A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019

Varotsos, C.; Krapivin, Vladimir F.; Mkrtchyan, F.A.

doi:10.3390/rs12050835

Cited by 23 publications

(14 citation statements)

References 64 publications

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“…Інша, принципово відмінна, група методів збирання інформації ґрунтується на даних дистанційного зондування Землі, яке здійснюється за допомогою безпілотних літальних апаратів (дронів) [2], літаків [3] або штучних супутників Землі [4]. Супутники дистанційного зондування Землі можуть розміщуватися як на геліостаціонарній орбіті (сканують Землю двічі на день [5]), так і на геостаціонарній орбіті, на якій період сканування може становити 2,5 хв, зокрема, як у японського супутника Himawari-8 [6].…”

Section: виявлення і моніторинг потенційно небезпечних пожеж на теритunclassified

“…Дистанційне зондування Землі можливе також з використанням різних спектрів випромінювання. У статті [3] описано методику мікрохвильового сканування (електромагнітним випромінюванням) з літака для виявлення лісових пожеж на ранніх стадіях. Автори статті [7] за даними спектрального аналізу атмосфери з супутника Sentinel-5P визначали концентрацію в повітрі продуктів горіння лісу: монооксиду вуглецю, діоксиду азоту та показники аерозольного індексу.…”

Section: виявлення і моніторинг потенційно небезпечних пожеж на теритunclassified

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Detection and Monitoring of Potentially Dangerous Fires on the Territory of Ukraine Using the Data of Satellite Scanning

Oreshchenko¹,

Osadchyi²,

Savenets³

et al. 2020

Visn. Nac. Akad. Nauk Ukr.

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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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Section: виявлення і моніторинг потенційно небезпечних пожеж на теритunclassified

Detection and Monitoring of Potentially Dangerous Fires on the Territory of Ukraine Using the Data of Satellite Scanning

Oreshchenko¹,

Osadchyi²,

Savenets³

et al. 2020

Visn. Nac. Akad. Nauk Ukr.

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“…Forest fires, which can be manmade or naturally occurring, are events that can threaten our planet leaving behind catastrophic circumstances such as damages and losses [3][4][5][6][7]. Forest fires have long-term devastating effects on ecosystems such as destroying vegetation dynam-ics, emission of greenhouse gases, loss of wildlife habit, and also devastation of land covers [8][9][10][11]. Therefore, accurate and timely detection of active forest fires is critical to minimize and/or prevent such hazards.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, advanced remote sensing (RS) technologies have been adopted to monitor and observe the earth and land covers, which can be on a large scale [12][13][14][15]. Availability of high spatio-temporal resolution data and multispectral imagery allow tremendous applications of forest monitoring such as burned area mapping [7], active fire detection [11,16], burning biomass detection [17,18], and forest disturbance monitoring [19,20]. It is worth noting that one widely and freely available RS data source can be used for all the tasks, which are imagery form Landsat satellites.…”

Section: Introductionmentioning

confidence: 99%

Fire-Net: A Deep Learning Framework for Active Forest Fire Detection

Seydi

Saeidi²,

Kalantar

et al. 2022

Journal of Sensors

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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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“…Compared to optical or infrared sensors, measuring instruments working at the microwave range are more efficient for fire detection in conditions of insufficient visibility [17]. Varotsos et al [18] deployed a platform IL-18 equipped with passive microwave radiometers of 1.43, 13.3 and 37.5 GHz frequencies and proposed a decision-making system in order to achieve early detection of forest fires. In contrast to the ground sensor warning systems, many researchers have focused on the several operational satellites aiming for early fire detection on a global scale [19,20].…”

Section: Introductionmentioning

confidence: 99%

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

et al. 2020

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The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.

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A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019

Cited by 23 publications

References 64 publications

Detection and Monitoring of Potentially Dangerous Fires on the Territory of Ukraine Using the Data of Satellite Scanning

Detection and Monitoring of Potentially Dangerous Fires on the Territory of Ukraine Using the Data of Satellite Scanning

Fire-Net: A Deep Learning Framework for Active Forest Fire Detection

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

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