Search Results (2,288)

Wildfires and drought stressors can significantly limit forest recovery in Mediterranean-type ecosystems. Since 2010, the region of central Chile has experienced a prolonged Mega Drought, which intensified into a Hyper Drought in 2019, characterized by record-low precipitation and high temperatures, further constraining forest recovery. This study evaluates short-term (5-year) post-fire vegetation recovery across drought gradients in two types of evergreen sclerophyllous forests and a thorny forest and shrubland, analyzing Landsat time series (1987–2022) from 42 wildfires. Using the LandTrendr algorithm, we assessed post-fire forest recovery based on NDVI changes between pre-fire values and subsequent years. The results reveal significant differences in recovery across drought gradients during the Hyper Drought period, among the three forest types studied. The xeric forest, dominated by Quillaja saponaria and Lithrea caustica, showed significant interaction effects between levels of drought and fire severity, while the thorny forest and shrubland displayed no significant interaction effects. The mesic forest, dominated by Cryptocarya alba and Peumus boldus, exhibited additional significant differences in recovery between the Hyper Drought and Mega Drought periods, along with significant interaction effects. These findings underscore the critical role of prolonged, severe drought in shaping forest recovery dynamics and highlight the need to understand these patterns to improve future forest resilience under increasingly arid conditions. Full article

Forest fires, i.e., wildfires, often cause an inevitable strain on society and human living conditions. Incident Commanders (IC) at the Fire and Rescue Services (FRS) are challenged to handle forest fires and at the same time address the forest owners’ needs; this stipulates a need for collaboration, information, and communication. Hence, the aim of this study was to explore and describe the ICs’ experiences and actions in their interactions with forest owners during forest fires on private property. Interviews were conducted and analyzed using Flanagan’s Critical Incident Technique (CIT) to describe the experiences and actions of 22 ICs. The results showed that a firefighting operation needs clarity in information exchange with the forest owner as a stakeholder, not a victim. The trust between forest owner and IC accelerated the operational phase. The ICs demonstrate more care than the law stipulates, and they worry about the forest owners. Therefore, the FRS needs to form a strategic partnership with forest owners and their network on a local level. Also, future forest fire drills should not only include emergency stakeholders (i.e., police, ambulance, etc.) but also forest owners and local volunteer organizations. For a resilient community, FRS and forest owner collaboration is vital. Full article

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM. Full article

Although dune systems play a crucial ecological role and offer various ecosystem services, they are listed among the habitat types of community interest in the European Union that are undergoing the most severe conservation challenges. The subject of this study was the monitoring of habitat types protected under Directive 92/43/EEC (Habitats Directive) along the coastal dune systems of the Taranto Ionian Arc. Vegetation sociological surveys, GIS mapping, landscape metrics, NBR and dNBR indices were employed to assess the conservation status of the dune system and the impact of disturbance factors. Special attention was given to habitat 2250* (Coastal dunes with Juniperus spp.), revealing that it expanded from 2006 to 2019 but then significantly reduced between 2019 and 2022, with increasing fragmentation, mainly due to wildfires. The study also highlighted the impact of invasive species such as Acacia saligna and Carpobrotus acinaciformis, which compete for space and vital resources. These findings provide scientific evidence for the management and restoration of coastal dune ecosystems, emphasizing the need for targeted conservation strategies to mitigate the effects of these disturbances. Full article

The rapid and accurate detection of fire scenes in various environments is crucial for effective disaster management and mitigation. Fire scene classification is a critical aspect of modern fire detection systems that directly affects public safety and property preservation. This research introduced a novel hybrid deep learning model designed to enhance the accuracy and efficiency of fire scene classification across diverse environments. The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. By leveraging the strengths of pre-trained networks such as ResNet50, VGG16, and EfficientNet-B3, the model captures detailed features at multiple scales, ensuring robust detection capabilities. Including spatial and channel attention mechanisms further refines the focus on critical areas within the input images, reducing false positives and improving detection precision. Extensive experiments on a comprehensive dataset encompassing wildfires, building fires, vehicle fires, and non-fire scenes demonstrate that the proposed framework outperforms existing cutting-edge techniques. The model also exhibited reduced computational complexity and enhanced inference speed, making it suitable for deployment in real-time applications on various hardware platforms. This study sets a new benchmark for fire detection and offers a powerful tool for early warning systems and emergency response initiatives. Full article

Wind has a significant effect on pool fire behavior, which is relevant to many fire conditions, such as wildfires, building fires, and oil transportation fires. Although fire behavior and morphology changes have received considerable attention and been widely researched, there are few works concerning the flow and flam dynamics of pool fire. A large eddy simulation model is adopted to investigate the flow and flame dynamics of a rectangular pool fire considering the combined effects of wind and slope. The results show that, with a wind speed of 0.5 m/s, a flame develops immediately downstream of the fire source and sustains two flanks of plume. Further downstream, the plume starts to rise due to buoyant force. Temperature, velocity, and vorticity distributions show significantly different shapes at different streamwise locations. Near the fire source, the flame is confined to a small region around the fire source. The air circulation downstream shows a cylindrical spiring pattern. When the wind speed increases, the temperature and velocity become more parallel to the surface and their maximum values increase. On the contrary, the temperature fluctuations and turbulent kinetic energy decrease with the wind speed, and they are more frequent near the flame tails. Full article

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha⁻¹ × yr⁻¹ to 1.29 ton × ha⁻¹ × yr⁻¹, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha⁻¹ × yr⁻¹, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables. Full article

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts. Full article

In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 to 2020, skillfully incorporating a spatial autocorrelation analysis to uncover the mysteries of spatial heterogeneity, while comprehensively considering the influences of multiple factors such as climate, terrain, vegetation, and socioeconomic conditions. To simulate fire conditions under future climates, we adopted the BCC-CSM2-MR climate model, presetting temperature and precipitation data for two scenarios: a sustainable low-development path and a high-conventional-development path. The core findings of the study include the following: (i) In terms of spatial heterogeneity exploration, global autocorrelation analysis reveals a striking pattern: within the southern forest region, 63 cities exhibiting a low–low correlation are tightly clustered in provinces such as Hubei, Anhui, and Zhejiang, while 48 cities with a high–high correlation are primarily distributed in Guangxi and Guangdong. Local autocorrelation analysis further refines this observation, indicating that 24 high–high correlated cities are highly concentrated in specific areas, 14 low–low correlated cities are located in Hainan, and there are only 3 sparsely distributed cities with a low–high correlation. (ii) During the model construction and validation process, this study innovatively adopted the LR-RF-SVM ensemble model, which demonstrated exceptional performance indicators: an accuracy of 91.97%, an AUC value of 97.09%, an F1 score of 92.13%, a precision of 90.75%, and a recall rate of 93.55%. These figures, significantly outperforming those of the single models SVM and RF, strongly validate the superiority of the ensemble learning approach. (iii) Regarding predictions under future climate scenarios, the research findings indicate that, compared to the current fire situation in southern forest areas, the spatial distribution of wildfires will exhibit a noticeable expansion trend. High-risk regions will not only encompass multiple cities in Hunan, Hubei, southern Anhui, all of Jiangxi, and Zhejiang but will also extend northward into southern forest areas that were previously considered low-risk, suggesting a gradual northward spread of fire risk. Notably, despite the relatively lower fire risk in some areas of Fujian Province under the SS585 scenario, overall, the probability of wildfires occurring in 2090 is slightly higher than that in 2030, further highlighting the persistent intensification of forest fire risk due to climate change. Full article

Both topography and wildfire can exert significant influences on ecosystem processes and functions during boreal forest successions. However, their impacts on ecosystem multifunctionality (EMF) remain unclear. A mega-fire burned an area of 8700 hectares in the Great Xing’an Mountains in 2000, creating a wide range of fire severity levels across various topographic positions. This provided a unique opportunity to explore the impacts of mixed-severity fire disturbance in boreal forests. We evaluated the effect pathways of wildfire and topography on aboveground multifunctionality (AEMF), soil multifunctionality (SEMF), and overall multifunctionality (OEMF). We found that high-severity burning resulted in lower AEMF, SEMF, and OEMF relative to low-severity burning. Topographic positions significantly influenced SEMF and OEMF, but not AEMF. Specifically, both lower SEMF and OEMF were observed on south-facing slopes. The structure equation model analysis showed that aspect had exerted strong indirect effects on AEMF, SEMF, and OEMF by affecting soil moisture and regenerated tree density (RTD). Fire severity had indirect negative effects on AEMF and OEMF by reducing RTD and on SEMF by reducing soil bacterial diversity and RTD. Our study elucidates the necessity of considering postfire site environments to better manage forest ecosystems and, in turn, promote the rapid recovery of boreal ecosystem functions. Full article

Coarse woody debris (CWD) has historically been extensively utilized in Korea, with significant accumulation occurring mainly after the establishment of protected areas. This study, conducted in Odaesan National Park (designated in 1975), investigated the distribution and characteristics of CWD across five forest types with permanent plots. It also examined the effects of human and natural disturbances on CWD dynamics and evaluated its role in carbon storage. CWD mass varied significantly, ranging from 0.7 Mg ha⁻¹ in Pinus-Quercus (PQ) forests to 31.9 Mg ha⁻¹ in Broadleaved–Abies (BA) forests. The impacts of disturbances shifted markedly before and after the park’s designation; prior to this, human activities such as logging substantially affected BA, PQ, and Prunus-Salix (PS) forests, while Quercus-Tilia (QT) forests were primarily impacted by wildfires. After designation, natural disturbances became the primary contributors to CWD accumulation, with a major windstorm in BA forests adding 12.09 Mg ha⁻¹ of CWD (37.8% of the total). Late-successional forests exhibited higher CWD mass, advanced decay stages, and greater diversity, as well as elevated CWD-to-carbon storage ratios, highlighting their role as crucial carbon reservoirs. In light of climate change, these findings emphasize the need for forest management practices that enhance CWD’s contributions to biodiversity conservation and carbon storage. Full article

Relative to other Oregon counties, Klamath County experiences worse air quality due to wildfire smoke, as well as elevated rates of infant mortality and low birthweight. Klamath County Public Health (KCPH) raised concerns that wildfire smoke is a contributor to poor infant health. Thus, we built a multidisciplinary team and designed a community-engaged research (CEnR) project to capture community and individual-level exposure to wildfire smoke contaminants, alongside perinatal health outcomes. Through partnerships, we identified 24 individuals across academic, public health, and community organizations that met five times over three months to develop a study design. We initially used a modified Delphi method, but adjusted our approach to find multidisciplinary areas of agreement across a highly diverse team. Our team used structured meetings, surveys, and iterative feedback to build consensus on a study design. KCPH and our community partners reviewed and approved all proposed activities to ensure community input was integrated. The resultant study, trialed in Klamath County, included the use of environmental, residential, and personal samplers and health surveys with a cohort of pregnant individuals during the wildfire season. We discuss the advantages and challenges of building a multidisciplinary CEnR study in a rural county disproportionately impacted by wildfire smoke and infant mortality. Full article

Wildfire risk has been exacerbated across Europe by climate change favoring more damaging and severe wildfire events. This evolving wildfire risk context interacts with a broad landscape of EU policies including those on nature conservation, forestry, bioeconomy or climate and energy, all of which may increase or reduce fire hazard and the level of exposure and vulnerability of the values at risk. Coherently addressed, policies may support wildfire disaster risk management synergistically while reducing potential dysfunctions. This research conducts a content analysis of EU policies and initiatives under the European Green Deal with respect to integrated wildfire risk management and related nature-based solutions. The results show that a consistent EU policy framework to address wildfire risk reduction in a synergic way exists, with no major conflicts in the policy design. Nevertheless, better guidance on fire-smart land management practices and the conceptualization of wildfire-related nature-based solutions may enhance a more coherent policy implementation. Additional suggestions around the legal status of wildfire protection and ‘whole of government’ governance frameworks are discussed. Notably, within the laws, policies and initiatives analyzed, the beneficial side of fire addressed by integrated fire management is either missing or not explicitly mentioned, although it is considered in policy-related supporting guidelines. Full article

Wildfire represents a significant threat to ecosystems and communities in the Department of Amazonas, Peru, causing losses in biodiversity and land degradation and affecting socioeconomic security. The objective of this study was to analyze the spatial and temporal distribution of burned areas (BAs) from 1986 to 2023 to identify recurrence patterns and their impact on different types of land use and land cover (LULC). Landsat 5, 7, and 8 satellite images, processed by Google Earth Engine (GEE) using a decision tree approach, were used to map and quantify the affected areas. The results showed that the BAs were mainly concentrated in the provinces of Utcubamba, Luya, and Rodríguez de Mendoza, with a total of 1208.85 km² burned in 38 years. The most affected land covers were pasture/grassland (38.25%), natural cover (forest, dry forest, and shrubland) (29.55%) and agricultural areas (14.74%). Fires were most frequent between June and November, with the highest peaks in September and August. This study provides crucial evidence for the implementation of sustainable management strategies, fire prevention, and restoration of degraded areas, contributing to the protection and resilience of Amazonian ecosystems against future wildfire threats. Full article

Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual–reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies. Full article