@article {bnh-7907, title = {High-Resolution Estimates of Fire Severity - An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia }, journal = {Fire}, volume = {4}, year = {2021}, month = {03/2021}, chapter = {14}, abstract = {

With an increase in the frequency and severity of wildfires across the globe and resultant changes to long-established fire regimes, the mapping of fire severity is a vital part of monitoring ecosystem resilience and recovery. The emergence of unoccupied aircraft systems (UAS) and compact sensors (RGB and LiDAR) provide new opportunities to map fire severity. This paper conducts a comparison of metrics derived from UAS Light Detecting and Ranging (LiDAR) point clouds and UAS image based products to classify fire severity. A workflow which derives novel metrics describing vegetation structure and fire severity from UAS remote sensing data is developed that fully utilises the vegetation information available in both data sources. UAS imagery and LiDAR data were captured pre- and post-fire over a 300 m by 300 m study area in Tasmania, Australia. The study area featured a vegetation gradient from sedgeland vegetation (e.g., button grass 0.2m) to forest (e.g., Eucalyptus obliqua and Eucalyptus globulus 50m). To classify the vegetation and fire severity, a comprehensive set of variables describing structural, textural and spectral characteristics were gathered using UAS images and UAS LiDAR datasets. A recursive feature elimination process was used to highlight the subsets of variables to be included in random forest classifiers. The classifier was then used to map vegetation and severity across the study area. The results indicate that UAS LiDAR provided similar overall accuracy to UAS image and combined (UAS LiDAR and UAS image predictor values) data streams to classify vegetation (UAS image: 80.6\%; UAS LiDAR: 78.9\%; and Combined: 83.1\%) and severity in areas of forest (UAS image: 76.6\%, UAS LiDAR: 74.5\%; and Combined: 78.5\%) and areas of sedgeland (UAS image: 72.4\%; UAS LiDAR: 75.2\%; and Combined: 76.6\%). These results indicate that UAS SfM and LiDAR point clouds can be used to assess fire severity at very high spatial resolutio

}, keywords = {3D remote sensing, drone, fire severity, fuel structure, Lidar, photogrammetry, RPAS, structure, UAS, vegetation}, doi = {https://doi.org/10.3390/fire4010014}, url = {https://www.mdpi.com/2571-6255/4/1/14/htm}, author = {Samuel Hillman and Bryan Hally and Luke Wallace and Darren Turner and Arko Lucieer and Karin Reinke and Simon Jones} } @article {bnh-5481, title = {Assessing the ability of image based point clouds captured from a UAV to measure the terrain in the presence of canopy cover}, journal = {forests}, volume = {10}, year = {2019}, month = {04/2019}, abstract = {

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5\% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.

}, keywords = {drones, Fire, forest measurement, image based point clouds, RPAS, structure from motion, UAS}, doi = {https://doi.org/10.3390/f10030284}, url = {https://www.mdpi.com/1999-4907/10/3/284}, author = {Luke Wallace and Chris Bellman and Bryan Hally and Jaime Hernandez and Simon Jones and Samuel Hillman} } @conference {bnh-6549, title = {Quantifying dynamic fire behaviour phenomena using Unmanned Aerial Vehicle technology }, booktitle = {23rd International Congress on Modelling and Simulation}, year = {2019}, month = {12/2019}, address = {Canberra}, abstract = {

Catastrophic wildfires are often a result of dynamic fire behaviours. Although some of these behaviours have been described and investigated, others require further study. Fire coalescence and junction fires are particular cases of merging fire fronts and are common phenomena observed during bushfires. They are important as they can cause rapid escalation of fire behaviour and be dangerous for ground-based emergency personnel. There are a few studies devoted to investigation of merging fires in field conditions. There is a need for high temporal and spatial measurements of fire behaviour in field conditions in order to better understand this phenomenon and evaluate risks during bushfires and prescribed burning. The aim of this study was to test emerging technologies for better quantification of fire behaviour at field scales and evaluate their potential as an operational tool.

Several small and medium scale field experiments were conducted during April 2019 on farmland in Victoria, Australia. Harvested wheat fields were used as experimental plots, as they form relatively homogeneous fuel beds. Fuel height varied from 18 to 40 cm with fuel load and moisture content at 1.1 t/ha and 11.9 \% respectively. Wind speed varied in the range of 1.5-6.5 m/s. An Unmanned Aerial Vehicle (UAV) was used to capture high definition video imagery of fire propagation in synchronisation with sensor data from the on-board Global Positioning System (GPS) and Inertial Measurement Unit (IMU). These sensors enabled the platform/camera orientation and position in space to be aligned with the video footage of fire propagation and to be georeferenced in GIS software.

Twenty-one junction fires and five inward parallel fire fronts (parts of the fire lines approaching each other) were identified during the experiments. The rate of spread (ROS) of merging fire fronts was found to be at least two times higher than for the basic fire fronts (the rate of spread of a linear fire front in the same fuel bed in no-slope conditions) and for junction fires with acute angles (\< 14{\textdegree}) it increased 6 times and more. Inward parallel fire fronts spread much slower, varying between 0.05 and 0.25 m/s. Forty-six percent of junction fires had increase of the ROS at the final stage of the merging process in contrast to Thomas et al. (2017) and Viegas et al. (2012). Also, it was observed that the angle between two oblique fire fronts did not change significantly in time for the initial angles smaller than 34{\textdegree}. It can be assumed that the main fire front influences on the shape and ROS respectively of junction fires and laboratory experiments cannot fully replicate these conditions.

Although the initial experimental conditions were very different in relation to scale, fuel and wind conditions, similar ROS to that shown in numerical simulations by Thomas et al. (2017) were observed in our field experiments. Further investigation is required to explain the similarities as the relationship between fuel load, wind speed and scale is not known. The comparison of corrected values of dimensionless ROS for different angles between fire fronts with laboratory experiments of Viegas et al. (2012) showed reasonable quantitative agreement.

These experiments have shown that the method of using UAV{\textquoteright}s to capture georeferenced video footage can be used reliably to quantify fire behaviour phenomena for research, operation and management purposes.

Catastrophic wildfires are often a result of dynamic fire behaviours. Although some of these behaviours have been described and investigated, others require further study. Fire coalescence and junction fires are particular cases of merging fire fronts and are common phenomena observed during bushfires. They are important as they can cause rapid escalation of fire behaviour and be dangerous for ground-based emergency personnel. There are a few studies devoted to investigation of merging fires in field conditions. There is a need for high temporal and spatial measurements of fire behaviour in field conditions in order to better understand this phenomenon and evaluate risks during bushfires and prescribed burning. The aim of this study was to test emerging technologies for better quantification of fire behaviour at field scales and evaluate their potential as an operational tool.

Several small and medium scale field experiments were conducted during April 2019 on farmland in Victoria, Australia. Harvested wheat fields were used as experimental plots, as they form relatively homogeneous fuel beds. Fuel height varied from 18 to 40 cm with fuel load and moisture content at 1.1 t/ha and 11.9 \% respectively. Wind speed varied in the range of 1.5-6.5 m/s. An Unmanned Aerial Vehicle (UAV) was used to capture high definition video imagery of fire propagation in synchronisation with sensor data from the on-board Global Positioning System (GPS) and Inertial Measurement Unit (IMU). These sensors enabled the platform/camera orientation and position in space to be aligned with the video footage of fire propagation and to be georeferenced in GIS software.

Twenty-one junction fires and five inward parallel fire fronts (parts of the fire lines approaching each other) were identified during the experiments. The rate of spread (ROS) of merging fire fronts was found to be at least two times higher than for the basic fire fronts (the rate of spread of a linear fire front in the same fuel bed in no-slope conditions) and for junction fires with acute angles (\< 14{\textdegree}) it increased 6 times and more. Inward parallel fire fronts spread much slower, varying between 0.05 and 0.25 m/s. Forty-six percent of junction fires had increase of the ROS at the final stage of the merging process in contrast to Thomas et al. (2017) and Viegas et al. (2012). Also, it was observed that the angle between two oblique fire fronts did not change significantly in time for the initial angles smaller than 34{\textdegree}. It can be assumed that the main fire front influences on the shape and ROS respectively of junction fires and laboratory experiments cannot fully replicate these conditions.

Although the initial experimental conditions were very different in relation to scale, fuel and wind conditions, similar ROS to that shown in numerical simulations by Thomas et al. (2017) were observed in our field experiments. Further investigation is required to explain the similarities as the relationship between fuel load, wind speed and scale is not known. The comparison of corrected values of dimensionless ROS for different angles between fire fronts with laboratory experiments of Viegas et al. (2012) showed reasonable quantitative agreement.

These experiments have shown that the method of using UAV{\textquoteright}s to capture georeferenced video footage can be used reliably to quantify fire behaviour phenomena for research, operation and management purposes.

}, keywords = {fire front propagation, merging fire fronts, Remote measurements, UAS}, url = {https://www.researchgate.net/publication/338412609_Quantifying_dynamic_fire_behaviour_phenomena_using_Unmanned_Aerial_Vehicle_technology}, author = {Alex Filkov and Brett Cirulis and Trent Penman} }