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Title: Improving fire severity prediction in south-eastern Australia using vegetation-specific information
Abstract. Wildfire is a critical ecological disturbance in terrestrial ecosystems. Australia, in particular, has experienced increasingly large and severe wildfires over the past 2 decades, while globally fire risk is expected to increase significantly due to projected increases in extreme weather and drought conditions. Therefore, understanding and predicting fire severity is critical for evaluating current and future impacts of wildfires on ecosystems. Here, we first introduce a vegetation-type-specific fire severity classification applied to satellite imagery, which is further used to predict fire severity during the fire season (November to March) using antecedent drought conditions, fire weather (i.e. wind speed, air temperature, and atmospheric humidity), and topography. Compared to fire severity maps from the fire extent and severity mapping (FESM) dataset, we find that fire severity prediction results using the vegetation-type-specific thresholds show good performance in extreme- and high-severity classification, with accuracies of 0.64 and 0.76, respectively. Based on a “leave-one-out” cross-validation experiment, we demonstrate high accuracy for both the fire severity classification and the regression using a suite of performance metrics: the determination coefficient (R2), mean absolute error (MAE), and root-mean-square error (RMSE), which are 0.89, 0.05, and 0.07, respectively. Our results also show that the fire severity prediction results using the vegetation-type-specific thresholds could better capture the spatial patterns of fire severity and have the potential to be applicable for seasonal fire severity forecasts due to the availability of seasonal forecasts of the predictor variables.  more » « less
Award ID(s):
2243809
PAR ID:
10585205
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
EGU
Date Published:
Journal Name:
Natural Hazards and Earth System Sciences
Volume:
24
Issue:
10
ISSN:
1684-9981
Page Range / eLocation ID:
3337 to 3355
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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