Modelling spatial patterns of wildfire occurrence in South-Eastern Australia

Abstract

This paper describes the development and validation of spatial models for wildfire occurrence at a broad landscape scale. The hotspots databases from the Moderate Resolution Imaging Spectroradiometer (MODIS) and logistic regression models are investigated for the comprehensive understanding of environmental and socioeconomic determinants regulating the spatial distribution of wildfires over the 11-year period 2003À2013. The probability of occurrence of at least one fire on a 1 km 2 grid cell in a 1,030,000 km 2 region located in South-Eastern Australia is studied for the prediction of future fire occurrence. Our research shows that wildfires are most likely to occur in mountainous areas, forests, savannas and lands with high vegetation coverage, and are less likely to occur on grasslands and shrublands. Wildfires also tend to occur in areas near human infrastructures. Environmental variables are strong individual predictors of fire occurrence while socioeconomic variables contribute more to the final model. The influence of environmental and socioeconomic conditions on wildfire occurrence and the spatial patterns of wildfires identified in this study can assist fire managers in implementing appropriate management actions in South-Eastern Australia. This paper also demonstrates the potential of applying the MODIS active fire product in wildfire occurrence studies.

Human-caused fire occurrence modelling in perspective: a review

“…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.…”

“…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.…”

Improving Fire Behaviour Data Obtained from Wildfires

“…Firstly, as new satellites are launched the quality and quantity of data available will increase. In Australia, research and management have both used the Advanced Very High Resolution Radiometer (AVHRR) imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra (1999) and Aqua (2002) [74]. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia.…”

Modeling the spatial patterns of human wildfire ignition in Yunnan province, China

“…Humans usually have an extent of mobility and a geographic range, which is largely determined by the infrastructure and settlements. Consequently, previous work associated with human activity has commonly utilized land cover, distance or proximity to roads, settlements or other infrastructure as straight distance for buffer analyses (Fusco et al, 2016;Gralewicz et al, 2012a;Hawbaker et al, 2013;Kwak et al, 2012;Maingi & Henry, 2007;Romero-Calcerrada et al, 2010;Zhang et al, 2016). However, the effects of different factors on fire occurrence can vary among ecosystems and across spatial scales (Catry et al, 2010).…”

“…Consequently, an improved understanding of wildfire risk should address the patterns of human activity and its relation to fire ignition (Dickson et al, 2006;Narayanaraj & Wimberly, 2012;Prestemon, Pye, Butry, Holmes, & Mercer, 2002). Significant research effort has been undertaken to explore the relationship between wildfire and its causative factors with the goal of building predictive models (Cardille et al, 2001; Chas-Amil, Prestemon, McClean, & Touza, 2015;Maingi & Henry, 2007;Narayanaraj & Wimberly, 2012;Romero-Calcerrada, BarrioParra, Millington, & Novillo, 2010;Román-Cuesta et al, 2003;Salis et al, 2013;Syphard et al, 2007;Watts & Hall, 2016;Ye, Wang, Guo, & Li, 2017), and has concluded that wildfire tends to occur in areas near human infrastructure on the human-wildland interface (Zhang, Lim, & Sharples, 2016), and frequently exhibits nonlinear relationships (Hawbaker et al, 2013). However, previous studies mainly concentrate on overall wildfire risk integrating numerous factors simultaneously, yet the importance of human factors on ignition has not received much attention.…”