Relevant bibliographies by topics / Forest fires detection

Academic literature on the topic 'Forest fires detection'

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Published: 14 March 2023

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Journal articles on the topic "Forest fires detection"

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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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Dissertations / Theses on the topic "Forest fires detection"

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Luisi, Domenico. "Conceptual design and specification of a microsatellite forest fire detection system /." Online version of thesis, 2007. http://hdl.handle.net/1850/5771.

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Boynton, Ansel John. "EARLY WILDFIRE DETECTION USING TEMPORAL FILTERING AND MULTI-BAND INFRARED ANALYSIS." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1048.

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Every year wildfires threaten or destroy ecological habitats, man-made infrastructure and people’s lives. Additionally millions of dollars are spent each year trying to prevent and control these fires. Ideally if a wildfire can be detected before it rages out of control it can be extinguished and avoid large scale devastation. Traditional manned fire lookout towers are neither cost effective nor particularly efficient at detecting wildfire. It is proposed that temporal filtering can be used to isolate the signals created at the beginnings of potential wildfires. Temporal filtering can remove any background image and any periodic signals created by the camera movement. Once typical signals are analyzed, digital filters can be designed to pass fire signals while blocking the unwanted signals. The temporal filter passes only fire signals and signals generated by moving objects. These objects can be distinguished from each other by analyzing the objects mid and long wave energy profile. This algorithm is tested on 17 data sources and its results analyzed.
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Davenport, Timothy M. "Early Forest Fire Detection using Texture Analysis of Principal Components from Multispectral Video." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/795.

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The aim of this study is to incorporate the spectral, temporal and spatial attributes of a smoke plume for Early Forest Fire Detection. Image processing techniques are used on multispectral (red, green, blue, mid-wave infrared, and long-wave infrared) video to segment and indentify the presence of a smoke plume within a scene. The temporal and spectral variance of a smoke plume is captured through Principal Component Analysis (PCA) where the Multispectral-Multitemporal PCA is performed on a sequence of video frames simultaneously. The presence of a plume existing in one of the higher order principal components is determined by the texture of its spatial content. The texture is characterized by statistical descriptors derived from the principal component‟s joint probability density distribution of intensities occurring within a spatial relationship, known as a Gray Level Co-Occurrence Matrix (GLCM). Initial analysis is performed on selected frames where only a subset of time is considered. Once the parameters are chosen from the static analysis, the algorithms are executed on video through time to validate the method. The results show that a smoke plume is readily segmented via PCA. Based on the five spectral bands over 3 seconds sampled at 1 second, the plume exists in the 7th principal component. Within these principal components, the smoke‟s presence is best identified by the correlation texture descriptor. The smoke is very spatially correlated compared to the scene at large. Therefore a spike in the spatial correlation of the principal components is all that is needed to identify the start of the smoke plume.
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Moussa, Georges Fouad Mr. "EARLY FOREST FIRE DETECTION USING TEXTURE, BLOB THRESHOLD, AND MOTION ANALYSIS OF PRINCIPAL COMPONENTS." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/881.

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Forest fires constantly threaten ecological systems, infrastructure and human lives. The purpose behind this study is minimizing the devastating damage caused by forest fires. Since it is impossible to completely avoid their occurrences, it is essential to accomplish a fast and appropriate intervention to minimize their destructive consequences. The most traditional method for detecting forest fires is human based surveillance through lookout towers. However, this study presents a more modern technique. It utilizes land-based real-time multispectral video processing to identify and determine the possibility of fire occurring within the camera’s field of view. The temporal, spectral, and spatial signatures of the fire are exploited. The methods discussed include: (1) Range filtering followed by entropy filtering of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively. Cooled Midwave Infrared (IR) camera is used to capture the heat distribution within the field of view. The fire core is then isolated using texture analysis techniques: first, range filtering applied on two consecutive IR frames, and then followed by entropy filtering of their absolute difference. Since smoke represents the earliest sign of fire, this study also explores multiple techniques for detecting smoke plumes in a given scene. The spatial and temporal variance of smoke plume is captured using temporal Principal Component Analysis, PCA. The results show that a smoke plume is readily segmented via PCA applied on the visible Blue band over 2 seconds sampled every 0.2 seconds. The smoke plume exists in the 2nd principal component, and is finally identified, segmented, and isolated, using either motion analysis or adaptive intensity threshold. Experimental results, obtained in this study, show that the proposed system can detect smoke effectively at a distance of approximately 832 meters with a low false-alarm rate and short reaction time. Applied, such system would achieve early forest fire detection minimizing fire damage. Keywords: Image Processing, Principal Component Analysis, PCA, Principal Component, PC, Texture Analysis, Motion Analysis, Multispectral, Visible, Cooled Midwave Infrared, Smoke Signature, Gaussian Mixture Model.
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Garges, David Casimir. "Early Forest Fire Detection via Principal Component Analysis of Spectral and Temporal Smoke Signature." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1456.

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The goal of this study is to develop a smoke detecting algorithm using digital image processing techniques on multi-spectral (visible & infrared) video. By utilizing principal component analysis (PCA) followed by spatial filtering of principal component images the location of smoke can be accurately identified over a period of exposure time with a given frame capture rate. This result can be further analyzed with consideration of wind factor and fire detection range to determine if a fire is present within a scene. Infrared spectral data is shown to contribute little information concerning the smoke signature. Moreover, finalized processing techniques are focused on the blue spectral band as it is furthest away from the infrared spectral bands and because it experimentally yields the largest footprint in the processed principal component images in comparison to other spectral bands. A frame rate of .5 images/sec (1 image every 2 seconds) is determined to be the maximum such that temporal variance of smoke can be captured. The study also shows eigenvectors corresponding to the principal components that best represent smoke and are valuable indications of smoke temporal signature. Raw video data is taken through rigorous pre-processing schemes to align frames from respective spectral band both spatially and temporally. A multi-paradigm numerical computing program, MATLAB, is used to match the field of view across five spectral bands: Red, Green, Blue, Long-Wave Infrared, and Mid-Wave Infrared. Extracted frames are aligned temporally from key frames throughout the data capture. This alignment allows for more accurate digital processing for smoke signature. v Clustering analysis on RGB and HSV value systems reveal that color alone is not helpful to segment smoke. The feature values of trees and other false positives are shown to be too closely related to features of smoke for in solely one instance in time. A temporal principal component transform on the blue spectral band eliminates static false positives and emphasizes the temporal variance of moving smoke in images with higher order. A threshold adjustment is applied to a blurred blue principal component of non-unity principal component order and smoke results can be finalized using median filtering. These same processing techniques are applied to difference images as a more simple and traditional technique for identifying temporal variance and results are compared.
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Kohler, Daniel G. "STUDY OF STATISTICAL AND COMPUTATIONAL INTELLIGENCE METHODS OF DETECTING TEMPORAL SIGNATURE OF FOREST FIRE HEAT PLUME FROM SINGLE-BAND GROUND-BASED INFRARED VIDEO." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/796.

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This thesis will analyze video from land-based, cooled mid-wave infrared cameras to identify temporal features indicative of a heat plume from a forest fire. Desirable features and methods will show an ability to distinguish between heat plume movement and other movements, such as foliage, vehicles, humans, and birds in flight. Features will be constructed primarily using combinations of statistics and principal component analysis (PCA) with intent to detect key characteristics of fire and heat plume: persistence and growth. Several classification systems will combine and filter the features in an attempt to classify pixels as either heat or non-heat. The classification systems will be tuned and compared with common metrics of error rate and computation time. It was found that the movement pattern of a heat plume could be distinguished from the similar movement pattern of foliage by detecting outlier movement patterns, a phenomenon associated with the growth property of fire. Outlier movement patterns were best detected by thresholding the quotient of mean and median of a set of variance measurements over time. The best tested classifier in terms of minimizing false positives without losing the heat signal came from PCA of a dual-range moving average difference.
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Radjabi, Ryan F. "WILDFIRE DETECTION SYSTEM BASED ON PRINCIPAL COMPONENT ANALYSIS AND IMAGE PROCESSING OF REMOTE-SENSED VIDEO." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1621.

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Early detection and mitigation of wildfires can reduce devastating property damage, firefighting costs, pollution, and loss of life. This thesis proposes the method of Principal Component Analysis (PCA) of images in the temporal domain to identify a smoke plume in wildfires. Temporal PCA is an effective motion detector, and spatial filtering of the output Principal Component images can segment the smoke plume region. The effective use of other image processing techniques to identify smoke plumes and heat plumes are compared. The best attributes of smoke plume detectors and heat plume detectors are evaluated for combination in an improved wildfire detection system. PCA of visible blue images at an image sampling rate of 2 seconds per image effectively exploits a smoke plume signal. PCA of infrared images is the fundamental technique for exploiting a heat plume signal. A system architecture is proposed for the implementation of image processing techniques. The real-world deployment and usability are described for this system.
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Aldama, Raul-Alexander. "Early Forest Fire Heat Plume Detection using Neural Network Classification of Spectral Differences between Long-Wave and Mid-Wave Infrared Regions." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1021.

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It is difficult to capture the early signs of a forest fire at night using current visible-spectrum sensor technology. Infrared (IR) light sensors, on the other hand, can detect heat plumes expelled at the initial stages of a forest fire around the clock. Long-wave IR (LWIR) is commonly referred to as the “thermal infrared” region where thermal emissions are captured without the need of, or reflections from, external radiation sources. Mid‑wave IR (MWIR) bands lie between the “thermal infrared” and “reflected infrared” (i.e. short-wave IR) regions. Both LWIR and MWIR spectral regions are able to detect thermal radiation; however, they differ significantly in regards to their detection sensitivity of forest-fire heat plumes. Fires fueled by organic material (i.e. wood, leaves, etc.) primarily emit hot carbon dioxide (CO2) gas at combustion. Consequently, because CO2 is also present in the atmosphere, re-emission restricts the spectral transmittance and hence spectral radiance over a wide range of frequencies in the MWIR region. Moreover, as the distance between the detector and fire’s heat plume becomes greater, the additional CO2 introduced into the detection path leads to further attenuation of photon emittance. Since these absorption frequencies also lie within the response bandwidth of the MWIR sensor material, captured heat plume radiation manifests itself as a group of “flooded” or saturated pixels that exhibit very little dynamic behavior. Meanwhile, since the LWIR spectral region is not significantly affected by atmospheric gas absorption, its sensor is able to capture the forest fire’s heat plume thermal signature at far range without such complications. By exploiting the underlying spectral differences between LWIR and MWIR regions, this study aims to achieve early forest fire heat plume detection via direct identification of its dynamic characteristics whist concurrent attenuation of detected non-fire-related radiation. A land‑based, co‑located, cooled-LWIR/cooled-MWIR (CLWIR/CMWIR) detector camera is used to capture and normalize synchronized video from which sequential spatial-domain difference frames are generated. Processed frames allow for effective extraction of the heat plume’s “flickering” features, which are characteristic to the early stages of a forest fire. A multilayer perceptron (MLP) neural network classifier is trained with feature points generated from known target samples (i.e. supervised learning). Resulting detection performance is assessed via detection time, error metrics, computation time, and parameter variation. Results indicate that heat plumes expelled at the early stages of a forest fire can be identified with high sensitivity, low false alarm, and at a farther range than commercial detectors.
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Chen, Wei. "Detection of forest disturbance and recovery after a serious fire in the Greater Hinggan Mountain area of China based on remote sensing and field survey data." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/192219.

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Fickers, Jessica. "Modulation formats and digital signal processing for fiber-optic communications with coherent detection." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209204.

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A débit de données élevé, typiquement supérieur à 10 Gsymboles/s, les lignes de

télécommunication optique à fibre monomode souffrent de façon accrue des distorsions

inhérentes à la fibre et à l’architecture de transmission. Nous pouvons classer les

effets de fibre en plusieurs catégories:

– Les effets linéaires. La dispersion chromatique est entraînée par la dépendance en

fréquence de l’indice de réfraction de la fibre. Il en résulte un élargissement des

bits optiques. La dispersion des modes de polarisation prend son origine dans

la biréfringence de la fibre. La modélisation de cet effet est compliquée par son

caractère stochastique et variable dans le temps.

– Les effets non linéaires prennent leur origine dans un indice de réfraction de

fibre qui dépend du champ optique. Ces effets peuvent être classés en deux

catégories. Premièrement, les effets intérieurs à un canal dont le plus influant

est l’automodulation de phase qui découle de l’effet Kerr optique :l’intensité

d’une impulsion lumineuse influence sa propre propagation. Deuxièmement, il

existe des conséquences de l’effet Kerr par lesquelles les différents canaux, se

propageant au sein de la même fibre, s’influencent mutuellement. Le phénomène

le plus influent parmi ces derniers est la modulation de phase croisée :l’intensité

d’un canal influence la propagation dans un canal voisin.

– Les pertes par diffusion Rayleigh sont compensées par les amplificateurs distribués

le long de la ligne de transmission. L’amplification optique par l’intermédiaire

d’émission stimulée dans des dispositifs dopés aux ions Erbium est

accompagnée d’émission spontanée amplifiée. Ceci entraîne la présence d’un

bruit blanc gaussien se superposant au signal à transmettre.

– La gestion des canaux dans le réseau optique implique la présence dans les noeuds

du réseau de filtres de sélection, des multiplexeurs et démultiplexeurs.

Nous examinerons aussi les effets de ligne non inhérents à la fibre mais à l’architecture

de transmission. Les modèles de l’émetteur et du récepteur représentent les imperfections

d’implémentation des composants optiques et électroniques.

Un premier objectif est de définir et évaluer un format de modulation robuste aux

imperfections introduites sur le signal par la fibre optique et par l’émetteur/récepteur.

Deux caractéristiques fondamentales du format de modulation, determinants pour la

performance du système, sont étudiés dans ce travail :

– La forme d’ onde. Les symboles complexes d’information sont mis en forme par

un filtre passe-bas dont le profil influence la robustesse du signal vis-à-vis des

effets de ligne.

– La distribution des fréquences porteuses. Les canaux de communication sont

disposés sur une grille fréquentielle qui peut être définie de manière électronique

par traitement de signal, de manière optique ou dans une configuration hybride.

Lorsque des porteuses optiques sont utilisées, le bruit de phase relatif entre lasers

entraîne des effets d’ influence croisée entre canaux. En revanche, les limites des

implémentations électroniques sont données par la puissance des architectures

numériques.

Le deuxième objectif est de concevoir des techniques de traitement numérique du

signal implémentées après échantillonnage au récepteur afin de retrouver l’information

transmise. Les fonctions suivantes seront implémentées au récepteur :

– Les techniques d’estimation et d’égalisation des effets linéaires introduits par la

fibre optique et par l’émetteur et le récepteur. Le principe de l’égalisation dans

le domaine fréquentiel est de transformer le canal convolutif dans le domaine

temporel en un canal multiplicatif qui peut dès lors être compensé à une faible

complexité de calcul par des multiplications scalaires. Les blocs de symboles

émis doivent être rendus cycliques par l’ajout de redondance sous la forme d’un

préfixe cyclique ou d’une séquence d’apprentissage. Les techniques d’égalisation

seront comparées en termes de performance (taux d’erreurs binaires, efficacité

spectrale) et en termes de complexité de calcul. Ce dernier aspect est particulièrement

crucial en vue de l’optimisation de la consommation énergétique du

système conçu.

– Les techniques de synchronisation des signaux en temps/fréquence. Avant de

pouvoir égaliser les effets linéaires introduits dans la fibre, le signal reçu devra

être synchronisé en temps et en fréquence sur le signal envoyé. La synchronisation

est généralement accomplie en deux étapes principales :l’acquisition réalisée

avant de recevoir les symboles d’information don’t l’objectif est une première

estimation/compensation des effets de manière "grossière", le tracking réalisé en

parallèle à l’estimation des symboles d’information dont l’objectif est l’estimation

/compensation des effets de manière "fine". Les algorithmes d’acquisition et

de tracking peuvent nécessiter l’envoi d’informations connues du récepteur.

– Les techniques d’estimation et de compensation des imperfections de fonctionnement

de l’émetteur et du récepteur. Une structure de compensation des effets

introduits par les composants optiques et électroniques sera développée afin de

relâcher les contraintes d’implémentation de l’émetteur et du récepteur.

Etant donné la très haute cadence à laquelle les échantillons du signal sont produits

(plusieurs dizaines de Gech/s), une attention particulière est portée à la complexité de

calcul des algorithmes proposés.
Doctorat en Sciences de l'ingénieur
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Books on the topic "Forest fires detection"

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1961-, Gomez Eduards, and Alvarez Kristina 1964-, eds. Forest fires: Detection, suppression, and prevention. Hauppauge, NY: Nova Science Publishers, 2009.

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1961-, Gomez Eduards, and Alvarez Kristina 1964-, eds. Forest fires: Detection, suppression, and prevention. Hauppauge, NY: Nova Science Publishers, 2009.

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Bennett, Roger P., and Roger P. Bennett. Fire detection. New York: Nova Science Publishers, 2011.

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Matvienko, G. G. Early detection of forest fires from space. New York: Nova Science Publishers, 2011.

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Bennett, Roger P. Fire detection. New York: Nova Science Publishers, 2011.

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Burgan, Robert E. Using NDVI to assess departure from average greenness and its relation to fire business. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.

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Rothermel, Richard C. Predicting behavior and size of crown fires in the northern Rocky Mountains. [Ogden, Utah]: U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 1991.

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Shahrī, ʻAlī ibn ʻAbd Allāh and Akādīmīyat Nāyif al-ʻArabīyah lil-ʻUlūm al-Amnīyah, eds. Asālīb al-tadābīr al-maydānīyah li-muwājahat ḥarāʼiq al-ghābāt. al-Riyāḍ: Jāmiʻat Nāyif al-ʻArabīyah lil-ʻUlūm al-Amnīyah, 2011.

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Lawson, Bruce D. Diurnal variation in the fine fuel moisture code: Tables and computer source code. Victoria, B.C: Canada-British Columbia Partnership Agreement on Forest Resource Development, 1996.

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Bell, Samantha. Detecting wildfires. Lake Elmo, MN: Focus Readers, 2017.

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Book chapters on the topic "Forest fires detection"

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Fernández-Berni, Jorge, Ricardo Carmona-Galán, and Ángel Rodríguez-Vázquez. "Case study: early detection of forest fires." In Low-Power Smart Imagers for Vision-Enabled Sensor Networks, 127–46. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2392-8_7.

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Honma, Toshihisa, Kazuya Kaku, Aswin Usup, and Agus Hidayat. "Detection and Prediction Systems of Peat-Forest Fires in Central Kalimantan." In Tropical Peatland Ecosystems, 397–406. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55681-7_26.

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Andreev, Ivelin. "Advanced Open IoT Platform for Prevention and Early Detection of Forest Fires." In Advances in Intelligent Systems and Computing, 319–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77700-9_32.

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Dogra, Roopali, Shalli Rani, and Bhisham Sharma. "A Review to Forest Fires and Its Detection Techniques Using Wireless Sensor Network." In Lecture Notes in Electrical Engineering, 1339–50. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_101.

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Sonkin, M. A., A. A. Khamukhin, A. V. Pogrebnoy, P. Marinov, V. Atanassova, O. Roeva, K. Atanassov, and A. Alexandrov. "Intercriteria Analysis as Tool for Acoustic Monitoring of Forest for Early Detection Fires." In Advances in Intelligent Systems and Computing, 205–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47024-1_22.

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Fonseca C., Efraín R., Diego Marcillo, Santiago P. Jácome-Guerrero, Tatiana Gualotuña, and Henry Cruz. "Identifying Technological Alternatives Focused on Early Alert or Detection of Forest Fires: Results Derived from an Empirical Study." In Artificial Intelligence, Computer and Software Engineering Advances, 354–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68080-0_27.

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Thapa, Sunil, Vishwas Sudhir Chitale, Sudip Pradhan, Bikram Shakya, Sundar Sharma, Smriety Regmi, Sameer Bajracharya, Shankar Adhikari, and Gauri Shankar Dangol. "Forest Fire Detection and Monitoring." In Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region, 147–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73569-2_8.

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Kavitha, K. R., S. Vijayalakshmi, B. Murali Babu, D. Rini Roshan, and K. Kalaivani. "Forest Fire Detection and Prevention System." In International Conference on Innovative Computing and Communications, 629–35. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3679-1_53.

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Lalitha, Kakarapalli, and Geesala Veerapandu. "Forest Fire Detection Using Satellite Images." In Smart Innovation, Systems and Technologies, 277–84. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0108-9_29.

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Ahlawat, Harsh Deep, and R. P. Chauhan. "Forest Fire Detection Based on Wireless Sensor Network." In Lecture Notes in Electrical Engineering, 751–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5558-9_65.

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Conference papers on the topic "Forest fires detection"

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Ko, A., N. M. Y. Lee, R. P. S. Sham, C. M. So, and S. C. F. Kwok. "Intelligent wireless sensor network for wildfire detection." In FOREST FIRES 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/fiva120121.

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Kharchenko, V. S., A. A. Orekhov, D. A. Kotchkar, and V. V. Bogomolov. "Monitoring network-based infrastructure for forest fire detection." In FOREST FIRES 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/fiva120081.

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Fernández-Berni, J., R. Carmona-Galán, and L. Carranza-González. "A vision-based monitoring system for very early automatic detection of forest fires." In FOREST FIRES 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/fiva080171.

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Neubauer, B., J. Sidén, C. Olofsson, M. Gulliksson, A. Koptyug, H. E. Nilsson, and M. Norgren. "A new thermally activated battery cell-based forest fire detection and monitoring system." In FOREST FIRES 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/fiva120101.

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Lalkovič, M., and J. Pajtíková. "Forestwatch® wildfire smoke detection system: lessons learned from its two-year operational trial." In FOREST FIRES 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/fiva100121.

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von Wahl, N., S. Heinen, H. Essen, W. Kruell, R. Tobera, and I. Willms. "An integrated approach for early forest fire detection and verification using optical smoke, gas and microwave sensors." In FOREST FIRES 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/fiva100091.

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Losso, A., L. Corgnati, S. Bertoldo, M. Allegretti, R. Notarpietro, and G. Perona. "SIRIO: an integrated forest fire monitoring, detection and decision support system - performance and results of the installation in Sanremo (Italy)." In FOREST FIRES 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/fiva120071.

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Joshi, Priyanka, Sheetal Deshmukh, Shivani Handigol, Abhishek Deshmukh, Ram Deshmukh, and KBVSR Subrahmanyam. "Intelligent detector: Detection of forest fires using LoRaWSN technology." In INTERNATIONAL CONFERENCE ON RESEARCH IN SCIENCES, ENGINEERING & TECHNOLOGY. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0084147.

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Hefeeda, Mohamed, and Majid Bagheri. "Wireless Sensor Networks for Early Detection of Forest Fires." In 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems. IEEE, 2007. http://dx.doi.org/10.1109/mobhoc.2007.4428702.

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Cai, Min, Xiaobo Lu, Xuehui Wu, and Yifei Feng. "Intelligent video analysis-based forest fires smoke detection algorithms." In 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016. http://dx.doi.org/10.1109/fskd.2016.7603399.

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Reports on the topic "Forest fires detection"

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Aalto, Juha, and Ari Venäläinen, eds. Climate change and forest management affect forest fire risk in Fennoscandia. Finnish Meteorological Institute, June 2021. http://dx.doi.org/10.35614/isbn.9789523361355.

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Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.
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Patel, Reena. Complex network analysis for early detection of failure mechanisms in resilient bio-structures. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41042.

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Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented.
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