A VOXEL-BASED METHOD TO ESTIMATE NEAR-SURFACE AND ELEVATED FUEL FROM DENSE LIDAR POINT CLOUD FOR HAZARD REDUCTION BURNING

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.