Abstract
ABSTRACTThis 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 2003–2013. The probability of occurrence of at least one fire on a 1 km2 grid cell in a 1,030,000 km2 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.
Highlights
Wildfire is a major environmental and ecological issue across the world
In the group of Moderate Resolution Imaging Spectroradiometer (MODIS) land cover, wildfires are most likely to occur on forests and savannas, while least likely to occur on shrublands and grasslands
We used logistic regression in combination with land cover, vegetation index, topographic and socioeconomic information to characterize the spatial pattern of the fire occurrence at least one fire on a 1 km2 grid in South-Eastern Australia over the period 2003À2013
Summary
Wildfire is a major environmental and ecological issue across the world. Wildfires can alter the structure of ecosystems, affect ecological processes and functions, threaten human lives and increase fire-suppression costs (Bowman et al 2009). Australia is recognized as one of the most flammable continents in the world. The limitation of resources and the requirement for quick responses demand accurate prediction of where fires will occur. Employing fire occurrence records within empirical models is essential to quantify the characteristics of fire activities to support planning and decision-making (Andrews & Finney 2007). These models can be used to identify fireprone areas and help forest managers target suppression efforts (Pew & Larsen 2001; Syphard et al 2008; Romero-Calcerrada et al 2010; Wang & Anderson 2010; Renard et al 2012)
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