Utilizing The Unexplored Potential of Remote Sensing Derived Fire Characteristics in Fire Danger Modeling
Creators: name: Shah, Sami
Publisher: name: The Australian National University
Year: 2024

description: Remote sensing data possesses a significant potential to improve the existing fire danger models and develop remote sensing based fire danger components (fire spread and intensity) that could be integrated to develop composite fire danger index (CFDI). This thesis aimed to explore the unexplored potential of the remote sensing-derived fire characteristics for improving the existing fire danger models and developing remote sensing-based fire spread and intensity components using machine learning algorithms for Australia. The thesis integrated these components using GIS to develop CFDI. McArthur's fire danger indices (FDIs) forecast fire danger in six fire danger classes. These classes are linked with the rate of fire spread which does not exist at a continental scale across Australia due to its measurement challenges, which hinder further improvements of the fire danger classes. In contrast, the satellite-derived burned area is continuously available. The first chapter investigated the relationship between the McArthur fire danger classes and remote sensing derived burned area, which could provide a robust quantifiable metric for the fire danger classes. A negative binomial regression model was used which presented a positive relationship between the fire danger classes from Low Moderate to Very high in the inland regions of mainland Australia only. The burned area was not a well-correlated metric for the McArthur fire danger classes especially elevated classes. This lack of relationship was attributed to many inadequacies in the McArthur FDIs and the satellite derived burned area metric. Given the satellite-derived metric (burned area) did not correlate well with the McArthur FDIs, we used satellite-derived fire sizes and fire radiative power (FRP) to develop a new CFDI for Australia. The second chapter explored the potential of satellite-derived fire sizes as a scalable target variable proxy for developing the fire spread component of fire danger. A whole range of fire sizes could present a broader range of fire danger conditions that drive those fire sizes. We extracted satellite-derived fire sizes and corresponding ten predictors from 2001 to 2020 for six regional zones based on Koppen-Geiger's broad vegetation classification for Australia. Random forest and logistic regression were trained with 2001 to 2016 data for each zone and tested with 2017 to 2020 data. Random Forest demonstrated the best performance to predict the fire size probabilities and predicted correspondingly high probabilities for the increasing fire sizes. After developing the fire spread component, we developed the fire intensity component based on satellite-derived FRP (using RF) in the same way we developed fire size models in chapter 2. We then integrated the fire spread and intensity components using GIS to develop CFDI for Australia. We developed CFDI using the cartesian coordinates and simple average approaches. The CFDI demonstrated improved probabilities for larger fire sizes and reduced fire counts for lower probability categories. The CFDI did not show improvement for the FRP values. Future research can generate improved fire intensity components and CFDI by developing and incorporating grid-based fuel loads data and integration techniques based on fire behavior processes. The fourth chapter compared the performances of the fire danger components (fire spread and intensity), CFDI, normalized FFDI, and the operationally used FFDI against house loss data in various wildfire events across Australia. The fire danger components, CFDI, and normalized FFDI demonstrated the best performance in characterizing house loss. However, despite better performance, the machine learning models need calibration to suppression difficulty before operational application. However, the suppression difficulty data does not exist in quantifiable form and developing such data through future research will help pave their operational application.