@article {bnh-8328, title = {Post-fire impact assessment of Tasmanian sphagnum bogs}, number = {724}, year = {2022}, month = {03/2022}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In the summer of 2015-2016 several large dry lightning storms ignited wildfires that burned across Western Tasmanian landscapes desiccated by the combination of severe spring drought and above average summer temperatures. Approximately 20 000 ha of the Tasmanian Wilderness World Heritage Area burnt including Sphagnum moss communities, or {\textquoteleft}sphagnum bogs{\textquoteright}, and other significant areas of high conservation value Gondwanan refugia. The scale of the fires prompted the Tasmanian government to fund research in techniques to rehabilitate fire-impacted Gondwanan refugia, including sphagnum bogs, in the Lake Mackenzie area of the Northern Central Plateau. A key step in sphagnum bog restoration is the development of rapid, broad-scale, cost-effective and low impact survey techniques to identify sites in need of rehabilitation. We trialled unmanned aerial systems (UAS) with visible spectrum sensor technology to map fire extent and severity.\  Three sphagnum bogs were used to develop an ENVI classification workflow based on a training data set of burn severity categories and other ecological surface cover measurements to map burn severity.\  These maps were validated using ocular assessments of ground cover and burn severity in pre-existing permanently marked quadrats. We found poor to moderate agreement between the UAS and field fire severity with considerable variation amongst the sampled bogs. Total burnt area sphagnum bog maps were then produced by combining burnt severity categories for five bogs, including the three used for fire severity mapping, The congruence between Sphagnum burn extent maps with field data collected by differential Global Positioning System was found to range from substantial to poor according to Cohen{\textquoteright}s Kappa index. Multivariate analysis of the quadrat ground cover assessments showed that high grass cover was most strongly associated with poor field validation agreement (low Kappa scores), whereas acceptable field validation agreement (high Kappa scores), were associated with high cover of Sphagnum. A major constraint of this study was the two-year time gap between the fire and remote sensing mapping and field survey. Higher accuracy fire severity mapping and area burned mapping would be possible if imagery was acquired immediately after a fire event. In light of our findings, we provide recommendations for future UAS surveys of fire impacted sphagnum bogs, the most critical of which is acquiring remote sensing and field data in the same growing season as the fire.

}, keywords = {bogs, impact assessment, post-fire, sphagnum, Tasmanian}, issn = {724}, author = {Morgan Harding and Darren Turner and Grant Williamson and Scott Nichols and David Bowman} } @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-7523, title = {A comparison of terrestrial and UAS sensors for measuring fuel hazard in a dry sclerophyll forest}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {95}, year = {2020}, month = {11/2020}, abstract = {

In recent years, Unoccupied Aircraft Systems (UAS) have been used to capture information on forest structure in unprecedented detail. Pioneering studies in this field have shown that high spatial resolution images and Light Detecting And Ranging (LiDAR) data captured from these platforms provide detailed information describing the dominant tree elements of canopy cover and biomass. However, to date, few studies have investigated the arrangement of vegetation elements that contribute directly to fire propagation in UAS LiDAR point clouds; that is the surface, near-surface, elevated and intermediate-canopy vegetation. This paper begins to address this gap in the literature by exploring the use of image-based and LiDAR 3D representations collected using UAS platforms, for describing forest structure properties. Airborne and terrestrial 3D datasets were captured in a dry sclerophyll forest in south-eastern Australia. Results indicate that UAS LiDAR point clouds contain information that can describe fuel properties in all strata. Similar estimates of canopy cover (TLS: 68.27\% and UAS LiDAR: 64.20\%) and sub-canopy cover (Elevated cover TLS: 44.94\%, UAS LiDAR: 32.27\%, combined surface and near-surface cover TLS: 96.10\% UAS LiDAR: 93.56\%) to TLS were achieved using this technology. It was also shown that the UAS SfM photogrammetric technique significantly under performed in the representation of the canopy and below canopy structure (canopy cover - 20.31\%, elevated cover 10.09\%). This caused errors to be propagated in the estimate of heights in the elevated fuel layer (TLS: 0.51\ m, UAS LiDAR: 0.34\ m, UAS SfM: 0.15\ m). A method for classifying fuel hazard layers is also presented which identifies vegetation connectivity. These results indicate that information describing the below canopy vertical structure is present within the UAS LiDAR point clouds and can be exploited through this novel classification approach for fire hazard assessment. For fire prone countries, this type of information can provide important insight into forest fuels and the potential fire behaviour and impact of fire under different scenarios.

}, author = {Samuel Hillman and Luke Wallace and Arko Lucieer and Karin Reinke and Darren Turner and Simon Jones} }