Volume Estimation of Fuel Load for Hazard Reduction Burning: First Results to a Voxel Approach

Eusuf, M. S. R. S.; Barton, James D.; Gorte, B.G.H.; Zlatanova, S.

doi:10.5194/isprs-archives-xliii-b3-2020-1199-2020

Cited by 8 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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].…”

Section: Voxelization and Fuel Load Quantificationmentioning

confidence: 99%

See 1 more Smart Citation

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

Hoffrén,

Lamelas,

de la Riva

2024

Fire

View full text Add to dashboard Cite

The exposure of Mediterranean forests to large wildfires requires mechanisms to prevent and mitigate their negative effects on the territory and ecosystems. Fuel models synthesize the complexity and heterogeneity of forest fuels and allow for the understanding and modeling of fire behavior. However, it is sometimes challenging to define the fuel type in a structurally heterogeneous forest stand due to the mixture of characteristics from the different types and limitations of qualitative field observations and passive and active airborne remote sensing. This can impact the performance of classification models that rely on the in situ identification of fuel types as the ground truth, which can lead to a mistaken prediction of fuel types over larger areas in fire prediction models. In this study, a handheld mobile laser scanner (HMLS) system was used to assess its capability to define Prometheus fuel types in 43 forest plots in Aragón (NE Spain). The HMLS system captured the vertical and horizontal distribution of fuel at an extremely high resolution to derive high-density three-dimensional point clouds (average: 63,148 points/m2), which were discretized into voxels of 0.05 m3. The total number of voxels in each 5 cm height stratum was calculated to quantify the fuel volume in each stratum, providing the vertical distribution of fuels (m3/m2) for each plot at a centimetric scale. Additionally, the fuel volume was computed for each Prometheus height stratum (0.60, 2, and 4 m) in each plot. The Prometheus fuel types were satisfactorily identified in each plot and were compared with the fuel types estimated in the field. This led to the modification of the ground truth in 10 out of the 43 plots, resulting in errors being found in the field estimation between types FT2–FT3, FT5–FT6, and FT6–FT7. These results demonstrate the ability of the HMLS systems to capture fuel heterogeneity at centimetric scales for the definition of fuel types in the field in Mediterranean forests, making them powerful tools for fuel mapping, fire modeling, and ultimately for improving wildfire prevention and forest management.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Voxelization and Fuel Load Quantificationmentioning

confidence: 99%

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

Hoffrén,

Lamelas,

de la Riva

2024

Fire

View full text Add to dashboard Cite

show abstract

“…Similar conditions are emerging in Asia and the Americas as extended drier and hotter summer seasons produce increased volumes of dry understory fuels [23,24]. Most recent studies have concluded that this trend will continue [14,25].…”

Section: Introductionmentioning

confidence: 81%

A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis

Penglase,

Lewis,

Srivastava

2023

Remote Sensing

View full text Add to dashboard Cite

Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation’s environment. Juvenile Pinus species have a low fire tolerance. With Australia’s history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations.

show abstract

“…The influence of voxel size on the precision of canopy heigh was noted and a voxel size of 18 m was identified as the most fitting voxel size for this work. While Eusuf et al (2020) presented an automated method for measuring fuel load in a multi-layered forest in Newcastle, Australia. In this work a voxel size of 0.4 m was selected for the representation of the fuel load and classification of the near-surface fuel.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Homainejad

Zlatanova²,

Sepasgozar³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Point clouds acquired through laser scanning techniques are applied in the three-dimensional modelling of vegetation. They provide the three-dimensional coordinates of geometric surfaces with attributes. However, raw point clouds are unstructured and do not provide semantic, geometric, or topological information about an object. Voxelisation is a method for structuring point clouds. It is a generalisation of point clouds and therefore the voxel size and the voxel neighbourhood play a critical role in the processing. This research explores the influence of voxelisation of point clouds acquired of heathland in Australia and how it influences the three-dimensional modelling and the representation of important heathland structure using different voxel sizes and voxel connectivities. Voxel sizes of 0.4 m, 0.6 m, 1.0 m, 1.2 m and 1.6 m with a voxel neighbourhood connectivity of 6, 18 and 26 are examined for three-dimensional modelling and segmentation of heathland vegetation in Australia. The results indicate that the choice of voxel size and the voxel connectivity influence the representation of important heathland parameters. A smaller voxel size of 0.4 m provides a detailed representation of mallee structure while the the processing time is longer compared to a larger voxel size. While a larger voxel size produces blobs while the processing speed is shorter. The results from the voxel neighbourhood connectivity represent a stronger voxel connectivity of 26-connected voxels suitable for heathland modelling rather than a 6-connected voxels.

show abstract

Volume Estimation of Fuel Load for Hazard Reduction Burning: First Results to a Voxel Approach

Cited by 8 publications

References 14 publications

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

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

A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis

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

Contact Info

Product

Resources

About