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Background: In most wildland–urban interface (WUI) fires, damage to buildings results from poor surrounding vegetation management. No simulation had been conducted yet on historical WUI fires with Computational Fluid Dynamics modelling.Aims: It was interesting to check the feasibility of this modelling in simulating past fire cases for different scenarios of vegetation management and fire propagation.Methods: We studied three cases of WUI dwellings surrounded by gardens (subject to French regulations on fuel reduction) adjacent to forest affected by a past fire. The 3D fire propagation was assessed using the Fire Dynamic Simulator model (FDS) and taking into account accurate fire environment (fine vegetation distribution, terrain, etc.).Key results: Results showed that, in the current model state, brush-clearing mitigated fire intensity and propagation and damage to ornamental vegetation. However, it sometimes highlighted that this measure could be strengthened when the effects of topography and wind were combined.Conclusions: FDS modelling at the WUI scale using accurate vegetation distribution proved to be functionally satisfactory, exhibiting realistic fire behaviour.Implications: Once validated, this modelling will ultimately help to assess when fuel reduction is efficient in fire mitigation and to pinpoint possible limitations.

Climate change is expected to increase the frequency and size of large uncontrollable fires. The impact of this trend on forest vegetation is still poorly understood, especially in areas not commonly subject to recurrent fires, i.e. in areas where tree species may not be adapted to fire and where flammability may increase as warming decreases moisture. Here we use recent advances in remote sensing to simulate burned area development until the end of the century under different climate scenarios. We then combine these projections with an European forest resources model to assess the impact of projected fire regimes on forests in three major biomes in Europe (i.e. Mediterranean, temperate and boreal forests, here represented by three countries: Spain, Germany and Sweden). Burned area was projected to increase in all regions in the 21st century, with the biggest increase and highest absolute damage in the Mediterranean region under the most severe climate scenario. Furthermore, we found that future fire disproportionately affects temperate forests, where a higher level of damage occurs for the same relative increase in burned area, compared to the other biomes. This was mostly due to the combination of increasingly favourable weather conditions for fire and large standing biomass, which drove the increased susceptibility of temperate regions to emerging wildfire regimes. Our findings call for mainstreaming fire and fuel management strategies into forest planning to increase resilience to fires, particularly in temperate regions with limited past fire occurrence and a projected increase in favourable fire weather.

Olive tree orchards are suffering from wildfires in many Mediterranean countries. Following a wildfire event, identifying damaged olive trees is crucial for developing effective management and restoration strategies, while rapid damage assessment can support potential compensation for producers. Moreover, the implementation of real-time health monitoring in olive groves allows producers to carry out targeted interventions, reducing production losses and preserving crop health. This research examines the use of deep learning methodologies in true-color images from Unmanned Aerial Vehicles (UAV) to detect damaged trees, including withering and desiccation of branches and leaf scorching. More specifically, the object detection and image classification computer vision techniques area applied and compared. In the object detection approach, the algorithm aims to localize and identify burned/dry and unburned/healthy olive trees, while in the image classification approach, the classifier categorizes an image showing a tree as burned/dry or unburned/healthy. Training data included true color UAV images of olive trees damaged by fire obtained by multiple cameras and multiple flight heights, resulting in various resolutions. For object detection, the Residual Neural Network was used as a backbone in an object detection approach with a Single-Shot Detector. In the image classification application, two approaches were evaluated. In the first approach, a new shallow network was developed, while in the second approach, transfer learning from pre-trained networks was applied. According to the results, the object detection approach managed to identify healthy trees with an average accuracy of 74%, while for trees with drying, the average accuracy was 69%. However, the optimal network identified olive trees (healthy or unhealthy) that the user did not detect during data collection. In the image classification approach, the application of convolutional neural networks achieved significantly better results with an F1-score above 0.94, either in the new network training approach or by applying transfer learning. In conclusion, the use of computer vision techniques in UAV images identified damaged olive trees, while the image classification approach performed significantly better than object detection.

The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness.

What are the main findings? * By leveraging multiple data sources, innovative data processing techniques, and machine learning, an approach is presented for leveraging aerial thermal imagery to automate assessment of structural damage caused by active wildfires. * The effectiveness of the approach is demonstrated by applying it to the 2025 Eaton and Palisades wildfires in California. By leveraging multiple data sources, innovative data processing techniques, and machine learning, an approach is presented for leveraging aerial thermal imagery to automate assessment of structural damage caused by active wildfires. The effectiveness of the approach is demonstrated by applying it to the 2025 Eaton and Palisades wildfires in California. What are the implication of the main findings? * The proposed approach offers rapid and reliable assessment of damaged structures from wildfires using imagery that was once not widely available. With more rapid data collection in California, these refined techniques can provide more rapid and accurate assessments. * The proposed approach is suitable for operational wildfire damage assessment and provides insights to variation in fire behavior, as seen in heat intensities. The proposed approach offers rapid and reliable assessment of damaged structures from wildfires using imagery that was once not widely available. With more rapid data collection in California, these refined techniques can provide more rapid and accurate assessments. The proposed approach is suitable for operational wildfire damage assessment and provides insights to variation in fire behavior, as seen in heat intensities. Driven by dangerous Santa Ana winds and fueled by dry vegetation, the 2025 Eaton and Palisades wildfires in California caused historic levels of devastation, ultimately becoming the second and third most destructive fires in California history. Burning at the same time and drawing from the same resources, these fires burned a combined total of 16,251 structures. The first several hours of an emerging wildfire are a crucial period for fire officials to assess potential damage and develop a timely and appropriate response. A method to quickly generate accurate estimates of structural damage is essential to providing this crucial rapid response to wildfires. In this paper, we present a machine learning approach for automated assessment of structural damage caused by wildfires. By leveraging multiple data sources in model development (satellite-based building footprints, expert-labeled post-fire damage points, fire perimeters, and aerial thermal imagery) and innovative data processing techniques, the approach can be used to identify various levels of structural damage from just aerial thermal imagery during operational use. The resulting system offers an effective approach for rapid and reliable assessment of burned structures, suitable for operational wildfire damage assessment. Results on the Eaton and Palisades Fires demonstrate the effectiveness of this method and its applicability to real-world scenarios.

Bridge fires present unique challenges due to their potential for catastrophic structural failures, leading to extensive traffic disruptions, economic losses, and, in some cases, loss of life. In the aftermath of a fire incident, assessing the structural integrity and future viability of concrete bridges has become a paramount concern for civil engineers and safety inspectors. The critical decision to rehabilitate or demolish a fire-damaged structure hinges on accurately assessing the extent of damage incurred. Enhancing the fire resilience of concrete structures is a critical endeavor within civil engineering, necessitating accurate evaluation methods to analyze conditions after fire exposure. Focusing on concrete bridges, this study aimed to establish a comprehensive review of research on the effects of fire, providing engineers with the necessary means to develop guidelines for post-fire assessment to enhance safety and operational readiness. It proposes an in-depth examination of various methods as strategic decision-making tools. The assessment involves estimating the temperature, the extent of damage to concrete, and the reduction in the strength of both concrete and reinforcement. To achieve this, a detailed review of the existing literature on the impact of fire on concrete and its steel reinforcements is conducted. Current post-fire assessment tools have also been evaluated to improve the efficiency of the evaluation process. This study establishes a systematic post-fire assessment review framework that incorporates assessment information domains (including non-destructive testing, destructive testing, advanced computational modeling, and digital-twin technology) to provide a practical solution for accurately determining the safety and operational readiness of fire-damaged concrete bridges.

Deformation and stability of surrounding rock of a tunnel could be significantly affected by the fire accident-induced open-fire damage. In this study, the influence of open-fire damage on the P-wave velocity, Brazilian tensile strength (BTS), and the associated microstructure evolution of granite is investigated. The open-fire damage is generated by heating the rock sample with charcoal fire for 3 h prior to the subsequent tests. P-wave velocity of intact and heated specimens is measured. The microstructure of specimens with different distances to the open fire is observed utilizing a polarizing microscopy. Brazilian splitting tests on the heated specimens are then conducted combined with high-speed camera for monitoring the failure process. Both P-wave velocity and BTS are found to increase rapidly as the distance from the specimen to the open fire increases. When the distance between the specimen and the open fire is larger than 125 mm, the values of P-wave velocity and BTS tend to level off at the values of unheated specimen. The degradation of P-wave velocity and BTS with increasing open-fire damage is mainly attributed to the induced microcracks in the process of approaching the open fire. The trans-granular microcracking is found to be an effective indicator to evaluate the degree of open-fire damage. Two negative exponential models are proposed for the variation of the P-wave velocity and BTS with the damage distance from the specimen to the open fire. The proposed model is validated by the reasonable agreement between model predictions and experimental results.

PurposeThis study aims to develop an assessment strategy for fire damaged infrastructures based on the implementation of quick diagnostic techniques and consistent interpretation procedures, so to determine the residual safety margin and any need for repair works.Design/methodology/approachIn this perspective, several tailored non-destructive test (NDT) methods have been developed in the past two decades, providing immediate results, with no need for time-consuming laboratory analyses. Moreover, matching their indications with the calculated effects of a tentative fire scenario allows harmonizing distinct pieces of evidence in the coherent physical framework of fire dynamics and heat transfer.FindingsThis approach was followed in the investigations on a concrete overpass in Verona (Italy) after a coach violently impacted one supporting pillar and caught fire in 2017. Technical specifications of the vehicle made it possible to bound the acceptable ranges for fire load and maximum rate of heat release, while surveillance video footage indicated the duration of the burning stage. Some established NDT methods (evaluation of discolouration, de-hydroxylation and rebar hardness) were implemented, together with advanced ultrasonic tests based on pulse refraction and pulse-echo tomography.Originality/valueThe results clearly showed the extension of the most damaged area at the intrados of the box girders and validated the maximum heating depth, as predicted by numerical analysis of the heat transient ensuing from the localized fire model.

The application of remote sensing techniques for disaster management often requires rapid damage assessment to support decision-making for post-treatment activities. As the on-demand acquisition of pre-event very high-resolution (VHR) images is typically limited, PlanetScope (PS) offers daily images of global coverage, thereby providing favorable opportunities to obtain high-resolution pre-event images. In this study, we propose an unsupervised change detection framework that uses post-fire VHR images with pre-fire PS data to facilitate the assessment of wildfire damage. To minimize the time and cost of human intervention, the entire process was executed in an unsupervised manner from image selection to change detection. First, to select clear pre-fire PS images, a blur kernel was adopted for the blind and automatic evaluation of local image quality. Subsequently, pseudo-training data were automatically generated from contextual features regardless of the statistical distribution of the data, whereas spectral and textural features were employed in the change detection procedure to fully exploit the properties of different features. The proposed method was validated in a case study of the 2019 Gangwon wildfire in South Korea, using post-fire GeoEye-1 (GE-1) and pre-fire PS images. The experimental results verified the effectiveness of the proposed change detection method, achieving an overall accuracy of over 99% with low false alarm rate (FAR), which is comparable to the accuracy level of the supervised approach. The proposed unsupervised framework accomplished efficient wildfire damage assessment without any prior information by utilizing the multiple features from multi-sensor bi-temporal images.

Elephant and fire are considered to be among the most important agents that can modify the African savanna ecosystem. Although the synergistic relationship between these two key ecological drivers is well documented, it has proved much more difficult to establish the relative effects they have on savanna vegetation structure at a fine-scale over time. In this study, we explore the comparative impacts of fire and elephant on 2,522 individually identified large trees (> 5 m in height) in the Kruger National Park, South Africa. Data were collected from 21 transects first surveyed in April 2006 and resurveyed in November 2008, to determine the relative importance of past damage by these agents on subsequent impacts and mortality. The occurrence of fire or elephant damage in 2006 affected the amount of tree volume subsequently removed by both these agents; elephant removed more tree volume from previously burned trees and the impact of subsequent fire was higher on previously burned or elephant-utilized trees than on undamaged trees. Mortality was also affected by an interaction between previous and recent damage, as the probability of mortality was highest for trees that suffered from fire or elephant utilization after being pushed over. Subsequent fire damage, but not elephant utilization, on debarked trees also increased the probability of mortality. Mortality was twice (4.6% per annum) that of trees progressing into the > 5 m height class, suggesting an overall decline in large tree density during the 30-month study period. The responses of large trees were species and landscape-specific in terms of sensitivity to elephant and fire impacts, as well as for levels of mortality and progression into the > 5 m height class. These results emphasize the need for fine-scale site-specific knowledge for effective landscape level understanding of savanna dynamics.