A Voxel-Based Method to Estimate Near-Surface and Elevated Fuel From Dense Lidar Point Cloud for Hazard Reduction Burning

Abstract

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.

Evaluation of Handheld Mobile Laser Scanner Systems for the Definition of Fuel Types in Structurally Complex Mediterranean Forest Stands

“…They lacked information related to the presence of different vegetation parts, such as foliage, branches, trunks, or bark, which are relevant for wildfire considerations. In this context, some studies have attempted to categorize voxels according to their class to enhance fuel quantification (e.g., [59,60]). However, this can be a complex task in forest environments of very high structural heterogeneity, where different fuel classes are intermingled.…”

“…Estimation of the fuel load was performed by calculating the volume of the normalized point clouds. For this purpose, a voxelization process was conducted, which has been reported as a well-suited approach for estimating forest fuels (e.g., [59][60][61][62]) and allows for simplifying the huge amount of data coming from ground-based LiDAR systems [63][64][65][66][67]. In doing so, the effect of uneven point distributions, many of which tend to be located closer to the sensor, is normalized [64,65].…”

Quantitative Analysis Method of the Organizational Characteristics and Typical Types of Landscape Spatial Sequences Applied with a 3D Point Cloud Model

“…At present, the application of point cloud models in the landscape field has focused mainly on the visualization and presentation of 3D space and recording and identifying morphological characteristics [27][28][29][30] that correspond to the site survey and information acquisition stage in the early stages of design. Several researchers have also relied on 3D point cloud models to carry out spatial quantitative analysis, such as 3D green quantity analysis [31,32], visibility analysis [33,34], and climate simulation analysis [35][36][37][38]. There are also studies trying to use 3D point clouds to achieve 3D visual intelligent detection and analysis [39].…”

Influence of Voxel Size and Voxel Connectivity on the 3d Modelling of Australian Heathland Parameters

“…They are increasingly applied in three-dimensional city modelling and analysis (Gorte et al, 2019;Aleksandrov et al, 2019). They are also becoming favourable in three-dimensional vegetation modelling (Barton et al, 2020;Gorte and Pfeifer, 2004;Hancock et al, 2017;Homainejad et al, 2022a).…”