A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

Chen, Yuan; Zhang, Youmin

doi:10.1139/cjfr-2014-0347

Cited by 535 publications

(223 citation statements)

References 90 publications

(103 reference statements)

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“…UAS are characterised by high accuracy, flexibility, and the ability to be used in different atmospheric conditions [27]. UAS have been employed in several studies focusing on fires [28], evaluating insect infestations of forest stands [29], and for monitoring wildlife [30]. Despite the high resolution of the data [31], UAS have not been used to determine the size of windthrow areas.…”

Section: Introductionmentioning

confidence: 99%

Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery

Mokroš

Výbošťok

Merganič

et al. 2017

Forests

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Strong wind disturbances can affect large forested areas and often occur irregularly within a forest. Due to this, identifying damaged sites and estimating the extent of these losses are crucial for the harvesting management of salvage logging. Furthermore, the location should be surveyed as soon as possible after the disturbance to prevent the degradation of fallen trees. A fixed-wing type of unmanned aircraft system (UAS) with a compact digital camera was used in this study. The imagery was acquired on approximately 200 hectares where five large windthrow areas had occurred. The objective of the study was to determine the location of the windthrow areas using a semi-automatic approach based on the UAS imagery, and on the combination of UAS imagery with airborne laser scanning (ALS). The results were compared with reference data measured by global navigation satellite system (GNSS) devices. At the same time, windthrow areas were derived from Landsat imagery to investigate whether the UAS imagery would have significantly more accurate results. GNSS measurements and Landsat imagery are currently used in forestry on an operational level. The salvage logging was estimated for each forest stand based on the estimated areas and volume per hectare obtained from the forest management plan. The results from the UAS (25.09 ha) and the combined UAS/ALS (25.56 ha) methods were statistically similar to the reference GNSS measurements (25.39 ha). The result from Landsat, at 19.8 ha, was not statistically similar to the reference GNSS measurements or to the UAS and UAS/ALS methods. The estimate of salvage logging for the whole area, from UAS imagery and the forest management plan, overestimated the actual salvage logging measured by foresters by 4.93% (525 m 3 ), when only the most represented tree species were considered. The UAS/ALS combination improved the preliminary results of determining windthrow areas which lead to decreased editing time for all operators. The UAS imagery shows potential for application to early-stage surveys of windthrow areas in forests. The advantages of this method are that it provides the ability to conduct flights immediately after the disturbance, the foresters do not need to walk within the affected areas which decreases the risk of injury, and allows flights to be conducted on cloudy days. The orthomosaic of the windthrow areas, as a by-product of data processing in combination with forest maps and forest road maps, can be used as a tool to plan salvage logging.

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Section: Introductionmentioning

confidence: 99%

Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery

Mokroš

Výbošťok

Merganič

et al. 2017

Forests

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“…When mounted on winged UAVs for extended range, these sensors also greatly improve response times to forest fires [18]. Novel approaches, developed in the precision agriculture field, greatly rely on the automated use of UAVs and help farmers save resources and individually maintain individual orchards by assessing large areas of crops and soil on a daily basis [19].…”

Section: State Of the Artmentioning

confidence: 99%

Simulating and Executing UAV-Assisted Inspections on Construction Sites

Freimuth¹,

MÃ1⁄4ller²,

KÃ¶nig³

2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Unmanned Aerial Vehicles (UAV) are an established utility for remote sensing and mapping purposes. Practical applications emerge in the form of inspecting structures that are hard to reach, deterioration analysis, construction monitoring or quantifying material in landfills. From a technological perspective most of these applications rely on the same established method, manual piloting by experts. While ongoing research is focused on developing reliable means of obstacle avoidance, sophisticated mission planning solutions can greatly increase the practicality of UAVs for inspection purposes. We propose an inspection planning concept for highly automated inspections in BIM projects. The application, developed in the course of this research, interacts with the firmware and the simulation capabilities of the Dronekit framework, which makes for a highly integrated and sustainable UAV. The main contribution is the 3D simulation of planned flight missions with Software-In-The-Loop (SITL) technology. The inclusion of actual flight dynamics allows the operator to perceive an authentic representation of the UAV's behaviour before take-off. Furthermore, the webbased planning application renders preview images in the virtual scene which provide a realistic impression of the results to be expected. The application automatically generates the different waypoints for obtaining photographs of a Point of Interest (PoI).

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“…The systems consist of the imagers, a computer for image processing, the software, and a communication network to transmit the information. Over the years many research in the area of forest fire have been published [3][4][5][6]. Recent research in this area focus on RoTs which are flames and smoke based on the calculation of color indices, In their work the images obtained in the RGB space are transformed into grey scale, a thresholding process is used separating the region of interest from the rest of the image [7].…”

Section: Background Informationmentioning

confidence: 99%

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

EA¹

2017

JCSAIT

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Forest fires are increasing annually due to global warming. The cause of most forest fires are humans, followed by lightning, and winds enabling dry debris, such as small pieces of dry wood that rub against each other creating a spark. Forest fires are catastrophic events that annually claim lives, destroy property, kill domestic and wild animals, and destroy the trees and vegetation that provide oxygen, filter the water, food for animals, and playgrounds and shelter for humans and animals. Additionally after fires burn a forest, rain water runs down the mountain forming mudslides that often times cause damages to land, houses, other building that are on their way, cars, and other property and also kill people, and animals. Therefore forest fires are a disaster having negative effects on people, animals and flora in many ways. In this research paper we provide a solution to preventing forest fires by monitoring the forest using an autonomous UAS powered by solar panels, batteries and a brushless motor, equipped with two high quality cameras in the visual range an infrared camera and a LIDAR, capable of recognizing dry brash, people making camp fires, dead trees, and providing that and other relevant information via wireless transmission to the personnel designated to protect the forest. The intelligent system of the UAS is embedded into a Stratix-10 IntelAltera FPGA that is capable of performing 29.5 teraflops sustained, and is capable of detecting forest fires, as well as providing real time advice to the fire fighters how to efficiently fight the fire. In addition to that the UAS has a router a transceiver and an antenna, enabling the firefighters to communicate with each other and with command and control when the fire destroys the ground antenna system.

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A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

Cited by 535 publications

References 90 publications

Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery

Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery

Simulating and Executing UAV-Assisted Inspections on Construction Sites

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

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