Satellite Based Forest Fire Detection and Automatic Alert System — Pilot Experiment

Kelhä, Väinö; Herland, Einar-Arne; Lohi, Anssi

doi:10.1007/978-3-642-55903-7_89

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…To achieve and facilitate this, various sensing technologies are used to detect either (or a combination of) gas, open flames or smoke [14]. These sensors have in the past been deployed on various platforms, such as e.g., planes [4,18], drones [18][19][20][21] and even satellite systems [5,9,10,15,20,22,23] (even microsatellites [18]). Indeed, multiple test projects focusing on forest fire detection using satellite data were conducted in the late 90's in Finland and the surrounding countries [15].…”

Section: Monitoring Large Forests In Remote Areas Using Modern Sensin...mentioning

confidence: 99%

“…Forest fires occur somewhat unpredictably, but can be quite destructive [12]. The annual global impact of thousands of forest fires can hardly be assessed [9]; it is estimated to be anywhere from in the millions of hectares [13,14], ranging up to a billion hectares of forest destroyed annually [15]. This is only expected to get worse for the time being [1][2][3][4][5].…”

Section: Introduction 1wildfires a Major Global Environmental Issuementioning

confidence: 99%

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Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles

Saffre

Hildmann

Karvonen

et al. 2022

Drones

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Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial body of literature exists that emphasises the potential of autonomous drone swarms in various situational awareness missions, including in the context of environmental protection. In this paper, we present the results of a systematic investigation by means of numerical methods i.e., Monte Carlo simulation. We report our insights into the influence of key parameters such as fire propagation dynamics, surface area under observation and swarm size over the performance of an autonomous drone force operating without human supervision. We limit the use of drones to perform passive sensing operations with the goal to provide real-time situational awareness to the fire fighters on the ground. Therefore, the objective is defined as being able to locate, and then establish a continuous perimeter (cordon) around, a simulated fire event to provide live data feeds such as e.g., video or infra-red. Special emphasis was put on exclusively using simple, robust and realistically implementable distributed decision functions capable of supporting the self-organisation of the swarm in the pursuit of the collective goal. Our results confirm the presence of strong nonlinear effects in the interaction between the aforementioned parameters, which can be closely approximated using an empirical law. These findings could inform the mobilisation of adequate resources on a case-by-case basis, depending on known mission characteristics and acceptable odds (chances of success).

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Section: Monitoring Large Forests In Remote Areas Using Modern Sensin...mentioning

confidence: 99%

Section: Introduction 1wildfires a Major Global Environmental Issuementioning

confidence: 99%

Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles

Saffre

Hildmann

Karvonen

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection

Akyol

2023

Cluster Comput

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P-S protocol to detect fire in forest and fire alert system using sensors

Shankar

Gupta

Priyadarshini

et al. 2022

AIP Conference Proceedings

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Satellite Based Forest Fire Detection and Automatic Alert System — Pilot Experiment

Cited by 4 publications

References 2 publications

Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles

Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles

A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection

P-S protocol to detect fire in forest and fire alert system using sensors

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