The classification and quantification of fuel is traditionally a labour-intensive, costly and often subjective operation, especially in hazardous vegetation types, such as gorse (Ulex europaeus L.) scrub. In this study, unmanned aerial vehicle (UAV) technologies were assessed as an alternative to traditional field methodologies for fuel characterisation. UAV laser scanning (ULS) point clouds were captured, and a variety of spatial and intensity metrics were extracted from these data. These data were used as predictor variables in models describing destructively and non-destructively sampled field measurements of total above ground biomass (TAGB) and above ground available fuel (AGAF). Multiple regression of the structural predictor variables yielded correlations of R² = 0.89 and 0.87 for destructively sampled measurements of TAGB and AGAF, respectively, with relative root mean square error (RMSE) values of 18.6% and 11.3%, respectively. The best metrics for non-destructive field-measurements yielded correlations of R² = 0.50 and 0.49, with RMSE values of 40% and 30.8%, for predicting TAGB and AGAF, respectively, indicating that ULS-derived structural metrics offer higher levels of precision. UAV-derived versions of the field metrics (overstory height and cover) predicted TAGB and AGAF with R² = 0.44 and 0.41, respectively, and RMSE values of 34.5% and 21.7%, demonstrating that even simple metrics from a UAV can still generate moderate correlations. In further analyses, UAV photogrammetric data were captured and automatically processed using deep learning in order to classify vegetation into different fuel categories. The results yielded overall high levels of precision, recall and F1 score (0.83 for each), with minimum and maximum levels per class of F1 = 0.70 and 0.91. In conclusion, these ULS-derived metrics can be used to precisely estimate fuel type components and fuel load at fine spatial resolutions over moderate-sized areas, which will be useful for research, wildfire risk assessment and fuel management operations.

We present novel in‐field vegetation fire observations and the analyses using brightness temperatures recorded by longwave infrared camera and thermal image velocimetry. The brightness temperatures from a wind‐driven stubble wheat fire were obtained with a 60 frames per second (fps) video acquisition. Multi‐level sonic anemometers on a 10m in‐fire tower were used for measurements of turbulent velocity and air temperatures, while fuel level air and flame temperatures were collected by an array of thermocouples (TCs). The camera's image pixel resolution was adequate to discern thermal structures in accordance with the in‐fire TC measurements and its spacing distances. The in‐situ and remotely measured flaming zone dynamics were derived using two different methodologies, Thermal Image Velocimetry (TIV) and Image Segmentation (IS). The results highlight spatial and spectral information of coherent turbulent and mean velocity structures. The power spectra decomposition of the TIV showed similar spectral characteristics to the sonic speed measurements during the fire passage under the tower with a similar inertial subrange slope. This result reveals plausible evidence of interaction between the flaming zone and wind turbulence for a prescribed rapidly moving stubble wheat fire. This research presents a new field measurement methodology for understanding fire‐atmospheric interactions between the flaming zone and the immediate overlying atmospheric turbulent boundary layer.

Background: Complex interactions between fuel structure and fire substantially affect fire spread and spatial variability in fire behaviour. Heterogeneous arrangement of the fuel coupled with variability in fuel characteristics can impact heat transfer efficiency, preheating of unburned fuel and consequent ignition and spread. Aim: Study the influence of pre-burn fuel structure (canopy height, spatial arrangement) on fire behaviour (rate of spread, flame residence time) derived from high-resolution video of a prescribed gorse fire. Method: Rate of spread and flame residence time are calculated and mapped from high-resolution overhead visible-spectrum video, and compared with the Canopy Height Model derived from pre-burn Light Detection and Ranging (Lidar) scans. Results: Geospatial analytics can provide precision observations of fire behaviour metrics. Rates of spread under high wind conditions are influenced by local changes in canopy height and may be more dependent on other fuel characteristics, while flame residence time is better correlated with canopy height. Conclusions: These observational technology and spatio-temporal analytical techniques highlight how detailed fire behaviour characteristics can be derived from these data. Implications: The results have implications for wildfire modelling and Wildland–Urban Interface (WUI) building design engineers, as the reported dataset is suitable for model validation and the analysis contributes to further understanding of gorse fire hazard.

Improving the accuracy of fire behavior prediction requires better understanding of live fuel, the dominant component of tree crowns, which dictates the consumption and energy release of the crown fire flame-front. Live fuel flammability is not well represented by existing evaluation methods. High-flammability live fuel, e.g., in conifers, may maintain or increase the energy release of the advancing crown fire flame-front, while low-flammability live fuel, e.g., in boreal deciduous stands, may reduce or eventually suppress flame-front energy release. To better characterize these fuel–flame-front interactions, we propose a method for quantifying flammability as the fuel’s net effect on (contribution to) the frontal flame energy release, in which the frontal flame is simulated using a methane diffusion flame. The fuel’s energy release contribution to the methane flame was measured using oxygen consumption calorimetry as the difference in energy release between the methane flame interacting with live fuel and the methane flame alone. In-flame testing resulted in fuel ignition and consumption comparable to those in wildfires. The energy release contribution of live fuel was significantly lower than its energy content measured using standard methods, suggesting better sensitivity of the proposed metric to water content- and oxygen deficiency-associated energy release reductions within the combustion zone.

This study presents two new remote sensing approaches that can be used to derive rate of spread and flaming zone velocities of a wildfire at very high spatiotemporal resolution. Time sequential image tracking from thermal or visible video collected on uncrewed aerial vehicles is used to estimate instantaneous spatial rate of spread of a surface fire. The techniques were developed using experimental wheat‐stubble burns carried out near Darfield, New Zealand, in March 2019. The thermal tracking technique is based on Thermal Image Velocimetry, which tracks evolving temperature patterns within an infrared video. The visible tracking technique uses colour thresholding, and tracks fire perimeter progression through time at pixel resolution. Results show that the visible perimeter tracking creates a higher mean rate of spread compared to thermal image velocimetry. The visible perimeter tracking provides rate of spread measurements for fire front progression whereas the thermal tracking techniqueis computationally more expensive, but can resolve velocities of thermal structures within the flaming zone and provides spatiotemporal rate of spread measurements. Both techniques are available as open‐source code and providevital scientific data for new studies concerning e.g. fire–atmospheric interactions or model validation. They may be adapted for operational purposes providing rate of spread at high spatiotemporal resolution. This study presents two new remote sensing approaches that can be used to derive rate of spread and flaming zone velocities of a wildfire at very high spatiotemporal resolution. Time sequential image tracking from thermal or visible video collected on uncrewed aerial vehicles is used to estimate instantaneous spatial rate of spread of a surface fire. The techniques were developed using experimental wheat‐stubble burns carried out near Darfield, New Zealand, in March 2019. The thermal tracking technique is based on Thermal Image Velocimetry, which tracks evolving temperature patterns within an infrared video. The visible tracking technique uses colour thresholding, and tracks fire perimeter progression through time at pixel resolution. Results show that the visible perimeter tracking creates a higher mean rate of spread compared to thermal image velocimetry. The visible perimeter tracking provides rate of spread measurements for fire front progression whereas the thermal tracking techniqueis computationally more expensive, but can resolve velocities of thermal structures within the flaming zone and provides spatiotemporal rate of spread measurements. Both techniques are available as open‐source code and providevital scientific data for new studies concerning e.g. fire–atmospheric interactions or model validation. They may be adapted for operational purposes providing rate of spread at high spatiotemporal resolution.