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21 pages, 2044 KiB  
Review
Systematic Review of Post-Wildfire Landslides
by Stephen Akosah and Ivan Gratchev
GeoHazards 2025, 6(1), 12; https://doi.org/10.3390/geohazards6010012 - 3 Mar 2025
Viewed by 254
Abstract
This systematic literature review aims to review studies on post-wildfire landslides. A thorough search of Web of Science, Scopus, and other online library sources identified 1580 research publications from 2003 to 2024. Following PRISMA protocols, 75 publications met the inclusion criteria. The analysis [...] Read more.
This systematic literature review aims to review studies on post-wildfire landslides. A thorough search of Web of Science, Scopus, and other online library sources identified 1580 research publications from 2003 to 2024. Following PRISMA protocols, 75 publications met the inclusion criteria. The analysis revealed a growing interest in research trends over the past two decades, with most publications being from 2021 to 2024. This study is divided into categories: (1) systematic review methods, (2) geographical distributions and research trends, and (3) the exploitation of post-wildfire landslides in terms of susceptibility mapping, monitoring, mitigation, modeling, and stability studies. The review revealed that post-wildfire landslides are primarily found in terrains that have experienced wildfires or bushfires and immediately occur after rainfall or a rainstorm—primarily within 1–5 years—which can lead to multiple forms of destruction, including the loss of life and infrastructure. Advanced technologies, including high-resolution remote sensing and machine learning models, have been used to map and monitor post-wildfire landslides, providing some mitigation strategies to prevent landslide risks in areas affected by wildfires. The review highlights the future research prospects for post-wildfire landslides. The outcome of this review is expected to enhance our understanding of the existing information. Full article
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Figure 1
<p>The systematic review process and search outcome based on PRISMA protocol.</p>
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<p>Annual and cumulative research articles by year.</p>
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<p>Publications by subject area of interest.</p>
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<p>Geographical distribution of studies on post-wildfire landslides.</p>
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<p>Keyword co-occurrence cluster analysis.</p>
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<p>Contributions of studies on post-wildfire landslides within subtopics.</p>
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15 pages, 695 KiB  
Article
Exposures of Western Australian Wildland Firefighters: Insights from Real-Time Monitoring
by Kiam Padamsey, Adelle Liebenberg, Ruth Wallace and Jacques Oosthuizen
Fire 2025, 8(3), 98; https://doi.org/10.3390/fire8030098 - 27 Feb 2025
Viewed by 131
Abstract
Background: Inhalation of bushfire smoke is a risk to the health of firefighters, particularly across Australia where bushfires are becoming more frequent and intense. This study aimed to use real-time monitoring devices to assess the particle and chemical exposures of Western Australian [...] Read more.
Background: Inhalation of bushfire smoke is a risk to the health of firefighters, particularly across Australia where bushfires are becoming more frequent and intense. This study aimed to use real-time monitoring devices to assess the particle and chemical exposures of Western Australian firefighters during prescribed burns and bushfires. Methods: Participants included volunteer bushfire firefighters and forestry firefighters. Real-time gas and particulate monitors were used across nine unique fire events to evaluate the occupational exposures of firefighters. Findings: Firefighters (n = 40) were exposed to high concentrations of particulate matter (PM), particularly PM10, with concentrations varying widely between individuals and events. Exposures to carbon monoxide (CO) and volatile organic compounds (VOCs) were observed at elevated levels. No significant elevation in internal polycyclic aromatic hydrocarbons (PAHs) was observed. Conclusions: This study highlights the importance of respiratory protective equipment (RPE) and the need for health monitoring programmes for firefighters. Prescribed burns appear reflective of exposures at bushfires and could serve as valuable experimental settings for refining firefighting strategies and protective practises. Full article
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<p>Boxplot of firefighter average shift exposure to CO (ppm) and PM10 (ug/m<sup>3</sup>) stratified by personal exposure rating.</p>
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30 pages, 27446 KiB  
Article
Experimental and Numerical Studies of Window Shutters Under Bushfire Radiant Heat Exposure
by Birunthan Perinpalingam, Anthony Ariyanayagam and Mahen Mahendran
Fire 2025, 8(3), 94; https://doi.org/10.3390/fire8030094 - 25 Feb 2025
Viewed by 219
Abstract
The growing intensity and frequency of bushfires across the globe pose serious threats to building safety when it comes to the vulnerability of glass windows. During bushfires, extreme heat can cause significant damage to these windows, creating openings that allow embers, radiant heat, [...] Read more.
The growing intensity and frequency of bushfires across the globe pose serious threats to building safety when it comes to the vulnerability of glass windows. During bushfires, extreme heat can cause significant damage to these windows, creating openings that allow embers, radiant heat, and flames to enter buildings. This study investigated the effectiveness of various construction materials, including thin steel sheets, glass fibre blankets, aluminium foil layers, and intumescent layers on glass fibre blankets, as bushfire-resistant shutters for protecting windows in bushfire-prone areas. The shutters were tested under two scenarios of radiant heat exposure: rapid and prolonged exposures of 11 and 47 min, respectively. Heat transfer models of the tested shutters were developed and validated using fire test results, and then comparisons of the performance of materials were made through parametric studies for bushfire radiant heat exposure. The results show that a 0.4 mm glass fibre blanket with aluminium foil performed best, with very low glass temperatures and ambient heat fluxes due to the reflective properties of the foil. Similarly, a thin steel sheet (1.2 mm) also effectively maintained low glass temperatures and ambient heat fluxes. Additionally, graphite-based intumescent coating on a glass fibre blanket reduced the ambient heat flux. These results highlight the importance of bushfire-resistant shutters and provide valuable insights for improving their design and performance. Full article
(This article belongs to the Special Issue Advances in Building Fire Safety Engineering)
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Graphical abstract

Graphical abstract
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<p>Radiant heat profiles: (<b>a</b>) 11 min profiles in AS 1530.8.1 and (<b>b</b>) 47 min profile in NASH Bushfire Standard.</p>
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<p>Specimen-holding frame and test set-up at QUT Banyo Laboratory: (<b>a</b>) holding frame (<b>b</b>); wall and window frame; (<b>c</b>) test set-up; and (<b>d</b>) window.</p>
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<p>BAL-40 radiant heat curves: (<b>a</b>) rapid 11 min curve and (<b>b</b>) prolonged 47 min curve.</p>
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<p>Front and rear views of the experimental set-up: (<b>a</b>) fire side and (<b>b</b>) ambient side.</p>
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<p>Test specimens: (<b>a</b>) Test 1—1.2 mm steel sheet; (<b>b</b>) Test 2—0.4 mm glass fibre blanket; (<b>c</b>) Test 3—0.4 mm glass fibre blanket with aluminium foil; and (<b>d</b>) Test 4—0.4 mm glass fibre blanket with intumescent coating.</p>
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<p>Locations of temperature and heat flux sensors.</p>
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<p>Fire-side and ambient-side heat fluxes and shutter and glass temperatures of Test 1—1.2 mm steel sheet: (<b>a</b>) front heat flux; (<b>b</b>) ambient heat flux (at 365 mm); (<b>c</b>) front shutter temperature; (<b>d</b>) rear shutter temperature; (<b>e</b>) front glass temperature; and (<b>f</b>) rear glass temperature.</p>
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<p>Fire-side and ambient-side heat fluxes and shutter and glass temperatures of Test 2—0.4 mm thick glass fibre blanket: (<b>a</b>) front heat flux; (<b>b</b>) ambient heat flux (at 365 mm); (<b>c</b>) front shutter temperature; (<b>d</b>) rear shutter temperature; (<b>e</b>) front glass temperature; and (<b>f</b>) rear glass temperature.</p>
Full article ">Figure 8 Cont.
<p>Fire-side and ambient-side heat fluxes and shutter and glass temperatures of Test 2—0.4 mm thick glass fibre blanket: (<b>a</b>) front heat flux; (<b>b</b>) ambient heat flux (at 365 mm); (<b>c</b>) front shutter temperature; (<b>d</b>) rear shutter temperature; (<b>e</b>) front glass temperature; and (<b>f</b>) rear glass temperature.</p>
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<p>Fire-side and ambient-side heat fluxes and shutter and glass temperatures of Test 3—0.4 mm thick glass fibre blanket with 35-micron aluminium foil: (<b>a</b>) front heat flux; (<b>b</b>) ambient heat flux (at 365 mm); (<b>c</b>) front shutter temperature; (<b>d</b>) rear shutter temperature; (<b>e</b>) front glass temperature; and (<b>f</b>) rear glass temperature.</p>
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<p>Comparison of selected results from Tests 2 and 3: (<b>a</b>) front glass temperature and (<b>b</b>) ambient heat flux (at 365 mm).</p>
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<p>Fire-side and ambient-side heat fluxes and shutter and glass temperatures of Test 4—0.4 mm glass fibre blanket with 0.9 mm graphite-based intumescent coating: (<b>a</b>) front heat flux; (<b>b</b>) ambient heat flux (at 365 mm); (<b>c</b>) front shutter temperature; (<b>d</b>) rear shutter temperature; (<b>e</b>) front glass temperature; and (<b>f</b>) rear glass temperature.</p>
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<p>Test 4 specimen (<b>a</b>) during the test and (<b>b</b>) post-test.</p>
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<p>Typical model of shutter and glass for radiant heat exposure.</p>
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<p>Additional wind speed used in front of the specimen to incorporate the wind speed from the radiant heater in FDS modelling: (<b>a</b>) 11 min model and (<b>b</b>) 47 min model.</p>
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<p>Thermal properties of the materials used in FDS modelling: (<b>a</b>) steel sheet [<a href="#B31-fire-08-00094" class="html-bibr">31</a>]; (<b>b</b>) glass fibre blanket [<a href="#B32-fire-08-00094" class="html-bibr">32</a>]; and (<b>c</b>) glass [<a href="#B33-fire-08-00094" class="html-bibr">33</a>].</p>
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<p>FDS modelling results compared with Test 1 results—1.2 mm steel sheet: (<b>a</b>) front heat flux; (<b>b</b>) front shutter temperature; (<b>c</b>) rear shutter temperature; (<b>d</b>) front glass temperature; and (<b>e</b>) rear glass temperature.</p>
Full article ">Figure 16 Cont.
<p>FDS modelling results compared with Test 1 results—1.2 mm steel sheet: (<b>a</b>) front heat flux; (<b>b</b>) front shutter temperature; (<b>c</b>) rear shutter temperature; (<b>d</b>) front glass temperature; and (<b>e</b>) rear glass temperature.</p>
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<p>FDS modelling results compared with Test 2 results—0.4 mm thick glass fibre blanket: (<b>a</b>) front heat flux; (<b>b</b>) front shutter temperature; (<b>c</b>) rear shutter temperature; (<b>d</b>) front glass temperature; and (<b>e</b>) rear glass temperature.</p>
Full article ">Figure 17 Cont.
<p>FDS modelling results compared with Test 2 results—0.4 mm thick glass fibre blanket: (<b>a</b>) front heat flux; (<b>b</b>) front shutter temperature; (<b>c</b>) rear shutter temperature; (<b>d</b>) front glass temperature; and (<b>e</b>) rear glass temperature.</p>
Full article ">Figure 18
<p>FDS modelling results compared with Test 3 results—0.4 mm thick glass fibre blanket with 35-micron aluminium foil: (<b>a</b>) front heat flux; (<b>b</b>) front shutter temperature; (<b>c</b>) rear shutter temperature; (<b>d</b>) front glass temperature; and (<b>e</b>) rear glass temperature.</p>
Full article ">Figure 18 Cont.
<p>FDS modelling results compared with Test 3 results—0.4 mm thick glass fibre blanket with 35-micron aluminium foil: (<b>a</b>) front heat flux; (<b>b</b>) front shutter temperature; (<b>c</b>) rear shutter temperature; (<b>d</b>) front glass temperature; and (<b>e</b>) rear glass temperature.</p>
Full article ">Figure 19
<p>Parametric study results (ST—1.2 mm steel sheet; GF—0.4 mm glass fibre blanket; AL—0.4 mm glass fibre blanket with 35-micron aluminium foil): (<b>a</b>) front heat flux; (<b>b</b>) average front shutter temperature; (<b>c</b>) average rear shutter temperature; (<b>d</b>) average front glass temperature; and (<b>e</b>) average rear glass temperature.</p>
Full article ">Figure 19 Cont.
<p>Parametric study results (ST—1.2 mm steel sheet; GF—0.4 mm glass fibre blanket; AL—0.4 mm glass fibre blanket with 35-micron aluminium foil): (<b>a</b>) front heat flux; (<b>b</b>) average front shutter temperature; (<b>c</b>) average rear shutter temperature; (<b>d</b>) average front glass temperature; and (<b>e</b>) average rear glass temperature.</p>
Full article ">Figure 19 Cont.
<p>Parametric study results (ST—1.2 mm steel sheet; GF—0.4 mm glass fibre blanket; AL—0.4 mm glass fibre blanket with 35-micron aluminium foil): (<b>a</b>) front heat flux; (<b>b</b>) average front shutter temperature; (<b>c</b>) average rear shutter temperature; (<b>d</b>) average front glass temperature; and (<b>e</b>) average rear glass temperature.</p>
Full article ">Figure 20
<p>Comparison of incident, radiative, convective and net heat fluxes in Test 1 (1.2 mm steel sheet) from the parametric study: (<b>a</b>) 11 min test and (<b>b</b>) 47 min test.</p>
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15 pages, 3906 KiB  
Article
Performance Comparison of Solid Walls and Porous Fences in Protecting Structures from Firebrand Attack
by Ahmad Sharifian
Fire 2025, 8(3), 88; https://doi.org/10.3390/fire8030088 - 21 Feb 2025
Viewed by 247
Abstract
In bushfire-prone regions, solid walls and porous fences are commonly employed as mitigation measures against windblown embers (firebrands). This computational study evaluates and compares the performance of a 2 m high solid wall and a 2 m porous fence (38% porosity) in protecting [...] Read more.
In bushfire-prone regions, solid walls and porous fences are commonly employed as mitigation measures against windblown embers (firebrands). This computational study evaluates and compares the performance of a 2 m high solid wall and a 2 m porous fence (38% porosity) in protecting structures from firebrand showers. Using a numerical model subjected to free-stream velocities of up to 50 m/s, flow patterns and firebrand trajectories were analyzed. The findings indicate that impermeable walls offer superior protection for immediately adjacent structures by deflecting the incident flow upwards, creating a “jump board” effect. However, the deflected flow subsequently reattaches to the ground at a downstream distance, rendering structures further downwind vulnerable to ember attack. The porous fence also exhibits a similar, albeit less pronounced, upward deflection. The simulations reveal minimal flow descent downstream of the fence at lower free-stream velocities, suggesting extended downwind protection. In the immediate downstream vicinity of the porous fence, penetration by small firebrands is possible; however, prior studies have shown that the likelihood of ignition from these embers is minimal and decreases rapidly within a short downstream distance of several metres. Full article
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<p>Initial mesh configurations: (<b>a</b>) mesh around the solid wall; (<b>b</b>) mesh around the top wires of the porous fence (38% porosity).</p>
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<p>Final mesh configurations: (<b>a</b>) mesh around the top of the solid wall; (<b>b</b>) mesh around the top wires of the porous fence (38% porosity).</p>
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<p>Flow around a 2 m high wall at a free-stream velocity of 10 m/s and 35 °C. (<b>a</b>) Streamlines within the computational domain; (<b>b</b>) velocity vectors near the wall; (<b>c</b>) pressure distribution showing high- and low-pressure regions; (<b>d</b>) magnified view of velocity vectors near the wall; (<b>e</b>) downstream streamlines showing vortex formation and downward trajectory; (<b>f</b>) streamlines returning to ground level.</p>
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<p>Flow around a 2 m high porous fence (38% porosity) at a free-stream velocity of 10 m/s and 35 °C. (<b>a</b>) Streamlines within the computational domain; (<b>b</b>) close-up view of streamlines near the fence; (<b>c</b>) pressure contours near the top wires; (<b>d</b>) magnified view of pressure variations around two top wires; (<b>e</b>) velocity vectors around a single wire; (<b>f</b>) velocity vectors near the topmost wires.</p>
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<p>Streamline distribution around the fence with 38% porosity, subjected to a free-stream velocity of 1 m/s and an ambient air temperature of 35 °C.</p>
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24 pages, 13025 KiB  
Article
Modelling LiDAR-Based Vegetation Geometry for Computational Fluid Dynamics Heat Transfer Models
by Pirunthan Keerthinathan, Megan Winsen, Thaniroshan Krishnakumar, Anthony Ariyanayagam, Grant Hamilton and Felipe Gonzalez
Remote Sens. 2025, 17(3), 552; https://doi.org/10.3390/rs17030552 - 6 Feb 2025
Viewed by 739
Abstract
Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the [...] Read more.
Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)
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<p>Main steps of the proposed methodology.</p>
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<p>The WUI study site in Samford, Queensland. (<b>a</b>) The elevated building in the study site. (<b>b</b>) The vegetation surrounding the elevated building. (<b>c</b>) Location of study site in relation to Brisbane, Queensland.</p>
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<p>Survey paths (red lines) and point clouds generated from (<b>a</b>) handheld LiDAR survey and (<b>b</b>) UAS–LiDAR survey.</p>
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<p>(<b>a</b>) UAS–LiDAR and handheld LiDAR survey and (<b>b</b>) in situ data collection including DBH and height of surrounding vegetation.</p>
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<p>Visualization of branch voxelization process, showing (<b>a</b>) triangulated mesh, (<b>b</b>) point-to-mesh face distances in a colour continuum from blue (negative distances) through green (distance = 0) to red (positive distances), and (<b>c</b>) voxelized stem.</p>
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<p>Suboptimal voxelization result, showing (<b>a</b>) triangulated mesh of intersecting branches, (<b>b</b>) point distances from mesh faces in a colour continuum from blue (negative distances) through green (distance = 0) to red (positive distances), and (<b>c</b>) inadequate voxelization voxels excluded.</p>
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<p>Non-manifold edge on intersecting faces.</p>
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<p>Visualization of <span class="html-italic">V<sub>t</sub></span>, <span class="html-italic">V</span>, <span class="html-italic">V<sub>m</sub></span>, and the normal vector of the inner and outer face pairs for the selected non-manifold edge.</p>
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<p>Output of the automated Raycloudtools segmentation showing 21 trees closest to the building.</p>
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<p>Volume estimates from 3D meshes reconstructed with three tree reconstruction tools vs. volumes estimated with a biomass allometric equation (BAE) using field-measured height and DBH.</p>
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<p>The <span class="html-italic">Eucalyptus siderophloia</span> selected for geometric representation, depicted in (<b>a</b>) a photograph taken during the handheld LiDAR survey and (<b>b</b>) the manually segmented point cloud.</p>
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<p>The reconstructed mesh of the <span class="html-italic">Eucalyptus siderophloia</span> produced by (<b>a</b>) TreeQSM, (<b>b</b>) AdTree, and (<b>c</b>) Raycloudtools.</p>
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<p>(<b>a</b>) The result of the face differentiation process at a cylinder intersection in which the outer faces (shown in red) have been retained in the mesh, which is a surface representation. (<b>b</b>) The inner faces (shown in yellow) were discarded.</p>
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<p>The result of removing the border faces depicted in red in (<b>a</b>) and (<b>b</b>) can be seen in (<b>c</b>), which shows a cylinder intersection from which all border faces have been eliminated.</p>
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<p>The end result of our voxelization process showing the successful geometric representation (blue squares) of branches. The red squares denote the voxels that were missed while the inner faces were present.</p>
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<p>Geometric representations of (<b>a</b>) a deciduous <span class="html-italic">Eucalyptus siderophloia</span>, and (<b>b</b>) a coniferous <span class="html-italic">Araucaria bidwillii</span>.</p>
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<p>FDS model of Eucalyptus siderophloia (tree #20) showing particles identified as (<b>a</b>) foliage, (<b>b</b>) wooden core and bark, and (<b>c</b>) a comprehensive view of the whole tree.</p>
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<p>Simulated fire spread in the FDS tree model.</p>
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<p>(<b>a</b>) The incomplete point cloud of a <span class="html-italic">Callitris columellaris</span> (tree #9) where the trunk is hidden by foliage, and the 3D mesh reconstructions of this tree produced by (<b>b</b>) AdTree (<b>c</b>) TreeQSM, and (<b>d</b>) Raycloudtools.</p>
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21 pages, 6490 KiB  
Article
Uncertainty Modelling of Groundwater-Dependent Vegetation
by Todd P. Robinson, Lewis Trotter and Grant W. Wardell-Johnson
Land 2024, 13(12), 2208; https://doi.org/10.3390/land13122208 - 17 Dec 2024
Viewed by 507
Abstract
Groundwater-dependent vegetation (GDV) is threatened globally by groundwater abstraction. Water resource managers require maps showing its distribution and habitat preferences to make informed decisions on its protection. This study, conducted in the southeast Pilbara region of Western Australia, presents a novel approach based [...] Read more.
Groundwater-dependent vegetation (GDV) is threatened globally by groundwater abstraction. Water resource managers require maps showing its distribution and habitat preferences to make informed decisions on its protection. This study, conducted in the southeast Pilbara region of Western Australia, presents a novel approach based on metrics summarising seasonal phenology (phenometrics) derived from Sentinel-2 imagery. We also determined the preferential habitat using ecological niche modelling based on land systems and topographic derivatives. The phenometrics and preferential habitat models were combined using a framework that allows for the expression of different levels of uncertainty. The large integral (LI) phenometric was capable of discriminating GDV and reduced the search space to 111 ha (<1%), requiring follow-up monitoring. Suitable habitat could be explained by a combination of land systems and negative topographic positions (e.g., valleys). This designated 13% of the study area as requiring protection against the threat of intense bushfires, invasive species, land clearing and other disturbances. High uncertainty represents locations where GDV appears to be absent but the habitat is suitable and requires further field assessment. Uncertainty was lowest at locations where the habitat is highly unsuitable (87%) and requires infrequent revisitation. Our results provide timely geospatial intelligence illustrating what needs to be monitored, protected and revisited by water resource managers. Full article
(This article belongs to the Special Issue Geospatial Data in Landscape Ecology and Biodiversity Conservation)
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Figure 1
<p>Study area located on the southeastern boundary of the Pilbara bioregion in the arid zone of Australia. Elevation was sourced from the SRTM mission. Arid zone is based on Zomer et al. [<a href="#B42-land-13-02208" class="html-bibr">42</a>]. The Pilbara bioregion is based on the IBRA (2012) version 7 dataset [<a href="#B41-land-13-02208" class="html-bibr">41</a>]. Land system abbreviations are defined in <a href="#land-13-02208-t0A2" class="html-table">Table A2</a>.</p>
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<p>(<b>A</b>) Groundwater-dependent vegetation species appear as “green islands” in the background in contrast to the saltbush (<span class="html-italic">Atriplex</span> sp.) in the foreground, which has no groundwater dependence. (<b>B</b>) Example of a healthy <span class="html-italic">Eucalyptus victrix</span> with a health rating of 5 (see text). (<b>C</b>) Example of a <span class="html-italic">E. calmuldulensis</span> with a health rating of 4 due to some leaf loss on lower limbs. (<b>D</b>) Example of a healthy <span class="html-italic">Melaleuca argentea</span>. (<b>E</b>) <span class="html-italic">Acacia</span> species, which are common vadophytes in the uplands. (<b>F</b>) Dead mature <span class="html-italic">M. argentea</span> tree. (<b>G</b>) Dead <span class="html-italic">E. calmuldulensis</span>.</p>
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<p>Illustration of the phenometrics extracted from the Sentinel time series. See <a href="#land-13-02208-t001" class="html-table">Table 1</a> for definitions.</p>
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<p>Correlation matrix with individual area under the curve (AUC) statistics for (<b>A</b>) phenometric variables defined in <a href="#land-13-02208-t001" class="html-table">Table 1</a> and (<b>B</b>) DEM derivatives (CON = convexity, TPI = topographic position index, SWI = SAGA wetness index) used in ecological niche modelling.</p>
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<p>Response curves for each of the retained variables of (<b>A</b>) the large integral, where MAVI is expressed as an 8-byte integer. The probability of GDV increases with higher values of the large integral. (<b>B</b>) The topographic position index showing the probability of GDV increases when it is negative. (<b>C</b>) Land systems showing the majority of GDV samples are found within the “River” land system (RGERIV) with a minor association with the “Newman” land system (RGENEW). See <a href="#land-13-02208-t0A2" class="html-table">Table A2</a> for other definitions.</p>
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<p>Groundwater-dependent vegetation model results. (<b>A</b>) The BELIEF map, which represents locations in support of GDV presence. Thresholding GDV is delineated in blue. (<b>B</b>) The DISBELIEF map, which represents locations in support of GDV absence. (<b>C</b>) The PLAUSIBILITY map, which illustrates potential GDV habitat that needs protection. Suggested habitat for protection is delineated in purple. (<b>D</b>) The BELIEF INTERVAL map, where high belief intervals present an opportunity for further sampling to reduce uncertainty.</p>
Full article ">Figure 7
<p>Receiver operating characteristic (ROC) curves illustrating the true and false-positive rates overall threshold values. Chosen thresholds are shown in blue and purple and correspond to the models of (<b>A</b>) belief and (<b>B</b>) plausibility, respectively, and relate to the delineations in the same colour in <a href="#land-13-02208-f006" class="html-fig">Figure 6</a>.</p>
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34 pages, 23866 KiB  
Article
Experimental and Numerical Investigations of Crest-Fixed Corrugated Steel Claddings Under Wind Uplift Loading at Elevated Temperatures
by Lisa Pieper and Mahen Mahendran
Fire 2024, 7(12), 473; https://doi.org/10.3390/fire7120473 (registering DOI) - 12 Dec 2024
Viewed by 601
Abstract
The 2019–2020 Black Summer bushfire in Australia is a good example of the frequent and severe bushfires (wildfires) observed around the world in recent years. Fire-enhanced winds and fire–wind interactions during those bushfire events have caused increased wind velocities in the vicinity of [...] Read more.
The 2019–2020 Black Summer bushfire in Australia is a good example of the frequent and severe bushfires (wildfires) observed around the world in recent years. Fire-enhanced winds and fire–wind interactions during those bushfire events have caused increased wind velocities in the vicinity of a bushfire front. This can lead to a premature failure of the building envelope, making it vulnerable to ember attack and direct flame contact. In Australia, crest-fixed cold-formed steel (CFS) claddings are commonly used for buildings in bushfire-prone areas because of their non-combustibility. Therefore, this study investigated the pull-through failure behaviour of corrugated CFS claddings under wind uplift/suction loading at elevated temperatures, simulating fire-enhanced winds during a bushfire by means of experimental and numerical studies. Experimental results showed a negligible influence of the thermal expansion of the cladding system on the pull-through failure behaviour, while a significant decrease in pull-through capacity was observed with increasing temperatures. Suitable finite element models were developed, validated and used in a detailed numerical parametric study. Based on the findings from these studies, a design equation was proposed for the pull-through capacity of the crest-fixed corrugated claddings at elevated temperatures. Full article
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<p>Damage to the building envelope caused by fire–wind interaction during the 2009 Black Saturday bushfire [<a href="#B4-fire-07-00473" class="html-bibr">4</a>].</p>
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<p>Pull-through failures of crest-fixed CFS claddings: (<b>a</b>) local plastic dimpling of corrugated cladding and (<b>b</b>) splitting failure of trapezoidal cladding.</p>
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<p>(<b>a</b>) Overall test set-up and (<b>b</b>) loading system.</p>
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<p>(<b>a</b>) Screw arrangement and (<b>b</b>) 15 kN load cells attached to the critical screws underneath the cladding.</p>
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<p>Locations of laser sensors and load cells on the corrugated cladding specimen.</p>
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<p>(<b>a</b>) Movable frame and insulation layers between cladding and heating blanket and (<b>b</b>) test set-up for elevated temperature tests.</p>
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<p>Locations of thermocouples on the surface of the corrugated cladding.</p>
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<p>Validation of the load–deflection curve at ambient temperature.</p>
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<p>Load–deflection curves at ambient temperature.</p>
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<p>Local dimpling failure at the central support at (<b>a</b>) ambient temperature, (<b>b</b>) 200 °C, (<b>c</b>) 400 °C and (<b>d</b>) localised failure at the critical screw fastener.</p>
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<p>Displacement versus temperature curves at 400 °C.</p>
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<p>Load versus temperature curves at 200 °C and 400 °C.</p>
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<p>Overall applied load-deflection curves.</p>
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<p>Midspan line-loading FE model of corrugated cladding. Note: Different screw labelling compared with the test set-up in <a href="#fire-07-00473-f005" class="html-fig">Figure 5</a>.</p>
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<p>Boundary conditions and load application of (<b>a</b>) the corrugated FE model, (<b>b</b>) the screw head and washer and (<b>c</b>) at the screw fastener hole.</p>
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<p>Mesh of the midspan line-load FE model of corrugated cladding.</p>
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<p>FE model of corrugated cladding subject to uniform pressure loading.</p>
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<p>Load–deflection curves based on (<b>a</b>) the overall applied load from the line-load FE models and (<b>b</b>) the critical screw fastener load in the line-load FE model.</p>
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<p>Comparison of the localised dimpling failure around the critical screw fastener at (<b>a</b>) ambient temperature, (<b>b</b>) 200 °C and (<b>c</b>) 400 °C.</p>
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<p>Load–deflection curves of the critical screw in the large-scale FE model.</p>
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<p>Stress distribution at the pull-through failure of corrugated cladding at 600 °C.</p>
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<p>Influence of Young’s modulus on the pull-through capacity.</p>
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<p>Influence of pitch on the pull-through capacity.</p>
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<p>Influence of crest height on the pull-through capacity.</p>
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<p>Influence of radius on the pull-through capacity.</p>
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<p>Influence of span on the pull-through capacity.</p>
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<p>Load reduction factors from uniform pressure loading and line-loading FE models and line-load and small-scale tests.</p>
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17 pages, 759 KiB  
Article
Better Be Ready! Evacuation Experiences During a Bushfire Emergency
by Carina C. Anderson, Susan F. Rockloff, Lucinda P. Burton, Victoria R. Terry, Sally K. Jensen, Anne T. Nolan and Peter C. Terry
Fire 2024, 7(12), 458; https://doi.org/10.3390/fire7120458 - 5 Dec 2024
Viewed by 944
Abstract
This paper reports on research undertaken for the Building Resilience for Bushfire-Affected Communities in Noosa Shire project, funded by the Australian Government. Being evacuated from a home in the path of a bushfire can be traumatic. Therefore, it is important for evacuees to [...] Read more.
This paper reports on research undertaken for the Building Resilience for Bushfire-Affected Communities in Noosa Shire project, funded by the Australian Government. Being evacuated from a home in the path of a bushfire can be traumatic. Therefore, it is important for evacuees to have safe places to stay, both physically and psychologically. Using a qualitative approach, we aimed to (a) understand the experiences of people who were displaced from their homes and sheltered at evacuation centres during the Noosa Shire bushfires and (b) understand what support is needed during disasters, such as bushfires, to help create positive experiences for future evacuees. Twelve participants displaced by bushfires in Noosa, Australia, in 2019 recalled their experiences in semi-structured interviews (conducted in 2022–2023). Inductive thematic analysis using NVivo 13 identified three themes that can inform government and public disaster preparation and response: planning, support, and communication. Findings from this study centred around building community resilience and offer valuable insights for organising disaster evacuation processes and evacuation centres on a broader scale. For individuals, it involves planning optimal evacuation routes, gathering necessary personal items, feeling safe and calm in evacuation centres, and receiving regular and accurate communication from authorities during disaster events. Full article
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<p>Bureau of Meteorology rainfall map showing unprecedented dry conditions and widespread decline in rainfall across Australia in 2019. (Image available under Creative Commons Attribution CC-BY 3.0 AU Deed 3.0).</p>
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<p>Diagrammatic representation of the core theme Building Community Resilience informed by Planning, Support, and Communication.</p>
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23 pages, 8345 KiB  
Article
Daily PM2.5 and Seasonal-Trend Decomposition to Identify Extreme Air Pollution Events from 2001 to 2020 for Continental Australia Using a Random Forest Model
by Nicolas Borchers-Arriagada, Geoffrey G. Morgan, Joseph Van Buskirk, Karthik Gopi, Cassandra Yuen, Fay H. Johnston, Yuming Guo, Martin Cope and Ivan C. Hanigan
Atmosphere 2024, 15(11), 1341; https://doi.org/10.3390/atmos15111341 - 8 Nov 2024
Cited by 1 | Viewed by 1171
Abstract
Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to [...] Read more.
Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m3. We predicted daily PM2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM2.5 attributable to extreme pollution events in different locations across the country. Full article
(This article belongs to the Section Air Quality)
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<p>Summarized methods.</p>
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<p>Australian State/Territories with 185 PM monitoring stations (black dots), and climate (temperature/humidity) zones as defined by the Australian Bureau of Meteorology (<a href="http://www.bom.gov.au/climate/maps/averages/climate-classification/" target="_blank">http://www.bom.gov.au/climate/maps/averages/climate-classification/</a> (accessed on 15 September 2024)). NT = Northern Territory, QLD = Queensland, NSW = New South Wales, ACT = Australian Capital Territory, TAS = Tasmania, VIC = Victoria, SA = South Australia, WA = Western Australia.</p>
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<p>R-squared between predicted and observed PM<sub>2.5</sub>: (<b>A</b>) out of bag—daily, (<b>B</b>) testing set—daily, (<b>C</b>) training set—daily, (<b>D</b>) complete dataset—daily, (<b>E</b>) complete dataset—monthly, and (<b>F</b>) complete dataset—annual. NOTE: dashed red line represents the identity function.</p>
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<p>Correlation (Pearson’s) between predicted and observed PM<sub>2.5</sub> for each monitoring site.</p>
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<p>PM<sub>2.5</sub> prediction results (μg/m<sup>3</sup>): (<b>a</b>) mean PM<sub>2.5</sub> concentrations Jan 2001–June 2020, (<b>b</b>) standard deviation (SD) of PM<sub>2.5</sub> concentrations Jan 2001–June 2020, (<b>c</b>) Population-weighted mean PM<sub>2.5</sub> concentration by financial year 2001–2020 and State/Territory. (*) A financial year starts on July 1 and ends on June 30 (i.e., the 2001 financial year runs from 1 July 2001 to 30 June 2002).</p>
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<p>Example of extreme air pollution days identified with 95th percentile and 2SD remainder flags for: (<b>A</b>,<b>B</b>) Launceston 2005–2007 showing three winter smoke seasons and the 2006/2007 bushfire season, (<b>C</b>,<b>D</b>) Darwin 2011–2013 showing impact on smoke during three dry seasons, and (<b>E</b>,<b>F</b>) Sydney July 2017 to June 2020 showing three winter smoke seasons and the devastating 2019/2020 Black summer bushfires. NOTES: (1) For comparison purposes, panels (<b>E</b>,<b>F</b>) <span class="html-italic">y</span>-axis do not show values above 50 µg/m<sup>3</sup>. (2) For illustration purposes “seasonal + trend” and “2SD remainder + seasonal + trend” time series have been smoothed, and probable smoke days flagged with these.</p>
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8 pages, 757 KiB  
Reply
Conservation Agendas and the Denial of History. Reply to Penna, I. and Feller, M.C. Comments on “Laming et al. The Curse of Conservation: Empirical Evidence Demonstrating That Changes in Land-Use Legislation Drove Catastrophic Bushfires in Southeast Australia. Fire 2022, 5, 175”
by Michael-Shawn Fletcher, Anthony Romano, Simon Connor, Alice Laming, S. Yoshi Maezumi, Michela Mariani, Russell Mullett and Patricia S. Gadd
Fire 2024, 7(11), 391; https://doi.org/10.3390/fire7110391 - 30 Oct 2024
Viewed by 2004
Abstract
This is a reply to the comments of Penna [...] Full article
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<p>Quantifying the differences in tree/shrub cover using aerial photography and GIS. (<b>a</b>) 1969 aerial photograph CAD7011-134 (16 January 1969) over Snowy River National Park [<a href="#B34-fire-07-00391" class="html-bibr">34</a>]. (<b>b</b>) Focus area of the 1969 aerial photograph CAD7011-134 (16 January 1969) (~37°29.392′ S, 148°20.544′ E) [<a href="#B34-fire-07-00391" class="html-bibr">34</a>]. (<b>c</b>) Raster result of inferred canopy cover change between 1969 and 1986 aerial photographs. The darker the colour (green), the greater the shift between light-frequency band inferred canopy cover between the 1969 and 1986 aerial photographs. Raw aerial photographs were processed in Adobe Photoshop 2023 to standardise the two photographs before quantifying temporal changes in ArcGIS Pro 3.1.0 [<a href="#B28-fire-07-00391" class="html-bibr">28</a>,<a href="#B29-fire-07-00391" class="html-bibr">29</a>,<a href="#B30-fire-07-00391" class="html-bibr">30</a>,<a href="#B31-fire-07-00391" class="html-bibr">31</a>,<a href="#B32-fire-07-00391" class="html-bibr">32</a>,<a href="#B33-fire-07-00391" class="html-bibr">33</a>]. (<b>d</b>) 1986 aerial photograph CAD2822-209 (29 January 1986) over Snowy River National Park [<a href="#B34-fire-07-00391" class="html-bibr">34</a>]. (<b>e</b>) Focus area of the 1986 aerial photograph CAD7011-134 (16 January 1969) (~37°29.392′ S, 148°20.544′ E) [<a href="#B34-fire-07-00391" class="html-bibr">34</a>]. (<b>f</b>) Frequency distribution plot of the magnitude of change (%) and the proportion of inferred canopy change (%) within the study area presented in (<b>c</b>). Table insert demonstrates the magnitude of change and proportion of pixels, with 88.10% of pixels demonstrating &gt; 20% of inferred canopy cover change between 1969 and 1986.</p>
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18 pages, 6929 KiB  
Article
Characterising the Chemical Composition of Bushfire Smoke and Implications for Firefighter Exposure in Western Australia
by Kiam Padamsey, Adelle Liebenberg, Ruth Wallace and Jacques Oosthuizen
Fire 2024, 7(11), 388; https://doi.org/10.3390/fire7110388 - 28 Oct 2024
Viewed by 1352
Abstract
This study evaluates bushfire smoke as a workplace hazard for firefighters by characterising its chemical composition and potential health risks in Western Australia. Portable Fourier Transform Infrared (FTIR) spectrometry was used to measure airborne chemical concentrations at prescribed burns across five regions, including [...] Read more.
This study evaluates bushfire smoke as a workplace hazard for firefighters by characterising its chemical composition and potential health risks in Western Australia. Portable Fourier Transform Infrared (FTIR) spectrometry was used to measure airborne chemical concentrations at prescribed burns across five regions, including peat (acid sulphate) fire events. Samples were collected during both flaming and smouldering phases, as well as in perceived “clear” air resting zones. Results indicated that carbon monoxide (CO) was the dominant gas, reaching concentrations of 205 ppm at the fire front, followed by nitrogen monoxide (26 ppm) and methane (19 ppm). Peat fires produced distinct profiles, with ammonia (21.5 ppm) and sulphur dioxide (9.5 ppm) concentrations higher than those observed in typical bushfires. Smouldering phases emitted higher chemical concentrations than flaming phases 75% of the time. Even clear air zones contained measurable chemicals, with CO levels averaging 18 ppm, suggesting that firefighters are not free from exposure during rest periods. These findings highlight the need for fit-for-purpose respiratory protective equipment (RPE) and improved rest protocols to minimise exposure. The study underscores the importance of comprehensive health monitoring programs for firefighters to mitigate long-term health risks. Full article
(This article belongs to the Special Issue Patterns, Drivers, and Multiscale Impacts of Wildland Fires)
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<p>Scenes at a typical burn.</p>
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<p>The principal researcher in full wildland PPE collecting data inside a smouldering area of Jarrah Forest.</p>
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<p>The dense vegetation of Blackwood Forest.</p>
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<p>Manjimup peat fire, pictured with the Gasmet Technology DX4040.</p>
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<p>The sandy soils and low-lying vegetation of Wilbinga, pictured with the DBCA commencing a prescribed burn.</p>
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<p>The laterite soils and kwongan heathland of Badgingarra.</p>
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<p>The tall, open canopies and sparse groundcover of the Julimar region.</p>
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<p>The ten most prevalent chemicals present in bushfire smoke, reported in parts per million (ppm).</p>
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<p>The tenth to nineteenth most prevalent gases in general bushfire smoke reported in ppm.</p>
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<p>The ten most prevalent chemicals present in peat fire smoke, reported in ppm.</p>
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<p>Mean concentration of chemicals present inside prescribed burn smoke across five unique ecoregions in WA expressed as parts per million (ppm). * Sulphur dioxide concentration at Manjimup was 9.5 ppm.</p>
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<p>Comparison of the mean chemical emissions across the flaming and smouldering phases of prescribed burns across four different prescribed burns (ppm).</p>
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36 pages, 9570 KiB  
Article
Random Forest Spatial-Temporal and State Space Models to Assess the Impact of Bushfire-Induced Aerosol Events on Ozone Depletion in Australia
by Irene Hudson, Phillip Pedro-Suvorov and Servet Kocak
Appl. Sci. 2024, 14(21), 9825; https://doi.org/10.3390/app14219825 - 28 Oct 2024
Viewed by 1142
Abstract
Serious concerns exist that the increasing frequency of fires may delay the recovery of ozone given increasing temperatures due to climate change. Australian bushfires from September 2019 to February 2020 were catastrophic. A random forest spatial-temporal (RF sp) analysis using satellite data to [...] Read more.
Serious concerns exist that the increasing frequency of fires may delay the recovery of ozone given increasing temperatures due to climate change. Australian bushfires from September 2019 to February 2020 were catastrophic. A random forest spatial-temporal (RF sp) analysis using satellite data to detect an association between Australian bushfires and stratosphere ozone on the local depletion of ozone in the vicinity of fires in three regions of Australia (Pacific Ocean, Victoria, NSW) has shown a significant reduction in ozone attributable to aerosols from fires. By intervention analysis, increases in aerosols in all three regions were shown to have a significant and ongoing impact 1–5 days later on reducing ozone (p < 0.0001). Intervention analysis also gave similar periods of aerosol exceedance to those found by Hidden Markov models (HMMs). HMMs established a significant and quantifiable decline in ozone due to bushfire-induced aerosols, with significant lags of 10–25 days between times of aerosol exceedance and subsequent ozone level decline in all three regions. Full article
(This article belongs to the Special Issue AI-Enhanced 4D Geospatial Monitoring for Healthy and Resilient Cities)
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<p>(<b>left</b>) NASA measurements of aerosols optical thickness over April 2019. (<b>right</b>) NASA measurements of aerosols’ optical thickness over December 2020.</p>
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<p>(<b>A</b>) Ammonium sulfate particles containing soot (marked by the small arrows) and fly-ash spheres (marked by the bold arrow in the lower-right corner). (<b>B</b>) In a typical branching soot aggregate; the arrows point to a carbon film that connects individual spherules within the aggregate. (<b>C</b>) Fly-ash spheres. Source: [<a href="#B12-applsci-14-09825" class="html-bibr">12</a>].</p>
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<p>Pathway for smoke particles from intense bushfires to enter the stratosphere and for those same particles to, in turn, lead to ozone depletion. Source: [<a href="#B26-applsci-14-09825" class="html-bibr">26</a>].</p>
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<p>Schematic of ozone levels ozone within a column of air from the Earth’s surface (0 km) to the top of the atmosphere (&gt;35 km). Taken from <a href="https://csl.noaa.gov/assessments/ozone/2018/twentyquestions/" target="_blank">https://csl.noaa.gov/assessments/ozone/2018/twentyquestions/</a>, accessed on 30 June 2023.</p>
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<p>Outline of the map of Australia showing the locations of the three regions. The regions in order from top to bottom: Region 1 (Blue), Region 2 (Red) and Region 3 (Green). The checkerboard shading pattern identifies the study area.</p>
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<p>Observed from data of daily gridded aerosol values plot of entire study area over the 20 days from 3 November 2019.</p>
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<p>Observed from data of daily gridded ozone values plot of entire study area over the 20 days from 3 November 2019.</p>
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<p>Time series plot of Total Sum Ozone for each region.</p>
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<p>Time series plot of Total Sum Aerosol for each region.</p>
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<p>Graphical models associated with extensions to the basic HMM. (<b>A</b>) state sequence with a memory order of 2. (<b>B</b>) influence of covariate <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>1</mn> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <msub> <mi>z</mi> <mi>T</mi> </msub> </mrow> </semantics></math> on state dynamics. (<b>C</b>) observations depending on both states and previous observations. (<b>D</b>) bivariate observation sequence, conditionally independent given the states. Source: [<a href="#B72-applsci-14-09825" class="html-bibr">72</a>].</p>
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<p>Spatial-temporal predictions of daily gridded ozone values over 20 days from 3 November 2019.</p>
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<p>Spatial-temporal predictions of daily gridded ozone values over 20 days from 1 December 2019.</p>
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<p>Spatial-temporal predictions of daily gridded ozone values over 20 days from 20 January 2020.</p>
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<p>Quantile prediction intervals of daily gridded ozone values over 20 days from 3 November 2019.</p>
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<p>Quantile prediction intervals of daily gridded ozone values over 20 days from 1 December 2019.</p>
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<p>Quantile prediction intervals of daily gridded ozone values over 20 days from 20 January 2020.</p>
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<p>CPs for TSA and TSO for Region 1 (Pacific Ocean) as shown by vertical grey dashed lines.</p>
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<p>CPs for TSA and TSO for Region 2 (Vic) as shown by vertical grey dashed lines.</p>
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<p>CPs for TSA and TSO for Region 3 (NSW) as shown by vertical grey dashed lines.</p>
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<p>HMM bivariate state changes in total sum ozone time-series with respect to total sum aerosol time-series as a covariate for Region 3.</p>
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<p>Summary of the BinSeg CPs and the HMM derived CPs. In the RHS table, the O columns show the ozone CP (day) based on HMM without covariate adjustment. The A columns show the ozone CP (day) for HMMs with aerosol as a covariate.</p>
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<p>Detected Change Points for Total Sum Ozone (Region 3).</p>
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<p>Intervention Analysis Output for Region 3 with Model R3.</p>
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<p>Detected Change Points for Total Sum Ozone (Region 1).</p>
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<p>Intervention Analysis Output for Region 1.</p>
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<p>Detected Change Points for Total Sum Ozone (Region 2).</p>
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<p>Intervention Analysis Output for Region 2.</p>
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<p>HMM bivariate state changes in total sum ozone time-series with respect to total sum aerosol time-series as a covariate for Region 1.</p>
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<p>HMM bivariate state changes in total sum ozone time-series with respect to total sum aerosol time-series as a covariate for Region 2.</p>
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20 pages, 19130 KiB  
Article
Spatiotemporal Analysis of Land Use and Land Cover Dynamics of Dinderesso and Peni Forests in Burkina Faso
by Alphonse Maré David Millogo, Boalidioa Tankoano, Oblé Neya, Fousseni Folega, Kperkouma Wala, Kwame Oppong Hackman, Bernadin Namoano and Komlan Batawila
Geomatics 2024, 4(4), 362-381; https://doi.org/10.3390/geomatics4040019 - 4 Oct 2024
Cited by 1 | Viewed by 1153
Abstract
The sustainable management of protected areas has increasingly become difficult due to the lack of updated information on land use and land cover transformations caused by anthropogenic pressures. This study investigates the spatiotemporal dynamics of the Dinderesso and Peni classified forests in Burkina [...] Read more.
The sustainable management of protected areas has increasingly become difficult due to the lack of updated information on land use and land cover transformations caused by anthropogenic pressures. This study investigates the spatiotemporal dynamics of the Dinderesso and Peni classified forests in Burkina Faso from 1986 to 2022. First, a data driven method was adopted to investigate these forests degradation dynamics. Hence, relevant Landsat images data were collected, segmented, and analyzed using QGIS SCP plugin Random Forest algorithm. Ninety percent of the overall adjusted classification accuracies were obtained. The analysis also showed significant degradation and deforestation with high wooded vegetation classes such as clear forest and wooded savannah (i.e., tree savannah) converging to lower vegetation classes like shrub savannah and agroforestry parks. A second investigation carried out through surveys and field trips revealed key anthropogenic drivers including agricultural expansion, demographic pressure, bad management, wood cutting abuse, overexploitation, overgrazing, charcoal production, and bushfires. These findings highlight the critical need for better management to improve these protected areas. Full article
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<p>Dinderesso and Peni classified forest location.</p>
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<p>Landsat land use land cover assessment and household heads survey flowchart.</p>
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<p>Land uses land cover classes in Dinderesso and Peni classified forests.</p>
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<p>Land use land cover map of Dinderesso classified forest in 1986, 2006, 2010, 2016, and 2022.</p>
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<p>Land use land cover map of Peni classified forest in 1986, 2006, 2010, 2016, and 2022.</p>
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<p>Land use land cover change in the classified forest of Dinderesso in 1986, 2006, 2010, 2016, and 2022.</p>
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<p>Land use land change in the classified forest of Peni in 1986, 2006, 2010, 2016, and 2022.</p>
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<p>Anthropogenic drivers of Dinderesso and Peni classified forests degradation and deforestation.</p>
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<p>Dinderesso classified forest degradation and deforestation drivers.</p>
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<p>Peni classified forest degradation and deforestation drivers.</p>
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12 pages, 2387 KiB  
Article
Preliminary Assessment of Tunic Off-Gassing after Wildland Firefighting Exposure
by Kiam Padamsey, Adelle Liebenberg, Ruth Wallace and Jacques Oosthuizen
Fire 2024, 7(9), 321; https://doi.org/10.3390/fire7090321 - 14 Sep 2024
Cited by 1 | Viewed by 774
Abstract
Evidence has previously shown that outer tunics (turnout coats) worn by firefighters at structural fires are contaminated with harmful chemicals which subsequently off-gas from the material. However, there is limited research on whether this phenomenon extends to wildland firefighter uniforms. This pilot study [...] Read more.
Evidence has previously shown that outer tunics (turnout coats) worn by firefighters at structural fires are contaminated with harmful chemicals which subsequently off-gas from the material. However, there is limited research on whether this phenomenon extends to wildland firefighter uniforms. This pilot study aimed to explore if the tunics of volunteer bushfire and forestry firefighters in Western Australia off-gas any contaminants after exposure to prescribed burns or bushfires, and whether there is a need to explore this further. Nine tunics were collected from firefighters following nine bushfire and prescribed burn events, with a set of unused tunics serving as a control. Chemical analysis was performed on these tunics to assess levels of acrolein, benzene, formaldehyde, and sulphur dioxide contamination. The assessment involved measuring chemical off-gassing over a 12 h period using infrared spectrometry. Tunics worn by firefighters appear to adsorb acrolein, benzene, formaldehyde, and sulphur dioxide from bushfire smoke and these contaminants are emitted from firefighting tunics following contamination at elevated concentrations. Further investigation of this research with a larger study sample will be beneficial to understand this phenomenon better and to determine the full extent and range of chemical contaminants absorbed by all firefighter clothing. Full article
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<p>Summary of acrolein off-gassing from fire-fighting tunics.</p>
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<p>Summary of benzene off-gassing from firefighting tunics.</p>
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<p>Summary of formaldehyde off-gassing from firefighting tunics.</p>
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<p>Summary of sulphur dioxide off-gassing from firefighting tunics.</p>
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19 pages, 3020 KiB  
Article
Assessing the Provisioning of Ecosystem Services Provided by the Relics Forest in Togo’s Mono Biosphere Reserve
by Kokouvi Gbétey Akpamou, Somiyabalo Pilabina, Hodabalo Egbelou, Kokou Richard Sewonou, Yvonne Walz, Luca Luiselli, Gabriel H. Segniagbeto, Daniele Dendi and Kouami Kokou
Conservation 2024, 4(3), 486-504; https://doi.org/10.3390/conservation4030030 - 10 Sep 2024
Viewed by 1207
Abstract
In most Sub-Saharan African countries, such as Togo, forest ecosystems provide ecosystem services to the local population. These ecosystem services are of vital importance to the local populations, who depend on the benefits derived from their use to meet their socio-economic needs. The [...] Read more.
In most Sub-Saharan African countries, such as Togo, forest ecosystems provide ecosystem services to the local population. These ecosystem services are of vital importance to the local populations, who depend on the benefits derived from their use to meet their socio-economic needs. The permanent dependence of these populations on ecosystem services is a major factor accelerating the degradation of natural resources, which are already under pressure from climatic factors. The present study assesses the provisioning of ecosystem services provided by the relics forest in the southeast region of the Mono Biosphere Reserve in Togo. Individual interviews and group discussions were carried out with 420 households in fourteen villages around the reserve to identify the current uses of woody species. The results show that 100% of the respondents cited plant species, such as Mitragyna inermis, Lonchocarpus sericeus, and Diospyros mespiliformis, as used for wood. Species, such as Mimusops andogensis and Triplohiton scleroxylon, were cited as exclusively used for wood by 94% and 86%, respectively. Other species, such as Vitex doniana and Dialium guineense, in addition to their use for wood (93% and 70%), were cited, respectively, by 97% and 98% of respondents as used for fruit, and by 82% and 90% for their leaves. The heavy daily use of these species compromises their sustainability. An analysis of Sorensen’s similarity index, according to gender, age, ethnic group, and sector of activity, revealed a variation in this index ranging from 0.6 to 1, reflecting households’ knowledge of the use of these seven species. The local populations are already feeling the effects of the low availability of these commonly used species. According to them, the depletion of these resources is caused mainly by agricultural clearing, illegal logging, and bushfires. Full article
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<p>Location of Togo’s Mono Biosphere Reserve.</p>
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<p>Location of TMBR villages surveyed.</p>
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<p>Principal component analysis (PCA) of the matrix, for 7 woody species X 6 use categories.</p>
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<p>Local people’s knowledge of woody species.</p>
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<p>Availability of woody species over the last ten (10) years.</p>
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<p>Sought-after organ parts of seven most commonly used woody species.</p>
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<p>Uses of seven woody species.</p>
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<p>Reasons for the disappearance of woody species.</p>
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