Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires

Zhang, Z. X.; Zhang, H. Y.; Zhou, Daowei

doi:10.1016/j.jaridenv.2009.09.024

Cited by 52 publications

(35 citation statements)

References 44 publications

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“…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%

“…Temporal variables drive short-term models. High mean and maximum temperatures (Alonso-Betanzos et al 2003;Preisler et al 2004;Carvalho et al 2008Carvalho et al , 2010Vilar et al 2010;Magnussen and Taylor 2012;Bedia et al 2014;Karouni et al 2014;Najafabadi et al 2015), low precipitation (Albertson et al 2009;Vasilakos et al 2009;Zhang et al 2010;Plucinski et al 2014;Guo et al 2016a) and low relative humidity (AlonsoBetanzos et al 2003;Padilla and Vega-Garcia 2011;Chang et al 2013;Karouni et al 2014) favour fires and are often used in models. However, fire science has developed methods to estimate the decrease in moisture content caused by weather on litter and fine fuels, medium compact organic layers and deep organic soil layers or heavy fuels for fire danger rating (Dimitrakopoulos et al 2011).…”

Section: Predictors For Short-term Studiesmentioning

confidence: 99%

“…Precipitation in spring increases vegetation biomass, especially in fine fuels like grasses or shrubs, which may be available later to burn. Weather conditions favourable to fire occur mainly in summer (Albertson et al 2009;Badia et al 2011;Ager et al 2014), but also happen at other times of the year when fuels are dry, such as early or late winter in some regions (Maingi and Henry 2007;Reineking et al 2010;Zhang et al 2010). In Europe (Reineking et al 2010;Ganteaume et al 2013), for example, fires have two well-defined peaks, one higher in summer, and another lower in winter.…”

Section: Predictors For Long-term Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

138

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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: Spatialising Ignition Datamentioning

confidence: 99%

Section: Predictors For Short-term Studiesmentioning

confidence: 99%

Section: Predictors For Long-term Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

138

View full text Add to dashboard Cite

show abstract

“…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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“…Several studies on spatial and temporal distribution patterns of forest fires have been previously conducted (Larsen 1997;Pew and Larsen 2001;Wittenberg and Malkinson 2009;Yang et al 2009). Some scientists have studied or predicted the wildfire occurrence with the logistic regression or geographic information system (GIS) spatial analysis methods (Yang et al 2007;Bartlein et al 2008;Syphard et al 2008;Vadrevu and Badarinath 2009;Grala and Cooke 2010;Zhang et al 2010). In the past, Chinese researchers usually studied the patterns of forest or grass fires in one or several provinces under the basis of fire frequency, burned area and time of occurrence (Tian et al 2007;Yang et al 2009;Zhao et al 2009).…”

Section: Introductionmentioning

confidence: 99%