@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-8326, title = {Fuels3D - final project report}, number = {723}, year = {2022}, month = {03/2022}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Understanding fuel hazard is essential. Effective management of Australia{\textquoteright}s fire prone landscapes relies on accurate consistent and up-to-date fuel characterisation. This project seeks to create a quantitative methodology for calculating fuel hazard, in surface and near surface fuel layers, using affordable consumer grade equipment. It is hoped that this methodology will enhance and supplement existing visual estimation methods used by land management agencies across Australia and demonstrate the utility of moving towards new approaches capable of creating quantitative outputs. The \ method uses a series of systematically acquired photographs to create a 3D point cloud that captures vegetation elements in the surface and near surface vegetation layers and their horizontal and vertical structure. These point clouds are then processed to create the metrics for deriving fuel hazard estimates.

The project methodological tool-chain is divided into five major components:

Each of these methods are embedded in an AWS workflow. The aim being to provide firefighting and land management agencies with an end-to-end semi-automated methodology for collecting, analysing and visualising fuel hazard information.

Although a viable methodology was developed and implemented, results varied by ecosystem. Woodlands, plantations, low open forest, open grasslands and low open shrublands systems all had good image matching and end metric conversion rates (\>90\%). In contrast, closed and other grasslands, shrublands and tall closed forest fuel types all had sample conversion rates below 65\%. The explanation of these large variances in success rates were explored with a number of image acquisition and processing factors identified.

There are many benefits to standardising data collection and harmonising metrics for reporting fuel hazard. Unlike visual assessments the reference photographs and associated point clouds exist in perpetuity and can be re-processed when new techniques emerge for their analysis. Comparing data gathered in different states, territories and jurisdictions also becomes much easier.

Feedback from end users was mixed. While many land managers felt this quantitative methodology had much merit others commented it was too time-consuming to replace current practices. Other (more costly) point cloud collection methods (Terrestrial Laser Scanners and Mobile Laser Scanners (LiDAR) as well as optical depth camera systems) have presented themselves as alternatives during the course of the project. With this in mind the research team has enabled the AWS tool chain to ingest other point cloud data into the fourth and fifth workflow elements. \ 

}, keywords = {fuel hazards, fuels3D, image-based, near-surface, point clouds, surface}, issn = {723}, author = {Simon Jones and Karin Reinke and Johann Tiede and Luke Wallace and Bryan Hally and Mark Robey} } @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-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-7380, title = {An early exploration of the use of the Microsoft Azure Kinect for estimation of urban tree Diameter at Breast Height}, journal = {Remote Sensing Letters}, volume = {11}, year = {2020}, month = {09/2020}, pages = {963-972}, abstract = {
Forest and urban tree inventory measurements are increasingly adopting Remote Sensing (RS) techniques due to the accurate and rapid estimates available compared to conventional methods. The focus of this study is to assess the accuracy and potential application of the Microsoft Azure Kinect {\textendash} a lightweight depth sensor {\textendash} for outdoor measurement of tree stem Diameter at Breast Height (DBH). Individual urban trees (n\ =\ 51) were recorded from one viewing angle at a distance of 1 m to 5 m away using the various Field of View (FOV) settings on the depth sensor, from which resultant point clouds provided DBH estimates using a circle-fitting approach. The optimal capture method was observed at a distance of 2 m using the binned Near Field of View (NFOV) setting. Root Mean Square Error (RMSE) of DBH using this method was 8.43 cm; however, after removing trees with irregular or non-circular stems, this improved to 3.53 cm. Variations in ambient light were observed to have little effect on DBH estimates. The results of this study suggest when in an outdoor environment, the Azure Kinect should be used at a distance no greater than 3 m away, using the binned NFOV sensor setting, for DBH estimates.
}, keywords = {DBH, remote sensing, trees}, doi = {https://doi.org/10.1080/2150704X.2020.1802528}, url = {https://www.tandfonline.com/doi/abs/10.1080/2150704X.2020.1802528}, author = {James McGlade and Luke Wallace and Bryan Hally and Andrew White and Karin Reinke and Simon Jones} } @article {bnh-7501, title = {Quantifying fuel hazard assessments - Fuels3D annual report 2018-2019}, number = {627}, year = {2020}, month = {12/2019}, 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, fuels3D, hazard assessments, quantifying}, issn = {627}, author = {Bryan Hally and Karin Reinke and Luke Wallace and Simon Jones} } @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} } @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} } @mastersthesis {bnh-5777, title = {Methods for background temperature estimation in the context of active fire detection}, volume = {Philosophy}, year = {2019}, month = {08/2019}, school = {RMIT University and the University of Twente}, type = {Doctorate}, address = {Melbourne}, abstract = {

Fire is one of the most important ecological drivers on the planet. Whilst its initial impacts upon the environment are seemingly destructive, fire also plays an important role in the functioning and regeneration of ecosystems, along with providing a catalyst for changes to ecosystem successional processes. With this in mind,\ finding methods to efficiently measure and monitor\ wildfire is of vital importance. Information about fire in the environment can assist\ in mitigation planning and asset management for fire authorities, assists in the minimisation\ of fire impacts on human lives and communities, and of course can help\ to address problems such as carbon accounting and ecosystem impacts of fire. This thesis addresses four research questions, the current accepted method of background temperature estimation imagery and presents a new method of temperature estimation.\ 

}, keywords = {estimation, temperature, Wildfire; Community safety; Risk perception; Mitigation; Decision making}, author = {Bryan Hally} } @article {bnh-5136, title = {Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data}, journal = {International Journal of Digital Earth}, year = {2018}, month = {07/2018}, abstract = {

A vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire{\textquoteright}s absence, assisting in identification and attribution of fire activity. New geostationary sensors increase the data available to describe background temperature in the temporal domain. Broad area methods to extract the expected diurnal cycle of a pixel using this temporally rich data have shown potential for use in fire detection. This paper describes an application of a method for priming diurnal temperature fitting of imagery from the Advanced Himawari Imager. The BAT method is used to provide training data for temperature fitting of target pixels, to which thresholds are applied to detect thermal anomalies in 4 μm imagery over part of Australia. Results show the method detects positive thermal anomalies with respect to the diurnal model in up to 99\% of cases where fires are also detected by Low Earth Orbiting (LEO) satellite active fire products. In absence of LEO active fire detection, but where a burned area product recorded fire-induced change, this method also detected anomalous activity in up to 75\% of cases. Potential improvements in detection time of up to 6 h over LEO products are also demonstrated.

}, doi = {https://doi.org/10.1080/17538947.2018.1497099}, url = {https://www.tandfonline.com/doi/abs/10.1080/17538947.2018.1497099?journalCode=tjde20}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones and Andrew Skidmore} } @article {bnh-5137, title = {Estimating fire background temperature at a geostationary scale - an evaluation of contextual methods for AHI-8}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {08/2018}, chapter = {1368}, abstract = {

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel{\textquoteright}s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel{\textquoteright}s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65\% of a target pixel{\textquoteright}s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50\% increase in temperature variability when using a larger window size than\ 5{\texttimes}5pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4\%{\textendash}4.4\% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

}, doi = {https://doi.org/10.3390/rs10091368}, url = {https://www.mdpi.com/2072-4292/10/9/1368}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones and Chermelle Engel and Andrew Skidmore} } @article {bnh-5338, title = {Estimating Fire Background Temperature at a Geostationary Scale{\textemdash}An Evaluation of Contextual Methods for AHI-8}, journal = {Remote Sensing }, volume = {10}, year = {2018}, month = {08/2018}, chapter = {1368}, abstract = {

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel{\textquoteright}s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel{\textquoteright}s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65\% of a target pixel{\textquoteright}s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50\% increase in temperature variability when using a larger window size than\ 5{\texttimes}5pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4\%{\textendash}4.4\% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

}, keywords = {contextual methods, fire attribution, fire background temperature, geostationary sensors}, doi = {10.3390/rs10091368}, url = {https://www.mdpi.com/2072-4292/10/9/1368}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones and Chermelle Engel and Andrew Skidmore} } @article {bnh-3514, title = {A Broad-Area Method for the Diurnal Characterisation of Upwelling Medium Wave Infrared Radiation}, journal = {Remote Sensing}, volume = {9}, year = {2017}, month = {02/2017}, chapter = {167}, abstract = {

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location{\textquoteright}s recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area{\textquoteright}s local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

}, doi = {10.3390/rs9020167}, url = {http://www.mdpi.com/2072-4292/9/2/167}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones} } @conference {bnh-3892, title = {Enhanced estimation of background temperature for fire detection using new geostationary sensors}, booktitle = {AFAC17}, year = {2017}, month = {09/2017}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Sydney}, abstract = {

Recent increases in the frequency and intensity of active fire has heightened the importance of remote sensing as a source of early warning information for fire incidents. The launches of new geostationary sensors, such as Himawari-8 over the Asia-Pacific, have vastly increased the information available with which to detect and attribute these incidents, with observations every 10 minutes possible over key parts of the electromagnetic spectrum (3.8 -- 4{\textmu}m). Remotely sensed fire products such as Sentinel Hotspots and the MODIS active fire product have focussed upon use of contextually derived background temperatures for isolating hotspots, dictated by the low temporal frequency of available images. This research proposes a new paradigm in fire detection, which utilises the increased temporal resolutions of geostationary sensor imagery to provide a baseline dataset for land surface temperature estimation based upon location and time of day. To achieve this, a multi-temporal diurnal characterisation of temperature is calculated for each pixel based upon a large area latitudinal transect. Hot spot anomalies are then identified based upon the deviation of the location{\textquoteright}s temperature from the expected diurnal cycle. Validation of the fire detection algorithm has focussed upon case study fires from the 2016/17 fire season, by way of inter-comparison with commonly used MODIS and VIIRS active fire products, and a burned area product. Results show increased capability for early fire detection using the new algorithm in comparison to traditional single image contextual algorithms employed for polar orbiting systems. Other advantages include notable resilience to sources of occlusion such as cloud and smoke. Further research will focus on the wider application of this method across the Australian continent and methods for countering more challenging detection conditions.

}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Chathura Wickramasinghe and Simon Jones} } @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} }