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Forests in Brazil play a crucial role in maintaining ecological balance and the environment, but this has been threatened by deforestation, forest fires, and the effects of climate change. Among these, forest fire has happened frequently in areas where there have been changes in land use for activities of agriculture and livestock farming, that motivate the destruction of forests particularly in biomes like the Amazon and Cerrado. In those biomes, the forest fires initiated to clear land for pasture are the most worrying because promote ecological and socioeconomic consequences, contributing to greenhouse gas emissions and impacts the regional flora and biogeochemical cycles. As a strategy to understand and identify areas at risk of forest fires, this study aimed to develop a risk zoning framework for fire hotspots in the biomes of Brazilians. This framework combines multiple variables, incorporating factors like physical terrain, land use, and climatic data, to assess the potential fire risk. The areas with greater fire risk are located in the Caatinga, Cerrado, and Pantanal biomes, in which the physical and climate variables influence directly in incidence and propagation of fire. In the Amazon biome there is a fire risk, some possibly intentional, but can be regulated by elevated precipitation in the region. The identification of areas at high fire risk allows the implementation of proactive strategies for fire prevention for safeguarding Brazil's biomes and ecosystems, which are integral to the environment and biodiversity. [ABSTRACT FROM AUTHOR]

Early detection and distinguish of mine fires can avoid or reduce casualties, property damage, and secondary accidents. There are no natural light sources such as sunlight, moonlight, starlight, and lightning underground, and the main factor affecting the recognition of mine fire images is the mine light source. Circularity can eliminate interference from circular light sources, but it is difficult to exclude interference from non-circular light sources. Rectangularity can eliminate interference from rectangular light sources, but it is difficult to exclude interference from non-rectangular light sources. In engineering practice, due to the different shooting angles of the camera, the image of the mine light source may deform and cannot present an ideal regular shape. It is difficult to eliminate the interference of the mine light source using circularity and rectangularity algorithms. It has been revealed that the area of the external connection graphic area of the flame image is significantly larger than the actual area of the image, and the external connection graphic area of the actual light source image of the mine, such as circular lamps, rectangular lamps, and square lamps, is approximately equal to the actual area of the light source image. In this study, a mine fire recognition and anti-interference method is proposed based on the internal concavity of image, the ratio of the target image area to the external connection graphic area of the image (i.e. internal concavity of image) is calculated, and the flames and mine light sources are distinguished based on the small concavity value in the flame image and the large concavity value in the mine light source image. The internal concavity method proposed in this paper is not affected by the distance from the camera to the detection target and the size of the image, the installation position and angle of the camera to capture the detection target, and the shape of the mine light source. It has strong adaptability and high recognition accuracy. The experiment shows that the internal concave recognition method calculates the maximum average difference between the mine interference light source and the flame image, with the smallest fluctuation and the best discrimination. It is also least affected by the camera’s shooting angle and distance, and has the strongest anti-interference ability, with an accuracy of 91.6%. The rectangular recognition method calculates the average difference between the mine interference light source and the flame image, with small fluctuations and good discrimination. It is less affected by the camera’s shooting angle and distance, and has average anti-interference ability, with an accuracy rate of 72.5%. The roundness recognition method calculates the minimum average difference between the mine interference light source and the flame image, with the highest fluctuation and the worst discrimination. It is less affected by the camera shooting distance and more affected by the camera shooting angle, and has the worst anti-interference ability, with an accuracy of 12.0%. Therefore, the internal concave recognition method proposed in this paper is superior to rectangular and circular degrees, with the best discrimination, minimal influence from camera shooting angle and distance, and the strongest anti-interference ability.

This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast, which is equivalent to the pixel informativeness indicator of the image. Proposed preprocessing methods give a delay of no more than 0.03 s due to the use of a pipeline method for data processing. Experimental results show that the horizon clipping algorithm improves fire and smoke detection accuracy by approximately 11%. The best results with the neural network were achieved with YOLO 5m, which yielded an F1 score of 76.75% combined with a processing speed of 45 frames per second. The obtained results differ from existing analogs by utilizing a comprehensive approach to early fire detection, which includes image enhancement and alternative real-time video processing methods. [ABSTRACT FROM AUTHOR]

Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform's architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers' burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries. [ABSTRACT FROM AUTHOR]

In order to strengthen the construction of mine fire intelligent monitoring and early warning system, the research idea of mine fire intelligent monitoring and early warning technology, and summarizes the research progress of mine fire intelligent monitoring and early warning technology is put forward. The research progress from three aspects of mine fire intelligent perception technology and equipment, prediction technology and modeling, and intelligent early warning system and platform is summarized. First, the internal and external cause fire information monitoring technology and equipment are summarized, the recognition process based on image-video recognition is summarized, and the content of multi-source information fusion and its application in the fire monitoring process are described. Secondly, the prediction techniques and models for mine fires are introduced, including machine learning algorithms such as support vector machine, artificial neural network, and random forest algorithms. Then, the mine fire early warning system and platform are elaborated. On the basis of summarizing the graded early warning technology for spontaneous coal combustion and exogenous fires in mines, the progress of the mine fire early warning system platform in terms of perception layer, network layer, service fusion layer and application layer is introduced. The connotation of each layer of the early warning system and the application framework are summarized, and the intelligent monitoring and early warning system for fires in mines is constructed. Finally, the future development trend of mine fire intelligent monitoring and early warning technology in China is outlooked including following aspects. In terms of intelligent perception technology of mine fire information, it is proposed to strengthen the research and development of sensing technology and equipment. In terms of intelligent prediction technology for mine fires, it is proposed to strengthen the research on the location exploration method of hidden fire sources and construct a transparent model of fire disasters. In terms of the construction of an intelligent early warning system for mine fires, it is proposed to integrate big data into intelligent judgment, identify the risk sources of mine fire and forecast the sources’ location of hidden fire, and realize the early warning of spontaneous coal combustion under special conditions. In terms of the linkage between the intelligent judgment and prevention and control technology for mine fires, it is proposed to utilize large-scale linguistic models to realize the self-adaptive prevention and control of mine fires on the basis of intelligent judgment.

The "Forest Fires Prediction and Detection" edition highlights the importance of research on fires worldwide. In recent years, the increased frequency of fires caused by climate change has rendered the planet uninhabitable. Several works have been prepared and published in an effort to raise awareness among civil society and government bodies about the importance of developing new technologies for monitoring areas prone to mega-fires. This special issue includes nine important works from various countries. The goal is to better understand the impacts on the world's most diverse regions, ecosystems, and forest phytophysiognomies. New geotechnologies and fire models were used, both of which are important and could be used in the future to improve short- and long-term planning in firefighting. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from climate-related monitoring products obtained from an integration of remote sensing and in situ data from weather stations. It contains useful indicators, such as rainfall, vegetation, and fire conditions, that provide an overview of conditions across the country. The present study demonstrates how these natural resource indices are integrated and consolidated for utilization by farmers, policy-makers, private organizations, and the general public to make day-to-day decisions on the management and mitigation of natural disasters. However, there is a need to expand these baseline observation initiatives, including the following: (1) forecasting future conditions to strengthen coping mechanisms of government, farmers, and communities at large; and (2) incorporating information on other natural disasters such as floods and extreme heat. In the context of South Africa, this information is important to improve disaster preparedness and management for agricultural productivity. In a global context, the Umlindi newsletter can be insightful for developing and disseminating natural resources information on adaptation to and mitigation of climate change and variability impacts to other regions facing similar risks. Furthermore, while international organizations also provide natural resource information, the Umlindi newsletter may be distinguished by its regional focus and linkages to individual communities. It bridges the gap between global environmental data and local decision-making by illustrating how global scientific knowledge may be applied locally. [ABSTRACT FROM AUTHOR]

The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.

In order to overcome the problems of poor real-time performance, high false alarm rate and underreport alarm rate of mine external fire monitoring, a method of fire monitoring using the fusion of visible visual features is proposed.Firstly, the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed, and the extraction methods of fire source texture, sharp corners, similarity coefficient and flicker frequency are designed.Then, an improved seed region growth algorithm is used to segment the suspected fire area, and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly, the extracted dynamic and static features are used to construct fire feature vectors.Finally, a fire monitoring model using BP neural network is constructed, and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60% and 99.06%, respectively, the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.

To reduce the losses caused by fire, this paper proposes an intelligent fire monitoring system based on multi-sensor data fusion. Through the design of the temperature, smoke and flame sensors, based on the weighted least squares WLS and improved fuzzy support degree of similar sensors and dissimilar sensors data fusion, respectively, in the ZigBee wireless network based on the design of the fire remote monitoring and control alarm for monitoring, and add the local storage and emergency module. In addition, whether the router nodes work on the ZigBee wireless network during the fire test depends on multi-sensor data fusion. The test shows that the node communication distance is extended and can reach the acceptable requirements in the range of 70m, the highest probability of fire occurrence is 0.92, the lowest probability is 0.14, the overall probability of occurrence is in the range of 0.4 to 0.6, and the error between the predicted value of the probability of occurrence of the fire and the expected value is within ±0.05.

Several studies have already evaluated the ability to identify wildfires and their characteristics by using NOAA-AVHRR and EO-MODIS images, which have adequate spectral bands for these targets, although with a spatial resolution limited to 1 km and a daily revisit time. In many cases, the latter features can limit the timely identification of a fire and the monitoring of its evolution. Conversely, sensors operating on geostationary platforms could acquire images within less than half an hour, yet still with a nominal spatial resolution of 1 km. In this study, we perform an analysis at different spatial resolutions of a sequence of OLI-Landsat-8 images referring to a natural fire that occurred near Massarosa, Tuscany, in July 2022. In particular, we investigate the potential of the SWIR bands, which are useful for monitoring high temperature wildfires. The results suggest that the use of sensors onboard a geostationary platform with relatively high nominal spatial resolution (of the order of 1 km) and frequent revisit time could enable the timely detection of fires and their monitoring. [ABSTRACT FROM AUTHOR]

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial resolution (1 km, which is the spatial resolution of infrared channels in most satellites used for wildfire monitoring in daily operational mode). The Medium-Resolution Spectral Imager II onboard the Fengyun-3D polar orbiting meteorological satellite (FY-3D/MERSI-II) contains far-infrared channels with a spatial resolution of 250 m at the wavelengths of 10.8 μm and 12.0 μm, which promotes the application of far-infrared channels in wildfire monitoring. In this study, the features of FY-3D/MERSI-II far-infrared channels in fire monitoring are discussed. The sensitivity of 10.8 μm (250 m) to fire spots and the influence of solar radiation reflection on the infrared channels are quantitatively analyzed. The method of using 10.8 μm (250 m) as a major data source to detect fire spots is proposed, and several typical wildfire cases are used to verify the proposed method. The results show that the 10.8 μm (250 m) far-infrared channel has the same advantages as the existing method in wildfire monitoring in terms of a more precise positioning of the detected fire pixel, avoiding interference by solar radiation reflections, and reflecting stronger fire regions in large fire fields. [ABSTRACT FROM AUTHOR]

This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast, which is equivalent to the pixel informativeness indicator of the image. Proposed preprocessing methods give a delay of no more than 0.03 s due to the use of a pipeline method for data processing. Experimental results show that the horizon clipping algorithm improves fire and smoke detection accuracy by approximately 11%. The best results with the neural network were achieved with YOLO 5m, which yielded an F1 score of 76.75% combined with a processing speed of 45 frames per second. The obtained results differ from existing analogs by utilizing a comprehensive approach to early fire detection, which includes image enhancement and alternative real-time video processing methods.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers. [ABSTRACT FROM AUTHOR]

With the increase of coal mining intensity in mines, the tendency of coal to spontaneous combustion increases gradually. Due to the existing coal mine spontaneous combustion fire beam tube chromatography monitoring system has the problems of easy distortion of gas detection, long analysis period and easy pollution of gas sample, it cannot provide an effective scientific basis for the prevention and control of coal mine spontaneous combustion fire. In this paper, based on the traditional beam tube chromatography system, we combine micro gas chromatography detection with data network transmission and other high technologies to develop an intrinsically safe underground micro chromatography and beam tube positive pressure gas delivery pump station, forming a coal mine spontaneous combustion fire monitoring system based on micro chromatography and positive pressure gas delivery technology. The system is capable of analyzing 10 gas components simultaneously within 2 to 3 min with relative standard deviation ≤1.54%, relative accuracy up to 1.0% and lower detection limit up to 1×10-6. It improves the reliability and stability of the coal mine spontaneous combustion fire monitoring system, realizes real-time and accurate prediction of spontaneous combustion fires, and guarantees the safe production of coal mines.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole‐system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre‐fire vegetation inventories, (2) active‐fire monitoring, (3) post‐fire assessment, and (4) multi‐scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co‐development of information products using cloud computing that benefits from open‐access Earth observation and other geospatial data layers. [ABSTRACT FROM AUTHOR]

Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform’s architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers’ burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries.

The “Forest Fires Prediction and Detection” edition highlights the importance of research on fires worldwide. In recent years, the increased frequency of fires caused by climate change has rendered the planet uninhabitable. Several works have been prepared and published in an effort to raise awareness among civil society and government bodies about the importance of developing new technologies for monitoring areas prone to mega-fires. This special issue includes nine important works from various countries. The goal is to better understand the impacts on the world’s most diverse regions, ecosystems, and forest phytophysiognomies. New geotechnologies and fire models were used, both of which are important and could be used in the future to improve short- and long-term planning in firefighting.

The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from climate-related monitoring products obtained from an integration of remote sensing and in situ data from weather stations. It contains useful indicators, such as rainfall, vegetation, and fire conditions, that provide an overview of conditions across the country. The present study demonstrates how these natural resource indices are integrated and consolidated for utilization by farmers, policy-makers, private organizations, and the general public to make day-to-day decisions on the management and mitigation of natural disasters. However, there is a need to expand these baseline observation initiatives, including the following: (1) forecasting future conditions to strengthen coping mechanisms of government, farmers, and communities at large; and (2) incorporating information on other natural disasters such as floods and extreme heat. In the context of South Africa, this information is important to improve disaster preparedness and management for agricultural productivity. In a global context, the Umlindi newsletter can be insightful for developing and disseminating natural resources information on adaptation to and mitigation of climate change and variability impacts to other regions facing similar risks. Furthermore, while international organizations also provide natural resource information, the Umlindi newsletter may be distinguished by its regional focus and linkages to individual communities. It bridges the gap between global environmental data and local decision-making by illustrating how global scientific knowledge may be applied locally.

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

MODIS and VIIRS fire products have been widely used to detect and monitor fire activity at a global scale, as they provide highly relevant information on fire events, on their spatial and seasonal trends. Although these products have some limitations in detecting fires in forested areas due to closed canopy and smoke, they have been widely used to monitor and assess forest fires in many scientific studies. This study analyzes the performance of MODIS (MCD14ML) and VIIRS S-NPP (VNP14IMG) active fire/hotspot products in fire detection in five different land cover types (closed and open forests, shrublands, herbaceous vegetation and croplands) and compares the results to the ground-based fire database from 2015 to end of the 2019 in Turkey. Detected fires with a confidence value above 30% (nominal and high confidence) were used in the study. The land cover was assessed using the European Space Agency (ESA) Copernicus Global Land Service (CGLS) Dynamic Land Cover Layers at 100 m resolution in the study area. The performance assessment of two fire/hotspot products were conducted in three fire size classes, namely: fire size 10 ha in five different land cover types. The results indicated that the overall accuracy of MODIS ranged from 0.6% to 16.6% and VIIRS S-NPP ranged from 1.3% to 25.6% of all ground-based fires in five different land cover types. The detection rates increased as the fire size increased. This study indicates that some limitations still exist to use MODIS and VIIRS S-NPP active fire/hotspot data in the assessment of wildfires.

Several studies have already evaluated the ability to identify wildfires and their characteristics by using NOAA-AVHRR and EO-MODIS images, which have adequate spectral bands for these targets, although with a spatial resolution limited to 1 km and a daily revisit time. In many cases, the latter features can limit the timely identification of a fire and the monitoring of its evolution. Conversely, sensors operating on geostationary platforms could acquire images within less than half an hour, yet still with a nominal spatial resolution of 1 km. In this study, we perform an analysis at different spatial resolutions of a sequence of OLI-Landsat-8 images referring to a natural fire that occurred near Massarosa, Tuscany, in July 2022. In particular, we investigate the potential of the SWIR bands, which are useful for monitoring high temperature wildfires. The results suggest that the use of sensors onboard a geostationary platform with relatively high nominal spatial resolution (of the order of 1 km) and frequent revisit time could enable the timely detection of fires and their monitoring.

In general, the far-infrared channel in the wavelength range of 10.5–12.0 µm plays an auxiliary role in wildfire detection as its sensitivity to high-temperature targets is far lower than the mid-infrared channel in the wavelength range of 3.5–4.0 µm at the same spatial resolution (1 km, which is the spatial resolution of infrared channels in most satellites used for wildfire monitoring in daily operational mode). The Medium-Resolution Spectral Imager II onboard the Fengyun-3D polar orbiting meteorological satellite (FY-3D/MERSI-II) contains far-infrared channels with a spatial resolution of 250 m at the wavelengths of 10.8 μm and 12.0 μm, which promotes the application of far-infrared channels in wildfire monitoring. In this study, the features of FY-3D/MERSI-II far-infrared channels in fire monitoring are discussed. The sensitivity of 10.8 μm (250 m) to fire spots and the influence of solar radiation reflection on the infrared channels are quantitatively analyzed. The method of using 10.8 μm (250 m) as a major data source to detect fire spots is proposed, and several typical wildfire cases are used to verify the proposed method. The results show that the 10.8 μm (250 m) far-infrared channel has the same advantages as the existing method in wildfire monitoring in terms of a more precise positioning of the detected fire pixel, avoiding interference by solar radiation reflections, and reflecting stronger fire regions in large fire fields.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem that most of the current mine external fire monitoring methods do not have fire source positioning function, a binocular vision-based perception and positioning method of mine external fire is proposed. Firstly, the method sets up mine visible binocular cameras or near-infrared binocular cameras at multiple points in roadways, chambers and working faces with cables, tapes and electromechanical equipment to capture images of the monitoring area. Then, the adopted images are preprocessed and binarized images are obtained by threshold segmentation. The roundness, rectangularity and number of sharp corners in the image are calculated, and the images are identified by flames according to the roundness, rectangularity and number of sharp corners. If there is flame in the image detection area, a fire alarm signal will be issued and the temperature, smoke, carbon dioxide, carbon monoxide, oxygen and infrared sensor information are taken into consideration to improve the accuracy of alarms. Finally, the method uses mine visible binocular cameras or near-infrared binocular cameras to measure the distance of the fire source, locates the fire source based on the camera position, and outputs the fire source location information to control the fire extinguishing device near the fire source. Far-infrared binocular cameras can also be used for fire perception and fire source positioning, but the cost is high. The binocular vision-based perception and positioning method of mine external fire can sense and locate the fire source. This method has the advantages of wide monitoring range, low cost, fast response and visualization, and solves the problem of locating the fire source of mine external fire.

The monitoring data of coal spontaneous combustion in super large mining height working face includes gas concentration, temperature, etc. The research methods include experiments, numerical simulations, on site measurements, etc. Most of the existing studies have not considered the relationship between the indicators, and the research methods and data analysis methods are single. In order to solve this problem, taking the working face with a mining height of 8.8 m in Shangwan Coal Mine as an example, this paper analyzes the correlation between gas concentration and temperature in the process of coal spontaneous combustion and summarizes the law and characteristics of fire in the goaf area through a combination method of coal spontaneous combustion experiment, on-site 'three zone' measurement and numerical simulation. A surface borehole nitrogen injection model is established, and the changes of O2 concentration field, CO concentration field, temperature field and 'three zone' distribution before and after the nitrogen injection measures are inverted. For the high temperature abnormal area, the nitrogen injection location is selected according to the numerical simulation results. And the combined method of surface nitrogen injection and mine nitrogen injection is adopted to reduce the fire hazard. The research results show that CO can be used as an indicator gas for predicting coal spontaneous combustion, O2 and CH4 cannot be used as indicator gases, and C2H6, C2H4, C2H2 and H2 can be used as auxiliary indicator gases. After the nitrogen injection measures are taken, the width of the oxidation heating zone is greatly reduced, and the CO volume fraction is significantly reduced. The highest temperature drops rapidly and the inerting effect is significant. The CO volume fraction and temperature in the high temperature abnormal area show a gradual decrease trend, which verifies the rationality of the nitrogen injection location and the effectiveness of nitrogen injection measures.

The patrol in forests is difficult and low efficiency for safety guards. A novel fire monitoring method based on multisource data was proposed. The concentration of smoke particulate was firstly used to recognize the region where fire might break out. In order to identify the high risk regions, human activities and weather factors related to the forest fire were combined to evaluate the fire risks. Experimental results show that a number of fake forest fire can be filtered so that the guards can be massed effectively and dispose of the fire quickly. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main objective of this paper was to demonstrate the capability of dedicated small satellite infrared sensors with cooled quantum detectors, such as those successfully utilized three times in Germany's pioneering BIRD and FireBIRD small satellite infrared missions, in the quantitative characterization of high-temperature events such as wildfires. The Bi-spectral Infrared Detection (BIRD) mission was launched in October 2001. The space segment of FireBIRD consists of the small satellites Technologie Erprobungs-Träger (TET-1), launched in July 2012, and Bi-spectral InfraRed Optical System (BIROS), launched in June 2016. These missions also significantly improved the scientific understanding of space-borne fire monitoring with regard to climate change. The selected examples compare the evaluation of quantitative characteristics using data from BIRD or FireBIRD and from the operational polar orbiting IR sensor systems MODIS, SLSTR and VIIRS. Data from the geostationary satellite "Himawari-8" were compared with FireBIRD data, obtained simultaneously. The geostationary Meteosat Third Generation-Imager (MTG-I) is foreseen to be launched at the end of 2022. In its application to fire, the MTG-I's Flexible Combined Imager (FCI) will provide related spectral bands at ground sampling distances (GSD) of 3.8 µm and 10.5 µm at the sub-satellite point (SSP) of 1 km or 2 km, depending on the used FCI imaging mode. BIRD wildfire data, obtained over Africa and Portugal, were used to simulate the fire detection and monitoring capability of MTG-I/FCI. A new quality of fire monitoring is predicted, if the 1 km resolution wildfire data from MTG-1/FCI are used together with the co-located fire data acquired by the polar orbiting Visible Infrared Imaging Radiometer Suite (VIIRS), and possibly prospective FireBIRD-type compact IR sensors flying on several small satellites in various low Earth orbits (LEOs). [ABSTRACT FROM AUTHOR]

The electric leakage of underground cables and electrical equipment in coal mine, electromotive force discharge induced by high-power radio transmission on metal support and electromechanical equipment metal will generate electric spark, which will then lead to mine fire, gas and coal dust explosion. The early perception of mine electric spark and taking preventive measures can avoid or reduce mine fire, gas and coal dust explosion. The early perception of mine fire, gas and coal dust explosion and timely emergency rescue can reduce casualties and property losses. The ultraviolet image perception method of mine electric spark and thermal power disaster is proposed. The ultraviolet camera, pickup and air pressure sensor are set in coal working face, heading face and roadway to collect mine ultraviolet image, sound and air pressure in real time. The ultraviolet images are preprocessed. The interference of fluorescent lamps, incandescent lamps and LED lamps is eliminated. If there was an electric spark ultraviolet image, the electric spark alarm would be given. If there was a fire ultraviolet image, the fire alarm would be given. If the ultraviolet image was not an electric spark image nor a fire ultraviolet image, and if the pickup detected an explosion sound or the air pressure sensor detected a sudden increase in air pressure, the gas and coal dust explosion alarm would be given. The ultraviolet image is greatly interfered by incandescent lamps and fluorescent lamps, and is less interfered by LED lamps. With LED lamp replacing incandescent lamp and fluorescent lamp in coal mine, various lighting and signal equipment in coal mine have little interference to ultraviolet image in coal mine. Therefore, the ultraviolet image perception method of mine electric spark and thermal power disaster has the advantages of small external interference, high reliability and simultaneously perception of electric spark, external fire, gas and coal dust explosion disasters.

Decades of successful active fire mapping from space, have led to global informational products of growing importance to scientific community and operational agencies. In contrast, detecting fires from space faster than current conventional capabilities in the continental U.S. has not been considered attainable, except in remote, sparsely populated areas. We present a research prototype version of the GOES Early Fire Detection (GOES-EFD) algorithm focused on minimizing the time to first detection of a wildfire incident. The algorithm is designed for regional-scale surveillance and combines multitemporal anomaly tests developed in our previous work, contextual hot-spot tests, and dynamic event classification and tracking. The GOES-EFD version 0.4 was initially tested with 40-day summer 2006 data over central California. The algorithm identified most of large (final size>2ha) wildfires within 30min and 31% of the wildfires were detected before they were reported by the public. Under identical operation conditions, GOES-EFD 0.4 provided quicker initial detection than the temporally filtered operational WF-ABBA algorithm (version 6.1) and committed fewer false alarms. There is a substantial potential for further reducing detection latency and increasing reliability. Following the ongoing optimizations, tests, and integration in collaboration with the fire management agencies and first responders, GOES-EFD could be deployed for regional scale real-time surveillance to complement existing fire identification methods.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The issues of increasing the efficiency of fire situation monitoring are discussed, during which the search for objects of interest is being conducted to organize rescue operations and plan activities to fight the fire and reduce the consequences. Modern unmanned aerial vehicles equipped with vision systems, allow us to automatically explore the search area and detect various objects of interest. Monitoring is realized by controlling the trajectories and altitude of the aircraft, as well as receiving and analyzing images of the underlying surface, taken by onboard visual systems. The article offers a method for determining the altitude of flight of unmanned aerial vehicles during monitoring of the fire situation, taking into account the safety of aircraft and the conditions of observability of objects of interest. We consider some options for assessing the effectiveness of monitoring based on the search criteria of objects and the safety of unmanned aerial vehicles.

Research status of coalfield fire monitoring technologies was reviewed including electromagnetic detection method, resistivity method, radio wave method, geological radar method, gas measurement method, isotope radon measurement method, temperature method, remote sensing method and so on. Advantages and disadvantages of each technology were compared. It was pointed out that there were some questions existed in coalfield fire monitoring technologies at current such as lots of environmental factor limits, high cost, large technical limits and so on. Prospect of coalfield fire monitoring technology was taken out. It was pointed out that low altitude remote sensing technology of unmanned aerial vehicle equipped with an infrared thermal imager was development direction of coalfield fire monitoring technology. Moreover, development ideas were proposed which took high efficiency, intelligence and integration as leading role.

Based on analysis of principle and characteristics of mine fire monitoring methods such as temperature monitoring method, gas monitoring method, smoke monitoring method, visible light image monitoring method and infrared image monitoring method, a mine fire monitoring and trend prediction method based on multi-parameter fusion was proposed. Conclusions were got as following:① Thermocouple temperature measurement method and semiconductor temperature measurement method have advantages of high accuracy, strong real-time performance and early detection of early fire, but there are disadvantages such as large sensor consumption and large maintenance workload; Infrared temperature measurement method has advantages of wide monitoring range and easy sensor arrangement, but shielding and coal dust between sensor and measured object will affect monitoring result; Fiber distributed temperature measurement method has advantages of multi-point temperature monitoring and small amount of cable consumption, but there are disadvantages such as easy damage, complicated installation and difficult maintenance of optical fiber. ② Gas monitoring method and smoke monitoring method have advantages of wide monitoring range, convenient use and maintenance, but there are disadvantages such as cannot monitor early fire with low concentration of index gas and less smoke. ③ Visible light image monitoring method has advantages of wide monitoring range, convenient use and maintenance, low cost, but interference sources such as roadway light, miner's lamp, vehicle lamp and red clothes and smoke will affect monitoring effect, and the method cannot monitor early fire with no flame and less smoke.④ Infrared image monitoring method has advantages of wide monitoring range, convenient use and maintenance, but cost is high, and shielding and coal dust between infrared camera and measured object will affect monitoring effect. ⑤ Mine fire monitoring and trend prediction method based on multi-parameter fusion of temperature, smoke, gas concentration, odor, wind direction, wind speed, air volume, visible light image and infrared image can not only detect fire in time, but also locate fire source and predict fire development trend.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main objective of this paper was to demonstrate the capability of dedicated small satellite infrared sensors with cooled quantum detectors, such as those successfully utilized three times in Germany’s pioneering BIRD and FireBIRD small satellite infrared missions, in the quantitative characterization of high-temperature events such as wildfires. The Bi-spectral Infrared Detection (BIRD) mission was launched in October 2001. The space segment of FireBIRD consists of the small satellites Technologie Erprobungs-Träger (TET-1), launched in July 2012, and Bi-spectral InfraRed Optical System (BIROS), launched in June 2016. These missions also significantly improved the scientific understanding of space-borne fire monitoring with regard to climate change. The selected examples compare the evaluation of quantitative characteristics using data from BIRD or FireBIRD and from the operational polar orbiting IR sensor systems MODIS, SLSTR and VIIRS. Data from the geostationary satellite “Himawari-8” were compared with FireBIRD data, obtained simultaneously. The geostationary Meteosat Third Generation-Imager (MTG-I) is foreseen to be launched at the end of 2022. In its application to fire, the MTG-I’s Flexible Combined Imager (FCI) will provide related spectral bands at ground sampling distances (GSD) of 3.8 µm and 10.5 µm at the sub-satellite point (SSP) of 1 km or 2 km, depending on the used FCI imaging mode. BIRD wildfire data, obtained over Africa and Portugal, were used to simulate the fire detection and monitoring capability of MTG-I/FCI. A new quality of fire monitoring is predicted, if the 1 km resolution wildfire data from MTG-1/FCI are used together with the co-located fire data acquired by the polar orbiting Visible Infrared Imaging Radiometer Suite (VIIRS), and possibly prospective FireBIRD-type compact IR sensors flying on several small satellites in various low Earth orbits (LEOs).

Context and Background. Active fires have the potential to provide early estimates of fire perimeters, but there is a lack of information about the best active fire aggregation distances and how they can vary between fuel types, particularly in large areas of study under diverse climatic conditions. Objectives. The current study aimed at analyzing the effect of aggregation distances for mapping fire perimeters from active fires for contrasting fuel types and regions in Mexico. Materials and Methods. Detections of MODIS and VIIRS active fires from the period 2012–2018 were used to obtain perimeters of aggregated active fires (AGAF) at four aggregation distances (750, 1000, 1125, and 1500 m). AGAF perimeters were compared against MODIS MCD64A1 burned area for a total of 24 fuel types and regions covering all the forest area of Mexico. Results/findings. Optimum aggregation distances varied between fuel types and regions, with the longest aggregation distances observed for the most arid regions and fuel types dominated by shrubs and grasslands. Lowest aggregation distances were obtained in the regions and fuel types with the densest forest canopy and more humid climate. Purpose/Novelty. To our best knowledge, this study is the first to analyze the effect of fuel type on the optimum aggregation distance for mapping fire perimeters directly from aggregated active fires. The methodology presented here can be used operationally in Mexico and elsewhere, by accounting for fuel-specific aggregation distances, for improving rapid estimates of fire perimeters. These early fire perimeters could be potentially available in near-real time (at every satellite pass with a 12 h latency) in operational fire monitoring GIS systems to support rapid assessment of fire progression and fire suppression planning.

Underground coal mines are susceptible to many kinds of hazards. Mine fire is one of the most vulnerable threats that leads to loss of human lives and other resources inside the mines. Hence, continuous monitoring of underground coal mines is very important for early detection of mine fire. Nowadays, wireless sensor networks are being widely used for monitoring underground coal mines. However, the collected data from sensor nodes are inaccurate in nature, and there is a need for analyzing such data for taking preventive measure. In this article, we propose an optimized fuzzy logic-based fire monitoring system for wireless underground sensor network, which strengthens reliability of making decision in preventing mine fire. However, storing of large fuzzy rule set in the memory constraint sensor nodes and their traversal for every sensor reading are very challenging. Therefore, we use the binary particle swarm optimization (BPSO) algorithm to optimize the proposed fuzzy system that eliminates redundant rules but preserves event detection accuracy of the monitoring system. The proposed system is simulated using BPSO algorithm and Fuzzy Logic Toolbox inbuilt in the MATLAB. The simulation results demonstrate that the proposed system outperforms the existing monitoring systems for underground coal mines. [ABSTRACT FROM AUTHOR]