Search Results (831)

Forests play a crucial role in climate regulation through atmospheric CO₂ sequestration. However, disturbances like wildfires can severely compromise this function. This study assesses the ecological and economic consequences of a 2018 wildfire in The Roaches Nature Reserve, UK, focusing on post-fire carbon dynamics. A mixed woodland dominated by Pinus sylvestris L. and Larix decidua Mill. was evaluated via satellite imagery (remote sensing indices), dendrochronological analysis (wood cores sampling), and soil properties analyses. Remote sensing revealed areas of high fire severity and progressive vegetation decline. Tree-ring data indicated near-total mortality of L. decidua, while P. sylvestris showed greater post-fire resilience. Soil properties (e.g., soil organic carbon, biomass and microbial indices, etc.) assessed at a depth of 0–5 cm showed no significant changes. The analysis of CO₂ sequestration trends revealed a marked decline in burned areas, with post-fire sequestration reduced by approximately 70% in P. sylvestris and nearly 100% in L. decidua, in contrast to the stable patterns observed in the control stands during the same period. To estimate this important ecosystem service, we developed a novel CO₂ Sequestration Loss (CSL) index, which quantified the reduction in forest carbon uptake and underscored the impaired sequestration capacity of burned area. The decrease in CO₂ sequestration also resulted in a loss of regulating ecosystem service value, with burned areas showing a marked reduction compared to pre-fire conditions. Finally, a carbon loss of ~208 Mg ha⁻¹ was estimated in the burnt area compared to the control, mainly due to tree mortality rather than shallow soil carbon stock. Overall, our findings demonstrate that wildfire can substantially compromise the climate mitigation potential of temperate forests, highlighting the urgency of proactive management and restoration strategies. Full article

In recent years, frequent wildfires have posed significant threats to both the ecological environment and socioeconomic development. Investigating the mechanisms underlying the influencing factors of forest fires and accurately predicting the likelihood of such events are crucial for effective prevention strategies. However, the field currently faces challenges, including the unclear characterization of influencing factors, limited accuracy in forest fire predictions, and the absence of models for mountain fire scenarios. To address these issues, this study proposes a research framework of “decoupling analysis-model prediction-scenario validation” and employs Principal Component Analysis (PCA) and Shapley Additive Explanations (SHAP) value quantification to elucidate the significant roles of meteorological as well as combustible state indicators through multifactor coupling. Furthermore, the Attention-based Long Short-Term Memory (ALSTM) network trained on PCA-decoupled data achieved mean accuracy, recall, and area under the precision-recall curve (PR-AUC) values of 97.82%, 94.61%, and 99.45%, respectively, through 10-time cross-validation, significantly outperforming traditional LSTM neural networks and logistic regression (LR) methods. Based on digital twin technology, a three-dimensional mountain fire scenario evolution model is constructed to validate the accuracy of the ALSTM network’s predictions and to quantify the impact of key factors on fire evolution. This approach offers an interpretable solution for predicting forest fires in complex environments and provides theoretical and technical support for the digital transformation of forest fire prevention and management. Full article

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies. Full article

Artificial intelligence (AI) offers several opportunities in wildfire management, particularly for improving short- and long-term fire occurrence forecasting, spread modeling, and decision-making. When properly adapted beyond research into real-world settings, AI can significantly reduce risks to human life, as well as ecological and economic damages. However, despite increasingly sophisticated research, the operational use of AI in wildfire contexts remains limited. In this article, we review the main domains of wildfire management where AI has been applied—susceptibility mapping, prediction, detection, simulation, and impact assessment—and highlight critical limitations that hinder practical adoption. These include challenges with dataset imbalance and accessibility, the inadequacy of commonly used metrics, the choice of prediction formats, and the computational costs of large-scale models, all of which reduce model trustworthiness and applicability. Beyond synthesizing existing work, our survey makes four explicit contributions: (1) we provide a reproducible taxonomy supported by detailed dataset tables, emphasizing both the reliability and shortcomings of frequently used data sources; (2) we propose evaluation guidance tailored to imbalanced and spatial tasks, stressing the importance of using accurate metrics and format; (3) we provide a complete state of the art, highlighting important issues and recommendations to enhance models’ performances and reliability from susceptibility to damage analysis; (4) we introduce a deployment checklist that considers cost, latency, required expertise, and integration with decision-support and optimization systems. By bridging the gap between laboratory-oriented models and real-world validation, our work advances prior reviews and aims to strengthen confidence in AI-driven wildfire management while guiding future research toward operational applicability. Full article

Wildfire detection remains a critical challenge for authorities, with human activity being the leading cause. The historical conditions prevailing in burned forest areas require a comprehensive analysis at both the environmental and anthropogenic levels. This study presents a multidimensional dataset comprising data from 2008 to 2024 and integrating Earth observation data and anthropogenic, environmental, meteorological, topographic, and fire-related features. This study evaluates, through time series analysis, the impact of climate trends such as increased temperature in comparison with anthropogenic activities such as deliberate fires. Time series analysis reveals that although climatic conditions with increased temperature and reduced precipitation in Cyprus intensify the risk of fire, the presence of fire events is primarily due to deliberate actions. The findings of this study support national-scale fire modeling, offering a foundation for targeted prevention, early warning systems, and sustainable forest fire management strategies. Full article

The restoration of forest ecosystems damaged by wildfires and pest outbreaks has become increasingly urgent. However, the public-good nature of forests, the involvement of diverse stakeholders, and the spatial variability of degradation present significant challenges to effective policy design. In particular, previous studies have largely examined these threats in isolation, and few have provided integrated economic analyses of their combined impacts. This gap underscores the need to better understand heterogeneous public preferences and their implications for restoration policy. To address this, we conducted a discrete choice experiment (DCE) with 1021 Korean citizens and applied a two-stage analytical framework combining principal component analysis (PCA) and latent class choice modeling (LCM). Five distinct preference segments were identified, each exhibiting substantial variation in willingness to pay (WTP) for restoration attributes. Policy simulations further revealed that public acceptance declines sharply at higher cost levels, highlighting the importance of setting realistic financial thresholds for broad support. While visual materials, consequentiality checks, and cheap talk scripts were employed to mitigate hypothetical bias, the limitations of external validity and potential sampling biases should be acknowledged. Our findings provide empirical evidence for tailoring restoration policies to different stakeholder groups, while also stressing the financial and institutional constraints of implementation. In particular, the results suggest that cost thresholds, citizen engagement, and awareness-raising strategies must be carefully balanced to ensure both effectiveness and public acceptance. Taken together, these insights contribute to evidence-based forest policymaking that is both economically efficient and socially acceptable, while recognizing the context-specific limitations of the Korean case and the need for comparative studies across countries. Full article

Conventional wildfire assessment products emphasize regional-scale ignition likelihood and potential spread derived from fuels and weather. While useful for broad planning, they do not directly support boundary-aware, scenario-specific decision-making for localized threats to communities in the Wildland–Urban Interface (WUI). This limitation constrains the ability of fire managers to effectively prioritize mitigation efforts and response strategies for ignition events that may lead to severe local impacts. This paper introduces WUI-BTI—a scenario-based, simulation-driven boundary-threat index for the Wildland–Urban Interface that quantifies consequences conditional on an ignition under standardized meteorology, rather than estimating risk. WUI-BTI evaluates ignition locations—referred to as Fire Amplification Sites (FAS)—based on their potential to compromise the defined boundary of a community. For each ignition location, a high-resolution fire spread simulation is conducted. The resulting fire perimeter dynamics are analyzed to extract three key metrics: (1) the minimum distance of fire approach to the community boundary ($D_{min}$) for non-breaching fires; and for breaching fires, (2) the time required for the fire to reach the boundary ($T_p$), and (3) the total length of the community boundary affected by the fire ($L_c$). These raw outputs are mapped through monotone, sigmoid-based transformations to yield a single, interpretable score: breaching fires are scored by the product of an inverse-time urgency term and an extent term, whereas non-breaching fires are scored by proximity alone. The result is a continuous boundary-threat surface that ranks ignition sites by their potential to rapidly and substantially compromise a community boundary. By converting complex simulation outputs into scenario-specific, boundary-aware intelligence, WUI-BTI provides a transparent, quantitative basis for prioritizing fuel treatments, pre-positioning suppression resources, and guiding protective strategies in the WUI for fire managers, land use planners, and emergency response agencies. The framework complements regional hazard layers (e.g., severity classifications) by resolving fine-scale, consequence-focused priorities for specific communities. Full article

The Gadouras Reservoir, Rhodes Island’s primary water source, experiences recurrent whiting events—milky turbidity from calcium carbonate precipitation—that challenge treatment operations, with impacts compounded by a major 2023 wildfire in this fire-prone Mediterranean setting. To elucidate these dynamics, a pragmatic, multi-source monitoring framework integrates archived Sentinel-2 and Landsat imagery with treatment-plant records (2017–mid-2025). Unitless spectral indices (e.g., AreaBGR) for whiting detection and chlorophyll-a proxies are combined with laboratory measurements of turbidity, pH, total organic carbon, manganese, and hydrological metrics, analyzed via spatiotemporal Hovmöller diagrams, Pearson correlations, and interrupted time-series models. Two seasonal whiting regimes are identified: a biogenic summer mode (southern origin; elevated chlorophyll-a; water temperature > 15 °C; pH > 8.5) and a non-biogenic winter mode (northern inflows). Following the wildfire, the system exhibits characteristics that could be related to possible hypolimnetic anoxia, prolonged whiting, a ~50% rise in organic carbon, and a manganese excursion to ~0.4 mg L⁻¹ at the deeper intake. Crucially, the post-fire period shows a decoupling of AreaBGR from turbidity (r ≈ 0.233 versus ≈ 0.859 pre-fire)—a key diagnostic finding that confirms a fundamental shift in the composition and optical properties of suspended particulates. The manganese spike is best explained by the confluence of a wildfire-induced biogeochemical predisposition (anoxia and Mn mobilization) and a consequential operational decision (relocation to a deeper, Mn-rich intake). This framework establishes diagnostic baselines and thresholds for managing fire-impacted reservoirs, supports the use of remote sensing in data-scarce systems, and informs adaptive operations under increasing climate pressures. Full article

Drought is an extremely terrifying environmental calamity, causing declining agricultural production, escalating food prices, water scarcity, soil erosion, increased wildfire risks, and changes in ecosystem. Drought data is noisy and poses challenges to accurate forecasts due to it being nonstationary and non-linear. This research aims to construct a contemporary and novel approach termed as TVFEMD-GPR, crossbreeding time varying filter-based empirical mode decomposition (TVFEMD) and gaussian process regression (GPR), to model multi-scaler standardized precipitation index (SPI) to forecast droughts. At first, the statistically significant lags at (t − 1) were computed via partial auto-correlation function (PACF). In the second step, the TVFEMD splits the (t − 1) lag into several factors named as intrinsic mode functions (IMFs) and residual components. The third step is the final step, where the GPR model took the IMFs and residual as input predictors to forecast one-month SPI (SPI1), three-months SPI (SPI3), six-months SPI (SPI6), and twelve-months SPI1 (SPI12) for Mackay and Springfield stations in Australia. To benchmark the new TVFEMD-GPR model, the long short-term memory (LSTM), boosted regression tree (BRT), and cascaded forward neural network (CFNN) were also developed to assess their accuracy in drought forecasting. Moreover, the TVFEMD was integrated to create TVFEMD-LSTM, TVFEMD-BRT, and TVFEMD-CFNN models to forecast multi-scaler SPI where the TVFEMD-GPR surpassed all comparable models in both stations. The outcomes proved that the TVFEMD-GPR outperformed comparable models by acquiring ENS = 0.5054, IA = 0.8082, U95% = 1.8943 (SPI1), ENS = 0.6564, IA = 0.8893, U95% = 1.5745(SPI3), ENS = 0.8237, IA = 0.9502, U95% = 1.1123 (SPI6), and ENS = 0.9285, IA = 0.9813, U95% = 0.7228 (SPI12) for Mackay Station. For Station 2 (Springfield), the TVFEMD-GPR obtained these metrics as ENS = 0.5192, IA = 0.8182, U95% = 1.9100 (SPI1), ENS = 0.6716, IA = 0.8953, U95% = 1.5163 (SPI3), ENS = 0.8289, IA = 0.9534, U95% = 1.1296 (SPI6), and ENS = 0.9311, IA = 0.9829, and U95% = 0.7695 (SPI12). The research exhibits the practicality of the TVFEMD-GPR model to anticipate drought events, minimize their impacts, and implement timely mitigation strategies. Moreover, the TVFEMD-GPR can assist in early warning systems, better water management, and reducing economic losses. Full article

Escalating extreme wildfires, fueled by the confluence of climate change, land use patterns alterations, ignitions by humans, and flammable fuels accumulation, pose significant and increasingly destructive risks to critical infrastructure (CI). This study presents a comprehensive comparative analysis of wildfire impacts and the corresponding CI resilience strategies employed across the EU and the US. It examines the vulnerability of CIs to the devastating effects of wildfires and their inadvertent contribution to wildfire ignition and spread. The study evaluates the EU’s CER Directive and the US National Infrastructure Protection Plan and assesses European Commission wildfire resilience-related initiatives, including FIRELOGUE, FIRE-RES, SILVANUS, and TREEADS flagship projects. It synthesizes empirical evidence and extracts key lessons learned from major wildfire events in the EU (2017 Portuguese fires; 2018 Mati wildfire) and the US (2023 Lahaina disaster; 2025 Los Angeles fires), drawing insights regarding the effectiveness of various resilience measures and identifying areas for improvement. Persistent challenges impeding effective wildfire resilience are identified, including governance fragmentation, lack of standardization in risk assessment and mitigation protocols, and insufficient integration of scientific knowledge and data into policy formulation and implementation. It concludes with actionable recommendations aimed at fostering science-based, multi-stakeholder approaches to strengthen wildfire resilience at both policy and operational levels. Full article

Our study focuses on investigating the present-day influence of wildfires on the global atmosphere. To achieve this, we utilized four observational biomass burning (BB) emissions datasets for present-day simulations employing the TM5 Chemical Transport Model (CTM). To assess how different emissions estimates influence the model’s ability to simulate the atmosphere, we compared the following datasets over the period 2003–2015: GFED4s, GFASv1.2, FEERv1.0-G1.2 and QFEDv2.6r1. Our study aims to investigate the role of wildfires in affecting important trace gases and aerosols. Their impact on atmospheric composition and their interactions with solar radiation affect the radiative balance at the Earth’s surface and, consequently, temperature trends in the troposphere. Full article

Both sex-related factors and gender-related factors affect the immediate and long term mental and physical health impacts of disasters, including those resulting from public health emergencies, climate-related events, and naturally occurring phenomena. These include sex-specific biological, physiological and genetic processes, mechanisms underlying reproduction, disease outcomes, and stress, immune, and trauma responses. Gendered factors such as roles, relations, identity, and institutional policies that have an impact on caregiving, occupation, gender-based violence, and access to healthcare, also influence the impacts of disasters and emergencies. Sex/gender factors interact with a range of social determinants to affect the equitability of impacts. A rapid review was conducted to examine evidence from Australia, Canada, countries from the European Union, New Zealand, the United Kingdom (UK), and the United States of America (USA) on the influence of sex- and gender-related factors in the context of disasters, such as COVID-19, earthquakes, floods, hurricanes, and wildfires. This article describes and categorizes this evidence with attention to real-world impacts of the interactions between sex, gender, and other equity related factors. Broad considerations for improving research and practices to support more sex and gender research in this area and ultimately, to improve emergency and disaster management, are discussed. Full article

Wildfires are a complex and underexplored aspect of the Earth system, significantly affecting climate, as they emit greenhouse gases and aerosols that alter the Earth’s radiative balance. This study utilizes the EC-Earth3 Earth System Model to investigate how interannual variability in biomass burning emissions influences variability in total aerosol optical depth (AOD), as well as carbon monoxide (CO) and ozone (O₃) tropospheric columns. We demonstrate that fluctuations in biomass burning emissions impact AOD, CO, and O₃ variability at regional and global scales, emphasizing the need for improved understanding of wildfires and their climate effects. Full article

The impacts of climate change, including rising temperatures and severe droughts, have intensified wildfires globally, with increased frequency, severity, and extent. Forests reduce the occurrence of rockfalls and increase their intensity since the slope’s vegetation constrains the trajectory. Consequently, the destruction of vegetation following a wildfire may potentially cause higher and more intense rockfall activity. In this paper, we first evaluate the effects of forest destruction on a local scale by studying a specific site impacted by the 2023 Evros Wildfire, aiming to identify the key factors. Next, we modify existing rockfall hazard rating systems to incorporate these key factors in a user-friendly way. Finally, we apply this system on a regional scale to the area affected by the 2023 Evros Wildfire. The modified system produced results indicating a significant increase in exposure and risk following the wildfire. This information helps to identify vulnerable sites and prioritize them systematically, facilitating informed decision-making regarding restoration strategies. Full article

A significant wildfire event occurred in Korinthos, Greece, on 22 July 2020, releasing large amounts of smoke into the atmosphere. This episode provided the opportunity to develop and apply the methodology described in this work, where the synergistic use of ground data, satellite remote sensing data and dispersion modeling is utilized to demonstrate highly accurate source detection, emission transport, and dispersion of the smoke plumes. The Fire Radiative Power (FRP) data from SEVIRI, on board Meteosat Second Generation, are used to estimate hourly fire top-down emissions. These emissions are used as input for the FLEXPART Lagrangian particle dispersion model, driven by GFS meteorological data. Simulated smoke transport is compared with TROPOMI satellite CO observations and lidar profiles from the PANhellenic GEophysical observatory of Antikythera (PANGEA) station. The model includes key atmospheric processes such as advection and deposition, providing a framework for assessing wildfire impacts on air quality and transport. The results highlight the effectiveness of combining high temporal resolution FRP data with the WARM START configuration of FLEXPART versus the Standard FLEXPART Simulation. Full article