Linking wildland fuel combustibility, emission factors, biochemistry and fire behaviour using imaging spectroscopy

Remote sensing to quantify attributes of vegetation necessary for predicting regional-scale wildland fire danger lags significantly behind other applications in the wildfire management domain, largely due to insufficient connection between what is remotely sensed and what attributes are important for fire behaviour. To address this gap, we introduce and test an experimental methodology that integrates imaging spectroscopy with laboratory-based fire behaviour experiments, biochemical and calorimetry measurements, and greenhouse gas and particulate emissions sampling to link fuel bed spectra with fire behaviour, burn severity and fuel consumption. In a pilot study, three fuel types (eucalypt canopy branchlets, eucalypt litter, and annual ryegrass) were treated to produce two levels of combustibility (high, low) and burned by free-spreading fire under controlled laboratory conditions. Pre- and post-burn hyperspectral images (400–2500 nm) were collected during 26 fire experiments, alongside measurements of fuel moisture content, combustion efficiency, rate of spread, biochemistry, calorimetry, and emissions of CO2, CO, CH4, PM2.5. This dataset sumarizes this information.
Type
collection
Title
Linking wildland fuel combustibility, emission factors, biochemistry and fire behaviour using imaging spectroscopy
Collection Type
Dataset
Access Privileges
Fenner School of Environment & Society
Metadata Language
English
Data Language
English
Full Description
Remote sensing to quantify attributes of vegetation necessary for predicting regional-scale wildland fire danger lags significantly behind other applications in the wildfire management domain, largely due to insufficient connection between what is remotely sensed and what attributes are important for fire behaviour. To address this gap, we introduce and test an experimental methodology that integrates imaging spectroscopy with laboratory-based fire behaviour experiments, biochemical and calorimetry measurements, and greenhouse gas and particulate emissions sampling to link fuel bed spectra with fire behaviour, burn severity and fuel consumption. In a pilot study, three fuel types (eucalypt canopy branchlets, eucalypt litter, and annual ryegrass) were treated to produce two levels of combustibility (high, low) and burned by free-spreading fire under controlled laboratory conditions. Pre- and post-burn hyperspectral images (400–2500 nm) were collected during 26 fire experiments, alongside measurements of fuel moisture content, combustion efficiency, rate of spread, biochemistry, calorimetry, and emissions of CO2, CO, CH4, PM2.5. This dataset sumarizes this information.
Contact Email
nicolas.younes@anu.edu.au
Principal Investigator
Nicolas Younes
Collaborators
Sullivan, Andrew; Roulston, Christopher; Bright, Courtney; Reisen, Fabienne; Hay, Eric; Allen, Andy; Plucinski, Matt; Abdelaziz, Misarah; Yebra, Marta; Tuhý, Marek; Kitchen, Mark; Lymburner, Leo
Fields of Research
3108 - Plant biology; 370903 - Natural hazards; 401304 - Photogrammetry and remote sensing
Keywords
hyperspectral; wildfire; bushfire; Pyrotron; greenhouse gases; particulates; calorimetry; rate of spread; fire danger; spectroscopy; plant traits
Date of data creation
2025
Year of data publication
2025
Creator(s) for Citation
Nicolas
Younes
Andrew
Sullivan
Christopher
Roulston
Courtney
Bright
Fabienne
Reisen
Eric
Hay
Andy
Allen
Matt
Plucinski
Misarah
Abdelaziz
Marta
Yebra
Marek
Tuhý
Mark
Kitchen
Leo
Lymburner
Publisher for Citation
The Australian National University Data Commons
Related Websites
https://brcoe.org/
ANU Bushfire Research Centre of Excellence
Embargo Date
2026-10-29
Retention Period
Indefinitely
Data Management Plan
No

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Number of files: 1

Size: 82.1 KB

Identifier: anudc:6418

Status: Published
Published to:
  • Australian National University
  • Australian National Data Service
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