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Abstract. Drastic changes in the climate has revised the face of disaster management: it is contributing to abnormal intensity, frequency and duration of extreme weather and climate events. The year 2020 started with more than 100 fires burning across Australia. Hazard reduction burning has become a resolute and primary land management technique that contribute to the reduction of bushfire severity. One of the key variables to consider for this application is fuel load, as the accumulation of vegetation in a forest profile affects the intensity of the burn. Conventionally, fuel loads are measured by manually cutting the vegetation and physically measuring the quantity after dry heating. This process is expensive, and time consuming. There is an opportunity for these techniques to be digitised and automated to give results in a timely manner and work as a decision support tool for practitioners. This paper proposes a voxel-based approach that can be used for estimating fuel load and percentage cover of the vegetation, at the elevated and near-surface fuel/vegetation layer as a method to augment manual estimation. We use an airborne LiDAR pointcloud dataset of Vermont Place Park, Newcastle, Australia to test the method. The preliminary inspection of the results confirms the technique that can approximate conventional manual method. Next steps include performance testing including more dataset to derive quantitative measures on the approach.

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Abstract. In 2019/20 over 100 severe bushfires burned across the continent of Australia. The severity of these fires was exacerbated by many factors, including macroclimatic effects of global warming and, at the meso and micro scales, land management practices. The bushfire phenomenon cannot be stopped, however better management practices can help counter the increasing severity of fires. Hazard reduction burning is a method where certain vegetation is deliberately burned under controlled circumstances to thin the fuel to reduce the severity of bushfires. Fuel load is an important parameter to assess when hazard reduction burning, as the accumulation of vegetation in a forest profile affects the intensity of the burn. Conventional methods of measuring fuel load are time consuming and costly, and therefore it becomes increasingly important to investigate automated approaches for assessing fuel loads. This paper provides an overview of hazard reduction burning while explaining the methods to quantify fuel load. Then the paper presents our voxel approach in estimating the volume of fuel loads. The first results regarding different voxel resolutions are reported and analysed. This paper concludes with future steps and developments.

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