Journal articles on the topic 'Forest fires detection'
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1
Zatserkovnyi, V., P. Savkov, I. Pampukha, and К. Vasetska. "APPLICATION OF GIS AND REMOTE SENSING OF THE EARTH FOR THE FOREST FIRE MONITORING." Visnyk Taras Shevchenko National University of Kyiv. Military-Special Sciences, no. 2 (44) (2020): 54–58. http://dx.doi.org/10.17721/1728-2217.2020.44.54-58.
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The paper considers the problems of the forest industry, namely forest fires. Emphasis is placed on the suffering of theforests of Ukraine from large-scale fires. The main factors in reducing forest areas are forest fires. Despite the constantimplementation of preventive and precautionary fire-fighting measures, fires affect large areas of forests, which places a heavyburden on the country's budget. In addition to direct detection of fires, assessment of their power and development forecast, theurgent task is to monitor the parameters of fires: area, perimeter of the edge and radiation power of the fire, damage,quantification of vegetation changes and more. The ability to determine the areas burned during large forest fires, allows you tomake an inventory of the post-fire condition of forests. An important task of both economic and strategic nature is the study ofdynamic changes and the state of forests. Highly informative observations from artificial satellites of the Earth make it possible to quickly and objectively assess the reserves of forest resources and investigate changes in them: fires, damage assessments,reforestation in fires and deforestation, clarification of estimates of forest damage by diseases and pests, fires, identification ofcutting activities for the purpose of further control of their legality, solution of inventory problems, assessment of forest cover ofterritories, mapping of forest cover of areas and breed structure of forests. This allows to take timely measures for the rat ionaluse of forest resources and prevent damage.
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Huy, Long Tran, Chinh Tran Thien, Hoai Trung Tran, and Quynh Le Chi. "Monitoring, Detecting and Early Warning of Forest Fires using Blockchain in Wireless Sensor Network." International Journal of Computer Science and Mobile Computing 11, no. 11 (November 30, 2022): 165–76. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.013.
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Forest fires cause economic and material losses and destroy the environment, causing many negative impacts on human habitats and forest ecosystems. The unpredictable threat of wildfires leads to climate change and the greenhouse effect. It is worth noting that up to 90% of forest fires occur mainly due to human activities [1], even due to tampering, vandalism, and hostile actions. Therefore, to minimize the destruction caused by forest fires as well as accurately identify the area where forest fires occur and support government agencies, forest rangers, and search and disaster relief forces, ... it is necessary to monitor, detect and warn of forest fires right from the early stage (early warning). This paper proposes to use static sensor nodes combined with mobile in wireless sensor network (WSN) to Forest fire Monitoring, Detection, and Early Warning - FMDW. At the same time, integrating Blockchain technology (BC) in WSN solves the problem of secure routing, and distributed data storage, and prevents destructive and tampering attacks. This also suggests new research directions on monitoring, detecting, warning, and protecting forests in countries using Blockchain security technology in WSN.
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Seydi, Seyd Teymoor, Vahideh Saeidi, Bahareh Kalantar, Naonori Ueda, and Alfian Abdul Halin. "Fire-Net: A Deep Learning Framework for Active Forest Fire Detection." Journal of Sensors 2022 (February 21, 2022): 1–14. http://dx.doi.org/10.1155/2022/8044390.
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Forest conservation is crucial for the maintenance of a healthy and thriving ecosystem. The field of remote sensing (RS) has been integral with the wide adoption of computer vision and sensor technologies for forest land observation. One critical area of interest is the detection of active forest fires. A forest fire, which occurs naturally or manually induced, can quickly sweep through vast amounts of land, leaving behind unfathomable damage and loss of lives. Automatic detection of active forest fires (and burning biomass) is hence an important area to pursue to avoid unwanted catastrophes. Early fire detection can also be useful for decision makers to plan mitigation strategies as well as extinguishing efforts. In this paper, we present a deep learning framework called Fire-Net, that is trained on Landsat-8 imagery for the detection of active fires and burning biomass. Specifically, we fuse the optical (Red, Green, and Blue) and thermal modalities from the images for a more effective representation. In addition, our network leverages the residual convolution and separable convolution blocks, enabling deeper features from coarse datasets to be extracted. Experimental results show an overall accuracy of 97.35%, while also being able to robustly detect small active fires. The imagery for this study is taken from Australian and North American forests regions, the Amazon rainforest, Central Africa and Chernobyl (Ukraine), where forest fires are actively reported.
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Abdusalomov, Akmalbek Bobomirzaevich, Bappy MD Siful Islam, Rashid Nasimov, Mukhriddin Mukhiddinov, and Taeg Keun Whangbo. "An Improved Forest Fire Detection Method based on the Detectron2 Model and a Deep Learning Approach." Sensors 23, no. 3 (January 29, 2023): 1512. http://dx.doi.org/10.3390/s23031512.
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With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires. Fast detection with high accuracy is the key to controlling this unexpected event. To address this, we proposed an improved forest fire detection method to classify fires based on a new version of the Detectron2 platform (a ground-up rewrite of the Detectron library) using deep learning approaches. Furthermore, a custom dataset was created and labeled for the training model, and it achieved higher precision than the other models. This robust result was achieved by improving the Detectron2 model in various experimental scenarios with a custom dataset and 5200 images. The proposed model can detect small fires over long distances during the day and night. The advantage of using the Detectron2 algorithm is its long-distance detection of the object of interest. The experimental results proved that the proposed forest fire detection method successfully detected fires with an improved precision of 99.3%.
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Namburu, Anupama, Prabha Selvaraj, Senthilkumar Mohan, Sumathi Ragavanantham, and Elsayed Tag Eldin. "Forest Fire Identification in UAV Imagery Using X-MobileNet." Electronics 12, no. 3 (February 1, 2023): 733. http://dx.doi.org/10.3390/electronics12030733.
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Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India. Between 1 January and 31 March 2022, the country had 136,604 fire points. They activated an alerting system that indicates the location of a forest fire detected using MODIS sensor data from NASA Aqua and Terra satellite images. However, the satellite passes the country only twice and sends the information to the state forest departments. The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial vehicle equipped with a GPS and a high-resolution camera can acquire quality images referencing the fire location. Further, deep learning frameworks can be applied to efficiently classify forest fires. In this paper, a cheaper UAV with extended MobileNet deep learning capability is proposed to classify forest fires (97.26%) and share the detection of forest fires and the GPS location with the state forest departments for timely action.
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Lin, Ji, Haifeng Lin, and Fang Wang. "STPM_SAHI: A Small-Target Forest Fire Detection Model Based on Swin Transformer and Slicing Aided Hyper Inference." Forests 13, no. 10 (September 30, 2022): 1603. http://dx.doi.org/10.3390/f13101603.
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Forest fires seriously destroy the world’s forest resources and endanger biodiversity. The traditional forest fire target detection models based on convolutional neural networks (CNNs) lack the ability to deal with the relationship between visual elements and objects. They also have low detection accuracy for small-target forest fires. Therefore, this paper proposes an improved small-target forest fire detection model, STPM_SAHI. We use the latest technology in the field of computer vision, the Swin Transformer backbone network, to extract the features of forest fires. Its self-attention mechanism can capture the global information of forest fires to obtain larger receptive fields and contextual information. We integrated the Swin Transformer backbone network into the Mask R-CNN detection framework, and PAFPN was used to replace the original FPN as the feature fusion network, which can reduce the propagation path of the main feature layer and eliminate the impact of down-sampling fusion. After the improved model was trained, the average precision (AP0.5) of forest fire target detection at different scales reached 89.4. Then, Slicing Aided Hyper Inference technology was integrated into the improved forest fire detection model, which solved the problem that small-target forest fires pixels only account for a small proportion and lack sufficient details, which are difficult to be detected by the traditional target detection models. The detection accuracy of small-target forest fires was significantly improved. The average precision (AP0.5) increased by 8.1. Through an ablation experiment, we have proved the effectiveness of each module of the improved forest fire detection model. Furthermore, the forest fire detection accuracy is significantly better than that of the mainstream models. Our model can also detect forest fire targets with very small pixels. Our model is very suitable for small-target forest fire detection. The detection accuracy of forest fire targets at different scales is also very high and meets the needs of real-time forest fire detection.
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Suastika, I. Made, I. Gusti Agung Gede Arya Kadyanan, Ngurah Agus Sanjaya ER, Made Agung Raharja, I. Komang Ari Mogi, and Agus Muliantara. "Optimization Of Wsn Deployment Using Pso Algorithm For Forest Fire Detection." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 11, no. 2 (July 19, 2022): 421. http://dx.doi.org/10.24843/jlk.2022.v11.i02.p21.
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Abstract
Forest fires are disasters that have often occurred in recent years. This has a huge impact on both the environment and society itself. Delayed handling of fires is one of the triggering factors for the large losses caused by the disaster. The use of a Wireless Sensor Network is one solution so that information related to fires is conveyed to the authorities quickly so that the handling can be done more quickly. In this study, a simulation was made to determine the optimal position of a node to detect fires optimally. This simulation is run on NS3 Software on Ubuntu 18.04 Linux Operating System. In the optimization process, the PSO algorithm is run with Google Colab. The results of each iteration on the PSO will be simulated in NS3 and the communication between nodes will be seen. There are 12 iterations of the maximum 30 iterations specified, and there are 12 simulations according to the number of iterations. From 12 simulations that have been carried out, it is known that in the last iteration of the 10 nodes installed, all nodes communicate. Communication between nodes can be seen through .pcap files and graphs on NetAnim, the communication is characterized by sending fire messages to each installed node. In the last iteration, 10 nodes received a fire message.
Keywords: Wireless Sensor Network, Forest fires, Particle swarm optimization
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Ковалев, Борис, Boris Kovalev, Наталия Сакович, Nataliya Sakovich, Евгений Христофоров, Evgeniy Khristoforov, Юрий Баранов, and Yu Baranov. "ABOUT THE CONDITION AND FIRE-FIGHTING MEASURES OF PROTECTION IN THE BRYANSK FORESTRY." Forestry Engineering Journal 8, no. 1 (March 19, 2018): 189–98. http://dx.doi.org/10.12737/article_5ab0dfc6c3aba1.38810767.
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Bryansk forestry is located in the north-east of the Bryansk region, in the territories of the Bryansk and Karachev administrative districts, with a total area of 62,339 hectares, including 59,219 hectares of forest, and 16593 ha of forest cultures. Forests of the forest range are classified as protective, they are used in recreational, water-protective, environmental-forming purposes, grow on sands, moraines, sandy loam, loam. Forest management in the Bryansk forestry is aimed at rational forest management and management, enhancement of the forest resource potential, protection and protection of forests, expansion and rational use of forests through the systematic implementation of a set of forest management measures, growing forests to meet the needs of the Bryansk region in wood. The climatic conditions in the area of Bryansk forestry are mainly favorable for forest-forming coniferous and deciduous species. However, in recent years, dry periods of different duration and intensity have regularly occurred, which create conditions for the occurrence of fires. Research indicators that for the period from 2003 to 2015 in the territory of the forestry there were 2039 fires, a total area of 4,499.9 hectares. Only in 2014, the damage from forest fires amounted to 17434.2 thousand rubles, while directly to extinguish fires spent 1,434.2 thousand rubles. In 2015, the main causes of fires are: the human factor - 66 fires; grass fires - 27 fires; through the fault of the railway -10; other reasons - 3 fires and others. Elimination of forest fires in the forestry is carried out by land means, while the artificial and natural fire barriers created in advance, in particular, created with the help of a tractor, aggregated by the device for laying and reconstructing mineralized strips, and fire-fighting mineralized strips, are of great help in eliminating fires. In order to improve the microclimate in the tractor cab, the authors propose to use a control pedal with a hermetic terminal. Timely detection of fire and liquidation of a fire, reduces economic, natural, technological and social damage
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Supriya, Y., and Thippa Reddy Gadekallu. "Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires." Sustainability 15, no. 2 (January 5, 2023): 964. http://dx.doi.org/10.3390/su15020964.
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Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires.
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Jang, Eunna, Yoojin Kang, Jungho Im, Dong-Won Lee, Jongmin Yoon, and Sang-Kyun Kim. "Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea." Remote Sensing 11, no. 3 (January 30, 2019): 271. http://dx.doi.org/10.3390/rs11030271.
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Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.
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Xiong, Ding, and Lu Yan. "Early smoke detection of forest fires based on SVM image segmentation." Journal of Forest Science 65, No. 4 (April 26, 2019): 150–59. http://dx.doi.org/10.17221/82/2018-jfs.
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A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.
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Oreshchenko, Andrii V., Volodymyr I. Osadchyi, Mykhailo V. Savenets, and Vira O. Balabukh. "DETECTION AND MONITORING OF POTENTIALLY DANGEROUS FIRES ON THE TERRITORY OF UKRAINE USING THE DATA OF SATELLITE SCANNING." Visnik Nacional'noi' academii' nauk Ukrai'ni, no. 11 (November 20, 2020): 33–44. http://dx.doi.org/10.15407/visn2020.11.033.
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The study presents the classification of systems for fires detection and monitoring including forest fires according to the method of fires data collection. In Ukrainian Hydrometeorological Institute of State Emergency Service of Ukraine and National Academy of Sciences of Ukraine are developed the methods of heat emissions geocoding from data provided by artificial satellites in order to obtain information about the geographic features in which these emissions are recorded. The original method for detecting forest and other potentially dangerous fires is also developed in the Institute. We created the cartographic and analytical system for monitoring heat emissions and detecting potentially dangerous fires that successfully passed check studies and is used in operations of State Emergency Service of Ukraine.
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Chen, Gong, Hang Zhou, Zhongyuan Li, Yucheng Gao, Di Bai, Renjie Xu, and Haifeng Lin. "Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s." Forests 14, no. 2 (February 6, 2023): 315. http://dx.doi.org/10.3390/f14020315.
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The frequent occurrence of forest fires causes irreparable damage to the environment and the economy. Therefore, the accurate detection of forest fires is particularly important. Due to the various shapes and textures of flames and the large variation in the target scales, traditional forest fire detection methods have high false alarm rates and poor adaptability, which results in severe limitations. To address the problem of the low detection accuracy caused by the multi-scale characteristics and changeable morphology of forest fires, this paper proposes YOLOv5s-CCAB, an improved multi-scale forest fire detection model based on YOLOv5s. Firstly, coordinate attention (CA) was added to YOLOv5s in order to adjust the network to focus more on the forest fire features. Secondly, Contextual Transformer (CoT) was introduced into the backbone network, and a CoT3 module was built to reduce the number of parameters while improving the detection of forest fires and the ability to capture global dependencies in forest fire images. Then, changes were made to Complete-Intersection-Over-Union (CIoU) Loss function to improve the network’s detection accuracy for forest fire targets. Finally, the Bi-directional Feature Pyramid Network (BiFPN) was constructed at the neck to provide the model with a more effective fusion capability for the extracted forest fire features. The experimental results based on the constructed multi-scale forest fire dataset show that YOLOv5s-CCAB increases AP@0.5 by 6.2% to 87.7%, and the FPS reaches 36.6. This indicates that YOLOv5s-CCAB has a high detection accuracy and speed. The method can provide a reference for the real-time, accurate detection of multi-scale forest fires.
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Nörthemann, K., J. E. Bienge, J. Müller, and W. Moritz. "Early forest fire detection using low-energy hydrogen sensors." Journal of Sensors and Sensor Systems 2, no. 2 (November 1, 2013): 171–77. http://dx.doi.org/10.5194/jsss-2-171-2013.
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Abstract. Most huge forest fires start in partial combustion. In the beginning of a smouldering fire, emission of hydrogen in low concentration occurs. Therefore, hydrogen can be used to detect forest fires before open flames are visible and high temperatures are generated. We have developed a hydrogen sensor comprising of a metal/solid electrolyte/insulator/semiconductor (MEIS) structure which allows an economical production. Due to the low energy consumption, an autarkic working unit in the forest was established. In this contribution, first experiments are shown demonstrating the possibility to detect forest fires at a very early stage using the hydrogen sensor.
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Trpeski, Pavle, Samir Ajdini, and Almendina Mehmedi. "PREVENTION AND AVOIDANCE OF FIRES WITH INNOVATIVE MEANS IN THE PUBLIC INSTITUTION MAVROVO." Knowledge International Journal 34, no. 5 (October 4, 2019): 1517–22. http://dx.doi.org/10.35120/kij34051517t.
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Forests are the lungs of the planet Earth. As in all countries, one of the natural treasures of RSM is the forests in our country. protection of forests is the responsibility of state owned forest enterprises and national parks that manage them. Forests today have numerous risks where they are reduced or destroyed and one of the major risks is forest fires which we as a state cannot afford. and we are exempt.Forest fires are the spontaneous and uncontrolled spread of fire in the natural environment. The size of the burned area and the severity of the fire depend on the type of vegetation affected by the fire. The dimensions of these natural disasters are often of such magnitude as are visible from space, such as the fires in Siberian rainforest and the Amazon this year. Characteristic of forest fires is the very rapid spread and sudden changes of direction due to weather conditions.The strategy for combating forest fires includes their prevention to prevent, early detect and suppress and to develop means to effectively combat this type of natural disaster.European Commission reports on forest fires in Europe, the Middle East and North Africa for 2017 say more than 1.2 million hectares of forest and land in Europe have been destroyed - more than the total area of Cyprus.The forest fires killed 127 civilians and firefighters and caused nearly 10 billion euros in economic damage.Mavrovo National Park undertakes fire prevention measures in the area covered primarily by appropriate forest endangerment plans, operational fire protection measures as well as innovative means of drone drone use in the area. as part of the ASPires Advanced Forest Fire Prevention and Early Detection Systems that control the area for early fire warning.The strategy for combating forest fires includes their prevention to prevent, early detect and suppress and to develop means to effectively combat this type of natural disaster.
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Pan, Ligong. "Preventing forest fires using a wireless sensor network." Journal of Forest Science 66, No. 3 (March 30, 2020): 97–104. http://dx.doi.org/10.17221/151/2019-jfs.
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Forest fire is a natural phenomenon in many ecosystems across the world. The forecasting of fire danger conditions resembles one of the most important parts in forest fire management. A ZigBee-based wireless sensor network was proposed for monitoring fire danger and predicting the behaviour of fire after occurrence. This technique is intended for real-time operation, given the urgent need for forest protection against fires. The architecture of a wireless sensor network for forest fire detection is described. From the information collected by the system, decisions on firefighting or fire prevention can be made more quickly by the relevant government departments. We believe that by making the sensor network able to reconfigure rapidly in response to changes in the local conditions upon which the network is dependent, we will generate an adaptable weather monitoring and fire detection system.
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Xue, Qilin, Haifeng Lin, and Fang Wang. "FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5." Forests 13, no. 12 (December 12, 2022): 2129. http://dx.doi.org/10.3390/f13122129.
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Intense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this paper considers the classification and identification of surface fires and canopy fires. Deep learning-based forest fire detection uses convolutional neural networks to automatically extract multidimensional features of forest fire images with high detection accuracy. To accurately identify different forest fire types in complex backgrounds, an improved forest fire classification and detection model (FCDM) based on YOLOv5 is presented in this paper, which uses image-based data. By changing the YOLOv5 bounding box loss function to SIoU Loss and introducing directionality in the cost of the loss function to achieve faster convergence, the training and inference of the detection algorithm are greatly improved. The Convolutional Block Attention Module (CBAM) is introduced in the network to fuse channel attention and spatial attention to improve the classification recognition accuracy. The Path Aggregation Network (PANet) layer in the YOLOv5 algorithm is improved into a weighted Bi-directional Feature Pyramid Network (BiFPN) to fuse and filter forest fire features of different dimensions to improve the detection of different types of forest fires. The experimental results show that this improved forest fire classification and identification model outperforms the YOLOv5 algorithm in both detection performances. The mAP@0.5 of fire detection, surface fire detection, and canopy fire detection was improved by 3.9%, 4.0%, and 3.8%, respectively. Among them, the mAP@0.5 of surface fire reached 83.1%, and the canopy fire detection reached 90.6%. This indicates that the performance of our proposed improved model has been effectively improved and has some application prospects in forest fire classification and recognition.
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Kasyap, Varanasi LVSKB, D. Sumathi, Kumarraju Alluri, Pradeep Reddy CH, Navod Thilakarathne, and R. Mahammad Shafi. "Early Detection of Forest Fire Using Mixed Learning Techniques and UAV." Computational Intelligence and Neuroscience 2022 (July 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/3170244.
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Over the last few decades, forest fires are increased due to deforestation and global warming. Many trees and animals in the forest are affected by forest fires. Technology can be efficiently utilized to solve this problem. Forest fire detection is inevitable for forest fire management. The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective. The mixed learning technique is composed of YOLOv4 tiny and LiDAR techniques. Unmanned aerial vehicles (UAVs) are promising options to patrol the forest by making them fly over the region. The proposed model deployed on an onboard UAV has achieved 1.24 seconds of classification time with an accuracy of 91% and an F1 score of 0.91. The onboard CPU is able to make a 3D model of the forest fire region and can transmit the data in real time to the ground station. The proposed model is trained on both dense and rainforests in detecting and predicting the chances of fire. The proposed model outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.
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Lin, Ji, Haifeng Lin, and Fang Wang. "A Semi-Supervised Method for Real-Time Forest Fire Detection Algorithm Based on Adaptively Spatial Feature Fusion." Forests 14, no. 2 (February 11, 2023): 361. http://dx.doi.org/10.3390/f14020361.
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Forest fires occur frequently around the world, causing serious economic losses and human casualties. Deep learning techniques based on convolutional neural networks (CNN) are widely used in the intelligent detection of forest fires. However, CNN-based forest fire target detection models lack global modeling capabilities and cannot fully extract global and contextual information about forest fire targets. CNNs also pay insufficient attention to forest fires and are vulnerable to the interference of invalid features similar to forest fires, resulting in low accuracy of fire detection. In addition, CNN-based forest fire target detection models require a large number of labeled datasets. Manual annotation is often used to annotate the huge amount of forest fire datasets; however, this takes a lot of time. To address these problems, this paper proposes a forest fire detection model, TCA-YOLO, with YOLOv5 as the basic framework. Firstly, we combine the Transformer encoder with its powerful global modeling capability and self-attention mechanism with CNN as a feature extraction network to enhance the extraction of global information on forest fire targets. Secondly, in order to enhance the model’s focus on forest fire targets, we integrate the Coordinate Attention (CA) mechanism. CA not only acquires inter-channel information but also considers direction-related location information, which helps the model to better locate and identify forest fire targets. Integrated adaptively spatial feature fusion (ASFF) technology allows the model to automatically filter out useless information from other layers and efficiently fuse features to suppress the interference of complex backgrounds in the forest area for detection. Finally, semi-supervised learning is used to save a large amount of manual labeling effort. The experimental results show that the average accuracy of TCA-YOLO improves by 5.3 compared with the unimproved YOLOv5. TCA-YOLO also outperformed in detecting forest fire targets in different scenarios. The ability of TCA-YOLO to extract global information on forest fire targets was much improved. Additionally, it could locate forest fire targets more accurately. TCA-YOLO misses fewer forest fire targets and is less likely to be interfered with by forest fire-like targets. TCA-YOLO is also more focused on forest fire targets and better at small-target forest fire detection. FPS reaches 53.7, which means that the detection speed meets the requirements of real-time forest fire detection.
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Attri, Varun, Rajeev Dhiman, and S. Sarvade. "A review on status, implications and recent trends of forest fire management." Archives of Agriculture and Environmental Science 5, no. 4 (December 25, 2020): 592–602. http://dx.doi.org/10.26832/24566632.2020.0504024.
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Forest fire spread out in an area having combustible material in the summer season with high temperature. It burns the area and looks like a misery. Forest fire is one of the factors that severely affects the forest biodiversity. Burning actions in a forest affects not only flora and fauna but also soil properties changed due to the forest fire. In summer season on sloppy topography forest fire originates in tropical forests. While in coniferous forests, forest fire outbreaks during the resin extraction activities. More than 350 million hectares (ha) was estimated to be affected by vegetation fires globally. In India about 55% of forest area is prone to the fire. Fires can be natural or man- made, but manmade fire affects mostly. Several forest types and areas are more susceptible to forest fires because of suitable weather, topography and inflammable material. Forest fires adversely affect the soil, water, flora and fauna and disrupt the ecological functions. The new advances in fire control are remote sensing and GIS where real time information can be gathered about the fire break and immediate follow can be done. The chemicals (as borate, ammonium sulfate and ammonium biphosphate) are used for fire control and various other types of fire retardants are used to keep the fire under control. Forest fire changes the composition of vegetation, extinction of species, development of the various adaptations in unwanted plants. Nutrient cycle and soils are affected. Frequent forest fire events cause global warming. Forest fire needed to be controlled at initial stage and the large fires should not be allowed to occur, the modern techniques of monitoring, detection and control must be used for avoiding the large fires happenings.
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Mohammed, Zouiten, Chaaouan Hanae, and Setti Larbi. "Comparative study on machine learning algorithms for early fire forest detection system using geodata." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5507. http://dx.doi.org/10.11591/ijece.v10i5.pp5507-5513.
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Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.
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Utkin, A. B., A. V. Lavrov, L. Costa, F. Simões, and R. Vilar. "Detection of small forest fires by lidar." Applied Physics B: Lasers and Optics 74, no. 1 (January 1, 2002): 77–83. http://dx.doi.org/10.1007/s003400100772.
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Xue, Zhenyang, Haifeng Lin, and Fang Wang. "A Small Target Forest Fire Detection Model Based on YOLOv5 Improvement." Forests 13, no. 8 (August 20, 2022): 1332. http://dx.doi.org/10.3390/f13081332.
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Forest fires are highly unpredictable and extremely destructive. Traditional methods of manual inspection, sensor-based detection, satellite remote sensing and computer vision detection all have their obvious limitations. Deep learning techniques can learn and adaptively extract features of forest fires. However, the small size of the forest fire target in the long-range-captured forest fire images causes the model to fail to learn effective information. To solve this problem, we propose an improved forest fire small-target detection model based on YOLOv5. This model requires cameras as sensors for detecting forest fires in practical applications. First, we improved the Backbone layer of YOLOv5 and adjust the original Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv5 to the Spatial Pyramid Pooling-Fast-Plus (SPPFP) module for a better focus on the global information of small forest fire targets. Then, we added the Convolutional Block Attention Module (CBAM) attention module to improve the identifiability of small forest fire targets. Second, the Neck layer of YOLOv5 was improved by adding a very-small-target detection layer and adjusting the Path Aggregation Network (PANet) to the Bi-directional Feature Pyramid Network (BiFPN). Finally, since the initial small-target forest fire dataset is a small sample dataset, a migration learning strategy was used for training. Experimental results on an initial small-target forest fire dataset produced by us show that the improved structure in this paper improves mAP@0.5 by 10.1%. This demonstrates that the performance of our proposed model has been effectively improved and has some application prospects.
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Đorđević, Goran, Mihailo Ratknić, Ljubinko Rakonjac, and Tatjana Dimitrijević. "Sheme of organisation and procedures in the actions of entities on forest fire protection: Prevention and suppression of forest fires." Sustainable Forestry: Collection, no. 85-86 (2022): 197–214. http://dx.doi.org/10.5937/sustfor2285197d.
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Organisations and procedures play an important role in the preventive fire protection of forest as well as in the calculation of effective forest fire suppression. In order for the fight against forest fires to be successful, all entities involved in risk management in forest fire protection must know their place and role, both in the segment of preparedness, preventive action, action on forest fires and in the field rehabilitation. Knowledge of the legality of forest fires, mode of action, equipment and extinguishing agents, rapid fire detection, preparation of planning documents is very important for the actions of entities in the protection of forest from fires to be effective.
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Belomutenko, D. V., M. V. Istochkina, S. V. Belomutenko, A. E. Peskov, D. A. Golatov, M. V. Medvedev, and E. A. Klenina. "Fighting forest and landscape fires in forestry." IOP Conference Series: Earth and Environmental Science 965, no. 1 (January 1, 2022): 012053. http://dx.doi.org/10.1088/1755-1315/965/1/012053.
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Abstract Wildfire is a serious disastrous phenomenon for nature and the population of our country and the whole world. Wildfires, which result in enormous economic losses, take a toll in human life and affect ecosystems, are a fundamental problem. The article assesses the fire hazards of Leshchevskoe forestry in the Volgograd region. The total area of the forest fund of the forestry is 17004 ha. The area covered by forests is 9313 ha. According to the forest zoning the territory of Leshchevskoe forestry attributed to the steppe region of the European part of Russia of the steppe forest zone. The article contains natural and climatic characteristics of the forestry territories, the indicators of the characteristics of forest and non-forest zones of the forestry fund, the fire hazard classes, the tree species composition and the major forest types. The main causes of forest fires and factors that increase the fire risks of forestry have been identified. The list and terms of measures for the organization of monitoring and control of fire hazards in forests and forest fires, as well as measures for the organization of fire detection, inventory and monitoring systems on the territory of Leshchevsky forestry are set out.
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Ari Kukuh Sentanu, I. G. A., I. Gst A. Komang Diafari Djuni, and Nyoman Pramaita. "RANCANG BANGUN SISTEM PENDETEKSI KEBAKARAN HUTAN BERBASIS NODE MCU ESP8266." Jurnal SPEKTRUM 8, no. 1 (March 28, 2021): 286. http://dx.doi.org/10.24843/spektrum.2021.v08.i01.p32.
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The problem that often occurs so far is the delay in the presence of the fire departmentat the fire site. So the authors make an early detection tool for forest fires based on the Internetof Things because forest fires occur in very large forests so that supervision from officers is notenough. This study aims to design a forest fire extinguishing system based on ESP8266NodeMCU. The research was conducted by designing a system and making it happen by usinga board and several sensors to obtain data. From the results of the design carried out in thisstudy, the prototype of forest fire detection systems based on NodeMCU ESP8266 andtemperature, fire and smoke sensors has been realized, which can send notifications ontelegram. And the pump can put out the fire. The actual application in the forest still needschanges to the pump construction and the addition of several sensors.
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Rais, Sasli, Erdianto Erdianto, and Mukhlis R. Mukhlis R. "KEBIJAKAN NON PENAL DALAM PENANGGULANGAN KEBAKARAN LAHAN DAN HUTAN OLEH KEPOLISIAN DAERAH RIAU BERBASIS TEKNOLOGI APLIKASI DASHBOARD LANCANG KUNING DIKAITKAN DENGAN UPAYA PENCEGAHAN TINDAK PIDANA PEMBAKARAN LAHAN DAN HUTAN." EKSEKUSI 4, no. 1 (June 2, 2022): 22. http://dx.doi.org/10.24014/je.v4i1.14188.
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ABSTRACT Forest fires and land fires are thus far an annual problem in the province of riau, with effects not only harmful to the people of the province of Riau, even to those of neighboring countries, advanced and efficient technologies are needed to address the karhutla's growing problem in the province of Riau. It is intended to prevent and anticipate the possibilities of increasing the phenomenon of land fires and forests both by natural or otherwise by irresponsible human behavior.The study aims to know about the treatment of forest fires and land fires by the riau county police for the past time, to assess the weaknesses and overcare of the land and forest fires by the riau area police by using polite-yellow dashboard applications and to deride legal formulations using a yellow crude dashboard application to prevent land fires and forest fires in the province of Riau.The study came to the conclusion that handling of forest fires and land by the riau county police is done with a preventive and repressive approach. A preventive approach to this is by performing socialization, empowering communities and conducting carhutla's early detection patrols. Tackling land fires and forest using the yellow sass-yellow dashboard app method presents several weaknesses and advantages. As for the weakness in the development of the application, it is not as high as the operation of the personnel of the Riau area. Not all personnel use the technological advances as field reporting reporting specifically to monitor land fires and forests. Rather, it is overdone in the use of the first information information regarding location and direction of the windfall, both known to nearby accessible sources of water or gas, the three personnel budget problems that could be addressed through police-integrated systems. It will require the creation of a by-law product of both the law and the pp which features a dashboard application model as a primary and integrated facility for sustainable use in the karhutla management to be coordinated by police institutions. Keywords: forest and land fires, non-penal policies, and the yellow sassy dashboard application.
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Yevstihnieiev, Andrii. "Judicial protection of several legitimate environmental interests as a way to prevent violations of environmental security." Law Review of Kyiv University of Law, no. 2 (August 10, 2020): 341–45. http://dx.doi.org/10.36695/2219-5521.2.2020.64.
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The article analyzes some aspects of the legal provision of fire safety in the forests of Ukraine as a component of environmentalsafety on the example of forest fires in Zhytomyr and Kyiv regions in the spring of 2020, the legislative regulation of subordinationand coordination of state and departmental fire protection units on prevention and elimination of forest fires is analyzed, identified someshortcomings of legal regulation in the relevant field. It is concluded that in order to increase the effectiveness of forest fire fighting, itis necessary to improve certain regulations on the interaction of fire departments of different authorities, strengthening their technicalsupport for timely detection of fires and their rapid extinguishing. It is emphasized that Forests play a surprisingly important role inensuring the human right to a safe environment for life and health. It is noted that provided that if fire safety in forests is properlyensured (resulting in the absence of fires or their prompt cessation) will ensure environmental safety, because the environmental risk offire (probability of causing damage to human health or life as a result of harm to human health and life due to environmental pollution(especially atmospheric air).It is concluded that current legislation regulates in detail the activities of the departmental fire protection of the State ForestAgency in the field of prevention and elimination of forest fires, while to the activities of fire departments of the SES of Ukraine aregiven less attention.
According to the results of the analysis of regulations and the main problems that arose during firefighting in Kyiv and Zhytomyrregions in the spring of 2020, we consider it expedient to highlight the following main shortcomings of regulatory and legal regulationof fire safety in the forests of Ukraine:– lack of clear provisions on coordination and subordination of fire brigades of different authorities in the elimination of forest fires;– lack of clear provisions on the coordination of fire brigades and other stare authorities in terms of involving their equipmentfor forest fires.
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Veretennikova, N., V. Kislov, and K. Eremenko. "The Problem of Timely Detection and Elimination of Forest Fires." Bulletin of Science and Practice 7, no. 6 (June 15, 2021): 56–59. http://dx.doi.org/10.33619/2414-2948/67/07.
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Up to 35 thousand forest fires are registered in Russia annually, the area of fire of which is up to 2.5 million hectares. The use of unmanned aerial vehicles as one of the effective ways to detect and prevent forest fires. The use of UAVs has more advantages over other means of fire detection. In conclusion, the authors conclude that if only an incipient forest fire can be detected, it will prevent large economic and environmental losses.
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Zinoveva, Irina, and P. Medvedev. "MONITORING FIRES IN THE TERRITORY OF FOREST FUND OF REGIONS OF THE RUSSIAN FEDERATION." Actual directions of scientific researches of the XXI century: theory and practice 8, no. 1 (October 26, 2020): 329–34. http://dx.doi.org/10.34220/2308-8877-2020-8-1-329-334.
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This work is devoted to the consideration of the problems of reducing forest resources, which is largely associated with fires that occur in hard-to-reach areas and in areas with increased recreational load. In this regard, the creation of an effective fire detection system using various types of monitoring – ground, aviation, and space – is of particular relevance and importance. An important monitoring task is not only monitoring the fire situation, but also assessing the effects of fires. To determine the location of forest fires on the lands of the forest fund and other categories of lands, as well as to collect and store data, the Avialesohrana Federal State Budgetary Institution uses remote monitoring of the ISDM-Rosleskhoz system, which helps to quickly detect fires using satellites, record them, and compile information on foci and give them an assessment. According to the ISDM-Rosleskhoz data, a comparative analysis of the dynamics of the number of forest fires in the regions of the Russian Federation for 2017-2019 was carried out, as well as an analysis of the area of forest land covered by fires in 2015-2019, the results of which revealed a growing trend in the number of fires and their increase area. As a result, it was concluded that it is necessary to improve the system of remote monitoring of forest fires with the aim of preventing, timely detecting and preventing spread.
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Tomkins, L., T. Benzeroual, A. Milner, J. E. Zacher, M. Ballagh, R. S. McAlpine, T. Doig, S. Jennings, G. Craig, and R. S. Allison. "Use of night vision goggles for aerial forest fire detection." International Journal of Wildland Fire 23, no. 5 (2014): 678. http://dx.doi.org/10.1071/wf13042.
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Night-time flight searches using night vision goggles have the potential to improve early aerial detection of forest fires, which could in turn improve suppression effectiveness and reduce costs. Two sets of flight trials explored this potential in an operational context. With a clear line of sight, fires could be seen from many kilometres away (on average 3584m for controlled point sources and 6678m for real fires). Observers needed to be nearer to identify a light as a potential source worthy of further investigation. The average discrimination distance, at which a source could be confidently determined to be a fire or other bright light source, was 1193m (95% CI: 944 to 1442m). The hit rate was 68% over the course of the controlled experiment, higher than expectations based on the use of small fire sources and novice observers. The hit rate showed improvement over time, likely because of observers becoming familiar with the task and terrain. Night vision goggles enable sensitive detection of small fires, including those that were very difficult to detect during daytime patrols. The results demonstrate that small fires can be detected and reliably discriminated at night using night vision goggles at distances comparable to those recorded for daytime aerial detection patrols.
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Ivanova, Svetlana, Alexander Prosekov, and Anatoly Kaledin. "A Survey on Monitoring of Wild Animals during Fires Using Drones." Fire 5, no. 3 (April 29, 2022): 60. http://dx.doi.org/10.3390/fire5030060.
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Forest fires occur for natural and anthropogenic reasons and affect the distribution, structure, and functioning of terrestrial ecosystems worldwide. Monitoring fires and their impacts on ecosystems is an essential prerequisite for effectively managing this widespread environmental problem. With the development of information technologies, unmanned aerial vehicles (drones) are becoming increasingly important in remote monitoring the environment. One of the main applications of drone technology related to nature monitoring is the observation of wild animals. Unmanned aerial vehicles are thought to be the best solution for detecting forest fires. There are methods for detecting wildfires using drones with fire- and/or smoke-detection equipment. This review aims to study the possibility of using drones for monitoring large animals during fires. It was established that in order to use unmanned aerial vehicles to monitor even small groups of wild animals during forest fires, effective unmanned remote sensing technologies in critical temperature conditions are required, which can be provided not only by the sensors used, but also by adapted software for image recognition.
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Huang, Jingwen, Jiashun Zhou, Huizhou Yang, Yunfei Liu, and Han Liu. "A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection." Forests 14, no. 1 (January 16, 2023): 162. http://dx.doi.org/10.3390/f14010162.
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Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, APS (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects.
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Husak, Olena, and Volodymyr Husak. "Improvement of forest fire monitoring system by expanding information and technological possibilities of modern quadcopters." System research and information technologies, no. 3 (September 30, 2021): 33–46. http://dx.doi.org/10.20535/srit.2308-8893.2021.3.03.
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The article proposes a solution to an important problem — the development of an information technology based on expanding the functionality of non-specialized unmanned aerial vehicles (drones) for early detection of forest fires. The proposed information technology is designed to increase the effectiveness of monitoring forest fires. Тhe existing level of information technology does not fully settle the issue of reliable fire protection of forests. Today, there is a contradiction between the high cost of developing high-tech fire-fighting equipment and lack of its efficiency. The elimination of this contradiction will be facilitated by the involvement of additional non-technical and technical resources in the information technology of early detection of forest fire hotspots. The results of the analysis of the use of modern drones prove that the involvement of unmanned aerial vehicles significantly increases the efficiency of many types of monitoring and they can successfully be used to solve the problems of early detection of forest fire hotspots. The results of experiments are presented, which were carried out both for a series of digital images and for video.
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Lohar, Shobha, Sachin Panhalkar, and Abhijit Patil. "Detection of forest fire burn-area using landsat-8 and sentinel-2: a case study of Nivale (Kolhapur) beat of Chandoli National Park, Maharashtra, India." Disaster Advances 15, no. 1 (December 25, 2021): 53–60. http://dx.doi.org/10.25303/1501da5360.
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Forests play an important role in maintaining environmental equilibrium in the ecosystem. Forest fires, which can occur for a variety of reasons, are the greatest threat to forests. In order to control forest fires, it is critical to assess the formation and behavioral characteristics of forest fires. Detecting fire areas and the severity is much easier with satellite images obtained with advancing technologies. The main objective of this study is to extract burn area from Landsat-8 and Sentinel-2 satellite images of 2017 using six vegetation indices. Remote sensing data was used to study a forest fire in the Nivale (Kolhapur) beat of Chandoli National Park of Maharashtra. The Department of Divisional Forest Officer (Wildlife)- Chandoli National Park at Karad (DDFO-CNP) provided reference data indicating that the fire had damaged 194 hectares of Scrub forest. In addition, forest fire areas were determined using an objectbased image classification technique. When the findings of the study are compared to the values obtained by DDFO- CNP, it is found that Sentinel-2's object-based analysis provided the highest accuracy with an overall accuracy of 84 % and 0.795 Kappa statistics. Landsat-8 image has 82 % overall accuracy and 0.765 Kappa value. The findings of Sentinel-2 and Landsat-8 spectral indices showed the Sentinel-2 had better results in all indices. Differenced Normalized Difference Vegetation Index (dNDVI) and Relative Difference Normalized Burn Ratio (RdNBR) performed better than other indexes with a difference of only 18.27 and 30.88 hectares respectively. According to the fire severity analysis, a burn area with high intensity in Sentinel-2 was identified as moderate-high in Landsat-8. As per the research findings, sentinel-2 had a high severity area of 28.72 ha and a low severity area of 37.04 ha. It shows that satellite images of Sentinel-2 are highly suitable than Landsat-8 for estimating scrub forest fire areas. Finally, the findings of this study could be useful to forest managers to monitor burn regions quickly after the fire and in reducing severity and frequency of forest fires.
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Nash, C. H., and E. A. Johnson. "Synoptic climatology of lightning-caused forest fires in subalpine and boreal forests." Canadian Journal of Forest Research 26, no. 10 (October 1, 1996): 1859–74. http://dx.doi.org/10.1139/x26-211.
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The coupling of synoptic scale weather conditions with local scale weather and fuel conditions was examined for 2551 fires and 1 537 624 lightning strikes for the May through August fire seasons in 1988, 1989, 1992, and 1993 in Alberta and Saskatchewan. The probability of lightning fire occurrence (number of fires/number of strikes) is near zero until the Fine Fuel Moisture Code reaches 87 (moisture content of 14% dry weight), after which the probability increases rapidly. Duff Moisture and Drought Codes show less clear increases. In all cases, the probability of fire occurrence was low (the number of strikes greatly exceeded the number of forest fires), suggesting that lightning fire ignition coupled with early spread to detection was an uncommon event. This low probability of fire occurrence even at low fuel moisture may be a result of the arrangement and continuity of fuels in the boreal and subalpine forests. The literature suggests a higher probability of lightning-ignited fires in qualitatively different fuels, e.g., grasslands. The higher probability of fire at lower fuel moistures occurred primarily when high pressure dominated (positive 50-kPa anomaly) for at least 3 days and less than 1.5 mm precipitation occurred. The highest number of lightning strikes and largest number of fires also occurred when high pressure dominated. The high lightning numbers during high pressure systems were logistically related to increasing atmospheric instability (K-index).
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Xu, Renjie, Haifeng Lin, Kangjie Lu, Lin Cao, and Yunfei Liu. "A Forest Fire Detection System Based on Ensemble Learning." Forests 12, no. 2 (February 13, 2021): 217. http://dx.doi.org/10.3390/f12020217.
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Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.
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Setiawan, Aep, Akmal Yusup, and Amir Machmud. "Prototype of Fire Detection Tool Using Short Message Service (SMS) Notification as A Disaster Mitigation Effort Against Environmental Damage Due to Forest Fires." International Journal of Hydrological and Environmental for Sustainability 1, no. 1 (February 6, 2022): 1–7. http://dx.doi.org/10.58524/ijhes.v1i1.38.
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In relation to the threat of drought, it is necessary to do a drought analysis to evaluate the drought that has occurred. Another disaster caused by water drought is forest fires. The purpose of this study using the prototype of a Forest Fire Detection Tool (FFDT) are to early detection of forest fires and providing Short Message Service (SMS) notification of forest fires to the officer. The method of this study used a prototype of a FFDT with SMS Notification. This tool is controlled using the ATmega328 microcontroller or better known as Arduino Uno R3 and also the GSM 800L v2 module so that this tool will provide notification to forest guards in the form of sirens and SMS sounds when there are indications of smoke or fire that are identical to the event of a forest fire. Monitoring uses fire sensors, namely fire for the detection of flames that are often associated with forest fires and uses sensors as soon as possible, namely MQ2 and MQ9, all of which are equipped in one device, a forest fire detector using the Arduino Mega microcontroller. When there is a good sign that the flame or as soon as possible is read, the tool will send an SMS notification to the forest officer, the process of sending the SMS makes the fire process can detect even though it is still in a small condition or not widened.
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Johnston, Joshua, Lynn Johnston, Martin Wooster, Alison Brookes, Colin McFayden, and Alan Cantin. "Satellite Detection Limitations of Sub-Canopy Smouldering Wildfires in the North American Boreal Forest." Fire 1, no. 2 (August 10, 2018): 28. http://dx.doi.org/10.3390/fire1020028.
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We develop a simulation model for prediction of forest canopy interception of upwelling fire radiated energy from sub-canopy smouldering vegetation fires. We apply this model spatially across the North American boreal forest in order to map minimum detectable sub-canopy smouldering fire size for three satellite fire detection systems (sensor and algorithm), broadly representative of the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea and Land Surface Temperature Radiometer (SLSTR) and Visible Infrared Imaging Radiometer Suite (VIIRS). We evaluate our results according to fire management requirements for “early detection” of wildland fires. In comparison to the historic fire archive (Canadian National Fire Database, 1980–2017), satellite data with a 1000 m pixel size used with an algorithm having a minimum MWIR channel BT elevation threshold of 5 and 3 K above background (e.g., MODIS or SLSTR) proves incapable of providing a sub-0.2 ha smouldering fire detection 70% and 45% of the time respectively, even assuming that the sensor overpassed the relevant location within the correct time window. By contrast, reducing the pixel area by an order of magnitude (e.g., 375 m pixels of VIIRS) and using a 3.5 K active fire detection threshold offers the potential for successfully detecting all fires when they are still below 0.2 ha. Our results represent a ‘theoretical best performance’ of remote sensing systems to detect sub-canopy smoldering fires early in their lifetime.
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Dadzie, Adams Elias, and Antwi Mary. "Modelling the risk of forest to fire for the Bosomkese Forest Reserve, Ahafo Region, Ghana." South African Journal of Geomatics 10, no. 1 (September 5, 2022): 60–74. http://dx.doi.org/10.4314/sajg.v10i1.5.
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Forest fire is a devastating phenomenon in real life, causing huge losses of lives, properties and ecologies. A risk assessment model to identify, classify and map forest fire risk areas is presented in this paper. This model considers four risk models, i.e. ignition model, detection model, response model and fuel model analysis. The first model concentrates on human influence factors in forest fires, including the land use, distance from roads, and distance from settlements and the second model is made up of the possibility of fire visibility from road and settlement viewpoint. The forest fire response included distance from fire stations and motion resistance is the third model. The type of fuel (dry or wet), fuel moisture content, health of the forest vegetation and topography of the area were analysed as the fourth model. The study results indicate that very high-risk zones covered 38.8km2 representing 25.6% of the total forest area. Findings of the research are helpful in developing forest fire management systems. Fast and appropriate direction could be used by management to stop the spread of fire effectively. It also helps to provide effective means for protecting forests from fires as well as to formulate appropriate methods to control and manage forest fire damages and its spread. Recommendations were made at the end of the work to implement fire towers, break lines and employ the use of modern detection techniques such drones, etc to improve fire detection and response.
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Kataev, Michael Yu, and Eugene Yu Kartashov. "Computer Vision Method for Forest Fires Detection Based on RGB Images Obtained by Unmanned Motor Glider." Light & Engineering, no. 05-2021 (October 2021): 71–78. http://dx.doi.org/10.33383/2021-009.
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The article proposes a method (algorithm) of forest fire detection by means of RGB images obtained by using an unmanned aerial vehicle (motor glider). It includes several stages associated with background detection and subtraction and recognition of fire areas by means of RGB colour space. The proposed method was tested using images of forest fires. It is proposed to use unmanned aerial vehicles capable to monitor large areas continuously for several hours. The results of calculations are shown, which demonstrate that the proposed method allows us to detect areas of images occupied by forest fires and may be used in automatic forest fire monitoring systems.
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Muid, A., H. Kane, I. K. A. Sarasawita, M. Evita, N. S. Aminah, M. Budiman, and M. Djamal. "Potential of UAV Application for Forest Fire Detection." Journal of Physics: Conference Series 2243, no. 1 (June 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2243/1/012041.
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Abstract Improved ground and aerial system technologies enable mapping and monitoring forests and land to mitigate forest fires. UAV plays a role in monitoring by collecting forest area images from the air, which could be processed into 2D and 3D images. They can be analyzed to identify land cover types and objects in forest areas. This image data collection uses the DJI Phantom 4 Pro UAV controlled automatically with a flight plan made with Pix4D Capture, which is then processed using Agisoft. The result of the mapping has an average GSD of 2,03 cm/px. The mapping result shows that the 3D image produced can show objects in various land cover types. Weather related parameters were measured using ground sensors both in forest and plain area. We had successfully gathered forest and plain area images in addition to weather related parameters in Tangkuban Perahu Mountain area.
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Praveena, Dr S. Mary, B. Akshaya, BB Devipriya, C. Divya, and K. Mirudhula. "FOREST FIRE DETECTION USING DRONE." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (November 1, 2021): 110–13. http://dx.doi.org/10.33564/ijeast.2021.v06i07.018.
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Forest fires are a major reason behind forest degradation and have wide ranging adverse ecological, economic and social impacts, including loss of valuable timber resources, degradation of catchment areas, loss of biodiversity and extinction of plants and animals. In the recent times forests of Amazon and Australia faced a serious a threat to both wildlife and mankind. It also caused an enormous loss to both the countries. This paper describes early detection of fire by sectionally dividing the forest for efficient monitoring. The flame sensor is used to detect fire and a drone is employed as a mobile object which monitors the respective section. This drone travels from one pole to a different every alternate hour and gets charged while it's on a pole, because the pole has solar array. There is a timer also used at the poles which indicate the time of arrival and time of departure of the drone. The transponders are accustomed receive and transmit the signals. By this detection the nearby department of local government can get the precise location of the wildfire and early measures will be taken accordingly
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Sadouni, Salheddine, Ouissal Sadouni, and Malek Benslama. "Design and Development of an Intelligent System Based on the Internet of Things for the Early Detection of Forest Fires." International Journal of Organizational and Collective Intelligence 12, no. 2 (April 2022): 1–28. http://dx.doi.org/10.4018/ijoci.286174.
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Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.
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Lu, Kangjie, Renjie Xu, Junhui Li, Yuhao Lv, Haifeng Lin, and Yunfei Liu. "A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV." Forests 13, no. 3 (February 25, 2022): 383. http://dx.doi.org/10.3390/f13030383.
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Forest fires have the characteristics of strong unpredictability and extreme destruction. Hence, it is difficult to carry out effective prevention and control. Once the fire spreads, devastating damage will be caused to natural resources and the ecological environment. In order to detect early forest fires in real-time and provide firefighting assistance, we propose a vision-based detection and spatial localization scheme and develop a system carried on the unmanned aerial vehicle (UAV) with an OAK-D camera. During the high incidence of forest fires, UAVs equipped with our system are deployed to patrol the forest. Our scheme includes two key aspects. First, the lightweight model, NanoDet, is applied as a detector to identify and locate fires in the vision field. Techniques such as the cosine learning rate strategy and data augmentations are employed to further enhance mean average precision (mAP). After capturing 2D images with fires from the detector, the binocular stereo vision is applied to calculate the depth map, where the HSV-Mask filter and non-zero mean method are proposed to eliminate the interference values when calculating the depth of the fire area. Second, to get the latitude, longitude, and altitude (LLA) coordinates of the fire area, coordinate frame conversion is used along with data from the GPS module and inertial measurement unit (IMU) module. As a result, we experiment with simulated fire in a forest area to test the effectiveness of this system. The results show that 89.34% of the suspicious frames with flame targets are detected and the localization error of latitude and longitude is in the order of 10−5 degrees; this demonstrates that the system meets our precision requirements and is sufficient for forest fire inspection.
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Qian, Jingjing, and Haifeng Lin. "A Forest Fire Identification System Based on Weighted Fusion Algorithm." Forests 13, no. 8 (August 16, 2022): 1301. http://dx.doi.org/10.3390/f13081301.
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The occurrence of forest fires causes serious damage to ecological diversity and the safety of people’s property and life. However, due to the complex forest environment, the changeable shape of forest fires, and the uncertainty of flame color and texture, forest fire detection becomes very difficult. Traditional image processing methods rely heavily on artificial features and are not generally applicable to different forest fire scenes. In order to solve the problem of inaccurate forest fire recognition caused by the manual extraction of features, some scholars use deep learning technology to adaptively learn and extract forest fire features, but they often use a single target detection model, and their lack of learning and perception makes it difficult for them to accurately identify forest fires in a complex forest fire environment. Therefore, in order to overcome the shortcomings of the manual extraction of features and achieve a higher accuracy of forest fire recognition, this paper proposes an algorithm based on weighted fusion to identify forest fire sources in different scenarios, fuses two independent weakly supervised models Yolov5 and EfficientDet, completes the training and prediction of data sets in parallel, and uses the weighted boxes fusion algorithm (WBF) to process the prediction results to obtain the fusion frame. Finally, the model is evaluated by Microsoft COCO standard. Experimental results show that compared with Yolov5 and EfficientDet, the proposed Y4SED improves the detection performance by 2.5% to 4.5%. The fused algorithm proposed in this paper has better feature extraction ability, can extract more forest fire feature information, and better balances the recognition accuracy and complexity of the model, which provides a reference for forest fire target detection in the real environment.
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Feng, Xuhong, Pengle Cheng, Feng Chen, and Ying Huang. "Full-Scale Fire Smoke Root Detection Based on Connected Particles." Sensors 22, no. 18 (September 7, 2022): 6748. http://dx.doi.org/10.3390/s22186748.
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Smoke is an early visual phenomenon of forest fires, and the timely detection of smoke is of great significance for early warning systems. However, most existing smoke detection algorithms have varying levels of accuracy over different distances. This paper proposes a new smoke root detection algorithm that integrates the static and dynamic features of smoke and detects the final smoke root based on clustering and the circumcircle. Compared with the existing methods, the newly developed method has a higher accuracy and detection efficiency on the full scale, indicating that the method has a wider range of applications in the quicker detection of smoke in forests and the prevention of potential forest fire spread.
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Tian, Yuping, Zechuan Wu, Mingze Li, Bin Wang, and Xiaodi Zhang. "Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images." Remote Sensing 14, no. 18 (September 6, 2022): 4431. http://dx.doi.org/10.3390/rs14184431.
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With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses.
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Yuan, Chi, Youmin Zhang, and Zhixiang Liu. "A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques." Canadian Journal of Forest Research 45, no. 7 (July 2015): 783–92. http://dx.doi.org/10.1139/cjfr-2014-0347.
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Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.
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Gebert, Krista M., David E. Calkin, and Jonathan Yoder. "Estimating Suppression Expenditures for Individual Large Wildland Fires." Western Journal of Applied Forestry 22, no. 3 (July 1, 2007): 188–96. http://dx.doi.org/10.1093/wjaf/22.3.188.
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Abstract The extreme cost of fighting wildland fires has brought fire suppression expenditures to the forefront of budgetary and policy debate in the United States. Inasmuch as large fires are responsible for the bulk of fire suppression expenditures, understanding fire characteristics that influence expenditures is important for both strategic fire planning and onsite fire management decisions. These characteristics then can be used to produce estimates of suppression expenditures for large wildland fires for use in wildland fire decision support or after-fire reviews. The primary objective of this research was to develop regression models that could be used to estimate expenditures on large wildland fires based on area burned, variables representing the fire environment, values at risk, resource availability, detection time, and National Forest System region. Variables having the largest influence on cost included fire intensity level, area burned, and total housing value within 20 mi of ignition. These equations were then used to predict suppression expenditures on a set of fiscal year 2005 Forest Service fires for the purpose of detecting “extreme” cost fires—those fires falling more than 1 or 2 SDs above or below their expected value.