@article {bnh-8371, title = {Terrestrial Laser Scanning: an operational tool for fuel hazard mapping?}, journal = {Fire}, volume = {5}, year = {2022}, month = {04/2022}, pages = {85}, abstract = {

Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited due to a lack of a sampling approach that balances efficiency and data efficacy. This study aims to assess whether an operational approach utilising Terrestrial Laser Scanning (TLS) to capture fuel information over an area commensurate with current fuel hazard assessment protocols implemented in South-Eastern Australia is feasible. TLS data were captured over various plots in South-Eastern Australia, utilising both low- and high-cost TLS sensors. Results indicate that both scanners provided similar overall representation of the ground, vertical distribution of vegetation and fuel hazard estimates. The analysis of fuel information contained within individual scans clipped to 4 m showed similar results to that of the fully co-registered plot (cover estimates of near-surface vegetation were within 10\%, elevated vegetation within 15\%, and height estimates of near-surface and elevated strata within 0.05 cm). This study recommends that, to capture a plot in an operational environment (balancing efficiency and data completeness), a sufficient number of non-overlapping individual scans can provide reliable estimates of fuel information at the near-surface and elevated strata, without the need for co-registration in the case study environments. The use of TLS within the rigid structure provided by current fuel observation protocols provides incremental benefit to the measurement of fuel hazard. Future research should leverage the full capability of TLS data and combine it with moisture estimates to gain a full realisation of the fuel hazard.

}, keywords = {fuel hazard, fuel structure, occlusion, remote sensing, Risk assessment, TLS}, doi = {https://doi.org/10.3390/fire5040085}, url = {https://www.mdpi.com/2571-6255/5/4/85}, author = {Luke Wallace and Samuel Hillman and Bryan Hally and Ritu Taneja and Andrew White and James McGlade} } @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} }