Search Results (2,921)

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

The background modification of ecosystems affected by fire can cause black or dark colours in animals to become adaptive, providing better protection against visually oriented predators. We surveyed fire-prone Mediterranean woodlands to describe the behaviour, position and background characteristics of the black cicada Tibicina quadrisignata Hagen, 1855 found in recently burnt and unburnt trees. A human detectability test, using cicada pictures in natural backgrounds taken during the fieldwork, was used to assess detection risk. Most cicadas found were solitary males uttering courtship song. Many cicadas flew when approached, with 82% of flight initiation distances being less than 3 m and half of the flights being less than 30 m. Cicadas favoured sunny locations in early morning, and shady sites as the temperature increased. Fire altered fine-scale microhabitat use by cicadas, since cicadas were found in 71% thicker stems and at 14% lower height on the tree, in burnt trees, in relation to unburnt trees. Generalised Linear Mixed Models (GLMMs) revealed a negative fire effect on cicada detection by human test participants. The probability of detection fell from 0.62 in unburnt backgrounds to 0.48 in burnt backgrounds, while the time needed for detection did not change between burnt and unburnt sites. Overall, these results show that T. quadrisignata cicadas adjust their substrate use after fire and are less detectable on burnt backgrounds. Real predation risk, however, also depends on thermoregulation-associated exposure, courtship song activity and predator densities. Full article

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas. Full article

Following bushfires (also known as wildfires), impacted free-living wildlife with welfare or conservation concerns are captured and presented for veterinary assessment where possible. This study represents an in-depth retrospective analysis of the veterinary records of 259 animals in Victoria, Australia, impacted by bushfire in 2019–2020. In total, 35 different species were assessed, including 196 koalas. Multivariable analyses of 126 koalas with complete medical records identified several clinical prognostic factors affecting 6-month survival outcomes. Increased odds of negative outcomes (death or euthanasia) were associated with increasing age (tooth wear class; odds ratio 2.70 for one unit increase), lower body condition score (one-unit decrease OR 7.27), and the earlier animals were presented after the fire event (OR 0.94 for each passing day). In 83 koalas with burn injuries, negative outcomes were also associated with burns more severe than minor (85% survival for minor burns only, compared to 31% survival with moderate or severe burns), and burns to more than 10 digits (12% survival). In burnt koalas, the combination of burn severity and digital involvement appear to be important prognostic factors for long-term outcomes. These findings can support veterinarians to more accurately evaluate prognosis for bushfire-affected koalas during initial assessment and will facilitate the strategic allocation of limited treatment and rehabilitation resources to the animals most likely to recover. The scope of this study was limited to the consideration of health outcomes, with the recognition of health as just one of many factors that must inform decisions about rehabilitating injured wildlife. Full article

Efficient and accurate active fire detection is crucial for timely firefighting and mitigating hazards. Geostationary satellites deliver high-frequency observations that offer valuable data for near-real-time fire monitoring. However, current operational fire detection algorithms often underutilize temporal information, failing to decouple fire-induced anomalies from inherent surface thermal heterogeneity, which results in frequent false alarms. To address this limitation, we constructed a ten-day historical background brightness temperature (BT) reference database from multi-year Himawari-8/9 data, serving as a stable, fire-undisturbed baseline. Based on this, an active fire detection algorithm was developed that integrates instantaneous spatial–contextual differences with historical deviations of these differences from the reference database. Evaluated against a robust dataset of over 55,000 fire pixels (cross-verified using 10 m Sentinel-2 burn-scar data), the proposed algorithm significantly outperforms the Himawari-8/9 Wildfire (WLF) product, achieving a commission error (CE) of 2.9%, an omission error (OE) of 37.5%, and an F1-score of 0.76. The framework demonstrated superior detection accuracy in challenging scenarios such as low-temperature, smoke-obscured, and early-stage fires, while maintained robust performance across diverse fire types. The approach enables rapid full-disk fire detection in less than one minute and can be adapted to other geostationary satellites, providing a technical foundation for building a globally coordinated fire monitoring system. Full article

Wildfires cause major damage, and their accurate detection is crucial. A common approach to near-real-time detection uses Geostationary (GEO) satellite algorithms. A standard scheme for evaluating the accuracy of a GEO-based algorithm is to compare its detections with higher-resolution Low Earth Orbit (LEO) images, considering the latter as ground truth. The primary objective of this study is to quantify the prevalence of GOES ABI/VIIRS fire detection misalignments and assess their impact on the accuracy evaluation of the GOES Fire Detection and Characterization (FDC) product. Thus, the key question is how this evaluation should be performed. To this end, a large dataset of matching FDC/VIIRS fire detections across Western U.S., Amazonas, and Patagonia was constructed. Our finding is that for nearly 12% of fire events, there are spatial misalignments between FDC and VIIRS detections. Next, we show that using VIIRS as ground truth without considering these misalignments yields highly biased estimates. This affects the evaluation of the FDC product detection capabilities. Finally, we demonstrate that using a GOES FDC/VIIRS buffer window substantially mitigates the effect of misalignments. For example, the estimated false alarm rate ranges between 26% and 36% without a window, whereas using a $3\times 3$ window yields values between 7% and 15%. Full article

Anthropogenic wildfires account for the majority of wildfire ignitions in human-dominated landscapes, yet their spatial drivers remain insufficiently understood at national scales. This study aims to identify key factors influencing anthropogenic wildfire occurrence and to develop a robust and interpretable prediction framework using nationwide data from South Korea. Wildfire occurrence records from 2011–2021 were integrated with daily meteorological, environmental, and socio-economic variables at a 1 km grid resolution. A stacking ensemble model combining Random Forest, XGBoost, LightGBM, Extra Trees, and logistic regression was implemented to improve predictive robustness under rare-event conditions. Model performance was evaluated using ROC–AUC, PR–AUC, and threshold-optimized F1-scores, and variable contributions were interpreted using feature importance and SHAP analyses. The ensemble model achieved a PR–AUC of 0.934 and an ROC–AUC of 0.941. Relative humidity and maximum temperature were identified as influential meteorological variables, while human-accessibility-related variables, particularly distance to roads and agricultural land, showed consistently high contributions to spatial ignition probability. These findings indicate that anthropogenic wildfire occurrence is shaped by interactions between fire-weather conditions and spatial patterns of human accessibility. The proposed framework provides a scalable approach for understanding anthropogenic wildfire mechanisms and supporting prevention strategies in forested landscapes. Full article

Thermal contextual algorithms for 375 m VIIRS active fire detection can produce substantial commission errors over persistent non-wildfire heat sources (e.g., refineries, gas flares, and volcanoes), and globally fixed thresholds may be suboptimal under heterogeneous thermal backgrounds. We present a lightweight spatiotemporal prior layer that augments by applying prior-guided, pixel-level parameter switching during the discrimination stage. The layer combines: (i) a persistent non-wildfire thermal anomaly mask (PFM) derived from multi-year VNP14IMG recurrence and seasonality statistics on a 0.004° grid, and (ii) a short-term heat-source mask (SHM) based on nighttime VIIRS I4/I5 brightness temperature stability to capture newly emerged or rapidly intensifying static sources. Pixels flagged by either prior are processed with a stricter parameter set, while other pixels follow the baseline setting. We evaluate the method using a stratified validation dataset (N = 3435) spanning industrial/urban clusters, volcanic regions, forest/grassland wildfires, and fragmented crop residue burning, with validation supported by independent high-resolution imagery (Sentinel-2/Landsat) and external POI datasets. The framework markedly reduces false positives in high-interference zones (industrial/urban false positive rate from 88.6% to 22.7%; volcanic from 100.0% to 57.3%) while preserving high performance for forest/grassland wildfires (F1 ≈ 0.999). For fragmented residue burning, omission error decreases from 11.2% to 1.3%, improving detection completeness without an apparent increase in commission errors. Overall, the results suggest that integrating long- and short-term spatiotemporal priors via threshold switching can improve the robustness and interpretability of contextual VIIRS fire detection under complex thermal backgrounds in the evaluated scenarios. Full article

Cellular automata-based wildfire simulation models are widely used to support fire management, risk assessment, and operational decision-making, due to their efficiency and computational advantages. However, the accuracy of these models heavily depends on the quality of input data provided by the user, including the composition and geospatial extend of forest fuels, current meteorological conditions and terrain information. This publication examines how quantitative and spatial input data uncertainties affect the estimates of the impacted areas. Using a series of simulation experiments, inaccurate data are introduced to specific input variables (such as the vegetation type and the fuel moisture content) to reflect realistic levels of uncertainty commonly observed in operational scenarios, where users with different cognitive backgrounds fail to properly identify key characteristics of a fire. Model outputs are then compared using spatial and temporal performance metrics, including the rate of spread and burned area extent. The results demonstrate that uncertainties in fuel models and meteorological inputs exert a dominant influence on simulated fire behavior. Our findings highlight the sensitivity of wildfire simulations to compounded input uncertainties and stress the need for improved in-field data acquisition strategies. Full article

School classrooms represent complex, interconnected systems where indoor environmental quality critically influences student health, cognitive performance, and educational equity. Yet traditional approaches operate in disciplinary silos, creating systemic failures in design, operation, and maintenance. This narrative review adopts a systems engineering framework to demonstrate how integrated interventions—spanning policy, design, technology, and operations—create resilient, sustainable, and healthy classroom climates. Amid escalating climate change impacts (rising temperatures, heatwaves, wildfires) and emerging threats (airborne pathogens, urban pollution), reactive measures like school closures prove pedagogically counterproductive. This review synthesizes evidence on natural, mechanical, and mixed-mode ventilation systems optimized through advanced control strategies, smart technologies, and health-centred policies. Key findings reveal that synergistic integration of Policy, Management, Construction, Operation, and Smart Technologies, in a systems engineering framework, outperforms singular strategies. Critical interventions include hybrid ventilation coupled with layered defences (HEPA filtration, UVGI), AI-driven adaptive controls using IoT sensors and Model Predictive Control to optimize energy while managing pollutant concentrations, and mandatory IAQ standards rooted in stakeholder education. By framing classrooms as interconnected engineering systems, this work provides actionable insights for architects, engineers, policymakers, and administrators, positioning future school design toward resilience, sustainability, and human-centred health outcomes. Full article

Recently, numerous countries have experienced devastating wildfires, leading to significant destruction and loss of life. These catastrophic events highlight the shortcomings in current building regulations and testing methods. There is a pressing need for a more profound understanding of the characteristics and behaviour of large outdoor fires to address these inadequacies effectively. Wildfires can spread to structures located at the wildland–urban interface, leading to further fire propagation from one building to another. In this study, the Fire Dynamics Simulator (FDS) model was validated using experimental data from the National Institute of Standards and Technology (NIST). The experiment consisted of a target wall and a small wooden shed containing six wooden cribs as fuel, with a separation distance of 3 m. Both FDS and the experiment proved that 3 m is the safe separation distance. Different shed materials, such as steel, were used, which reduced the total heat release rate by 40% and the flame height by 20%. The effects of wind speed and direction were investigated using two wooden sheds in FDS to observe fire spread between them. The safe separation distance was 3 m for both wind speeds (2 and 5 m/s) in all directions, where the critical temperature was not reached to cause self-ignition of the second shed, except in the north direction (inward) at a speed of 5 m/s. When the separation distance increased to 3.5 m, the average heat flux at the other shed reduced to 3.18 kW/m², which did not cause self-ignition. Therefore, the safe separation distance between two structures for a wind speed of 5 m/s should be 3.5 m to mitigate the spread of fire based on the shed dimensions and the fire source load. Full article

Southern Africa is highly sensitive to climate variability associated with the El Niño Southern Oscillation (ENSO), which strongly influences hydroclimate, vegetation dynamics, and atmospheric composition. This study examined the impacts of the 2015/16 El Niño on vegetation, meteorological conditions, and atmospheric emissions over Southern Africa using satellite observations and reanalysis data. Time-lagged cross-correlation analysis of seasonally adjusted time-series was applied to characterize synchronous and delayed interactions among vegetation indices, hydrological variables, meteorological drivers, and air-quality parameters. Bayesian causal impact analysis was further used to quantify El Niño-induced anomalies by comparing observed conditions with counterfactual scenarios representing the absence of the event. The results showed that vegetation greenness responds primarily to concurrent moisture availability, with strong positive associations between NDVI, precipitation, soil moisture, and canopy water. Moisture-related variables exert delayed influences on atmospheric composition, highlighting the role of wet scavenging and dilution. Carbonaceous aerosols (black carbon [BC] and organic carbon [OC]), particulate matter [PM_2.5], and aerosol optical depth exhibit strong synchronous coupling, indicating a dominant biomass-burning source. The causal impact analysis reveals statistically significant and sustained post-2015 increases in fire-related emissions (carbon monoxide [CO], BC, OC, PM_2.5, and aerosol optical depth [AOD]), particularly during austral winter and dry seasons. In contrast, precipitation, soil moisture, evapotranspiration, and vegetation greenness show persistent negative anomalies, reflecting widespread drought stress under elevated temperatures. Overall, the findings demonstrate that the 2015/16 El Niño amplified fire emissions while suppressing ecosystem functioning across Southern Africa, underscoring strong climate–fire–vegetation feedback with important air-quality and environmental implications. Full article

For the last three summers in Canada (2023–2025), and episodically in Siberia over the previous decade and a half, severe consequences from wildfires represent major ecological and societal impacts: the displacement of inhabitants; destruction of buildings, timber and infrastructure; and far-field air pollution. Wildfire occurrence is increasingly supported every summer by persistent surface warming and widespread atmospheric moisture deficits. The two recent major Canadian fire years in 2023 and 2025 show some contrasts: 2023 was dominated by an early June event with preconditioning, whereas 2025 saw repeated single events spanning June to early August, culminating in a significant late-summer event. Events in both years were associated with North Pacific–North American atmospheric blocking regimes. Over the longer term, 2003–2025, normalized June–September wildfire fraction anomalies in the Canadian sector (45–60° N, 150–60° W) show the post-2023 period as having new, clear, record-breaking fire intensities, highlighting wildfires as emerging dominant Arctic–subarctic extremes. Siberia shows an increase after 2010. Although multiple environmental Arctic–subarctic extremes are ongoing—such as sea-ice loss, storms, and glacial ice loss—the impacts from wildfires represent preeminent, growing societal consequences. Full article