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.Created: 2025
Subjects
Biological Sciences |
Earth Sciences |
Engineering |
Geomatic Engineering |
Natural Hazards |
Photogrammetry and Remote Sensing |
Physical Geography and Environmental Geoscience |
Plant Biology |
Pyrotron |
bushfire |
calorimetry |
fire danger |
greenhouse gases |
hyperspectral |
particulates |
plant traits |
rate of spread |
spectroscopy |
wildfire |
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