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.

Highlights

  • Fire is a fundamental process for maintaining diversity and health in Australian ecosystems [1,2].uncontrolled bushfires can pose a significant risk to human life and property [3,4], and result in substantial economic losses and environmental impacts [5]

  • Quantifying and characterising vegetation as fuel is essential to understanding fire behaviour [7], as fuel is the only factor influencing fire behaviour that can be manipulated by land managers [8]

  • This study has demonstrated the utility of image-based point clouds for consistently estimating surface and near-surface fuel attributes within lowland forest

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Summary

Introduction

Fire is a fundamental process for maintaining diversity and health in Australian ecosystems [1,2].uncontrolled bushfires can pose a significant risk to human life and property [3,4], and result in substantial economic losses and environmental impacts [5]. Fire risk is strongly related to properties of vegetation, such as the quantity, structure and orientation, the portion of live and dead biomass, and moisture content [6]. Quantifying and characterising vegetation as fuel is essential to understanding fire behaviour [7], as fuel is the only factor influencing fire behaviour that can be manipulated by land managers [8]. Understanding fuel helps to inform a wide range of fire management activities such as assessing bushfire risk, planning fuel treatments, and managing smoke emissions [8,9,10,11,12]. Classifying and quantifying fuel is complex and includes assessing a range of characteristics, such as fine fuel load, live-to-dead ratio, height, bulk density, vertical and horizontal connectivity, and the quantity of individual fuel layers [11,13]. Fine fuel load (

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