Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data

Abstract

About 90% of the wildland fires occurred in Southern Europe are caused by human activities. In spite of these figures, the human factor hardly ever appears in the definition of operational fire risk systems due to the difficulty of characterising it. This paper describes two spatially explicit models that predict the probability of fire occurrence due to human causes for their integration into a comprehensive fire risk-mapping methodology. A logistic regression technique at 1 9 1 km grid resolution has been used to obtain these models in the region of Madrid, a highly populated area in the centre of Spain. Socio-economic data were used as predictive variables to spatially represent anthropogenic factors related to fire risk. Historical fire occurrence from 2000 to 2005 was used as the response variable. In order to analyse the effects of the spatial accuracy of the response variable on the model performance (significant variables and classification accuracy), two different models were defined. In the first model, fire ignition points (x, y coordinates) were used as response variable. This model was compared with another one (Kernel model) where the response variable was the density of ignition points and was obtained through a kernel density interpolation technique from fire ignition points randomly located within a 10 9 10 km grid, which is the standard spatial reference unit established by the Spanish Ministry of Environment, Rural and Marine Affairs to report fire location in the national official statistics. Validation of both models was accomplished using an independent set of fire ignition points (years 2006-2007). For the validation, we used the area under the curve (AUC) obtained by a receiver-operating system. The first model performs slightly better with a value of AUC of 0.70 as opposed to 0.67 for the Kernel model. Wildland-urban interface was selected by both models with high relative importance.

Human-caused fire occurrence modelling in perspective: a review

“…Outdoor recreational activities (Romero-Calcerrada et al 2008Vilar del Hoyo et al 2011) have been found to be associated with a higher probability of HCF ignitions. Proximity to campgrounds (Pew and Larsen 2001;Gonzalez-Olabarria et al 2011;Mann et al 2016) or fishing areas (Chang et al 2013;Sitanggang et al 2013) is often related to negligent or careless fires.…”

“…Urban, forest and agricultural land uses coexist and intermix in these anthropic landscapes, and interfaces between them seem to favour HCF occurrence in those models that have taken them into consideration (63 interface out of 230 land-use variables) (Vilar del Hoyo et al 2011;Faivre et al 2014;Duane et al 2015;Mishra et al 2016;Modugno et al 2016;Rodrigues et al 2016). Configuration metrics have not been applied as extensively (only 13 variables) as composition or land-cover variables, but fire-prone landscapes often present high fragmentation (Martínez et al 2009;Ruiz-Mirazo et al 2012;Martínez-Fernández et al 2013) and non-complex shapes linked to the artificial boundaries set by humans (Henry and Yool 2004;Gralewicz et al 2012b;Costafreda-Aumedes et al 2013).…”

“…The location of human activities is highly dependent on site-related variables that determine the number and distribution of human sources of ignition. Human presence can be analysed from explicit spatial factors such as proximity to, or density of, infrastructure such as roads (Dickson et al 2006;Yang et al 2008Yang et al , 2015Gralewicz et al 2012b;Hegeman et al 2014;Syphard and Keeley 2015;Zhang et al 2016;Mhawej et al 2016;Vilar et al 2016b), tracks (Pew and Larsen 2001;Romero-Calcerrada et al 2008, trails (Syphard et al 2008;Vilar del Hoyo et al 2011;Arndt et al 2013) and railways (Sturtevant and Cleland 2007;Guo et al 2015;Alcasena et al 2016), all of which are associated with an increase in fire occurrence. For example, in Spain (MAGRAMA 2015), the United States (Morrison 2007) and south-eastern Australia (Penman et al 2013), more than half of HCFs start along road systems.…”

A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece

“…The ordinal recovery classes (Table 1) were expressed as binary variables by attributing the value "1" if pine woodland had recovered to its pre-fire state and the value "0" if not. Binary logistic regression was then performed to predict the value of a response variable (reference category "recovered" in this study) from the values of a set of environmental explanatory variables [47,55,56]. A regression analysis was performed by testing the best model applying the BIC criterion in the "bestglm" package in R [52] including the following continuous variables as predictors: "altitude," "slope," "aspect," "solar radiation," "distance from unburned sites," "distance from unburned P. brutia forest," and the categorical variables "soil depth," "bedrock type,"…”

Spatial point process modeling applied to the assessment of risk factors associated with forest wildfires incidence in Castellón, Spain

“…In Spain, the increase in forest fires incidence is partly explained by climate change as well as socio-economic transformation in rural areas. Climate change has resulted in higher mean temperature and lower relative humidity, whilst socio-economic change has lead to the abandonment of farms, resulting in an increase and an unusual accumulation of forest fuels (Villar del Hoyo et al, 2011). The accumulation of forest fuels and the higher temperature can potentially lead to the outbreak of wildfires.…”