Search Results (2,933)

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50–600 ha) with a ground smoke detector distance of 1–2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished. Full article

The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day⁻¹, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

The increasing frequency of global forest fires necessitates rapid and accurate detection methods. This study proposes a forest fire detection and segmentation framework based on the MST++ hyperspectral reconstruction model to improve the accuracy and robustness of wildfire monitoring under complex environmental conditions. The proposed method first reconstructs hyperspectral images from RGB inputs using an MST++ model trained on the NTIRE 2022 RGB-to-hyperspectral dataset (950 paired samples), followed by fire and smoke segmentation based on spectrally sensitive bands. For segmentation experiments, 118 flame images from the BoWFire dataset and 100 manually annotated smoke images from public datasets (D-Fire and DFS) were used. Quantitative results demonstrate that the proposed MST++-based method significantly outperforms the conventional U-Net baseline. In flame segmentation, MST++ achieved an IoU of 76.90%, an F1 score of 86.81%, and a Kappa coefficient of 0.8603, compared to 44.42%, 58.15%, and 0.5625 for U-Net, respectively. For smoke segmentation, MST++ achieved an IoU of 91.76% and an F1 score of 95.66%, surpassing U-Net by 17.08% and 10.32%, respectively. In fire–smoke overlapping scenarios, MST++ maintained strong robustness, achieving an IoU of 89.64% for smoke detection. These results indicate that hyperspectral reconstruction enhances discrimination capability among flame, smoke, and complex backgrounds, particularly under low-light and overlapping conditions. The proposed framework provides a reliable and efficient solution for early forest fire detection and demonstrates the potential of hyperspectral reconstruction approaches in disaster monitoring applications. Full article

The wildfires in the vicinity of the power transmission corridors are famous for their sudden occurrence, rapid growth, and susceptibility to interference from fire-like interferences at night, which can easily lead to line discharge and trip accidents, thus affecting the safe operation of the power system. In order to address the issue of the high false alarm rate and poor generalization performance of wildfire image recognition in complex power transmission corridor environments, a wildfire image recognition method based on an improved AlexNet is proposed in this paper. The proposed method improves the description of flame and smoke properties at different scales by designing a reparameterized multi-scale feature extraction structure, and effectively alleviates the influence of strong light reflection and fire-like interference at night by using lightweight multi-scale attention and hybrid pooling attention mechanisms. A wildfire image dataset is constructed based on 1246 on-site images of the power transmission corridor captured by a visual monitoring device and 600 wildfire images downloaded from the internet, and tested in real-world imbalanced distribution scenarios. The experimental results show that the proposed method can recognize wildfire images with an accuracy of 96.9% and an F1 value of 94.9% on the test dataset, which is much higher than that of the original AlexNet, and has a strong ability to adapt to cross-dataset tests. The research work can provide technical support for online monitoring and operation and maintenance of wildfires in power transmission corridors. Full article

A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management. Full article

Community land management plays an important role in wildfire-prone landscapes in Mediterranean Europe. However, in Portugal, information on the spatial extent and boundaries of community land remains fragmented across multiple institutions. This study addresses a critical but often overlooked issue in wildfire management: the fragmentation of institutional data on community land boundaries in mainland Portugal and its direct implications for forest fire risk management, planning, and accountability. We harmonized georeferenced datasets from various government and public institutions, applying multi-institutional spatial integration supported by legal land use criteria using the Land Use Land Cover map 2018 (LULC2018). The resulting national map represents the first fully harmonized spatial assessment of community land (baldios) in mainland Portugal. Our results show that baldios currently occupy approximately 595 thousand hectares, significantly exceeding official estimates. Of this total, around 74% are under partial forest regime law, and approximately 76% are classified as having a high or very high wildfire hazard. This means that three out of every four hectares of baldios in mainland Portugal are structurally susceptible to extreme wildfire conditions. Beyond improving cartographic data, the study’s findings demonstrate how the lack of land registry weakens the institutional foundations for community-based wildfire management. Without a functional, legally validated national map of community land boundaries, responsibilities, co-management mechanisms, and prevention measures remain spatially inconsistent. Full article

Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article

This study presents a comprehensive wildfire risk assessment framework for Rhodes Island, Greece, aimed at quantifying the impacts of climate change on hazard levels and vulnerability in a typical Mediterranean environment. The approach integrates Fire Weather Index (FWI) data, detailed fuel-type mapping, and multiple vulnerability indicators covering ecological, socioeconomic, and population factors, enabling spatially explicit estimates of current and future wildfire risk. Historically, Rhodes mostly faces moderate wildfire risk, mainly in central and northeastern regions, with localised areas of higher risk near settlements and key economic sites. Climate forecasts for 2025–2049 predict a notable increase in hazard, with areas experiencing extreme fire weather (FWI > 50) increasing from 15.19% to 66–72%, across all emission scenarios. Ecological vulnerability is particularly alarming, as 93% of the island is already highly susceptible; fire-prone forest and agricultural zones are expected to move into the highest ecological risk categories, especially in the central mountain areas. The devastating 2023 wildfire, which burned over 17,600 hectares, caused more than €5.8 million in direct damages and led to the largest evacuation in the island’s history, closely aligning with high-risk zones modelled in the framework. An important insight is the limited spatial variation in near-future risk between RCP 4.5 and RCP 8.5, indicating that significant wildfire intensification is largely unavoidable by mid-century, emphasising the urgent need for quick adaptation and risk mitigation efforts for Mediterranean critical infrastructure and communities. Full article

Cross-sectional surveys of psychological distress using the Kessler-6 tool (K6+) were conducted among training cohorts per year of New Jersey (NJ) secondary school teachers between January 2022 and December 2024. Data downloaded for 12–18 annual virtual synchronous live session training date ranges related to specified teacher cohorts, consisting of 30 calendar days prior to its date to relate to K6+ questions (575 unique participants across 42 total live sessions). Utilizing data from federal/state air quality monitoring stations (AQMS), we constructed a database of estimated exposures to ambient/outdoor air quality. Cohorts were broken down by school district (SD) and paired with AQMS based on approximate geographic proximity for each SD’s school’s physical address utilizing NJ-GeoWeb. Once addresses were reported and associated with two AQMS, associated reviewed daily criteria pollutant data (2021–2024) were retrieved for particulate matter (PM, PM₁₀ and PM_2.5) and ozone. Data were averaged for relevant stations. Analyses suggested prior 30-day PM_2.5 showed a significant negative correlation with K6+ scores, −0.32 with PM_2.5 concentration (p = 0.04) and −0.48 with PM_2.5 AQI (p = 0.002); however, wind speed had a positive association, 0.33, with K6+ scores (p = 0.03). These results suggested how specific events and meteorological conditions affected ambient air quality for only some of the prior 30 days yet still potentially influenced K6+ scores for some cohorts, e.g., large wildfires then prevailing winds. More research with improved exposure assessment is warranted. This initial environmental epidemiology study with ecological design can inform future collaborative research and practice work on mental health and the effects of environmental factors. Full article

With the increasing number of wildfire events, people living close to the wildland–urban interface (WUI) are more likely to be exposed to these events. To mitigate the hazards related to wildfires, it is of great importance to identify areas where human settlements are at a greater risk. Remote sensing-based techniques for mapping and quantifying the inhabitants possibly affected by these events are crucial to reduce the loss of life as well as reduce the negative impact that wildfires pose to the people living in WUIs, the surrounding areas, and the environment. Fine-scale mapping is a suitable auxiliary tool to indicate areas at greater risk. Hence, the dasymetric method was applied to generate a high-resolution map of the study area’s population, using products generated from Sentinel-2 imagery, a census, and Light Detection and Ranging (LiDAR) data. The findings of the proposed methodology show that around 59% of the population in the study area currently lives inside the WUI, while in 2025, most of the people affected by wildfires—77%—lived outside the WUI. This is expected, since wildfires vary in space and time, and they are seen as spatial–temporal processes. In addition, the results demonstrated that women are slightly more exposed to wildfires than other population groups. These results showed that the proposed methodology could not only help identify high-risk areas but also the number of people living in these areas due to the high-resolution dasymetric methodology. The proposed methodology described in this work shows that fine-scale mapping could enrich forest management in order to protect the populations susceptible to the negative impacts of wildfires, consequently protecting the environment. Full article

Climate hazards increasingly disrupt schooling, revealing the limits of preparedness models that treat teachers only as implementers. This study reframes teacher empowerment as a climate-resilience capability and examines how governance arrangements enable (or constrain) hazard-ready education systems. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), searches of Web of Science, Scopus, and Google Scholar (2000–2025) identified 53 eligible studies. Across diverse hazards and settings, the evidence converges on a governance-to-capability pathway: empowerment becomes resilient performance only when the delegated decision space is matched with financed capacity (time, training, contingency resources), timely risk information and functional communication/digital infrastructure, institutionalized cross-sector coordination (education–DRR–health–protection–local government), and learning-oriented accountability (after-action review and adaptive revision rather than punitive compliance). Reported outcomes include higher preparedness quality, earlier protective action, improved learning continuity and safeguarding, and more sustainable teacher well-being/retention. Predictable failure modes include mandate–resource mismatch, accountability overload, unstable centralization–autonomy dynamics, and inequitable empowerment distribution affecting rural schools, women, and contract teachers, and disability inclusion. The evidence gaps remain pronounced for chronic hazards (especially heat and wildfire smoke), high-vulnerability contexts (fragile/conflict settings and informal settlements), and standardized measures of equity, burden distribution, governance performance, and cost-effectiveness. Policies should prioritize integrated governance packages with explicit protection and equity safeguards. Full article

Floodplain disconnection caused by channel incision and/or levee construction has led to widespread loss of riparian habitats and ecosystem functions globally. Restoring full stream–floodplain connectivity is increasingly promoted, yet evidence of ecological outcomes remains limited. This study evaluates the initial performance of two Stage 0 restoration projects on Whychus Creek, Oregon, which reconnected incised channels to their historical floodplains in 2012 and 2016. We combined pre- and post-restoration vegetation surveys along fixed transects with hydrogeomorphic-based riparian and wetland function assessments and applied quantitative analyses, including Kruskal–Wallis tests, Jaccard correlations, Sorensen similarity indices, and factor analysis, to compare changes in plant assemblages and ecosystem functions across restored, transitional, and unrestored reaches. Our research results indicate that two years post-restoration, the active riparian area expanded 2.5-fold, species richness and structural diversity increased significantly, and riparian and wetland functions such as water storage, sediment retention, and habitat support for fish and amphibians improved markedly. Numbers of anadromous salmonids also increased markedly. This is important as salmon recovery is a regional stream restoration goal. Comparisons with a reach restored six years earlier suggest a positive trajectory toward mature, resilient ecosystems. These findings demonstrate that Stage 0 restoration can rapidly reestablish complex habitat mosaics and enhance ecosystem services critical for biodiversity, water quality, and flood resilience. Practically, this evidence supports process-based restoration strategies that prioritize full floodplain reconnection as a cost-effective approach to reversing long-term ecological degradation. Continued monitoring is essential to guide adaptive management and strengthen the evidence base for the wide-scale implementation of valley-floor wide stream restoration. Full article

Wildfires are increasingly complex and geographically dynamic phenomena that require timely and context-sensitive decision support across the management cycle. Artificial intelligence (AI) has been widely applied to wildfire detection, prediction, and remote sensing; however, a systemic understanding of how AI methods are structurally integrated into decision-support architectures remains limited. The present configurational mapping review, reported in alignment with PRISMA-ScR guidance, examines AI applications in rural wildfire management between 2020 and 2024. Using a configurational framework, explicit scope–algorithm–vector relations are mapped, identifying how specific AI paradigms are operationalised through technological infrastructures to support decision-relevant functions. A total of 27 articles were included, from which 168 scope–algorithm–vector triplets were extracted and analysed descriptively. The results reveal a concentration of applications in detection and evolution prediction tasks, predominantly supported by machine learning methods and remote sensing platforms. Explicitly linked configurations to action-oriented or prescriptive decision functions are less frequently documented. The findings contribute to a structured mapping of AI deployment patterns in wildfire management and provide a conceptual basis for future research addressing integrative and action-oriented system design. Full article

Wildfires have become more frequent and intense worldwide due to climate change and anthropogenic activities, which is why accurate and timely burned area mapping is essential for estimating damage and effective post-fire recovery planning. Synthetic Aperture Radar (SAR) data, which operates under all weather conditions and day-night cycles, offers a reliable source for burned area mapping. In this context, several studies have explored the use of dual-polarization SAR imagery and machine learning, yet the influence of multi-date, dual-orbit pass data and texture features remained unexplored. Therefore, this study aims to assess the Sentinel-1 acquisition configurations, varying in temporal depth and orbital direction, for wildfire burned area mapping, considering the recent Palisades wildfire event as a study area. A comparative study was conducted across different scenarios to evaluate the effectiveness of using single-date versus multi-date SAR imagery, the integration of ascending and descending orbit passes, and the contribution of Grey-Level Co-occurrence Matrix texture features. The performance of Random Forest (RF) and Extreme Gradient Boosting classifiers was analyzed through the scenarios mentioned above. The single-date configuration using RF achieved an accuracy of 82.34%, F1-score of 81.43%, precision of 83.07%, recall of 80.84%, and ROC-AUC of 90.88%, whereas the multi-date approach reached 85.78%, 85.15%, 86.45%, 84.56%, and 93.28%, respectively. Our study highlights the importance of acquisition configuration and texture information for reliable SAR-based wildfire burned area assessment. Full article