Search Results (413)

Wildfires cause severe consequences, including property loss, threats to human life, damage to natural resources, biodiversity, and economic impacts. Consequently, numerous wildland fire detection systems were developed over the years to identify fires at an early stage and prevent their damage to both the environment and human lives. Recently, deep learning methods were employed for recognizing wildfires, showing interesting results. However, numerous challenges are still present, including background complexity and small wildfire and smoke areas. To address these challenging limitations, two deep learning models, namely CT-Fire and DC-Fire, were adopted to recognize wildfires using both visible and infrared aerial images. Infrared images detect temperature gradients, showing areas of high heat and indicating active flames. RGB images provide the visual context to identify smoke and forest fires. Using both visible and infrared images provides a diversified data for learning deep learning models. The diverse characteristics of wildfires and smoke enable these models to learn a complete visual representation of wildland fires and smoke scenarios. Testing results showed that CT-Fire and DC-Fire achieved higher performance compared to baseline wildfire recognition methods using a large dataset, which includes RGB and infrared aerial images. CT-Fire and DC-Fire also showed the reliability of deep learning models in identifying and recognizing patterns and features related to wildland smoke and fires and surpassing challenges, including background complexity, which can include vegetation, weather conditions, and diverse terrain, detecting small wildfire areas, and wildland fires and smoke variety in terms of size, intensity, and shape. CT-Fire and DC-Fire also reached faster processing speeds, enabling their use for early detection of smoke and forest fires in both night and day conditions. Full article

Prescribed burning is a cost-effective method for reducing hazardous fuels in pine- and oak-dominated forests, but smoke emissions contribute to atmospheric pollutant loads, and the potential exists for exceeding federal air quality standards designed to protect human health. Fire behavior during prescribed burns influences above-canopy sensible heat flux and turbulent kinetic energy (TKE) in buoyant plumes, affecting the lofting and dispersion of smoke. A more comprehensive understanding of how enhanced energy fluxes and turbulence are related during the passage of flame fronts could improve efforts to mitigate the impacts of smoke emissions. Pre- and post-fire fuel loading measurements taken during 48 operational prescribed burns were used to estimate the combustion completeness factors (CC) and emissions of fine particulates (PM_2.5), carbon dioxide (CO₂), and carbon monoxide (CO) in pine- and oak-dominated stands in the Pinelands National Reserve of southern New Jersey. During 11 of the prescribed burns, sensible heat flux and turbulence statistics were measured by tower networks above the forest canopy. Fire behavior when fire fronts passed the towers ranged from low-intensity backing fires to high-intensity head fires with some crown torching. Consumption of forest-floor and understory vegetation was a near-linear function of pre-burn loading, and combustion of fine litter on the forest floor was the predominant source of emissions, even during head fires with some crowning activity. Tower measurements indicated that above-canopy sensible heat flux and TKE calculated at 1 min intervals during the passage of fire fronts were strongly influenced by fire behavior. Low-intensity backing fires, regardless of forest type, had weaker enhancement of above-canopy air temperature, vertical and horizontal wind velocities, sensible heat fluxes, and TKE compared to higher-intensity head and flanking fires. Sensible heat flux and TKE in buoyant plumes were unrelated during low-intensity burns but more tightly coupled during higher-intensity burns. The weak coupling during low-intensity backing fires resulted in reduced rates of smoke transport and dispersion, and likely in more prolonged periods of elevated surface concentrations. This research facilitates more accurate estimates of PM_2.5, CO, and CO₂ emissions from prescribed burns in the Pinelands, and it provides a better understanding of the relationships among fire behavior, sensible heat fluxes and turbulence, and smoke dispersion in pine- and oak-dominated forests. Full article

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

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and the debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires’ geoenvironmental impacts does not fit within the scope of this paper. In the end, and in no exhaustive way, some of the areas requiring future research are highlighted. Full article

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. Full article

Prescriptions for fuel management are universally applied across the forest types in British Columbia, Canada, to reduce the fire behaviour potential in the wildland–urban interface. Fuel thinning treatments have been advocated as a means of minimizing the likelihood of crown fire development in conifer forests. We hypothesized that these types of prescriptions are inappropriate for the coastal rainforests of the Whistler region of the province. Our study examined the impact of fuel thinning treatments in four stands located in the Whistler community forest. We measured several in-stand microclimatic variables beginning with snow melt in the spring up to the height of fire danger in late summer, at paired thinned and unthinned stand locations. We found that the thinning led to warmer, drier, and windier fire environments. The difference in mean soil moisture, ambient air temperature, and relative humidity between thinned and unthinned stands was significant in the spring with approximate p-values of 0.000217, 9.40 × 10⁻⁵, and 4.33 × 10⁻⁸, respectively, though there were no discernible differences in the late summer. The difference in mean solar radiation, average wind speed, and average cross wind between thinned and unthinned locations are significant in the spring and late summer (with approximate p-values for spring of 9.54 × 10⁻⁷, 0.02101, 1.92 × 10⁻⁹, and for late summer of 2.45 × 10⁻⁷, 4.08 × 10⁻⁶, and 2.45 × 10⁻⁵, respectively). Full article

The work of wildland firefighters, especially of the so-called ‘Brigadas de Refuerzo contra Incendios Forestales’, is characterised by high physical demands and extreme operating conditions. These professionals face long workdays (12 h), walking with heavy loads (~25 kg), being exposed to high temperatures (>30 °C), and handling specialised tools in high-risk environments. This study aimed to describe the prevalence of occupational injuries among members of the ‘Brigadas de Refuerzo contra Incendios Forestales’ and its relationship to variables such as age and work experience. A total of 217 wildland firefighters (18 female and 199 male) correctly answered a questionnaire developed on an ad hoc basis to meet the study’s objectives. A high prevalence of occupational injuries was observed among them (~76%). Age and work experience were shown to be significantly associated with injuries. Individuals over 35 years of age with more than 10 years’ experience had a higher probability of injury (OR = 2.14, CI = 1.12–4.06 and OR = 2.46, CI = 1.30–4.67, respectively). Injuries occurred mainly during physical training (~46%), followed by preventive work (~33%) and forest fires (~20%). The most common injuries were tendonitis and muscle pain (~44% and ~21% respectively), followed by sprains (~21%). The results underline the need for physical activity programmes adapted to help wildland firefighters, especially older and more experienced individuals. The identification of risk factors such as age and work experience can contribute to the prevention and management of occupational injuries among this group of highly specialised forestry workers. Specific preventative measures during training are required to mitigate the risk of injury among these crews, who play a crucial role in protecting the environment and public safety. Full article

Wildland firefighters are exposed to airborne particulates, polycyclic aromatic hydrocarbons (PAHs), and other hazardous substances. Respiratory protection is indicated, but information is lacking on the tasks and conditions for which mask wearing should be advised. Studies to assess respiratory protection in wildland firefighters were carried out in western Canada in 2021 and 2023. Sampling pumps measured airborne exposures and urinary 1-hydroxypyrene (1-HP) was assayed to indicate PAH absorption. Participants in 2021 reported the time for which they wore the mask during each task. In 2023, the use of masks was reported, and firefighters rated the smoke intensity. In 2021, 72 firefighters were monitored over 164 shifts and, in 2023, 89 firefighters were monitored for 263 shifts. In 2021, mask wearing was highest for those engaged in initial attack and hot spotting. Urinary 1-HP at the end of rotation was highest for those reporting initial attack, working on a prescribed fire and mop-up. In 2023, firefighter ratings of smoke intensity were strongly associated with measured particulate mass and with urinary 1-HP, but masks were not worn more often when there was higher smoke intensity. The data from the literature did not provide a clear indication of high-exposure tasks. Better task/exposure information is needed for firefighters to make informed decisions about mask wearing. Full article

The aim of this study was to investigate the pyrolysis of selected California foliage and estimate the energy content of the released volatiles to show the significance of the pyrolysis of foliage and its role during wildland fires. While the majority of the volatiles released during the pyrolysis of foliage later combust and promote fire propagation, studies on the energy released from combustion of these compounds are scarce. Samples of chamise (Adenostoma fasciculatum), Eastwood’s manzanita (Arctostaphylos glandulosa), scrub oak (Quercus berberidifolia), hoaryleaf ceanothus (Ceanothus crassifolius), all native to southern California, and sparkleberry (Vaccinium arboreum), native to the southern U.S., were pyrolyzed at 725 °C with a heating rate of approximately 180 °C/s to mimic the conditions of wildland fires. Tar and light gases were collected and analyzed. Tar from chamise, scrub oak, ceanothus and sparkleberry was abundant in aromatics, especially phenol, while tar from manzanita was mainly composed of cycloalkenes. The four major components of light gases were CO, CO₂, CH₄ and H₂. Estimated values for the high heating values (HHVs) of volatiles ranged between 18.9 and 23.2 (MJ/kg of biomass) with tar contributing to over 80% of the HHVs of the volatiles. Therefore, fire studies should consider the heat released from volatiles present in both tar and light gases during pyrolysis. Full article

Despite increasing wildfire severity and range, rapid development in the fire-prone Wildland–Urban Interface (WUI) has continued, and many neighborhoods are at risk of a constrained wildfire evacuation due to a high ratio of houses to community road-network exits. In Texas, Hays County is prone to fire, and rapid population growth has created a substantial WUI. Despite this, there is not sufficient research addressing neighborhood-level evacuation risks. The goal of this research, then, is to search Hays County for neighborhoods that face the highest combined risk of wildfire and potential evacuation difficulty. This research provides a limited use case wherein local decision-makers can quantify the combined risk of wildfire and constrained evacuation at the neighborhood scale by making use of standard spatial analysis techniques and publicly available datasets. The results show an alarming trend of low-egress neighborhoods in fire-prone areas within Hays County which carry the risk of a very difficult evacuation in cases when wildfire warning time is short. By using publicly available datasets and standard techniques, this research provides methods for local decision-makers across the state to identify these at-risk neighborhoods within their own jurisdictions which may aid in emergency planning and mitigation. Full article

This paper aimed to provide technical support for fuel management by exploring different strengths of fuel removal on the physical and chemical properties and flammability of Betula platyphylla forests in the wildland–urban interface. After investigating the northeastern region during the forest fire prevention period in May 2023, a typical WUI area was selected, and three different treatment strengths, combined with a control, were set up to carry out indoor and outdoor experiments for 27 weeks. Compared with previous studies, this study mainly investigated and analyzed the dynamic changes in the physical and chemical properties and fuel flammability after different intensities of treatments on a time scale. By processing and analyzing the data, the following results were obtained. Significant differences existed in the fuel loading of different time-lag fuels over time (p < 0.05). The ash and ignition point of 1 h time-lag fuel after different treatment intensities generally increased first and then decreased, and the higher heat value and ash-free calorific value generally decreased first and then increased. The physical and chemical properties of 10 h and 100 h time-lag fuel fluctuated with time, but the overall change was insignificant. The indicator that had the greatest impact on the combustion comprehensive score for different time-lag fuels was fuel loading. The change in the flammability of dead surface fuel with time varied significantly, and different treatment intensities effectively reduced the fuel’s flammability. The reduction effects, presented in descending order, were as follows: medium-strength treatment > low-strength treatment > high-strength treatment > control check. In conclusion, different treatment intensities have significant effects on the flammability of the fuel, and the medium-strength treatment has the best effect. Considering the ecological and economic benefits, adopting the medium-strength treatment for the WUI to regulate the fuel is recommended. Full article

Fire is a prevalent hazard that poses a significant risk to public safety and societal progress. The continuous expansion of densely populated urban areas, exacerbated by global warming and the increasing intensification of urban heat islands, has led to a notable increase in the frequency and severity of fires worldwide. Incorporating measures to withstand different types of calamities has always been a crucial aspect of urban infrastructure. Well-designed plant communities play a pivotal role as a component of green space systems in addressing climate-related challenges, effectively mitigating the occurrence and spread of fires. This study conducted field research on 21 sites in the green belt around Shanghai, China, quantifying tree morphological indexes and coordinate positions. The spatial structure attributes of different plant communities were analyzed by principal component analysis, CRITIC weighting approach, and stepwise regression analysis to build a comprehensive fire resistance prediction model. Through this research, the relationship between community spatial structures and fire resistance was explored. A systematic construction of a prediction model based on community spatial structures for fire resistance was undertaken, and the fire resistance performance could be quickly judged by easily measured tree morphological indexes, providing valuable insights for the dynamic prediction of fire resistance. According to the evaluation and ranking conducted by the prediction model, the Celtis sinensis, Sapindus saponaria, Osmanthus fragrans, Koelreuteria paniculata, and Distylium racemosum + Populus euramericana ‘I-214’ communities exhibited a high level of fire resistance. On the other hand, the Koelreuteria bipinnata + Ligustrum lucidum, Ginkgo biloba + Camphora officinarum + Ligustrum lucidum, and Ligustrum lucidum + Sapindus saponaria communities obtained lower scores and were positioned lower in the ranking. It is emphasized that the integration of monitoring and regulation is essential to ensure the ecological integrity and well-being of green areas in the Wildland–Urban Interface. Full article

Forest inventory data are useful for determining forest stand structure, growth, and change. Among the information collected on forest inventory plots by the USDA Forest Service Forest Inventory and Analysis Program, attributes characterizing various types of disturbance provide researchers a means of selecting plots specifically affected by disturbances, such as fire. We determine the performance of three of these attributes as indicators of recent fires on forest inventory plots of the United States by comparing them to independent records of wildland fire occurrence. The indicators are plot-level observations of fire effects on (1) general site appearance, (2) tree mortality, and (3) damage to live trees. Independent spatial layers of wildland fire perimeters provide an approach to test indicator performance and identify characteristics of fires that may affect detection. The sensitivities of indicators are generally higher in the West relative to the East. Detection rates exceed 90 percent for the Pacific Coast forests but seldom reach 80 percent in the East. Among the individual indicators, site appearance has higher identification rates than tree indicators for fires in the Pacific Coast, Great Plains, North, and South regions. Tree mortality is the most important single indicator for identifying Rocky Mountain fires. Tree damage is more important than tree mortality in the South; otherwise, the tree damage indicator is of relatively lower importance, particularly where high-severity fires are common, and tree survival is low. The rate of detection by the indicators is affected by the severity of the fire or the recency of the fire. The joint effect of severity and recency influence all three indicators for the Pacific Coast and Rocky Mountain fires, as well as the site appearance indicator in the South. Only a small proportion of fires are clearly missed by all three of the indicators. Full article

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h⁻¹ and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper’s results will be used to prevent future active fire occurrences in the CC. Full article

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities. Full article