Search Results (400)

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles. Full article

Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. Specifically, MEAC fuses (1) original local features, (2) multi-scale context captured via dilated convolutions, and (3) high-contrast edge cues derived from differential Gaussian filters. After fusing these branches, channel and spatial attention mechanisms are applied to adaptively emphasize critical regions, further improving feature discrimination. The MEAC module is fully compatible with standard convolutional layers and can be seamlessly embedded into various network architectures. Extensive experiments on three public infrared small-target datasets (SIRSTD-UAVB, IRSTDv1, and IRSTD-1K) demonstrate that networks augmented with MEAC significantly outperform baseline models using standard convolutions. When compared to eleven mainstream convolution modules (ACmix, AKConv, DRConv, DSConv, LSKConv, MixConv, PConv, ODConv, GConv, and Involution), our method consistently achieves the highest detection accuracy and robustness. Experiments conducted across multiple versions, including YOLOv10, YOLOv11, and YOLOv12, as well as various network levels, demonstrate that the MEAC module achieves stable improvements in performance metrics while slightly increasing computational and parameter complexity. These results validate the MEAC module’s significant advantages in enhancing the detection of small and weak objects and suppressing interference from complex backgrounds. These results validate MEAC’s effectiveness in enhancing weak small-target detection and suppressing complex background noise, highlighting its strong generalization ability and practical application potential. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region. Full article

In the Mediterranean region, the high frequency of fire events is combined with climatic conditions that hinder vegetation recovery. This underscores the urgent need for a post-fire restoration of natural ecosystems and implementation of emergency rehabilitation measures to prevent further degradation. In this study, we investigated the performance of three types of erosion control structures (log dams, log barriers, and wattles), two years after fire, in three Mediterranean areas that were burnt by severe forest fires in 2021. The wooden structures’ ability to infiltrate precipitation was evaluated by 100 infiltration experiments in 25 plots, one and two years after the wildfires. The unsaturated hydraulic conductivity K was determined at two zones formed between consecutive wooden structures, i.e., the erosion zone (EZ) where soil erosion occurs, and the deposition zone (DZ) where the soil sediment is accumulated. These zones showed significant differences concerning their hydraulic behavior, with DZ presenting enhanced infiltration ability by 130 to 300% higher compared to EZ, during both years of measurements. The findings suggest that the implementation of emergency restoration actions after a wildfire can highly affect the burned forest soils’ ability to infiltrate water, preventing surface runoff and erosion, whereas specific structures such as the log dams can be even more effective. Full article

This study analyzes forest fire legislation and policies in the Andean Community of Nations (ACN)—Colombia, Ecuador, Peru, and Bolivia—focusing on prevention and control. Using a comparative law approach, similarities, differences, and implementation challenges were identified. Ecuador and Peru have more comprehensive legal structures, while Colombia’s is simpler, and Bolivia falls in between. To address these gaps, this study proposes an Andean Directive for Integrated Fire Management (ADIFM) to harmonize policies and incorporate fire ecology, ancestral knowledge, education, monitoring technologies, and post-fire restoration. This regulatory framework, tailored to Andean ecological and sociocultural conditions, would optimize fire management and strengthen ecosystem resilience. Additionally, harmonizing sanctions and regulations at the regional level would ensure more coherent and effective governance. The ADIFM would provide strategic guidance for policymakers, fostering sustainable fire management and environmental restoration across Andean ecosystems. Full article

Fire regime (FR) is a key element in the study of ecosystem dynamics, supporting natural resource management planning by identifying gaps in fire patterns in time and space and planning to assess ecological conditions. Due to the insufficient consideration of integrated characterization factors, especially the insufficient research on fire season types (FST), the current understanding of the spatial heterogeneity of fire patterns in China is still limited, and it is necessary to use FST as a key dimension to classify FR zones more accurately. This study extracted 13 fire characteristic variables based on Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1), active fire data (MODIS Collection 6), and land cover data (MCD12Q1) from 2001 to 2023. The study systematically analyzed the frequency, intensity, spatial distribution and seasonal characteristics of fires across China. By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. The burned areas of the five FR zones account for 67.76%, 13.88%, 4.87%, 12.94%, and 0.55% of the total burned area across the country over the 23-year study period, respectively. Among them, fires in the Northeast China Plain and North China Plain cropland areas (FR 1) exhibit a bimodal distribution, with the peak period concentrated in April and June, respectively; the southern forest and savanna region (FR 2) is dominated by high-frequency, small-scale, unimodal fires, peaking in February; the central grassland region (FR 3) experiences high-intensity, low-frequency fires, with a peak in April; the east central forest region (FR 4) is characterized by low-frequency, high-intensity fires; and the western grassland region (FR 5) experiences low-frequency fires with significant inter-annual fluctuations. Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. This approach transcends the traditional single-factor classification, demonstrating that seasonal characteristics are indispensable for accurately delineating fire conditions. The resultant zoning system effectively overcomes the limitations of traditional methods, providing a scientific basis for localized fire risk warning and differentiated prevention and control strategies. Full article

Our research examines the dynamics of policy implementation in forest fire management and how local governments in Indonesia can successfully implement these policies. There are two main issues: first, the extent to which forest fire management practices are collaborative, which we assess by examining whether government implementation has focused on developing integrated forest fire management policies represented through collaborative networks. Second, we consider whether and how governments and other competing stakeholders move from conflict to collaboration to enable policy implementation. This research explores whether and how collaborative management can provide a foundation for successful forest fire management, particularly in Riau Province, Sumatra, Indonesia, an area that has experienced significant forest fires and expansion of plantations and oil palm industries. Data were collected through in-depth interviews and observations. We revealed a lack of coordination among local, central, and other stakeholders, which might result in policy “tyranny”. In order to effectively reduce the number of fires, the government needs to empower those responsible for fire prevention through law and policy. However, because forest fire management is inherently top-down and often excludes lower levels of bureaucracy, collaborative management remains challenging. Full article

Wildfires represent an increasing global concern, threatening ecosystems, human settlements, and economies. Chile, characterized by diverse climatic zones and extensive forested areas, has been particularly vulnerable to wildfire events over recent decades. In this context, real, long-term data are essential to understand wildfire dynamics and to design effective early warning and prevention systems. This paper introduces a unique dataset containing detailed wildfire occurrence and damage information across Chilean municipalities from 1985 to 2024. Derived from official records by the National Forestry Corporation of Chile CONAF, this dataset encompasses key variables such as the number of fires, total burned area, estimated material damages, and the number of affected individuals. It provides an invaluable resource for researchers and policymakers aiming to improve fire risk assessments, model fire behavior, and develop AI-driven early detection systems. The temporal span of nearly four decades offers opportunities for longitudinal analyses, the study of climate change impacts on fire regimes, and the evaluation of historical prevention strategies. Furthermore, by presenting a complete spatial coverage at the municipal level, it allows fine-grained assessments of regional vulnerabilities and resilience. Full article

Biogas produced from agricultural and forestry waste is emerging as a strategic and multifunctional solution to address climate change, inefficient waste management, and the need for renewable energy by transforming large volumes of biomass. Global estimates indicate that approximately 1.3 billion tons of waste is produced each year for these sectors; this waste is processed through anaerobic digestion, allowing it to be transformed into energy and biofertilizers. This reduces greenhouse gas emissions by up to 90%, promotes rural development, improves biodiversity, and prevents environmental risks, such as forest fires. However, despite its high global technical potential, which is estimated at 8000 TWh per year, its use remains limited as a result of its high initial costs, low efficiency in relation to lignocellulosic waste, and weak regulatory frameworks, especially in countries like Mexico, which use less than 5% of their available biomass. In response, emerging technologies, such as co-digestion with microalgae, integrated biorefineries, and artificial intelligence tools, are opening up new avenues for overcoming these barriers under a comprehensive approach that combines science, technology, and community participation. Therefore, biogas is positioned as a key pillar for a circular, fair, and resilient bioeconomy, promoting energy security and advancing toward a just and environmentally responsible future. Full article

In fields such as military reconnaissance, forest fire prevention, and autonomous driving at night, there is an urgent need for high-precision three-dimensional reconstruction in low-light or night environments. The acquisition of remote sensing data by RGB cameras relies on external light, resulting in a significant decline in image quality and making it difficult to meet the task requirements. The method based on lidar has poor imaging effects in rainy and foggy weather, close-range scenes, and scenarios requiring thermal imaging data. In contrast, infrared cameras can effectively overcome this challenge because their imaging mechanisms are different from those of RGB cameras and lidar. However, the research on three-dimensional scene reconstruction of infrared images is relatively immature, especially in the field of infrared binocular stereo matching. There are two main challenges given this situation: first, there is a lack of a dataset specifically for infrared binocular stereo matching; second, the lack of texture information in infrared images causes a limit in the extension of the RGB method to the infrared reconstruction problem. To solve these problems, this study begins with the construction of an infrared binocular stereo matching dataset and then proposes an innovative perspective projection positional encoding-based transformer method to complete the infrared binocular stereo matching task. In this paper, a stereo matching network combined with transformer and cost volume is constructed. The existing work in the positional encoding of the transformer usually uses a parallel projection model to simplify the calculation. Our method is based on the actual perspective projection model so that each pixel is associated with a different projection ray. It effectively solves the problem of feature extraction and matching caused by insufficient texture information in infrared images and significantly improves matching accuracy. We conducted experiments based on the infrared binocular stereo matching dataset proposed in this paper. Experiments demonstrated the effectiveness of the proposed method. Full article

Wildfires in central-south Chile, consistent with trends observed in other Mediterranean regions, are expected to become more frequent and severe, threatening ecosystems and impacting millions of people. This study aims to enhance wildfire resilience in the central-south regions of Chile through the provision of robust information on current wildfire management practices and comparison with successful alternatives implemented in other fire-prone Mediterranean regions. With this aim, we consulted 55 local stakeholders involved in wildfire management, and alongside a comparative analysis of wildfire statistics and resource allocation in selected Mediterranean regions, we critically assessed different strategies to improve wildfire prevention and management in central-south Chile. The comparative analysis indicated notable economic under-investment for wildfire prevention in Chile. Compared to other Mediterranean countries, Chile is clearly below in terms of investment in forest fire prevention, both in global (public investment) and specific terms ($ ha⁻¹, GDP per capita). The experts consulted included fuel management, governance and community participation, territorial management, landscape planning, socioeconomic evaluation, and education and awareness as key aspects for wildfire prevention. The results of the questionnaire indicated that there was a broad consensus regarding the importance of managing biomass to reduce fuel loads and vegetation continuity, thereby enhancing landscape resilience. Landscape planning and territorial management were also emphasized as critical tools to balance ecological needs with those of local communities, mitigating wildfire risks. Fire-Smart management emerged as a nature-based solution and a promising integrated approach, combining fuel treatments with modeling, simulation, and scenario evaluation based on local and regional environmental data. Additionally, educational and social engagement tools were considered vital for addressing misconceptions and fostering community support. Besides a better integration of rural planning with social demands, this study underscores the urgent need to substantially increase the investment and significance of wildfire prevention measures in central-south Chile, which are key to improving its wildfire resilience. Our work contextualizes the reality of wildfires in central-south Chile and directly contributes to mitigating this growing concern by critically examining successful wildfire resilience strategies from comparable fire-prone regions, complementing ongoing local efforts and offering a practical guide for stakeholders in wildfire management and prevention, with particular relevance to central-south Chile and other regions with similar characteristics. Full article

Wildfires pose increasing risks to human settlements, particularly in the Wildland–Urban Interface (WUI). This study examines the relationship between land cover (LC) characteristics and housing destruction during the 2023 wildfires in Chile’s Bío-Bío region. Using high-resolution remote sensing data and GIS-based multi-buffer spatial analysis (30 m and 100 m), we assessed LC patterns around affected and unaffected rural houses. Results indicate that the proximity of forest plantations significantly increased housing loss, with a notably higher presence of plantations within 30 m of destroyed houses. In contrast, agricultural and pasture mosaics demonstrated a protective function by reducing fire spread. Shrublands also showed moderate protection, albeit with statistical uncertainty. The findings highlight the critical role of immediate LC in determining wildfire impact, emphasizing the need for integrating LC considerations into wildfire risk management, land-use planning, and policy interventions. Strategies such as creating defensible spaces, enforcing zoning regulations, and promoting fire-resistant landscapes can help mitigate future wildfire damage. This research provides spatially explicit insights that contribute to wildfire risk reduction theory and inform targeted prevention and resilience-building strategies in Chile and other fire-prone regions. Full article

Fire is important in boreal forest ecosystems, but comprehensive recovery analysis is lacking for soil nutrients and plant traits in China boreal forests, where the strict “extinguish at sight” fire prevention policy has been implemented. Based on over 50 years of forest fire recordings in the Daxing’anling Mts, 48 pairs of burnt and unburnt controls (1066 plots) were selected for 0–20 cm soil sampling and plant surveys. We recorded 18 plant parameters of the abundance of each tree, shrub, grass, and plant size (height, diameter, and coverage), 7 geo-topographic data parameters, and 2 fire traits (recovery year and burnt area). We measured eight soil properties (soil organic carbon, SOC; total nitrogen, TN; total phosphorus, TP; alkali-hydrolyzed P, AP; organic P, Po; inorganic P, Pi; total glomalin-related soil protein, T-GRSP; easily-extracted GRSP, EE-GRSP). Paired T-tests revealed that the most significant impact of the fire was a 25%–48% reduction in tree sizes, followed by decline in the plant diversity of arbors and shrubs but increasing plant diversity in herbs. GRSP showed an >18% increase and P_o decreased by 17% (p < 0.05). Redundancy ordination showed that the post-fire recovery years and burnt area were the most potent explainer for the variations (p < 0.05), strongly interacting with latitudes and longitudes. Plant richness and tree size were directly affected by fire traits, while the burnt area and recovery times indirectly increased the GRSP via plant richness. A fire/control ratio chronosequence found that forest community traits (tree size and diversity) and soil nutrients could be recovered to the control level after ca. 30 years. This was relatively shorter than in reports on other boreal forests. The possible reasons are the low forest quality from overharvesting in history and the low fire severity from China’s fire prevention policy. This policy reduced the human mistake-related fire incidence to <10% in the 2010s in the studied region. Chinese forest fire incidences were 3% that of the USA. The burnt area/fire averaged 5 hm² (while the USA averaged 46 hm², Russia averaged 380 hm², and Canada averaged 527 hm²). Overharvesting resulted in the forest height declining at a rate of >10 cm/year. Our finding supports forest management and the evaluation of forest succession after wildfires from a holistic view of plant–soil interactions. Full article