What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

Abstract

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

“…Similar negative correlation between distance to railway and fire frequency was reported in the Upper Midwest states and Missouri and boreal forest in China [50][51][52]. On the contrary, Guo et al [53] reported a positive relationship between fire and distance to railway in subtropical forest in China and [3] reported both positive and negative relationships with spatial distribution in the study area. This discrepancy reflects that the effects of distance to railway on fire occurrences are site-specific.…”

Human-caused fire occurrence modelling in perspective: a review

“…Wildfire occurrence has been found to relate to social level (Mercer and Prestemon 2005;Vadrevu et al 2006;Oliveira et al 2012;Dondo Bühler et al 2013;Chas-Amil et al 2015), poverty levels (Dondo Bühler et al 2013), gross domestic product per capita (Chuvieco et al 2008;Guo et al 2016aGuo et al , 2016bGuo et al , 2016c, unemployment (Mercer and Prestemon 2005;Prestemon and Butry 2005;Martínez et al 2009;Oliveira et al 2012;Dondo Bühler et al 2013;Chas-Amil et al 2015;Nunes et al 2016), age (Koutsias et al 2010;Martínez-Fernández et al 2013;Nunes et al 2016) or literacy level (Vadrevu et al 2006). Law enforcement as a preventive factor has been included in the models from two perspectives: as police presence (Mercer and Prestemon 2005;Prestemon and Butry 2005), which discourages arson before it happens; and as the number of prosecutions and convictions after it has happened (Donoghue and Main 1985).…”

Modeling the Influence of Eucalypt Plantation on Wildfire Occurrence in the Brazilian Savanna Biome

“…Accordingly, RF showed a higher predictive ability than traditional modeling methods. Guo [39] applied Logistic Regression and RF to evaluate drivers of fire occurrence on a provincial scale in China. The RF model was able to identify significant driving factors and demonstrated a higher predictive ability than logistic regression on a regional scale.…”

“…Random Forest (RF) has been extensively used to model wildfire occurrence for large datasets. RF is a tree-based machine learning algorithm able to explore complex relationships among covariates [36], attaining high predictive performance in data mining while being able to capture fine-grained spatial patterns compared with other modeling methods [37][38][39][40]. Rodrigues and de la Riva [37] compared RF with…”