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Sensors (Basel). 2017 Apr 20;17(4). pii: E910. doi: 10.3390/s17040910.

Investigating Surface and Near-Surface Bushfire Fuel Attributes: A Comparison between Visual Assessments and Image-Based Point Clouds.

Author information

1
School of Science, RMIT University, Melbourne 3001, Australia. christine.spits@gmail.com.
2
School of Science, RMIT University, Melbourne 3001, Australia. luke.wallace2@rmit.edu.au.
3
Bushfire and Natural Hazards Cooperative Research Centre, East Melbourne 3002, Australia. luke.wallace2@rmit.edu.au.
4
School of Science, RMIT University, Melbourne 3001, Australia. karin.reinke@rmit.edu.au.
5
Bushfire and Natural Hazards Cooperative Research Centre, East Melbourne 3002, Australia. karin.reinke@rmit.edu.au.

Abstract

Visual assessment, following guides such as the Overall Fuel Hazard Assessment Guide (OFHAG), is a common approach for assessing the structure and hazard of varying bushfire fuel layers. Visual assessments can be vulnerable to imprecision due to subjectivity between assessors, while emerging techniques such as image-based point clouds can offer land managers potentially more repeatable descriptions of fuel structure. This study compared the variability of estimates of surface and near-surface fuel attributes generated by eight assessment teams using the OFHAG and Fuels3D, a smartphone method utilising image-based point clouds, within three assessment plots in an Australian lowland forest. Surface fuel hazard scores derived from underpinning attributes were also assessed. Overall, this study found considerable variability between teams on most visually assessed variables, resulting in inconsistent hazard scores. Variability was observed within point cloud estimates but was, however, on average two to eight times less than that seen in visual estimates, indicating greater consistency and repeatability of this method. It is proposed that while variability within the Fuels3D method may be overcome through improved methods and equipment, inconsistencies in the OFHAG are likely due to the inherent subjectivity between assessors, which may be more difficult to overcome. This study demonstrates the capability of the Fuels3D method to efficiently and consistently collect data on fuel hazard and structure, and, as such, this method shows potential for use in fire management practices where accurate and reliable data is essential.

KEYWORDS:

fuel structure; image-based point clouds; visual assessment

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