the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving fire severity prediction in south-eastern Australia using vegetation specific information
Kang He
Xinyi Shen
Cory Merow
Efthymios Nikolopoulos
Rachael V. Gallagher
Feifei Yang
Emmanouil N. Anagnostou
Abstract. Wildfire is a critical ecological disturbance in terrestrial ecosystems. Australia, in particular, has experienced increasingly large and severe wildfires over the past two decades while globally fire risk is expected to increase significantly due to the projected increase in fire weather severity and drought condition. Therefore, understanding and predicting fire severity is critical for evaluating current and future impacts of wildfires on ecosystems. Here, we firstly introduce a vegetation-type specific fire severity classification applied on satellite imagery, which is further used to predict fire severity using antecedent drought conditions, fire weather, and topography of the fire season. 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: determination coefficient (R2), mean absolute error (MPE) 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 has the potential to be applicable for seasonal fire severity forecasting due to the availability of seasonal forecasts of the predictor variables.
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Kang He et al.
Status: open (extended)
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CC1: 'Comment on nhess-2023-69', Carolina Ojeda, 19 May 2023
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This article deals with fire severity prediction in south-eastern Australia using GIS analysis over vegetation indexes. Methodologically, this article is very well written and explained.
My comment is about the main title ¿how this method will improve the fire severity prediction in south-eastern Australia if the authors did not present what the existing systems are?
In the introduction they presented abundant evidence of previous studies done in the region, however, those articles do not address the issue of how in south-eastern Australia the government institutions or private landowners work to predict fire disturbances.
In the discussion, this issue was not addressed and the text was focused on the methodological advantages of this vegetation index over others.
My suggestion is to change the title, adapt it to present those advantages, and leave Australia as a test rather than a true study case.
Citation: https://doi.org/10.5194/nhess-2023-69-CC1 -
CC2: 'Reply on CC1', Kang He, 27 Sep 2023
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We appreciate your comments on the paper and suggestions on how to make our study more explicit. Currently, most fire severity prediction models are based on the relationship between fire severity and some strong correlated variables, such as weather, terrain. These studies typically ignore the variability of fire severity from other variables such as landcover, vegetation type and their interaction with climate. Even though there have been some studies focusing on classifying the fire severity level according to vegetation type, these studies have not been connected to fire severity prediction. In our study, we firstly examine how fire severity varies according to vegetation type based on forest fire events in southeastern Australia and then demonstrate through Machine Learning modeling that using the vegetation-specific fire severity classification prior to fire severity prediction could significantly improve the performance of fire severity prediction results. Following the reviewer comments, in the revised manuscript we plan to modify the introduction and discussion sections to better clarify the objectives and unique contributions of this study, and consider modifying our paper title to a more suitable title for the subject matter of this study.
Citation: https://doi.org/10.5194/nhess-2023-69-CC2
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CC2: 'Reply on CC1', Kang He, 27 Sep 2023
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Kang He et al.
Kang He et al.
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