Mapping the probability of large fire occurrence in northern Arizona, USA

Dickson, Brett G.; Prather, John W.; Xu, Yaguang; Hampton, Haydee M.; Aumack, Ethan N.; Sisk, Thomas D.

doi:10.1007/s10980-005-5475-x

Cited by 111 publications

(82 citation statements)

References 38 publications

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“…Overall, from the Canadian boreal forest to the mixed chaparral and conifer forest of southern California, fire ignitions are often spatially clustered and the probability of human-caused wildfires decreases with an increase in the distance from human infrastructures, where more fires occur at the wildland-urban interfaces than in the backcountry (Chou et al 1993;Vega-García et al 1993;Cardille et al 2001;Zhai et al 2003;Syphard et al 2007aSyphard et al , b, 2008Pew and Larsen 2001;Pyne 2001;Keeley et al 2004;Yang et al 2007;Bar Massada et al 2009;Calef et al 2009). However, in places where humans do not cause most fires, the probability of occurrence was greater in areas where road density was lower (Dickson et al 2006). The same trend occurs in Russia, where the increasing exploitation of natural resources entails a high number of people per se, and an expanded road network that enables access to remote areas (Zakharov 1977;Noga and Tikhonov 1979;Sofronov and Vakurov 1981;Odintsov 1995;Sergienko 1996Sergienko , 1999Furyaev 1996;Karpachevskiy 2004;Achard et al 2008); fire occurrence…”

Section: Comparison With Countries Outside Europementioning

confidence: 97%

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A Review of the Main Driving Factors of Forest Fire Ignition Over Europe

Ganteaume¹,

Camia

Jappiot³

et al. 2012

Environmental Management

481

248

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Section: Comparison With Countries Outside Europementioning

confidence: 97%

“…(Dickson et al 2006). In the temperate rainforest on Vancouver Island (Canada), recreation and miscellaneous related fires tend to be significantly smaller than logging fires (Pew and Larsen 2001) and drivers of agricultural fires (mostly human-caused) differ from those of forest fires (Miranda et al 2012).…”

Section: Comparison With Countries Outside Europementioning

confidence: 99%

A Review of the Main Driving Factors of Forest Fire Ignition Over Europe

Ganteaume¹,

Camia

Jappiot³

et al. 2012

Environmental Management

481

248

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“…The same conclusions about how topography reflects the locations of human activities in relation to fire ignitions are indicated for a region of China by Xu et al (2006), showing that the anthropogenic factors are closely related to fires when altitudes of forests are lower than 900 m. However, at higher elevations their influence is much lower. In other studies the topography effect has been related with fires, using variables related with roughness or terrain shape index (Dickson et al, 2006, Nunes, 2012Padilla and Vega-Garcia, 2011;Narayanaraj and Wimberly, 2012).…”

Section: Driving Factors Of Long-term Fire Densitymentioning

confidence: 99%

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

Fernández¹,

Chuvieco

Koutsias³

2013

Nat. Hazards Earth Syst. Sci.

130

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Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983–2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character

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“…However, weighting the inputs is major problem in such kinds of techniques because of subjective ranking (Jaiswal et al 2002). Weight of evidence analyses can be used for weighting the input variables according to fire occurrence and location to solve this problem (Dickson et al 2006). This analysis needs categorical inputs to determine the weights for each category.…”

Section: Introductionmentioning

confidence: 99%

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

183

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Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R D ¡0.43), tree cover (R D 0.93) and temperature (R D 0.42) were strongly correlated with forest fire probability in the USB region. KEYWORDSForest fire probability and hazard; landscape feature; weighting; artificial neural network; Mediterranean region; fire weather index

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Mapping the probability of large fire occurrence in northern Arizona, USA

Cited by 111 publications

References 38 publications

A Review of the Main Driving Factors of Forest Fire Ignition Over Europe

A Review of the Main Driving Factors of Forest Fire Ignition Over Europe

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

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