@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-8165, title = {Using pre- and post-fire LiDAR to assess the severity of the 2019 Tasmanian bushfires}, number = {698}, year = {2021}, month = {08/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

In January 2019, over 64,000 ha of bushland burned in the Riveaux Road fire in Tasmania{\textquoteright}s southern forests. Most of area burned occurred in tall wet eucalypt forest. These forests are considered to be highly flammable in dry conditions, but fires are infrequent due to the generally cool, wet climate in which they grow. As a result, limited data exists on the behaviour and effects of wildfire in these forests. Prior to these fires, extensive areas of these southern forests have been studied in-depth. In 2014, a large area of the forests that burned were mapped with aerial LiDAR, a remote-sensing technology that can characterise three-dimensional forest structure. Further, in 2016, detailed field-based measurements of fuel load, structure, and hazard were taken at 12 permanent plots which subsequently burned in 2019. Hence, the 2019 fires in Tasmania represent a globally-rare opportunity to characterise the severity of a large wildfire using pre-fire and post-fire data. In October 2019, the Department of Primary Industries, Parks, Water and Environment (DPIPWE) in Tasmania, along with five other BNHCRC end-users and the University of Tasmania, launched a project to use remote-sensing and field-based data to create a detailed case study of the 2019 Riveaux Rd. Fire, and to untangle the drivers of fire severity in tall wet eucalypt forests.\  To do this we (i) remeasured plots to assess tree mortality and changes in fuel loads post fire; (ii) acquired LiDAR data from a transect across a burned buttongrass-forest boundary on the Weld River enabling comparison with pre-fire LiDAR data; (iii) established baseline postfire LiDAR buttongrass-forest boundary transect on the Huon River at Blakes Opening.\  Here we describe the data sets and report some preliminary analyses.

}, keywords = {Bushfire, fire severity, Lidar, post-fire, pre-fire, Tasmania}, issn = {698}, author = {Furlaud, James M. and Arko Lucieer and Scott Foyster and Anna Matala and David Bowman} } @article {bnh-7465, title = {Using pre- and post-fire LiDAR to assess the severity of the 2019 Tasmanian bushfires: field survey methods}, number = {620}, year = {2020}, month = {10/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In January 2019, over 64,000 ha of bushland burned in the Riveaux Road fire in Tasmania{\textquoteright}s southern forests. Most of this burning occurred in tall wet eucalypt forest. These forests are considered to be highly flammable in dry conditions, but fires are infrequent due to the generally cool, wet climate in which they grow. As a result, limited data exists on the behaviour and effects of wildfire in these forests. Prior to these fires, extensive areas of these southern forests have been studied in-depth. In 2014, a large area of the forests that burned were mapped with aerial LiDAR, a remote-sensing technology that can characterise three-dimensional forest structure. Further, in 2016, detailed field-based measurements of fuel load, structure, and hazard were taken at 12 permanent plot which subsequently burned in 2019. Hence, the 2019 fires in Tasmania represent a globally-rare opportunity to characterise the severity of a large wildfire using pre-fire and post-fire data. In October 2019, the Department of Primary Industries, Parks, Water and Environment (DPIPWE) in Tasmania, along with five other BNHCRC end-users and the University of Tasmania, launched a project to do just this, using remote-sensing and field-based data to create a detailed case study of the 2019 Riveaux Rd. Fire, and to untangle the drivers of fire severity in tall wet eucalypt forests.

Here, we present the Methodology this study. We took measurements at the 12 permanent plots that burned, dividing our measurements based on 2 levels of detail, based on the level of detail of the pre-fire measurements. In four plots we took {\textquoteleft}Level 1{\textquoteright} measurements, undertaking an overstorey census over 1 ha, assessing overstorey mortality. In 12 plots, we took {\textquoteleft}Level 2{\textquoteright} measurements, which involved characterising understorey phiognomy, structure fuel load, and fire hazard along 3-4 transects at each plot. For Level 1\&2 measurements we also took numerous fire severity measurements, such as char height and burnt tip diameter.\  We also developed a {\textquoteleft}Level 3{\textquoteright} methodology of mostly qualitative fire severity indices that could be quickly conducted at many sample points across the landscape. quantifying fuel load, structure, mortality, hazard, and fire severity indices such as char height and burnt tip diameter. We also outline the next steps to be taken for this project.

}, keywords = {2019 Tasmania fires, field survey method, fire severity, Lidar}, issn = {620}, author = {Furlaud, James M. and Scott Foyster and David Bowman} } @article {bnh-5437, title = {Historical background and current developments for mapping burned area from satellite Earth observation}, journal = {Remote Sensing of Environment}, volume = {225}, year = {2019}, month = {05/2019}, pages = {45-64}, abstract = {

Fire has a diverse range of impacts on Earth{\textquoteright}s physical and social systems. Accurate and up to date information on areas affected by fire is critical to better understand drivers of fire activity, as well as its relevance for\ biogeochemical cycles, climate, air quality, and to aid fire management. Mapping burned areas was traditionally done from field sketches. With the launch of the first Earth\ observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire occurrence. This review paper explores the physical basis to detect burned area from satellite observations, describes the historical trends of using\ satellite sensors\ to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). Finally, it identifies potential future opportunities to further improve burned area detection from Earth observation satellites.

}, keywords = {Burned area, Climate change, Fire, fire impacts, Lidar, Radar}, doi = {https://doi.org/10.1016/j.rse.2019.02.013}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719300689}, author = {Emilio Chuvieco and Florent Mouillot and Guido van der Werf and Jes{\'u}s San Miguel and Mihai Tanasse and Nikos Koutsias and Mariano Garcia and Marta Yebra and Marc Padilla and Ioannis Gitas and Angelika Heil and Todd Hawbaker and Louis Giglio} } @mastersthesis {bnh-5778, title = {Remote sensing of tree structure and biomass in north Australia mesic savanna}, volume = {Philosophy}, year = {2019}, month = {05/2019}, school = {Charles Darwin Unviersity}, type = {Doctorate}, address = {Melbourne}, abstract = {

The quantification of Above Ground Biomass (AGB) plays a major role in issues related to greenhouse gas emissions and carbon sequestration, pertaining to global warming and the effects of climate change. In Eucalyptus miniata/tetrodonta dominated open-forest in Australia{\textquoteright}s northern tropical savanna, AGB mapping is challenging, due to the complex structure of the canopy stand, highly dynamic woody cover, vast spatial extent, vulnerability to climatic effects and the impacts of extensive fire.\ 

Remotely sensed data allow for the mapping, quantification and monitoring of AGB at various scales. Quantification of AGB in tropical savanna by common medium and coarse spatial resolution optical sensor data is inappropriate to monitor finer-grained ecological processes responsible for measuring carbon stocks at an individual tree level and are limited in detecting vertical vegetation structure. There is limited research on the utility of airborne LiDAR (Light Detection and Ranging) and alternative high resolution (\< 0.5 m) remote sensing tools for Australian savanna structural assessment. The main goal of this research is to evaluate the efficiency of small footprint airborne LiDAR and determine whether Unmanned Aerial Systems (UAS) and Very High Resolution (VHR) satellite stereo remote sensing data can be used to extract tree biophysical and vertical structural parameters for the purposes of accurately estimating biomass stocks in Australian mesic savannas.\ 

This study utilized a two-phase LiDAR analysis procedure integrating both Individual Tree Detection (ITC) and Area-Based Approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. Regression analysis was applied on remote sensing data to develop biomass estimation models based on tree height allometry. This study demonstrated that where field-plot data are spatially limited, it is possible to use a hierarchical integration approach based on AGB uncertainty calculation and calibration to upscale AGB estimates from individual trees to broader landscapes.

}, keywords = {biomass, greenhouse gas, Lidar, northern Australia, remote sensing, tree structure}, url = {https://researchers.cdu.edu.au/en/studentTheses/remote-sensing-of-tree-structure-and-biomass-in-north-australia-m}, author = {Grigorijs Goldbergs} }