region experiences an increased recognition especially through sporting activities and outdoor recreation (Hall & Page 2009). Furthermore, the alpine region is crossed by important transit and trade routes. They aid in promoting tourism in the mountainous regions across Europe (Gambino & Romano 2003, Nordregio 2004, Brauchle 2006, which is leading to an increased development of touristic infrastructure besides the extensive use by naturebased tourism (Gambino & Romano 2003, Heinrichs et al. 2010). The herewith associated rise in pressure on forests through a growing number of tourists is potentially increasing danger of fire ignition. The increasing significance of transportation for the supply of living goods as well as for the provision of access to services potentially affects fire ignition as well. Several international studies have shown the significance of the distance of forest fires to roads, settlements and infrastructure, or specific land uses or even its abandonment as predisposition for fire ignition (Vega-Garcia et al. 1995, Goldammer 2003, Kalabokidis et al. 2002, Catry et al. 2009, Martinez et al. 2009).Although human factors are relatively important when analyzing forest fire ignition, little attention has been paid to their s...

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Evaluation of fire danger rating indexes using logistic regression and percentile analysis

Cited by 153 publications

References 0 publications

Human-caused fire occurrence modelling in perspective: a review

Human-caused fire occurrence modelling in perspective: a review

A critical assessment of the Burning Index in Los Angeles County, California

Modeling human-caused forest fire ignition for assessing forest fire danger in Austria

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