@article {bnh-8368, title = {Up-scaling fuel hazard metrics derived from terrestrial laser scanning using a machine learning model}, journal = {Remote Sensing}, volume = {15}, year = {2023}, month = {02/2023}, pages = {1273}, abstract = {

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}, keywords = {ALS, canopy, cover, elevated, field data, fuel hazard, fuel layers, fuel metrics, height, near-surface, random forest, up-scaling, visual assessments}, doi = {https://doi.org/10.3390/rs15051273}, url = {https://www.mdpi.com/2072-4292/15/5/1273}, author = {Ritu Taneja and Luke Wallace and Samuel Hillman and Karin Reinke and James Hilton and Simon Jones and Bryan Hally} } @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-7413, title = {Quantifying fuel hazard assessments - Fuels3D annual report 2019-2020}, number = {619}, year = {2020}, month = {10/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

This annual report summarises the year 2019 for the Fuels3D project.\  This project was funded as half a project; the work considers the lack of repeatability and reliability with current field fuel hazard assessments. It demonstrates the precision of a semi-automated non-visual based assessments as compared to those collected through traditional visual assessment.\  The opportunity to bring together off-the-shelf, smart phone cameras and consumer grade digital cameras with advances in computer vision and photogrammetric techniques provide a cheap alternative for quantitative assessments compared to more accurate, but more expensive 3D mapping technologies (i.e. Lidar TLS).\  A tool chain and suite of computer vision and photogrammetric algorithms that use images captured in the field to produce 3D point clouds from which fuel hazard metrics are calculated. The developed technique is adaptive to 3D point clouds captured from other terrestrial technologies and can allow for changes in data collection technologies.

Highlights of 2018-2019 have included:

}, keywords = {fuel hazard, fuels3D}, issn = {619}, author = {Bryan Hally and Karin Reinke and Luke Wallace and Simon Jones} } @conference {bnh-6527, title = {Fuels3D: barking up the wrong tree and beyond}, booktitle = {AFAC19 powered by INTERSCHUTZ - Bushfire and Natural Hazards CRC Research Forum}, year = {2019}, month = {12/2019}, publisher = {Australian Institute for Disaster Resilience}, organization = {Australian Institute for Disaster Resilience}, address = {Melbourne}, abstract = {

Improvement of the understanding of how fuel characteristics correlate with fire behaviour and severity is critical to the ongoing handling of risk and recovery in fire-prone environments. Current standards and protocols for describing fuel hazard (for example, {\textquoteleft}Overall Fuel Hazard Assessment Guide{\textquoteright}, Victorian Department of Sustainability and Environment) and post-burn severity (for example, {\textquoteleft}Fire Severity Assessment Guide{\textquoteright}, Victorian Department of Sustainability and Environment) were written for collection of information in the field. The data collected are largely subjective descriptions of the landscape. The ability of information from these assessment techniques to be adapted to modern risk assessment tools such as fire behavior models, or for the calibration and validation of datasets, is limited. Quantitative data-rich methods of measuring and assessing fuel load and structure are the missing link between the knowledge of land management personnel in the field, and the model drivers and decision makers at organizational level.

Handheld devices with high quality sensors, in the form of offthe-shelf cameras, are increasingly ubiquitous, as is the availability of 3D point cloud data collected from active sensing instruments on terrestrial and aerial platforms. Rapid and comprehensive capture of information by these devices, coupled with the use of computer vision techniques, allows for the 3D description of the surrounding environment to be exploited to provide robust measurement of metrics that can be built into existing fuel hazard assessment frameworks. Providing key metrics as data products rather than a single product enables flexibility across jurisdictions and ecosystem types, and capacity to adapt as end-user requirements change.

The Fuels3D project has created a suite of tools and methods for image capture in the field during fuel hazard assessments. 3D point clouds are generated using computer vision and photogrammetry techniques. From these 3D point clouds, scale is added, and decision rules are programmed to calculate quantifiable surface / near-surface metrics that replicate those
used in current fuel hazard visual assessment guides. Case studies are highlighted here.

Download the full non-peer reviewed research proceedings\ from the Bushfire and Natural Hazards CRC Research Forumhere.

}, keywords = {data collection, Fire behaviour, fuel hazard, risk management, technology}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Karin Reinke and Luke Wallace and Samuel Hillman and Bryan Hally and Simon Jones} } @mastersthesis {bnh-6627, title = {Measuring fire-induced change in the understorey of an Australian dry sclerophyll forest using remote sensing}, year = {2016}, month = {08/2016}, school = {RMIT University}, address = {Melbourne}, abstract = {

This research investigates the use of remote sensing technologies for measuring and mapping the changes in the forest understorey in response to prescribed burning. Remote sensing has been used extensively to map the burn areas in fire-affected landscapes, but less work has been done focussing on beneath the canopy. Sub-canopy vegetation layers are important for habitat and for understanding the fuel hazards they may pose to the risk of wildfire. Accordingly, instruments and approaches must be able to perform in both pre- and post-burn environments, and be able to provide meaningful measures of change.

Wildfires are increasing in intensity and frequency, and in response prescribed burning is used to mitigate threats posed by them. Quantifying post-fire effects is important for burn severity, ecosystem recovery and post-fire hazard assessments. This information will allow land managers and scientists to understand fires in their environmental, economic and social contexts and help formulate responses and policies accordingly. However, measures of fire effects and fuel hazards which are done via visual assessments are known to be subjective and inconsistent between assessors and over time. What is needed is an improvement in the reporting procedures around quantification of fire effects which are both repeatable and quantifiable.

In this research two remote sensing technologies were used to measure, map and track changes in the understorey of an Australian dry sclerophyll forest. Terrestrial Laser Scanning (TLS) was used to derive vegetation structure variables and HSR (HyperSpectral Radiometry) was used to derive vegetation physiological variables. The study site was located in St. Andrews, Victoria, Australia within which a control plot and three fire treatment plots were set-up and monitored over a two year period, before and after a prescribed fire event conducted in autumn 2012. The datasets collected were used for statistical and spatial analysis of changes in understorey vegetation, and to assess those metrics best suited for describing different vegetation responses to fire effects.

The first part of this research examined the potential of TLS to detect fire-induced change in the forest understorey. From TLS point clouds a total of 18 metrics were extracted which were tested against accuracy and reliability criteria. Three metrics; mean AGHchange (Above Ground Height), median AGHchange and point countchange were shortlisted. To report different post-fire changes in burnt understorey, mean AGHchange metric was used. This metric was able to report fire effects such as total burn area, measures of patchiness, spatial distribution of burnt and unburnt areas, fuel accumulation and prescribed burn efficiency across various temporal scales.

The second part of this research analysed hyperspectral data of the near-surface (grass) and surface fuel layer (litter). Spectral changes in the near-surface fuel layer were observed in Visible (550nm), Near-Infrared (680-750nm) and Middle Infrared (970nm, 1220nm, 1550nm) domains of the electromagnetic spectrum. For the surface fuel layer (litter) changes were observed in the Middle Infrared domain (1140nm, 1225nm and 1700nm). The greatest difference from pre-burn levels for both the fuel layers occurred within the first two weeks post-burn. Spectral indices corresponding to the above determined broad spectral bands were tested to ascertain which were best at characterising burnt from unburnt targets whilst also tracking recovery. Indices such as NDVI, NBR and D720 were found to be the most suitable for near-surface fuel layer whilst D1230 for surface fuel layer.

A preliminary investigation into comparing the change detected by the two remote sensing technologies suggested that physiological change detected by HSR, recorded vegetation recovery as early as six weeks post-burn. Structural change detected by TLS even after two years post-burn was recorded as being close to two weeks post-burn levels. This finding matched well with visual assessments of structural measures (plant cover and height).

The findings of this study suggest that improvements in reporting procedures around quantification of fire effects can be achieved using TLS and HSR technology. TLS-derived structural metric, mean AGHchange can accurately detect quantified measures of fire-induced change in forest understorey that can be validated with field assessments. It can also report post-fire effects at various temporal scales including area burnt, burn patchiness, fuel load accumulation and prescribed burn efficiency. Spectral indices such as NDVI, NBR and D720 were able to accurately detect both vegetation loss and recovery. There is merit in further investigating TLS and HSR in conjunction for quantified and robust reporting of fire effects. The change detected by these technologies can be linked to inform both vegetation recovery and fuel accumulation.

}, keywords = {burn severity, fuel hazard, post-fire effects, prescribed burns, remote sensing}, url = {https://researchbank.rmit.edu.au/view/rmit:161758}, author = {Vaibhav Gupta} }