@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-8257, title = {A comparison between TLS and UAS LiDAR to represent eucalypt crown fuel characteristics}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {181}, year = {2021}, month = {11/2021}, pages = {295-307}, abstract = {

Advances in fire behaviour modelling provide a catalyst for the development of next generation fuel inputs. Fire simulations underpin risk and consequence mapping and inform decisions regarding ecological and social impacts of different fire regimes. Unoccupied Aerial Systems (UAS) carrying Light Detection and Ranging (LiDAR) sensors have been proposed as a source of structural information with potential for describing fine fuel properties. Whilst these systems have been shown to be capable of describing general vegetation distribution, the ability to distinguish between vegetation elements that contribute to fire spread and those that do not (such as large woody elements) is yet to be explored. This study evaluates the ability of UAS LiDAR point clouds to provide a description of crown fuel elements in eucalypt trees. This is achieved through comparison with dense Terrestrial Laser Scanning (TLS) that were manually attributed with a fuel description. Using the TLSeparation package TLS and UAS LiDAR point clouds achieved 84.6\% and 81.1\% overall accuracy respectively in the separation of crown fuel and wood in nine reference trees. When applying the same separation process across a 30 by 50\ m plot consisting of approximately 75 trees, total canopy fuel volume was found to be strongly correlated between the TLS and UAS LiDAR point clouds (r: 0.96, RMSE: 1.53\ m3). A lower canopy base height and greater distance between crown fuel regions within each crown supported visual inspection of the point clouds that TLS point clouds were able to represent the crown to a greater extent than UAS LiDAR point clouds. Despite these differences it is likely that a less complete representation of canopy fuel such as that generated from UAS LiDAR point clouds will suitably represent the crown and canopy fuel objects effectively for fire behaviour modelling purposes. The research presented in this manuscript highlights the potential of TLS and UAS LiDAR point clouds to provide repeatable, accurate 3D characterisation of canopy fuel properties.

}, keywords = {UAS Drone LiDAR 3D remote sensing TLS Fuel}, doi = {https://doi.org/10.1016/j.isprsjprs.2021.09.008}, url = {https://www.sciencedirect.com/science/article/pii/S0924271621002409}, author = {Samuel Hillman and Luke Wallace and Karin Reinke and Simon Jones} } @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} } @article {bnh-7439, title = {Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations}, journal = {Fire}, volume = {3}, year = {2020}, month = {10/2020}, chapter = {59}, abstract = {

Site-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experience and the rigor applied in making assessments. Terrestrial remote sensing techniques have been demonstrated to be capable of capturing quantitative information on the spatial distribution of biomass to inform fuel hazard assessments. This paper explores the use of image-based point clouds generated from imagery captured using a low-cost compact camera for describing the fuel hazard within the surface and near-surface layers. Terrestrial imagery was obtained at three distances for five target plots. Subsets of these images were then processed to determine the effect of varying overlap and distribution of image captures. The majority of the point clouds produced using this image-based technique provide an accurate representation of the 3D structure of the surface and near-surface fuels. Results indicate that high image overlap and pixel size are critical; multi-angle image capture is shown to be crucial in providing a representation of the vertical stratification of fuel. Terrestrial image-based point clouds represent a viable technique for low cost and rapid assessment of fuel structure.

}, keywords = {Structure from Motion; vegetation structure; fuel hazard; Terrestrial Laser Scanning}, doi = {https://doi.org/10.3390/fire3040059}, url = {https://www.mdpi.com/2571-6255/3/4/59/htm}, author = {Luke Wallace and Bryan Hally and Samuel Hillman and Simon Jones and Karin Reinke} } @article {bnh-5481, title = {Assessing the ability of image based point clouds captured from a UAV to measure the terrain in the presence of canopy cover}, journal = {forests}, volume = {10}, year = {2019}, month = {04/2019}, abstract = {

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5\% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.

}, keywords = {drones, Fire, forest measurement, image based point clouds, RPAS, structure from motion, UAS}, doi = {https://doi.org/10.3390/f10030284}, url = {https://www.mdpi.com/1999-4907/10/3/284}, author = {Luke Wallace and Chris Bellman and Bryan Hally and Jaime Hernandez and Simon Jones and Samuel Hillman} } @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} } @article {bnh-6222, title = {A Method for Validating the Structural Completeness of Understory Vegetation Models Captured with 3D Remote Sensing}, journal = {Remote Sensing}, volume = {11}, year = {2019}, month = {09/2019}, abstract = {

Characteristics describing below canopy vegetation are important for a range of forest ecosystem applications including wildlife habitat, fuel hazard and fire behaviour modelling, understanding forest recovery after disturbance and competition dynamics. Such applications all rely on accurate measures of vegetation structure. Inherent in this is the assumption or ability to demonstrate measurement accuracy. 3D point clouds are being increasingly used to describe vegetated environments, however limited research has been conducted to validate the information content of terrestrial point clouds of understory vegetation. This paper describes the design and use of a field frame to co-register point intercept measurements with point cloud data to act as a validation source. Validation results show high correlation of point matching in forests with understory vegetation elements with large mass and/or surface area, typically consisting of broad leaves, twigs and bark 0.02 m diameter or greater in size (SfM, MCC 0.51{\textendash}0.66; TLS, MCC 0.37{\textendash}0.47). In contrast, complex environments with understory vegetation elements with low mass and low surface area showed lower correlations between validation measurements and point clouds (SfM, MCC 0.40 and 0.42; TLS, MCC 0.25 and 0.16). The results of this study demonstrate that the validation frame provides a suitable method for comparing the relative performance of different point cloud generation processes

}, keywords = {3D remote sensing, biomass, forest measurement, structure from motion, terrestrial laser scanning, validation, vegetation structure}, doi = {https://doi.org/10.3390/rs11182118}, url = {https://www.mdpi.com/2072-4292/11/18/2118}, author = {Samuel Hillman and Luke Wallace and Karin Reinke and Bryan Hally and Simon Jones and Daisy Saldias} } @conference {bnh-4778, title = {Experiences in the in-field utilisation of fuels3D}, booktitle = {AFAC18}, year = {2018}, month = {09/2018}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Perth}, abstract = {

Fuels3D provides a rapid method to collect quantified information describing fuel hazard using a smartphone. The method requires users to collect a number of photos along a transect within a fuel hazard environment. The photos are processed using photogrammetric algorithms to provide a three-dimensional representation of the fuel, and subsequently estimates of fuel hazard metrics including fuel height, cover and fate (dead/alive). This paper reports on the initial large scale utilisation trial of the Fuels3D fuel hazard workflow. Project end-users from Victoria, South Australia and ACT were provided with a smartphone app (iOS or Android) that allowed photos to be easily collected following the Fuels3D method. End-users were instructed to collect samples within a variety of fuel types and hazards in order to test the potential and limitations of the app.\  These photos were transferred utilising the cloudstor research infrastructure to a processing PC, where estimates of fuel hazard metrics were derived and reported back to end-users.\  Initial results of this trial indicate that Fuels3D is capable of quanitifed estimates of fuel hazard metrics that are more precise than those achieved with visual fuel hazard assessments.

}, author = {Luke Wallace and Karin Reinke and Simon Jones and Samuel Hillman and Adam J. Leavesley and Simeon Telfer and Ian Thomas} } @conference {bnh-3913, title = {Mapping the efficacy of an Australian fuel reduction burn using Fuels3D point clouds}, booktitle = {AFAC17}, year = {2017}, month = {09/2017}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Sydney}, abstract = {

Fuel reduction burns are commonly used in fire-prone forests to reduce the risk of wildfire and increase ecosystem resilience. As such producing quantified assessments of fire-induced change is important to understanding the success of the intervention. Remote sensing has also been employed for assessing fuel hazard and fire severity. Satellite, airborne and UAV remote sensing, for example, have shown potential for assessing the effects of large wildfires and fuel hazard in areas of open canopy. Fuel reduction burns, however, often take place under dense canopy and result in little or no change to the canopy cover. As such terrestrial techniques are needed to quantify the efficacy of these burns.

This study presents a case study on the use of image based point clouds, captured terrestrially following the fuels3D methodology outlined in Wallace et al. (2016), for describing the change in fuel structure induced by a low intensity fuel reduction burn. The specific objectives of this study were to evaluate whether fuel structure maps produced from fuels3D point clouds are sensitive to the changes that occur during a low intensity fuel reduction burn, and how these changes may be quantified.

}, author = {Luke Wallace and Karin Reinke and Simon Jones and Bryan Hally and Samuel Hillman and Christine Spits} } @article {bnh-5095, title = {Non-destructive estimation of above-ground surface and near-surface biomass using 3D terrestrial remote sensing techniques}, journal = {Methods in Ecology and Evolution}, volume = {8}, year = {2017}, month = {02/2017}, abstract = {
  1. Quantitative measurements of above-ground vegetation biomass are vital to a range of ecological and natural resource management applications. Remote-sensing techniques, such as terrestrial laser scanning (TLS) and image-based point clouds, are potentially revolutionary techniques for measuring vegetation biomass and deriving other related, structural metrics for these purposes.
  2. Surface vegetation biomass (up to 25\ cm) in pasture, forest, and woodland environments is estimated from a 3D point cloud derived from a small number of digital images. Volume is calculated, using the 3D cloud and regressed against dry weight to provide an estimate of biomass. Assessment of the method is made through comparison to 3D point clouds collected through TLS surveys.
  3. High correlation between destructively sampled biomass and vegetation volume derived from TLS and image-based point clouds in the pasture (TLS\ urn:x-wiley:2041210X:media:mee312759:mee312759-math-0001, image based\ urn:x-wiley:2041210X:media:mee312759:mee312759-math-0002), dry grassy forest (TLS\ urn:x-wiley:2041210X:media:mee312759:mee312759-math-0003, image based\ urn:x-wiley:2041210X:media:mee312759:mee312759-math-0004) and lowland forest (TLS\ urn:x-wiley:2041210X:media:mee312759:mee312759-math-0005, image based\ urn:x-wiley:2041210X:media:mee312759:mee312759-math-0006) environments was found. Occlusion caused by standing vegetation in the woodland environment resulted in moderate correlation between TLS derived volume and biomass (urn:x-wiley:2041210X:media:mee312759:mee312759-math-0007). The effects of surrounding vegetation on the image-based technique resulted in 3D point clouds being resolved for only 40\% of the samples in this environment.
  4. The results of this study demonstrate that image-based point cloud techniques are highly viable for the measurement of surface biomass. In contrast to TLS, volume and biomass data can be captured using low-cost equipment and relatively little expertise.

}, doi = {https://doi.org/10.1111/2041-210X.12759}, url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12759}, author = {Luke Wallace and Samuel Hillman and Karin Reinke and Bryan Hally} }