Forest fires have important ecological, social and economic consequences causing loss of life and property. In order to prevent these consequences, it is very important to intervene in active fires in a timely manner and to determine the extent of burnt areas as soon as possible. In such studies, remote sensing methods provide great benefits in terms of speed and cost. In recent years, various methods have been developed to segment active fires and burnt areas with satellite images. Deep learning methods successfully perform segmentation processes in many areas such as disease detection in the field of health, crop type determination in the field of agriculture, land use and building detection in the field of urbanization. In this study, a method has been developed that automatically detects both active fires and burned areas that need to be re-enacted in terms of location and area size by using the same Sentinel 2 scene in a single time using deep learning methods. In particular, a new training and validation data set was created to train the U-Net+InceptionResNetV2 (CNN) model. By combining the powerful features of U-Net with InceptionResNet V2, a convolutional neural network trained over more than one million images on the ImageNet very base, we aim to examine its capabilities in burned area and active fire detection. The model applied on the test data has been shown to give successful results with an overall accuracy of 0.97 and an IoU (Intersection over union) value of 0.88 in the detection of burnt areas, and an overall accuracy of 0.99 and an IoU value of 0.82 in the detection of active fires. Finally, when the test images that were not used in the training dataset were evaluated with the trained model, it was revealed that the results were quite consistent in the detection of active fires and burnt areas and their geographical locations.

The array of wildfire activities instigated by human endeavors has emerged as a significant source of atmospheric pollution, posing considerable risks to both public health and property safety. This study harnesses Sentinel-2 satellite data, employing a variety of methods including spectral index methods, thresholding, and the Random Forest (RF) model for active fire spot detection. The research encompasses a wide range of land cover types across various Chinese regions. Utilizing the Gini coefficient, the study assesses the importance of spectral and texture features in the RF, culminating in the selection of an optimal feature combination for the construction of a bespoke RF model tailored for active fire detection. The research utilized texture features based on the Grey Level Co-occurrence Matrix (GLCM), demonstrating their significant contribution to enhancing the accuracy of fire detection using the RF model. Our analysis reveals that GLCM-based texture features, which form 40% of the model’s final feature set, are crucial for improving detection accuracy. The optimized RF model demonstrates a marked superiority in identifying active fires, achieving an overall accuracy of 86.1%. The study results demonstrate that the bespoke RF model is suitable for detecting active fire across various land cover environments in China.

Conventional firefighting tools and methods can strain water sources, require toxic foams, or rely on pre-installed countermeasures. A low-cost, non-toxic, and portable option was previously overlooked in portable devices: electrically assisted “ionic wind” fire suppression. Conductive aerosols, carried by vortex rings, can potentially extend the length of an electric arc and suppress fires. After the simulation, two prototype vortex ring launchers were compared, one using compressed air and another using an elastic diaphragm. The efficiency of each test case was assessed with a purpose-built automated image analysis system. The compressed air vortex launcher had a significantly higher efficiency than the elastic diaphragm prototype, with a p-value of 0.0006. Regardless of the prototype or the use of conductive aerosols, the device had an effective range of up to 1.98 m. The highest reliability of 90 ± 4.1% was achieved at 1.52 m from the launcher. The observations with compressed air launcher results saw no significant difference regarding the use of the conductive aerosol. Further investigation of the concept requires a systematic examination of other types of fires, electronic optimization, permutations of chemicals and concentrations, other types of vortex generation, and human factors. The computer vision system could also be used to further detect and target active fires. Beyond firefighting, the device can be adapted to applications ranging from manufacturing to aerospace. Regardless of the use of conductive aerosols, handheld vortex ring generators are a versatile, potential firefighting tool.

The open burning of agricultural residues is a widespread practice with significant environmental implications. This study explores the potential of satellite remote sensing to detect and analyze small-scale agricultural fires in Portugal, focusing on their spatial and temporal characteristics. Using active fire detection products from various satellite platforms, including VIIRS, MODIS, SLSTR, and SEVIRI, we conducted a detailed analysis across two local case studies and a national-scale assessment. This study evaluates both active fire detections and post-fire burned area estimations, using high-resolution satellite imagery to overcome the limitations associated with the small size and low intensity of these fires. The results indicate that while active fire detections are feasible for larger-scale burning, challenges remain for smaller fires due to resolution constraints. A systematic comparison with an agricultural burning request database further highlights the need for the enhancement of temporal and spatial precision in data to improve detection reliability. Despite these limitations, this work underscores the importance of remote sensing tools in monitoring agricultural burning practices and enhancing environmental management efforts.

Frequent forest fires, driven by hotter and drier climates, threaten biodiversity and human health, causing significant economic losses, air pollution, soil erosion, and degeneration. Current active and passive fire protection methods often suffer from environmental pollution, poor flexibility, and limited availability in remote areas. However, fast-acting surface flame retardants for passive forest fire protection, particularly for foliage, are rare. Herein, we report an easily obtainable gelatin-based fire spray, which resulted in 1.8 and 16.3-fold extension in ignition time, 34% and 39% reductions in total heat release, 78% and 92% reductions in fire growth index for dead and fresh leaves, respectively. After the fire warning is suppressed, for instance by rain, the sprayed substances can decompose and provide nitrogen and phosphorus as leaf and soil fertilizers without affecting soil microbial function, which increase plant net photosynthesis by 84% and effective nitrogen and phosphorus by 664% and 140%, respectively. Our green flame retardant and fertilizer material allows for simultaneous tree fire protection and growth.

Introduction: Hospital fire can result in greater casualties, injuries to patients or staff, and loss of property and equipment compared to fires in other types of building. This is attributed to the presence of a large number of vulnerable individuals, including those who are ill, disabled, pregnant, children, elderly, immunocompromised, on life support, or incapable of moving independently. This study aims to assess the implementation of the fire protection system, life-saving facilities, and fire management in Hospital X. Methods: This was a quantitative study on active fire protection system facilities, passive protection system facilities, live-saving facilities, and fire management as the subjects. Data were collected through observation, interviews, and document review, as well as a checklist and then analyzed by comparing the actual conditions with applicable standards and regulations. The final result was presented as the percentage compliance level and categorization according to the criteria established by the Research and Development Agency of the Public Works Department. Results: The active fire protection system presented a standard fulfillment rate of 53%, categorizing it as poor. The passive fire protection systems similarly demonstrated a poor fulfillment rate at 42%, while the life-saving facilities achieved a 66.7% fulfillment rate, placing them in the quite good category. Additionally, fire management attained an 81% fulfillment level, which falls under the good category. Conclusion: Hospital X has a good fire prevention approach with a standard fulfillment level of 60%.

ABSTRACT Forest and land fires cause substantial economic, social, and environmental devastation. Interagency forest and land fire management has succeeded in decreasing the impact of these fires, particularly in Indonesia. Having comprehensive information on fire locations and frequencies will benefit national forest and land fire management programmes. This study describes nearly a decade of satellite-based burned area (BA) monitoring conducted by the Indonesian government. We discuss (1) the history of BA mapping in Indonesia, (2) the most recent techniques for producing monthly BA maps, (3) an evaluation of product accuracy, (4) advantages and disadvantages, and (5) recommendations for future research. The most recent approach combines manual and digital classification, primarily using Landsat images, but has been supplemented with Sentinel data since 2020. The digital analysis, named the normalized burn ratio difference index threshold, was used to distinguish between burned and unburned pixels, guiding the interpreter’s manual digitization. BA confidence levels were determined using active fire products and ground truth data. We engaged provincial and local agency stakeholders to verify the products and provide quality assurance. We also assessed product performance by examining high-resolution images captured at three different locations to ascertain the relative advantages and disadvantages, which varied depending on each region’s fire regime. Small fires and cloud cover reduced the accuracy of the national monthly BA product. However, the omission error decreased by 65% when all fires throughout the entire year were considered. The inclusion of all available Sentinel-2 (A and B) images yielded higher accuracy than using only Landsat 8 data. We conclude that Indonesia’s cloud coverage constraint requires additional observational data to obtain clear-sky imagery when using optical sensors, in addition to the adjusted technique. We urge the introduction of reliable digital classifications for all Indonesian fire regimes to simplify resource deployment and reduce manual labour operations.

Background Reducing the delay between the detection of a fire and the arrival of the initial attack (IA) crew can have a significant impact on the likelihood of the IA’s success. Aims The objective of this study was to identify factors influencing same-day getaway time, next-day getaway time and travel time of helitack IA crews in the province of Quebec, Canada. Methods Using generalised linear modelling and model selection, we analysed the impact of multiple factors on these three distinct times. Key results Our results show that factors such as the distance between the departure base and the fire, the number of flight legs to reach a fire, dispatch hour, departure base location, the fire’s rate of spread, Julian date, the number of active fires, fuel type and the fire’s size at detection all influenced getaway time and travel time with varying degrees of influence. Conclusions The factors with the highest influence were distance for travel time and dispatch hour for both same-day and next-day getaway times. Implications Addressing these high-impact factors through the modification of deployment policies and the positioning of helitack crews could help reduce response times.

The Anggrek Unit Steam Power Plant (PLTU) is a power generation facility whose construction began in 2007 and began operating in 2019 with a capacity of 2 x 25 MW. Since its inauguration, PLTU Anggrek has become one of the main sources of electricity distribution in Gorontalo Province, so the frequency of power outages in the area has now decreased significantly. The water supply system for fire fighting uses BSP pipes. In this research, the aim is to determine the active fire protection system. This research was carried out with a qualitative descriptive design with data collection: observation and interviews. The assessment of the results of existing suitability will be assessed using descriptive percentage calculations. The research results show that of the 7 variables discussed, the average value for the level of fulfillment is 98.57%. Based on the level of fire audit assessment that meets the requirements of Indonesian national standards and international standards, the results are in the Good criteria or have met the requirements.

Open biomass burning has significant adverse effects on regional air quality, climate change, and human health. Extensive open biomass burning is detected in most regions of China, and capturing the characteristics of open biomass burning and understanding its influencing factors are important prerequisites for regulating open biomass burning. The characteristics of open biomass burning have been widely investigated at the national scale, with regional studies often focusing on northeast China, but few studies have examined regional discrepancies in spatiotemporal variations over a long timescale in Guangxi province. In this study, we used the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), combined with land cover data and high-resolution remote sensing images, to extract open biomass burning (crop residue burning and forest fire) fire points in Guangxi province from 2012 to 2023. We explored the spatial density distribution and temporal variation of open biomass burning using spatial analysis methods and statistical methods, respectively. Furthermore, we analyzed the driving forces of open biomass burning in Guangxi province from natural (topography, climate, and plant schedule), policy, and social (crop production and cultural customs) perspectives. The results show that open biomass burning is concentrated in the central, eastern, and southern parts of the study area, where there are frequent agricultural activities and abundant forests. At the city level, the highest numbers of fire points were found in Baise, Yulin, Wuzhou, and Nanning. The open biomass burning fire points exhibited large annual variation, with high levels from 2013 to 2015 and a remarkable decrease from 2016 to 2020 under strict control measures; however, inconsistent enforcement led to a significant rebound in fire points from 2021 to 2023. Forest fires are the predominant type of open biomass burning in the region, with forest fires and crop residue burning accounting for 76.82% and 23.18% of the total, respectively. The peak period for crop residue burning occurs in the winter, influenced mainly by topography, planting schedules, crop production, and policies, while forest fires predominantly occur in the winter and spring, primarily influenced by topography, climate, and cultural customs. The results indicate that identifying the driving forces behind spatiotemporal variations is essential for the effective management of open biomass burning.

Smoke, flame, and heat detectors are included in the active fire protection system that must be present on board. The function of these detectors is to detect fires early. This research aims to optimize the maintenance of smoke, flame, and heat detectors on board ships. This research was conducted using a qualitative method with primary data sources obtained through notes from interviews, field observations, documentation, and secondary data obtained from various literacies. This research was conducted at MV Pan Mutiara. In this study, it can be concluded that the optimization of smoke, flame, and heat detector maintenance needs to be done on MV Pan Mutiara so that the detector can work properly because if the detector is damaged, this equipment loses its function and it can be a leading cause of fire that cannot be controlled. Then the maintenance optimization is carried out by making a special schedule, as evidenced by the results of the interview, where according to 3RD Officer the maintenance schedule is made by dividing the total number of detectors on board over 6 months, this period is taken into consideration of the workload of 3RD Officer at POS SM company which is more compared to other companies.

Forest fires are increasingly destructive, contributing to significant ecological damage, carbon emissions, and economic losses. Monitoring these fires promptly and accurately, particularly by delineating fire perimeters, is critical for mitigating their impact. Satellite-based remote sensing, especially using active fire products from VIIRS and MODIS, has proven indispensable for real-time forest fire monitoring. Despite advancements, challenges remain in accurately clustering and delineating fire perimeters in a timely manner, as many existing methods rely on manual processing, resulting in delays. Active fire perimeter (AFP) and Timely Active Fire Progression (TAFP) models were developed which aim to be an automated approach for clustering active fire data points and delineating perimeters. The results demonstrated that the combined dataset achieved the highest matching rate of 85.13% for fire perimeters across all size classes, with a 95.95% clustering accuracy for fires ≥100 ha. However, the accuracy decreased for smaller fires. Overall, 1500 m radii with alpha values of 0.1 were found to be the most effective for fire perimeter delineation, particularly when applied at larger radii. The proposed models can play a critical role in improving operational responses by fire management agencies, helping to mitigate the destructive impact of forest fires more effectively.

Satellite remote sensing is a critical tool for continental and synoptic monitoring and mapping of savannah wildfires. Satellite active fire products, which report on the time and location of a fire and may further characterise fire by estimating fire radiative power (FRP), provide valuable utility for savannah fire management and carbon accounting. These applications require that satellite measurements are of high accuracy, which can only be determined through validation. However, acquiring reference data for validation that is a representative of the fire conditions at the time of satellite image capture is challenging, due to rapid changes in fire behaviour and the inherent safety considerations of collecting field data during fire events. This review explores traditional and contemporary methods used to assess the accuracy and consistency of fire detections and FRP derived from satellite data in savannah ecosystems, with a focus on the approaches and challenges in collecting suitable reference data for a phenomenon as dynamic, ephemeral, and hazardous as wildfire. From this synthesis, we present generalised frameworks for the validation and intercomparison of satellite active fire products within savannah ecosystems.

Objective: Active fuel management operations, such as thinning, can minimize extreme wildfire conditions while preserving ecosystem services, including maintaining understory vegetation diversity. However, the appropriate thinning intensity for balancing the above two objectives has not been sufficiently studied. Methods: This study was conducted to assess the impact of various thinning intensities (light thinning, LT, 15%; moderate thinning, MT, 35%; heavy thinning, HT, 50%; and control treatment, CK) on fuel characteristics, potential fire behavior, and understory vegetation biodiversity in Platycladus orientalis forest in Beijing using a combination of field measurements and fire behavior simulations (BehavePlus 6.0.0). Results: A significant reduction in surface and canopy fuel loads with increasing thinning intensity, notably reducing CBD to below 0.1 kg/m3 under moderate thinning, effectively prevented the occurrence of active crown fires, even under extreme weather conditions. Additionally, moderate thinning enhanced understory species diversity, yielding the highest species diversity index compared to other treatments. Conclusions: These findings suggest that moderate thinning (35%) offers an optimal balance, substantially reducing the occurrence of active crown fires while promoting biodiversity. Therefore, it is recommended to carry out moderate thinning in the study area. Forest managers can leverage this information to devise technical strategies that simultaneously meet fire prevention objectives and enhance understory vegetation species diversity in areas suitable for thinning-only treatments.

Since 2001, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Aqua and Terra platforms has made great strides in providing information on global burned areas (BA). However, the MODIS mission is nearing its end. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, presented as the MODIS Aqua heritage, could be an excellent alternative to ensure the temporal continuity of this information at a moderate resolution. This paper describes and evaluates the effectiveness of our developed hybrid algorithm, which utilizes VIIRS reflectance and active fire products on the Google Earth Engine platform, in producing efficient information about BA. The study investigates the algorithm's performance in sub-Saharan Africa as the region of interest in 2019, using biweekly outputs and a spatial resolution of 250 m. The algorithm encompasses several steps, including pre-processing individual scenes, creating cloud-free composites, generating binary reference data for burned and non-burned areas, conducting a supervised classification using random forest, and performing region shaping. The VIIRS-BA final product, which includes three confidence levels (low, moderate, and high) known as the uncertainty layer, is compared to four other burned area products. The validation is conducted against 27 reference sampling units from the Sentinel-2 Burned Area Reference Database dataset, allowing for a comprehensive uncertainty assessment across five various biomes. The VIIRS-BA product identified 5.1 million km2 of BA, which was significantly larger than other global coarse resolution BA products such as FireCCI51, FireCCIS310, and MCD64A1 and close to the fine resolution FireCCISFD20 with a difference of 7.3%. The differences were less significant in biomes such as “Tropical Savannas” and “Temperate Grasslands” which are characterized by persistent biomass burning. Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. Notably, there is a significant reduction in OE (ranging from 40.7% to 50.5%) compared to global BA products like FireCCI51, FireCCIS310, and MCD64A1. When compared to VIIRS-BA, the FireCCISFD20 regional product has a 37% better OE performance. While VIIRS-BA shows great potential in detecting fires that global products miss, the VIIRS-BA with low confidence level tends to overestimate BA in regions with high fire activity. To address this, future versions of the algorithm will integrate the updated VIIRS reflectance data alongside VIIRS active fire from the National Oceanic and Atmospheric Administration to reduce CE and improve understanding spatial patterns.

The northern regions of the Irkutsk oblast have been prone to fires in recent decades as a result of oil and gas and infrastructure development, as well as changing climatic conditions. Geoinformation mapping and analysis of the spatial structure of the vegetation cover of two key areas located in the area of the Vershina Khandy village and the village of Tokma for two time slices: 2013–2014 and 2018–2019 (before and after active forest fires, mainly in 2016 and 2017) was carried out. Mapping was done using the Random Rorest supervised classification method, Landsat 8 space images. As a result, 10 classes of vegetation cover were identified on the territory. It is shown that in the territories in 2013–2014 coniferous (light coniferous – pine-larch, larch-pine, less often dark coniferous – cedar and spruce) forests prevailed, as well as mixed coniferous-deciduous and deciduous forests and swamps. From 2013 to 2019, more than 20% of Tokma and more than 5% of Khanda key area was covered by fires. Mostly coniferous forests, as well as ernik bogs in the Tokma area were burning.

Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Due to the irreversible nature of the consequences of fire, fire protection is a major challenge and source of problems for all types of built heritage. This study aims to establish sustainable fire protection technology strategies by generalizing fire prevention and control technologies and measures against extended burns. This study aims to explore Macau’s industrial heritage’s historical development and technological applications in the field of fire protection using literature analysis, field investigation, and spatial information visualization methods. It will be carried out using the industrial heritage of Macau as the object and systematic analyses from the screening and processing of fire protection historical data, fire risk assessment, and the migration of fire protection focus. The results show that (1) the fire protection of the industrial heritage of Macau has gone through a total of three phases: passive fire protection, transition of fire protection methods, and active fire protection, and the relied-upon fire protection technologies have been iterated and renewed continuously during this period. (2) When the fire load factors of industrial heritage increase, the fire vulnerability assessment substantially changes, and the center of gravity of heritage fire protection shifts from controlling the scope of disaster to reducing the fire risk. (3) The construction of a suitable and effective ecological model of fire protection technology can provide appropriate fire protection solutions for the preservation and reuse of Macau’s industrial heritage in a complex cultural context. Therefore, this study will help to solve the current dilemma of sustainable application and development of fire protection technology for industrial heritage. This study hopes to provide ideas and strategies for reference on industrial heritage fire protection issues in the development of similar world heritage cities.

Satellite detection of active fires has contributed to advance our understanding of fire ecology, fire and climate dynamics, fire emissions, and how to better manage the use of fires as a tool. In this study, we use active fire data of 12 years (2012–2023) combined with landcover information in the South-Central United States to derive a monthly, open-access dataset of categorized fires. This is done by calculating a fire predominance index used to rank fire-prone landcovers, which are then grouped into four main landscapes: grassland, forest, wildland, and crop fires. County-level aggregated analyses reveal spatial distributions, climatologies, and peak fire months that are particular to each fire type. Using the Standardized Precipitation Index (SPI), it was found that during the climatological fire peak-month, the SPI and fires exhibit an inverse relationship in forests and crops, whereas grassland and wildland fires show less consistent inverse or even direct relationship with the SPI. This varied behavior is discussed in the context of landscapes’ responses to anomalies in precipitation and fire management practices, such as prescribed fires and crop residue burning. In a case study of Osage County (OK), we find that large wildfires, known to be closely related to climate anomalies, occur where forest fires are located in the county and absent in areas of grassland fires. Weaker grassland fire response to precipitation anomalies can be attributed to the use of prescribed burning, which is normally planned under environmental conditions that facilitate control and thus avoided during droughts. Crop fires, on the other hand, are set to efficiently burn residue and are practiced more intensely in drier years than in wetter years, explaining the consistently strong inverse correlation between fires and precipitation anomalies. In our increasingly volatile climate, understanding how fires, vegetation, and precipitation interact has become imperative to prevent hazardous fire conflagrations and to better manage ecosystems.

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability.