Emerging Methods for Early Detection of Forest Fires Using Unmanned Aerial Vehicles and Lorawan Sensor Networks

Hristov, Georgi; Raychev, Jordan; Kinaneva, Diyana; Zahariev, Plamen

doi:10.1109/eaeeie.2018.8534245

Cited by 36 publications

(19 citation statements)

References 2 publications

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“…Martínez [137] performed studies based on the use of UAV-RGB sensor. Martínez-de Dios et al [129], Merino et al [130] and Hristov et al [131] studies also used a thermal infrared sensor (TIR). Martínez-de Dios et al [129] proposed a system to fill the gap in temporal and spatial resolution of forest fire information acquisition.…”

Section: Forest Fire and Post-fire Monitoringmentioning

confidence: 99%

“…The research continued in Merino et al [130], which used multiple UAVs to mitigate the errors and limitations from Martínez-de Dios et al [129] by having different perspectives of the fire front, avoiding the presence of smoke occluding the fire front, and to cover more extensive areas. Hristov et al [131], in turn, proposed a conceptual model combining medium altitude fixed-wing UAVs for permanent monitoring of a certain area. If a potential fire is detected, low altitude rotary-wing UAVs are triggered to confirm the forest fire.…”

Section: Forest Fire and Post-fire Monitoringmentioning

confidence: 99%

“…An example of such an approach is NASA's Ikhana UAS [168]. However, the complementary usage of small-sized UAVs, as suggested by Hristov et al [131], can reduce false positives, improving the management of field teams. Another aspect is that only one study was found covering wildfire prevention [49], which proved that the usage of UAV LiDAR data can be beneficial for mapping areas with higher fuel concentration.…”

Section: Forest Fire and Post-fire Monitoringmentioning

confidence: 99%

See 2 more Smart Citations

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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Section: Forest Fire and Post-fire Monitoringmentioning

confidence: 99%

Section: Forest Fire and Post-fire Monitoringmentioning

confidence: 99%

Section: Forest Fire and Post-fire Monitoringmentioning

confidence: 99%

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Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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“…There are also many other communications technologies that can be used by DL-UAVs, like 3G/4G/5G [106,107], ZigBee (IEEE 802.15.4) [103], Long-Range Wide Area Network (LoRaWAN) [108], Ultra Wide Band (UWB) [109], IEEE 802.11ah [110], or Wi-Fi Direct [111]. Table 3 shows a comparison on the main characteristics of the latest communications technologies for DL-UAVs [4], indicating their frequency band, coverage, data rate, power consumption, and potential applications.…”

Section: Communications Subsystemmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

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“…This technology is intended to achieve a long-range transmission of limited amounts of information between so-called nodes with very low power consumption. As a consequence, its usage has spread rapidly to a large portfolio of applications, including the early detection of forest fires [ 25 ], monitoring the occupation of boat spots in a Danish harbor [ 26 ], e-health [ 27 ], and agriculture [ 28 , 29 ]. Other IoT technologies can also be used to operate low-power wide area networks (LPWAN).…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

From Sensor to Cloud: An IoT Network of Radon Outdoor Probes to Monitor Active Volcanoes

Terray

Royer

Sarramia

et al. 2020

Sensors

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While radon in soil gases has been identified for decades as a potential precursor of volcanic eruptions, there has been a recent interest for monitoring radon in air on active volcanoes. We present here the first network of outdoor air radon sensors that was installed successfully on Mt. Etna volcano, Sicily, Italy in September 2019. Small radon sensors designed for workers and home dosimetry were tropicalized in order to be operated continuously in harsh volcanic conditions with an autonomy of several months. Two stations have been installed on the south flank of the volcano at ~3000 m of elevation. A private network has been deployed in order to transfer the measurements from the stations directly to a server located in France, using a low-power wide-area transmission technology from Internet of Things (IoT) called LoRaWAN. Data finally feed a data lake, allowing flexibility in data management and sharing. A first analysis of the radon datasets confirms previous observations, while adding temporal information never accessed before. The observed performances confirm IoT solutions are very adapted to active volcano monitoring in terms of range, autonomy, and data loss.

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Emerging Methods for Early Detection of Forest Fires Using Unmanned Aerial Vehicles and Lorawan Sensor Networks

Cited by 36 publications

References 2 publications

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

From Sensor to Cloud: An IoT Network of Radon Outdoor Probes to Monitor Active Volcanoes

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