Unmanned Aerial Vehicles for Wildland Fires: Sensing, Perception, Cooperation and Assistance

1. Introduction

2) were burned by more than 58,083 wildland fires in the U.S. [2) and destroying 18,733 structures. Losses were estimated to


16.5 billion [

Wildland fires are an important threat in rural and protected areas. Their control and mitigation are difficult as they can quickly spread to their surroundings, potentially burning large land areas and getting close to urban areas and cities. The occurrence of wildland fires results into substantial costs to the economy, ecosystems and climate [ 1 ]. Nevertheless, their frequency is on the rise. In fact, there has been an increase in the intensity and frequency of wildland fires in comparison to the past 10,000 years [ 2 ]. In the western U.S. alone, wildland fires increased by 400% in the last decades [ 3 4 ]. In 2018, 8.8 million acres (35,612.34 km) were burned by more than 58,083 wildland fires in the U.S. [ 5 ]. In Northern California, a single fire, known as “Camp Fire”, ended up killing 85 people. This fire was the most destructive in California history burning 153,336 acres (620.53 km) and destroying 18,733 structures. Losses were estimated to16.5 billion [ 3 ]. Experts estimate that wildland fires will increase in the coming years mainly as a result of climate change [ 6 ].

With wildland fires being a multifaceted issue, many different elements are relevant to the efforts to reduce their impact. Aspects such as meteorology, drought monitoring, vegetation status monitoring can help the prevention and the preparation to wildland fires. Other aspects such as fire suppression actions and post-fire recovery strategies must also be taken into account after the appearance of fire. Many of these aspects have been studied with unmanned aerial vehicles (UAVs). However, in the literature, two elements seem more prominent in relation to UAVs. First, the time span between the start of a fire and the arrival of firefighters. This response time needs to be reduced to a minimum in order to decrease the chances of the fire spreading to unmanageable levels. The second key element is the evaluation of the extent of the event and the monitoring of the emergency response. As manual wildland fire assessment is rendered difficult by several factors (e.g., limited visibility), the consideration of this aspect is necessary in order to elaborate better fighting strategies. These two key elements can only be properly addressed through the development of reliable and efficient systems for early stage fire detection and monitoring. As a result of this need, interest has grown in the research community and led to a large number of publications on the subject.

Remote sensing has been widely researched in the field as it allows the observation of wildland fire events without unnecessarily exposing humans to dangerous activities. For instance, satellite images have been used to report the fire risks [ 7 ] and the detection of active fires [ 8 9 ]. Wireless sensor networks (WSNs) have also been proposed for wildland fire detection [ 10 ], monitoring [ 11 ] and risk assessment [ 12 ]. However, both types of systems have practical limitations. Satellite imagery has limited resolution. Therefore, the data relevant to an area are often averaged and constrained to a single-pixel making it difficult to detect small fires [ 13 ]. Furthermore, satellites have limited ground coverage and necessitate a significant amount of time before being able to resurvey the same region. Limited precision and the lack of real-time data reporting are therefore rendering satellite imagery unsuitable for continuous monitoring. As for WSNs, they operate as an infrastructure that needs to be deployed beforehand. As the sensors are installed in the forest, their coverage and resolution are proportional to the investment that is made in their acquisition and deployment. Moreover, in the event of a fire, the sensors are destroyed, leading to additional replacement costs. Maintenance difficulties, the lack of power independence and the fact that they are not scalable due to their static nature are all factors known to limit their coverage and effectiveness [ 14 ]. As a result of the previous systems’ shortcomings, unmanned aerial vehicles (UAVs) have been proposed as a more convenient technology for this task. Their maneuverability, autonomy, easy deployment and relatively low cost are all attributes that made UAV the technology of choice for future wildland fire management efforts.


UAV technologies have seen an important progression in the last decade and they are now used in a wide range of applications. UAV has become smaller, more affordable and now have better computation capabilities than in the past making them reliable tools for remote sensing missions in hostile environments [ 15 ]. Furthermore, UAVs can fly or hover over specific zones to retrieve relevant data in real time with cameras or other airborne sensors. As a result, research has shown their benefits for surveillance and monitoring of wildland fire as well as tasks related to post-fire damage evaluation [ 16 20 ]. Additionally, UAVs have exhibited a positive economic balance in favor of their use in wildland fire emergencies [ 21 22 ]. This makes UAVs both a practical and an economical solution. Therefore, research efforts have been oriented towards the development of frameworks and techniques using UAVs with the goal of delivering optimal fire detection, coverage and firefighting.

The subject of this paper is a summarization of the literature pertaining to use of UAVs in the context of wildland fires. Research in this area revolve more predominantly around fire detection and monitoring, therefore the core of this review will be concentrated on technologies and approaches aimed at tackling these challenges. However, this paper also touches on other subjects when relevant such as fire prognosis and firefighting but less extensively as fewer works are available on the subject in the literature. The only other related works believed to exist are the work of Yuan et al. [ 19 ] and Bailon-Ruiz and Lacroix [ 23 ]. Yuan et al. [ 19 ] touch on subjects such as UAV wildland fire monitoring, detection, fighting, diagnosis and prognosis, image vibration elimination and cooperative control of UAVs. While the subject of this work overlaps with ours, it was performed 5 years ago and since then a lot of research has been produced on the subject. In fact, most of the papers reviewed have been published in 2015 or after and are not present in Yuan et al. [ 19 ]. Therefore, this work is much more current than Yuan et al. [ 19 ]. Bailon-Ruiz and Lacroix [ 23 ] have been published in 2020, and are therefore much more current. The authors discuss two components of the field of UAV wildfire remote sensing: system architecture (single UAV or multiple UAV) and autonomy level. The reviewed works are characterized by similar attributes (mission types, decision level, collaboration level, fielded) and include unique attributes such as information processing and airframe, while this paper also analyzes unique attributes such as sensing mode and coordination. Attributes such as information processing and airframe indicate that Bailon-Ruiz and Lacroix [ 23 ] put more focus on the type of UAV and the software that runs on it while this paper is focusing on sensing and communication. The most notable difference between both works is the depth of analysis of the reviewed works and the extent of the reviewed literature. While Bailon-Ruiz and Lacroix [ 23 ] discusses system architecture and autonomy level only, this paper discusses these topics as well as sensing instruments, fire detection and segmentation, available fire datasets, fire geolocation and modeling and UAV-unmanned ground vehicles (UGV) systems for wildfires. This paper also reviews more recent works (16 vs. 10 published in 2015 or after), more works in total (27 vs. 19), and this paper’s reference count is more than three times higher (121 vs. 35) indicating a more in-depth discussion of concepts related to the reviewed
works which in turn requires more referencing. Following these observations, it is believed that this paper is a significant contribution and is very relevant to the field.

The final goal of this review is to provide insight into the field towards the development of cooperative autonomous systems for wildland fires. Observations made after evolving for many years in the field indicate that the research community has provided many pieces of the solution to the problems that are wildland fires. However, these pieces, especially recent ones, often fail to come together in a unified framework to form a multifaceted solution to the underlying issue. A lot more could be accomplished by combining fire detection, monitoring, prognosis and firefighting under the same system. Therefore, this paper reviews fire assistance components, sensing modalities, fire perception approaches, relevant datasets and UAV/UGV coordination and cooperation strategies. In fact, this paper’s review approach is to break apart the reviewed works in these categories instead of discussing all the aspects of a reviewed work in the same paragraph. The idea is to bring existing approaches into light in such a way that it would be easier in the future to combine them into more complete systems instead of seeing them as individual systems. These subjects lead to the last section of this paper where cooperative autonomous systems are discussed and where all previously discussed technologies come together under the umbrella of a single framework.

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