Full description
Increases in consecutive days that meteorological, climatic and biophysical variables combine to influence fire is projecting an increase in fire size and intensity. This prompts investigation into the severity of recent fires to gain a better understanding of the factors that influence fire severity. Using a highly accurate spectral index, a geospatial analysis was undertaken to identify key variables that control the variability in fire severity of the 2021 Wooroloo Bushfire. Random Forest regression and General Additive Model (GAM) were utilised to determine variable importance in modelling fire severity and to create parsimonious models. Elevation and the Soil Dryness Index (SDI) were the two most influential factors in explaining the variability of the fire. Whilst a good variable importance was achieved, the parsimonious Random Forest and GAM models performed poorly, managing to explain 14% of variance (RF) and 17% of deviance (GAM). This work supports reports that the Wooroloo Bushfire was severe, unpredictable, and difficult to control. Further testing into fire behaviour variable interaction and the influence of fire on fire severity should be conducted to determine important variables in modelling fire severity.
Notes
The data sheet provided contains the random point location extracted numerical data from a variety of environmental, meteorological and fire-related dataset. Together with this data set is the statistical analysis files used to implement the Random Forest Regression, Generalised Additive Models, and Principal Component Analysis in R.
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- DOI : 10.25958/12KX-E775
