@article {bnh-6993, title = {Case study: use of remote sensing data to derive spatial and temporal explicit fuel accumulation curves across defence lands}, number = {579}, year = {2020}, month = {06/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fuel loads are a main driver of fire rate of spread. Therefore, a spatially explicit estimation of fuel loads, coupled with their variation through time may improve wildland fuel management and contribute to the design of more efficient active fire response strategies. However, the high frequency of planned and unplanned fires in large wild areas linked to varying fire severity levels that affect the rate at which fuels re-accumulate, make the continuous monitoring of wildland fuels challenging with field-based survey methods. Here we propose the use of satellite remote sensing to map fuel loads with a revisit time of 16 days. Fuel load maps are produced for five Defence Lands using Landsat and Sentinel-2 optical remote sensing data available at Digital Earth Australia and the National Computation Infrastructure. The fuel load maps are obtained by calculating the time series of the Vegetation Structure Perpendicular Index, an index that measures post-fire disturbance, and fitting these to fuel accumulation curves derived from literature.

}, keywords = {data analysis, defence lands, fuel accumulation, remote sensing}, issn = {579}, author = {Andrea Massetti and Marta Yebra and James Hilton and Christoph R{\"u}diger} } @article {bnh-5388, title = {The Vegetation Structure Perpendicular Index (VSPI): A forest condition index for wildlife predictions}, journal = {Remote Sensing of Environment}, volume = {224}, year = {2019}, month = {04/2019}, chapter = {167}, abstract = {

Wildfires are a major\ natural hazard, causing substantial damage to infrastructure as well as being a risk to lives and homes. An understanding of their progression and behaviour is necessary to reduce risks and to develop operational management strategies in the event of an active fire. Many empirical fire-spread models have been developed to predict the spread and overall behaviour of a wildfire, based on a range of parameters such as weather and fuel conditions. However, these parameters may not be available with sufficient accuracy or spatiotemporal resolution to provide reliable fire spread predictions. Fuel condition data include variables such as vegetation quantity, structure and moisture content and, in the event of previous wildfires, the burn severity and stage of ecosystem recovery. In this study, an index called the\ Vegetation Structure\ Perpendicular Index (VSPI) is introduced. The VSPI utilises the short-wave infrared reflectance in bands centred at 1.6 and 2.2 μm, essentially representing the amount and structure of the vegetation{\textquoteright}s woody biomass (as opposed to the photosynthetic activity and moisture content). The VSPI is quantified as the divergence from a linear regression between the two bands in a time series and represents vegetation disturbance and recovery more reliably than indices such as the Normalised Burn Ratio (NBR) and\ Normalised Difference Vegetation Index\ (NDVI). The VSPI index generally shows minor inter-annual variability and stronger post-wildfire detection of disturbance over a longer period than NBR and\ NDVI. The index is developed and applied to major wildfire events within eucalypt forests throughout southern Australia to estimate both burn severity and time to recovery. The VSPI can provide an improved information layer for fire risk evaluation and operational predictions of wildfire behaviour.

}, keywords = {Forest condition, Landsat, Vegetation recovery, Wildfire spread}, doi = {https://doi.org/10.1016/j.rse.2019.02.004}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719300586?dgcid=coauthor}, author = {Andrea Massetti and Christoph R{\"u}diger and Marta Yebra and James Hilton} } @article {bnh-5264, title = {Determining the minimum sampling frequency for ground measurements of burn severity}, journal = {International Journal of Wildland Fire}, year = {2018}, month = {06/2018}, abstract = {

Understanding burn severity is essential to provide an overview of the precursory conditions leading to fires as well as understanding the constraints placed on fire management services when mitigating their effects. Determining the minimum sampling frequency for ground measurements is not only essential for accurately assessing burn severity, but also for fire managers to better allocate resources and reduce the time and costs associated with sampling. In this study, field sampling methods for assessing burn severity are analysed statistically for 10 burn sites across Victoria, Australia, with varying spatial extents, topography and vegetation. Random and transect sampling methods are compared against each other using a Monte Carlo simulation to determine the minimum sample size needed for a difference of 0.02 (2\%) in the severity classes proportions relative to the population proportions. We show that, on average, transect sampling requires a sampling rate of 3.16\% compared with 0.59\% for random sampling. We also find that sites smaller than 400 ha require a sampling rate of between 1.4 and 2.8 times that of sites larger than 400 ha to achieve the same error. The information obtained from this study will assist fire managers to better allocate resources for assessing burn severity.

}, doi = {10.1071/WF17055}, url = {http://www.publish.csiro.au/WF/WF17055}, author = {Alexander Holmes and Sarah Harris and Nigel Tapper and Christoph R{\"u}diger} }