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1. Although anthropogenic edges are an important consequence of timber harvesting, edges due to natural disturbances or landscape heterogeneity are also common. Forest edges have been well studied in temperate and tropical forests, but less so in less productive, disturbance-adapted boreal forests. 2. We synthesized data on forest vegetation at edges of boreal forests and compared edge influence among edge types (fire, cut, lake/wetland; old vs. young), forest types (broadleaf vs. coniferous) and geographic regions. Our objectives were to quantify vegetation responses at edges of all types and to compare the strength and extent of edge influence among different types of edges and forests. 3. Research was conducted using the same general sampling design in Alberta, Ontario and Quebec in Canada, and in Sweden and Finland. We conducted a meta-analysis for a variety of response variables including forest structure, deadwood abundance, regeneration, understorey abundance and diversity, and non-vascular plant cover. We also determined the magnitude and distance of edge influence (DEI) using randomization tests. 4. Some edge responses (lower tree basal area, tree canopy and bryophyte cover; more logs; higher regeneration) were significant overall across studies. Edge influence on ground vegetation in boreal forests was generally weak, not very extensive (DEI usually < 20 m) and decreased with time. We found more extensive edge influence at natural edges, at younger edges and in broadleaf forests. The comparison among regions revealed weaker edge influence in Fennoscandian forests. 5. Synthesis. Edges created by forest harvesting do not appear to have as strong, extensive or persistent influence on vegetation in boreal as in tropical or temperate forested ecosystems. We attribute this apparent resistance to shorter canopy heights, inherent heterogeneity in boreal forests and their adaptation to frequent natural disturbance. Nevertheless, notable differences between forest structure responses to natural (fire) and anthropogenic (cut) edges raise concerns about biodiversity implications of extensive creation of anthropogenic edges. By highlighting universal responses to edge influence in boreal forests that are significant irrespective of edge or forest type, and those which vary by edge type, we provide a context for the conservation of boreal forests.

This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360° directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system’s capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks.

Recent wildfire events in Ukraine caused considerable economic and human losses, drawing the attention of public opinion in Ukraine to further research the issue related to the management of the risks of forest fires, specially, in the current context of climate change and due to the growing frequency of critical fire weather conditions. Current approaches to fighting wildfires in Ukraine are focused on fire extinction, currently omitting the management of vegetation fuels and their effect on wildfire behaviour to facilitate its mitigation. Due to current wildfire risks to the population and forests, and insufficient research on this issue in Ukraine, it is needed to further develop and test new approaches to reduce wildfire risk. For that purpose, it is required a deep understanding of the fire resilience of vegetation as well as the factors that make the communities vulnerable. In this manuscript, a method for assessing and mapping the Wildland-urban interface with a focus on fire risks for part of the Ukrainian Polissya is suggested. Wildland-urban interface zones were delineated for settlements in the study area and used to identify areas for wildfire risk remediation and silvicultural practices to increase forest resilience to fire. A biodiversity analysis of the main tree species, undergrowth, and understory of the study region, produced a list of local deciduous species that could be used to reduce fire intensity by increasing their proportion in pure pine forests. The volume of silvicultural efforts to increase forest resilience to fire and reduce wildfire risks to human settlements was assessed for one of the most forested regions of Ukraine. Moreover, the first comprehensive assessment of wildlands, which can potentially contribute to wildfire impacts on communities, was provided, making recommendations to reduce wildfire risks for the settlements. In this study, feasible and effective methods to assess Wildland-Urban-Interfaces, and reduce fire risks are suggested, suggesting a methodology concerning wildfire risks for a heavily forested region of Ukraine. Moreover, the suggested approaches that could be used in Ukrainian Forest Management to mitigate wildfire risks in the context of climate change, urbanization, and low resistance of pure pine stands to fires as well as pests and diseases

Interactions between climate and land-use change may drive widespread degradation of Amazonian forests. High-intensity fires associated with extreme weather events could accelerate this degradation by abruptly increasing tree mortality, but this process remains poorly understood. Here we present, to our knowledge, the first field-based evidence of a tipping point in Amazon forests due to altered fire regimes. Based on results of a large-scale, longterm experiment with annual and triennial burn regimes (B1yr and B3yr, respectively) in the Amazon, we found abrupt increases in fire-induced tree mortality (226 and 462%) during a severe drought event, when fuel loads and air temperatures were substantially higher and relative humidity was lower than long-term averages. This threshold mortality response had a cascading effect, causing sharp declines in canopy cover (23 and 31%) and aboveground live biomass (12 and 30%) and favoring widespread invasion by flammable grasses across the forest edge area (80 and 63%), where fires were most intense (e.g., 220 and 820 kW·m−1). During the droughts of 2007 and 2010, regional forest fires burned 12 and 5% of southeastern Amazon forests, respectively, compared with <1% in nondrought years. These results show that a few extreme drought events, coupled with forest fragmentation and anthropogenic ignition sources, are already causing widespread fire-induced tree mortality and forest degradation across southeastern Amazon forests. Future projections of vegetation responses to climate change across drier portions of the Amazon require more than simulation of global climate forcing alone and must also include interactions of extreme weather events, fire, and land-use change.

In densely populated fire-prone regions, interactions between global change drivers, such as landcover changes and climate change, may increase the frequency and severity of wildfires impacting forest ecosystems, thus diminishing their capability of provisioning key ecosystem goods and services for these societies. Yet, landscape mosaics play a crucial role in fire dynamics and behaviour. Here, we argue that promoting heterogeneous agro-forest mosaics could reduce the area affected by future fires. Specifically, we evaluated 24 landscape-scale management scenarios based on agricultural conversion, i.e. the creation of new agricultural land, that also explicitly incorporated fire suppression. Scenarios differed in the annual rate of such conversion, the spatial pattern (aggregate vs. scattered), and the location of new agricultural patches. To quantify the interactions between vegetation dynamics, fires, land-cover changes, and fire suppression, we coupled two spatially explicit models: a landscape dynamic fire-succession model and a land-cover change model. When applied to the Mediterranean region of Catalonia (NE Spain), new landscape mosaics favoured firefighting extinction capacity only after 15 years (on average) of cumulative land transformations. Agricultural conversion of at least 100 km² year⁻¹ was required to reduce total area burnt. A conversion rate of 200 km² year⁻¹ substantially improved fire suppression effectiveness, but subsequent conversion increases did not. When aggregated, new agriculture patches contributed more effectively to reduction in total area burnt and decreased the edge effect on remaining forest patches. Agricultural conversion in Mediterranean landscapes opens a new window for long-term spatial planning aimed at minimizing negative impacts of wildfire on forest ecosystems. These alternative strategies could help to develop landscape management practices in other fire-prone regions.

1. Widespread degradation of tropical forests is caused by a variety of disturbances that interact in ways that are not well understood. 2. To explore potential synergies between edge effects, fire and windstorm damage as causes of Amazonian forest degradation, we quantified vegetation responses to a 30-min, high-intensity windstorm that in 2012, swept through a large-scale fire experiment that borders an agricultural field. Our pre- and postwindstorm measurements include tree mortality rates and modes of death, above-ground biomass, and airborne LiDAR-based estimates of tree heights and canopy disturbance (i.e., number and size of gaps). The experimental area in the southeastern Amazonia includes three 50-ha plots established in 2004 that were unbumed (Control), burned annually (Blyr), or burned at 3-year intervals (B3yr). 3. The windstorm caused greater damage to trees (>10 cm DBH) in the burned plots (B1yr: 13 ± 9% of 785 trees; B3yr 17 ± 13% of 433) than in the Control plot (8 ± 4% of 2,300; ± CI). It substantially reduced vegetation height by 14% in B1yr, 20% in B3yr and 12% in the Control plots, while it reduced above-ground biomass by 18% of 77.7 Mg/ha (B1yr), 31% of 56.6 (B3yr), and 15% of 120 (Control). Tree damage was greatest near the agricultural field edge in all three plots, especially among large trees and in B3yr. Trunk snapping (70%) and uprooting (20%) were the most common modes of tree damage and mortality, with the height of trunk failure on the burned plots often corresponding with the height of historical fire scars. Of the windstorm-damaged trees, 80% (B1yr), 90% and s57% (Control) were dead 4 years later. Trees that had crown damage experienced the least mortality (22%-60%), followed by those that were snapped (55%-94%) and uprooted (88%-94%). 4. Synthesis. We demonstrate the synergistic effects of three kinds of disturbances on a tropical forest. Our results show that the effects of windstorms are exacerbated by prior degradation by fire and fragmentation. We highlight that understorey fires can produce long-lasting effects on tropical forests not only by directly killing trees but also by increasing tree vulnerability to wind damage due to fire scars and a more open canopy.

Fuel breaks are increasingly being implemented at broad scales (100s to 10,000s of square kilometers) in fire-prone landscapes globally, yet there is little scientific information available regarding their ecological effects (eg habitat fragmentation). Fuel breaks are designed to reduce flammable vegetation (ie fuels), increase the safety and effectiveness of fire-suppression operations, and ultimately decrease the extent of wildfire spread. In sagebrush (Artemisia spp) ecosystems of the western US, installation of extensive linear fuel breaks is also intended to protect habitat, especially for the greater sage-grouse (Centrocercus urophasianus), a species that is sensitive to habitat fragmentation. We examine this apparent contradiction in the Great Basin region, where invasive annual grasses have increased wildfire activity and threaten sagebrush ecosystems. Given uncertain outcomes, we examine how implementation of fuel breaks might (1) directly alter ecosystems, (2) create edges and edge effects, (3) serve as vectors for wildlife movement and plant invasions, (4) fragment otherwise contiguous sagebrush landscapes, and (5) benefit from scientific investigation intended to disentangle their ecological costs and benefits.

Currently, forest fires have become a major fire safety issue. To detect forest fires and optimize the accuracy, a forest fire detection and recognition model based on an improved YOLOv5-ACE algorithm is proposed. In response to the difficulties of small target detection in forest fires, poor adaptability to complex backgrounds, and deployment limitations of edge devices, the CBAM and the ASPP multi-scale feature extraction module are introduced to enhance the ability of target feature capture and small target detection. The algorithm is lightweight by combining the grouped convolution of ShuffleNet v2 and the global dependency capture of ViT, while improving the positioning accuracy and anti-interference ability. Compared with the traditional YOLOv5, the detection accuracy has increased by 11.5%, ultimately reaching 92.3%, and the recall has increased by 6.8% to 91.6%. Through hypothesis testing, all performance improvements have statistical significance (p < 0.05). The proposed method can detect forest fires more quickly and accurately, which has good guiding significance for preventing the occurrence of forest fires.

Forest fires pose an escalating global threat, severely impacting ecosystems, public health, and economies. Timely detection, especially during early stages, is critical for effective intervention. In this study, we propose a novel deep learning-based framework that augments the YOLOv4 object detection architecture with a modified EfficientNetV2 backbone and Efficient Channel Attention (ECA) modules. The backbone substitution leverages compound scaling and Fused-MBConv/MBConv blocks to improve representational efficiency, while the lightweight ECA blocks enhance inter-channel dependency modeling without incurring significant computational overhead. Additionally, we introduce a domain-specific preprocessing pipeline employing Canny edge detection, CLAHE + Jet transformation, and pseudo-NDVI mapping to enhance fire-specific visual cues in complex natural environments. Experimental evaluation on a hybrid dataset of forest fire images and video frames demonstrates substantial performance gains over baseline YOLOv4 and contemporary YOLO variants (YOLOv5–YOLOv9), with the proposed model achieving 97.01% precision, 95.14% recall, 93.13% mAP, and 92.78% F1-score. Furthermore, our model outperforms fourteen state-of-the-art approaches across standard metrics, confirming its efficacy, generalizability, and suitability for real-time deployment in UAV-based and edge computing platforms. These findings highlight the synergy between architectural optimization and domain-aware preprocessing for high-accuracy, low-latency wildfire detection systems.

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.