Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

Abstract

This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of to acquire information about live and dead fuel in the Mongolia-China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. 'Leave one out' cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001-2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.

Mapping Grassland Wildfire Risk of the World

“…A multivariate logistic regression model was used to predict grassland burning probability (Cao et al 2013). Logistic regression is used in the condition of the dichotomous (i.e., binary) response variable.…”

A Low-cost Normalized Difference Vegetation Index (NDVI) Payload for Cubesats and Unmanned Aerial Vehicles (UAVs)

“…Wildfire risk assessment, also referred to as fire potential, is based on the probability that ignition will occur and the potential propagation of the fire, or fire hazard. The fuel moisture content (FMC) in both live and dead biomass is directly related to the probability of ignition [9]. Currently, scientists at the NASA Goddard Space Flight Center are systematically investigating the relationship between wildfire and the water-cycle in sub-Saharan Africa.…”

“…In nearly all cases, areas having evergreen forest cover, and showing EXTREME (>30%) die-off, corresponded with forest fires as catalogued by the United States Department of Agriculture Forest Service" [32]. MODIS data was also used in a research study performed on wildfire propagation in the Mongolia-China grasslands and the effects of live and dead fuel [9]. Data was gathered from 2001 to 2007 during the wildfire seasons of April to May and September to October.…”

“…"Fuel is one of the most significant variables in fire ignition and behavior. Therefore its type, moisture level, and load must be carefully analyzed" [9]. Wildfire propagation and the resulting burnt areas are influenced by several factors, such as the location of the fire origin, topography, weather, fuel conditions, and active firefighting.…”

“…Figure 18 shows the grasslands in north China and eastern Mongolia that were selected as the study area. This area covers approximately 1.40 million km 2 and is characterized by cold and dry winters and warm and humid summers [9]. .…”