Introduction

1.1 Background

The study regards exploring the potential of using drones/RPAs as technological pathways to the early detection and prevention of bushfires. The problem of bushfires is associated with forest burns, and their effects are disastrous to the living organisms that inhabit the ecosystem (Hossain et al., 2020). Vardoulakis et al. (2020) contend that bushfires have usually characterised Australia’s natural environment. Still, their risks continue to increase over time because of the early commencement of fire seasons, their delayed end seasons, and their influence on climatic extremities. The bushfires are associated with extremely hot, dry, and windy conditions that render the fires move fast and become uncontrollable (Di Virgilio et al., 2019). Thus, bushfires are associated with climate change as part of their adverse effects (Harris and Lucas, 2019). For instance, the 2019-2020 bushfires witnessed in Victoria, Queensland, Australian Capital Territory, and New South Wales led to about 33 fatalities, heightened property damage, and flora and fauna destruction (Vardoulakis et al., 2020). The bushfires also exposed many people to extremities of air pollution (Vardoulakis et al., 2020). Smoke from bushfires and that which emanates from prescribed burns has complex particles and gases in them, which, once they are chemically transformed into the atmosphere, can be propelled by the wind to travel over a long distance (Vardoulakis et al. 2020). This aspect, therefore, contextualises bushfires as a cause of public health deterioration because of their adverse effects on air pollution.

Forest fires have emerged as part of international concerns because of the detrimental effects on the environment, thus impacting the imperatives of life for people and animals. According to Corcoran et al. (2021), there has been a consistent rising trend of forest fires and devastating economic and ecological impacts. Barmpoutis et al. (2020) assert that bushfires are linked to alterations in climatic patterns, hence the need to adopt novel pathways to facilitate their early detection and mitigation. Hughes (2021) argues that the effect of bushfires can be attested by the increase in the deviation of annual temperatures of 1.5℃ as of 2018 compared to the -0.5℃ recorded in 1910. Nithyavathy et al. (2021) found that Nigeria’s economy has lost about $4.4 billion in contemporary contexts due to bushfires, implying the need for urgency in addressing this challenge. Consequently, the use of technological pathways such as drones and RPAs in detecting bushfires has been argued as advantageous due to its features of video coverage or pictorials’ real-time transmission (Witwer, 2020). These characteristics are integral in helping to precisely model and predict the episodic happenings of bushfires.

There are various areas where the use of RPAs and drones can help detect bushfires earlier and help escalate the deployment of mitigation schemes. For instance, Kyuchukova et al. (2019) suggest that drones can be integral in providing detection and monitoring of bushfires since they produce big data that can be used to foster progress through continuous improvements. Tehseen et al. (2022) explain that drones are regarded as flying robots; hence, they can control the bushfires at early stages efficiently. In this regard, if the drones’ sensors detect anything that shows fire, then the information is relayed to the control room, whereby the controller will redirect the signals to the drone to help in visualising the forest area where the fire had been detected (Tehseen et al. 2022). Moreover, in the case of fire, the drones deployed for monitoring will identify the fire’s intensity and relay back information to the control room for timely decision-making on combating the fire before spreading (Kinaneva et al., 2019).

Fuquan et al. (2019) argue that there is a need for higher spatiotemporal imagery to facilitate the efficient monitoring as well as deployment of drones to offer high-resolution as well as images that are relayed in real-time, thus providing accurate mapping of the fire incident coupled with tracking, which is integral in making relevant decisions. Alkhatib (2014) indicates that optical sensors, digital cameras, sensor networks, and satellite systems constitute the widely adopted methodologies for detecting forest fires and offering their monitoring. Moreover, based on Fuquan et al. (2019), information gathered when such methods are deployed for fire monitoring and detection enables the creation of automation of warning systems for bushfires via advancing image and video processing. Bao et al. (2015) reveal that the utilisation of multi-objective algorithmic control that has visual analysis capabilities can help deescalate the detection expenses while simultaneously increasing the coverage area of fire detection. These methods provide novel ways of using artificial intelligence and machine learning capabilities to detect and map fire outbreak areas, facilitating faster control.

Gulcl et al. (2016) contend that the conventional watchtower method in detecting bushfires is constrained by inherent limitations that include its linkage to unfavourable weather conditions, thus making it inappropriate for detecting such fires over extended distances. Akay and Erdogan (2017) suggest that to prevent the inherent constraints of the conventional watchtower method, geographical information systems (GIS) and novel technologies can be applied to scale up the bushfire hotspots coverage. Gulcl et al. (2016) suggest that utilising Unmanned Aerial Vehicles (UAVs) can help achieve brushfires’ autonomous scanning. On the contrary, Kucuk et al. (2017) assert that efficiency in managing bushfires requires devices that have capabilities of relaying visibility data and detecting smoke coupled with other variables that indicate forest fires. Zhang et al. (2020) contend that the deployment of sensors that offer optical capabilities for terrestrial areas can alleviate the issues of visibility; however, the authors note that there are limitations on the field of view. This perspective is reiterated by Barmpoutis et al. (2020), whose study indicated that the use of surveillance algorithms could help detect such bushfires both during the day and at night. Alkhatib (2014) and Zhang et al. (2020) state that deploying many cameras can help improve brushfire detection. These assertions indicate the various technological pathways that can be utilised to scale up the identification and control of forest fires. In this study, the use of drones/RPAs to improve the detection and management of bushfires is examined.

1.2 Research Rationale

The rationale of this study is to examine how drones/RPAs can be used to facilitate the early detection and control of bushfires. Early detection of bushfires is integral to ameliorating the adverse effects of air pollution and climate change (Vardoulakis et al., 2020). Previous scholarship has disclosed that various technological solutions can be integrated into the detection, control, and mitigation of bushfires (Yuchukova et al., 2019; Yuan, 2015). However, the attention drawn to drones/RPAs does not provide a nuanced understanding of their capabilities and efficient application to improve detection outcomes (Tehseen et al., 2022). Therefore, this study is essential in providing in-depth insights into how drones/RPAs can be deployed to foster real-time detection of bushfires and transmission of relevant data to the control room, thus helping avert the losses associated with such fires in Australia.

1.3 Research Aims and Objectives

This inquiry aims to explore the use of innovative technologies and artificial intelligence to facilitate the aversion of bushfires in Australia through timely detection and deployment of control and mitigation strategies. The study’s specific objectives include:

To compare the various types of drones that can be deployed for bushfire detection and their effectiveness
To explore the efficiency outcomes of using drones/RPAs in bushfire detection and control
To examine how drones/RPAs can obtain optimal results in real-time transmission of bushfire information to the control room.

Research Question

How can technological innovations be optimised for bushfire detection and control?