Citation: Mohapatra, A.; Trinh, T.
Early Wildﬁre Detection Technologies
in Practice—A Review. Sustainability
2022,14, 12270. https://doi.org/
Academic Editor: Diamando
Received: 31 July 2022
Accepted: 16 September 2022
Published: 27 September 2022
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Early Wildﬁre Detection Technologies in Practice—A Review
Ankita Mohapatra * and Timothy Trinh
Computer Engineering Program, California State University, Fullerton, CA 92831, USA
As ﬁres grow in intensity and frequency each year, so has the resistance from their an-
thropic victims in the form of ﬁreﬁghting technology and research. Although it is impossible to
completely prevent wildﬁres, the potential devastation can be minimized if ﬁres are detected and
precisely geolocated while still in their nascent phases. Furthermore, automated approaches without
human involvement are comparatively more efﬁcient, accurate and capable of monitoring extremely
remote and vast areas. With this speciﬁc intention, many research groups have proposed numerous
approaches in the last several years, which can be grouped broadly into these four distinct categories:
sensor nodes, unmanned aerial vehicles, camera networks and satellite surveillance. This review
paper discusses notable advancements and trends in these categories, with subsequent shortcomings
and challenges. We also describe a technical overview of common prototypes and several analysis
models used to diagnose a ﬁre from the raw input data. By writing this paper, we hoped to create a
synopsis of the current state of technology in this emergent research area and provide a reference for
further developments to other interested researchers.
wildﬁre detection; unmanned aerial vehicle; satellite; infrared; wireless sensor networks
Wildﬁres are a necessary natural phenomenon and have been a critical part of many
ecosystems for years [
]. However, the last decade has witnessed a higher-than-normal
frequency and magnitude of wildﬁres affecting thousands of people in various continents
(Figure 1). Besides causing damage to life and property, wildﬁres negatively impact the
climate and air quality of the surrounding region. The European Space Agency estimates
that approximately four million square kilometers of land are affected globally due to
]. In the US, the National Interagency Fire Center reports 50 major wildﬁre
events in 2022 recorded between January 1 and June 29—a number that surpasses the
10-year average—with about 192,016 acres burnt .
In the Northern Hemisphere, the ﬁre season was typically marked as July to September.
However, due to shifting climate patterns and expanding drought areas, the ﬁre season
has lengthened by several months in many countries. CalFire reports that the ﬁre season
in the Sierras in California has increased by 75 days each year [
]. Furthermore, 75% of
the deadliest wildﬁres in the state have occurred since 2000 [
]. A scaled extrapolation for
similar impacts on other states and countries places wildﬁres as one of the most urgent
climate crises of this century.
The global impacts of wildﬁres are discussed in the following four categories.
Sustainability 2022,14, 12270. https://doi.org/10.3390/su141912270 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 12270 2 of 21
Plot of acres burned from 1984–2020 in the United States shows an overall increase in the
burn severity and impact over the years. Data was obtained from the Monitoring Trends in Burn
Severity (MTBS) program by US Geological Survey Center for Earth Resources Observation and
Science (EROS) and USDA Forest Service Geospatial Technology and Applications Center (GTAC).
1.1. Environmental Impact
Although pyrodiversity is essential for maintaining biodiversity and biome distri-
bution, the balance is delicate and dependent on the intensity, size and season of the
]. The recent trends in climate changes and severity of wildﬁres have tipped
the scales negatively against the sustenance of ﬂoral and faunal diversity. Studies have
documented the adverse effects of wildﬁres on critically endangered species like Bornean
orangutans in Indonesia [
] and Palethorpe’s pinwheel snails, koalas and native vegetation
groups in Australia [
], among countless other species that have been pushed to the
brink of extinction by such ﬁre events. Additionally, wildﬁres release copious amounts of
greenhouse gases, contributing to global warming and drought conditions that trigger new
ﬁres—a never-ending positive feedback loop. To put the wildﬁre emission into perspective,
the California Air Resources Board estimated that the state’s 2020 wildﬁres emitted about
112 million metric tons of carbon dioxide, the highest recorded in the past decade [
number is about three times higher than the second-highest emission value recorded in
2008 and is equivalent to the amount of greenhouse gases released by all the passenger
vehicles in California annually. The European Union’s Copernicus Atmosphere Monitoring
Service estimated that 1.76 billion tons of carbon were generated by wildﬁres globally in
2021, with the emissions projected to increase signiﬁcantly in the future.
1.2. Health Impact
The destructive nature of ﬁres may cause immediate bodily harm, but its emissions,
some largely unseen, also bring danger to those outside of the surrounding area. For
example, Oregon’s Bootleg Fire had its smoke carried over three thousand miles from its
origin, bringing health effects to people in distant cities who may not have been aware of
the ﬁre. It is a well-established fact that wildﬁres increase the concentration of particulate
matter and harmful gases in the air [
], which are directly linked to various diseases,
such as asthma, bronchitis, reduced lung volume, etc. [
]. Although the effect on
cardiovascular events are mixed, sufﬁcient studies indicate that exposure to ﬁre smoke
leads to respiratory morbidity [
]. Emerging research also hints at a possible correlation
between exposure to particulate matter and higher neurophysiological irregularities like
ADHD, autism, decline in memory, etc., in children [
]. Prenatal exposure to polycyclic
aromatic hydrocarbons, one of the constituents of ﬁre, has been associated with childhood
], while exposure to PM2.5 in the prenatal stage could possibly lead to higher
blood pressure in children [
]. Johnston et al., estimate that between 1997 to 2006, the
Sustainability 2022,14, 12270 3 of 21
average annual mortality attributed to ﬁre smoke was 339,000, with Southeast Asia and
Africa experiencing the maximum brunt of the crisis . Even short exposure to wildﬁre
smoke increases risk of premature death through lasting damages to the respiratory and
1.3. Sociological Impact
Disadvantaged, low-income communities, people of color and elderly people face an
increased risk to wildﬁre. They are also disproportionately affected by the aftermath of
wildﬁre events, like smoke-related morbidity and mortality, homelessness, displacement,
psychological stress, etc. [
]. The 2017 and 2018 wildﬁres in northern California killed
more than a hundred people, displaced thousands more and annihilated an entire city,
exacerbating the homelessness crisis in the area [
]. Wildﬁres also cause power outages [
and deplete long-term water supplies for the community .
1.4. Economic Impact
Fireﬁghting efforts and subsequent rehabilitation are excessively costly, and the costs
of mitigating wildﬁres are increasing exponentially each year. At a total loss of $12.5 billion,
California’s 2018 Camp Fire was the most expensive disaster globally in that year. Wildﬁres
incurred similar ﬁnancial burdens on other countries, with state expenditures increasing
by millions of dollars [
]. Along with the immediate property damage and recovery
expenses, each ﬁre disrupts the surrounding economy and workforce, causing a ripple
effect throughout the communities .
There are several ongoing global efforts to mitigate wildﬁres. As highlighted in
Figure 1, the frequency and impact of wildﬁres have escalated signiﬁcantly in the past few
years, and this has prompted numerous research groups to propose their unique solutions
to combat this issue. An ideal wildﬁre monitoring system should be able to survey a large
area (spatial coverage) with reasonable frequency (temporal coverage).
Two popular approaches are predicting the likelihood of a ﬁre in an area and detecting
wildﬁres still in early stages from the associated emission of gases, aerosols, temperature
rise, etc. Fires at young stages are simpler to control and mitigate. This paper discusses
many such efforts, with their merits and shortcomings. We hope to create a substantial
resource for the research community about the state of current technology and motivate
advancements needed to further reduce the losses from this disaster.
2. Literature Review
2.1. Review Methods
For this review, we chose published conferences and journal articles that are peer
reviewed. We have also presented a summary of the pivotal accomplishments in this area
of research, including those by companies that have contributed to the wildﬁre detection
technologies focused on in this review. A comprehensive review of all articles published in
this area is impossible, but we believe the selection of work in this review is an adequate
representation of the fundamental research trends. Figure 2shows a distribution of the
papers and articles we selected for this paper, in 5-year incremental periods. Satellites were
one of the ﬁrst to be utilized for wildﬁre detection, with a notable surge in other methods
observed in the last couple of decades. This pattern correlates with the rising frequency of
ﬁres shown in Figure 1.
Sustainability 2022,14, 12270 4 of 21
Figure 2. Frequency of papers and articles selected for review from each 5-year span.
Many methods have been proposed for wildﬁre detection in the past. The ﬁrst or-
ganized ﬁre alert networks in the US came in the form of ﬁre lookout towers built in the
early 1900s, after the Great Fire of 1910. These towers were built on top of high moun-
tains and staffed by “ﬁre spotters”. The spotters would inspect the surroundings for
signs of ﬁre and communicated using heliographs. As better technologies were made
available, ﬁre towers were retired from service by the end of the 20th century. In this
paper, we focus on the advanced mechanisms currently utilized for early wildﬁre detec-
tion, which can be broadly classiﬁed into the following main groups:
Unmanned Aerial Vehicles (UAV);
. Camera Networks;
Section 2.2.4. Satellite Surveillance.
2.2.1. Sensor Nodes
Sensor nodes typically consist of low-power sensors like humidity, temperature and
gases to monitor the surrounding area for ﬁre and generate alerts (Figure 3). When multiple
sensor nodes are arranged as a network distributed throughout an area of interest and capa-
ble of communicating wirelessly, they are referred to as a wireless sensor network (WSN).
Basic sequence of operation for the majority of WSNs. Many may not share the exact
steps but will follow these core functions. (Images sourced from Creative Commons, following the
guidelines on Attribution 3.0 Unported, CC By 3.0 [
]. The only modiﬁcations to the images are
cropping and resizing).
Sustainability 2022,14, 12270 5 of 21
In Table 1, we have summarized the salient features and capabilities of several
such kinds of nodes designed by different research groups. Each node is operated
by a microcontroller, typically powered by solar energy and rechargeable batteries
for long-term operability. In the event of a fire, the rate of change in environmental
variables is considerably different from their normal daily variation. For example, the
daily temperature is characterized by a gradual change within a predictable range,
whereas the temperature differential during a fire can quickly surpass several hundred
degrees Celsius. Several algorithms have been developed to distinguish between wildfire
and non-wildfire conditions from sensor data behavior. Varela et al., proposed a low-
complexity algorithm using only temperature and humidity [
]. They created two
base functions using regression analysis with temperature and humidity as dependent
variables, and time as an independent variable. The base functions were modeled to
represent the behavior of the dependent variables in a fire event. The data received
from the sensor nodes can then be compared with these base functions to determine the
occurrence of an actual fire. Other researchers have also proposed more sophisticated
algorithms like cellular automata and machine learning to detect wildfire. Khalid et al.,
logged flame, smoke, temperature, humidity and light intensity sensors at a central
station, and used a Bayesian classifier to categorize fire events from non-fire events
with an accuracy of 97.2% [
]. Ammar and Souissi fed temperature, humidity and
wind speed to a fuzzy logic controller. In the first step, the data was subjected to
trapezoidal and triangular fuzzification functions, then compared to presets to infer
estimated fire risk levels as Very Low, Low, Medium, High and Very High for each
]. Bolourchi and Uysal used a similar approach with temperature, smoke,
light, humidity and distance as inputs to a five-membership function-based fuzzy logic
system to determine the probability of fire. They also studied the dependency of fire on
each of these inputs as isolated variables [
]. Dampage et al., collected temperature,
relative humidity, light intensity and CO level from sensors under different climate
conditions and at various times of the day, and then calculated the threshold ratio R
between current data values and the values recorded 30 s ago. This further strengthened
the model with a machine learning algorithm to reduce false alarms. A dataset of
7000 sensor data samples was collected during no-fire and fire conditions to train the
algorithm, which was then used to corroborate the presence or absence of fire after
threshold analysis [
]. Yan et al., used an artificial neural network (ANN) to identify
the phase of combustion in real-time using CO2, air temperature and smoke sensors [
Their model considered three combustion phases: no fire, smoldering combustion and
flaming combustion, and was able to obtain a consistent accuracy of >82% when multiple
sensor input data were considered.
Abbassi et al.,
implemented a multiple-level alert
using a combination of KNN classifier and fuzzy logic [
]. In the first level, a cluster of
neighboring nodes generates an alert using K-means clustering. The second level adopts
a fuzzy inference system to evaluate the scale of fire and its direction of propagation.
This two-layer system was demonstrated to be more efficient than simple fuzzy logic
systems and generated fewer incorrect alarms.
These sensor nodes can also be used to predict the likelihood of a future wildﬁre
using algorithms such as the 30-30-30 rule of thumb [
] or the ﬁre weather index (FWI),
besides the machine learning or regression methods described above. The 30-30-30 rule
states that a temperature above 30
C, humidity less than 30% and wind speed above
30 kmph is indicative of extreme ﬁre risk. The FWI is a more elaborate indexing system
that predicts ﬁre behavior from moisture content in the organic matter on the forest ﬂoor,
availability of combustible fuel, ﬁre intensity and rate of ﬁre spread. The fuel moisture
content is categorized into three numeric fuel moisture codes, which are determined from
temperature, relative humidity, wind and precipitation readings .
To analyze and log sensor data at a central node, numerous wireless transmission
methods such as long-range radio (LoRa), Bluetooth, Zigbee, XBee, cellular networks, etc.,
have been explored. Cellular networks provide higher bandwidth and lower latencies
Sustainability 2022,14, 12270 6 of 21
but are power hungry and extremely complicated to setup in remote areas without prior
LTE connectivity. Bluetooth range extends to only a few meters; therefore, it is unsuitable
for supporting data transmission in vast networks. LoRa and Zigbee have been the most
popular choices for communication, as they can be easily used in remote areas without any
cellular connectivity or Wi-Fi. Both are capable of transmitting data over longer ranges
compared to Bluetooth, with LoRa surpassing Zigbee’s typical transmission range for two
devices connected directly. LoRa network architectures are arranged in a star pattern, with
a gateway node at the center that must be in line of sight of the sensor nodes to exchange
data. In contrast, Zigbee networks that are set up in a mesh pattern can bypass the need for
line of sight, as intermediary sensor nodes with Zigbee can be used for relaying data. The
Zigbee network can also be set up to be self-healing and re-route the data pathway through
healthy nodes if any node in the normal pathway is damaged. Although Zigbee has higher
bandwidth than LoRa, LoRa is more suited for outdoor low-power IoT applications that
also require longer communication range with fewer nodes and simple encryption methods
at a lower cost. Researchers have also proposed direct communication of sensor nodes with
geostationary and low orbit satellite networks using a novel collaborative beamforming
(CB) technique [
]. In CB, neighboring nodes form a virtual antenna array and transmit the
shared data synchronously. Doing so increases the transmission range signiﬁcantly without
the need of a signal booster or gateway, while still operating under low power consumption
restrictions on the sensor nodes. In a hybrid approach, unmanned aerial vehicles (UAVs)
sweep over the sensor node networks to facilitate data collection from sensor nodes, feasibly
solving the transmission issues faced by WSNs spread over large areas, data latency or
interruption due to damaged nodes. Such methods have been thoroughly reviewed by
Nguyen et al. .
Among the four technologies considered in this paper, sensor nodes are the simplest
and least expensive to design and implement. The resources required to build such
a node are widely available and supported by an extensive collection of open-source
codes, drivers, libraries, etc. These factors allow for a high degree of personalization
and customization, hence attracting relatively more users from diverse backgrounds and
promoting discoveries. Sensor nodes also consume significantly lesser amount of energy,
compared to their counterparts, for the same surveillance duration, therefore decreasing
the demands on design complexity for power supply and storage solutions. A well-
planned sensor node network can easily achieve the finest spatial coverage and temporal
frequency for monitoring an area for wildfires. In the last few years, this category has
received the most attention, with many large-scale projects funded by governments. For
example, in 2021 USA’s Department of Homeland Security (DHS) Science and Technology
directorate collaborated with four industry partners—Ai4 Technologies (San Francisco,
CA, USA), Breeze Technologies (Hamburg, Germany), N5 Technologies (Rockville, MD,
USA) and Valor Fire Safety of Londonderry (Londonderry, NH, USA)—to find innovative
solutions for early wildfire detection .
Despite its advantages, a sensor node network faces the crippling challenge of needing
human power or elaborate methods like autonomous helicopters for initial deployment.
This challenge can quickly devolve into an insurmountable hurdle when the target terrain
is remote, vast and hostile. However, once deployed, sensor nodes can function indepen-
dently for several years. The obvious physical proximity of the nodes to ﬁres presents
another hurdle. Some nodes are likely to be damaged in the intense heat generated by
ﬁres. Retrieving and replacing these non-functional nodes are additional maintenance
issues that will need to be accounted for during planning and resource allocation. Addi-
tionally, many wilderness zones are protected by laws or ordinances against introduction
of unnatural/non-native objects, which can complicate the installation of these nodes.
Sustainability 2022,14, 12270 7 of 21
Table 1. Summary of sensor nodes designed for early wildﬁre detection.
Prototype/Reference Location Sensor Types Range Communication
Type Processing Power Source Year
FireWxNet  USA Temperature, relative humidity, wind speed and
direction 138–393 m 900–930 MHz radio ATmega128
Solar (two panel,
24 V and 12 V) and
four batteries (12 V)
Bayo et al.  Spain
Temperature (NTC), relative humidity (H25K5A,
SHT11), pressure (MS5540B), soil moisture (Decagon
EC5), light intensity (S8265)
100 m (comm) XBee/LR-WPAN ATmega1281 Two AA batteries 2010
Firoxio  Lebanon Relative humidity and temperature (SHT10), smoke,
carbon monoxide (MQ-5) Unknown Zigbee PIC16F877A
Solar (17.26 V),
Yan et al.  China
Relative humidity and temperature (SHT11), smoke
(MS5100), carbon monoxide (EC805-CO), carbon
20 m Zigbee 8051 (included in
CC2430) Solar (12 V 7 W) 2016
Molina-Pico et al.  Spain Relative humidity and temperature (SHT75), gas
(carbon monoxide, carbon dioxide)
25 m (SN),
1.6 km (CN)
433 MHz ISM
between SN and GW,
868–870 MHz and
GW and CN
CN, MSP430 for SN
600 mAh Lithium
coin battery 2016
Kaijage Tanzania Temperature (LM35DZ), smoke (MQ-2), relative
humidity and temperature (DHT22)
100–120 m (SN
Zigbee between SN
and GW, GSM/GPS
between GW and CN
Two 3.7 V
SISVIA Vigilancia y
Spain Waspmote gas board (temperature, humidity, light
intensity, carbon monoxide, carbon dioxide) 70 m ZigBee ATmega1281 Rechargeable AA
and solar panel 2017
Smart Forests  Brazil Temperature, relative humidity 100 m WPAN, Bluetooth
Low Energy N/A Batteries 2018
Kadir  Indonesia Temperature, humidity, smoke, carbon dioxide Unknown ZigBee Unknown Direct power
LADSensors  Portugal Temperature, humidity, air pressure, carbon dioxide 300 m (SN) LoRa Unknown Solar 2018
Silvanet (Dryad)  Germany Temperature, humidity, air pressure, gases (hydrogen,
carbon monoxide, etc.) (BME 688) 100 m LoRaWAN STM microcontroller
Sustainability 2022,14, 12270 8 of 21
Table 1. Cont.
Prototype/Reference Location Sensor Types Range Communication
Type Processing Power Source Year
Khalid  Turkey IR ﬂame (760–1100 nm), smoke (MQ-2), light,
temperature and humidity (DHT-22) 250 m
NRF24L01+ (2.4 GHz
RF) ATmega328p Two Iithium-ion
cells (3.7 V) 2019
Knotiﬁre  Canada Unknown Surface ﬁre Internet Unknown Energy harvested
from ﬁre 2020
and US Humidity, temperature 50 m 3G Unknown Unknown 2020
Benzekri et al.  Morocco
Temperature, humidity, air pressure (BME280),
particulates (Nova SDS011), carbon dioxide
(MH-Z14A-CO2), carbon monoxide (ZE07-CO)
Unknown LoRa Lora32u4
U. Dampage et al.  Sri Lanka
Temperature, humidity (DHT22), light intensity (LDR),
carbon monoxide (MQ9) 5 m 2.4–2.5 GHz ISM Arduino Nano
Solar panel and
N5 sensors  USA Proprietary nanowire-based gas sensor array, IR
camera, particulate matter detector Unknown LoRa Unknown
30,000 mAh battery
SN: sensor node; GW: gateway, CN: control node.
Sustainability 2022,14, 12270 9 of 21
2.2.2. Unmanned Aerial Vehicles (UAV)
UAVs commonly refer to vehicles or systems that are remotely operated and travel
by ﬂight. The data gathered by UAVs are often in real-time, accurate and provide unique
vantage points that would otherwise be inaccessible, dangerous or time-consuming to
obtain by emergency responders. This is attributed to the mobility of the UAVs, which
allows rapid and continuous visual monitoring throughout the ﬁre’s progression and
movement. While UAVs may deﬁne any unmanned aerial vehicle such as military drones,
the majority of them that were ﬁrst designed for wildﬁre ﬁghting purposes were comparable
to consumer or hobby drones, operating within 1000–4000 feet, as shown in Figure 4[
This range has increased to 30,000 ft as drone technology has commendably progressed
in the past years [
]. The data collected commonly take the forms of GPS location,
images, video feeds or readings from sensor nodes such as those described in the previous
]. These systems may be remotely controlled by humans or automated systems.
Automated systems are easily adaptable by users with little to no experience compared to a
system that requires human pilots. For example, Fotokite Sigma can follow a programmed
ﬂight path set through an application on a mobile device [
]. This is particularly useful
for applications such as detecting wildﬁres, where the autonomy is a necessity to support
persistent vigilance. A few years ago, autonomous UAV were limited to low-complexity
surveillance tasks but have now been vastly improved by researchers. Chi Yuan et al.
discuss a system where ﬂeets of different types of semi-autonomous drones are deployed
in stages to search, conﬁrm and observe ﬁres [
]. Although each drone may not handle
dynamic and complex tasks such as those with dedicated pilots, the autonomy allows for
quick commanding and organization within a ﬂeet regardless of its size. With general
usages laid, a UAV system’s main limitation is summarized by its relatively low-capacity
power source. This is due to the structural constraints limiting usage of heavy energy-dense
batteries. UAVs are usually outﬁtted with various cameras (videos, IR and imaging) but
may contain many other features depending on the designer [
]. For example, some
systems are capable of controlling a ﬂeet of UAVs. Others may have additional devices
onboard, such as a specialized ﬁre sensor or a tank of water [
]. The IGNIS system
by Drone Ampliﬁed is an example of an advanced drone that utilizes several of these
]. It combats wildﬁres by initializing backﬁres through chemical payloads, and
can be actively controlled by both remote controller and/or by preset instructions sent by a
Possible roles of UAVs. (Images sourced from Creative Commons, following the guidelines
on Attribution 3.0 Unported, CC By 3.0 [
]. The only modiﬁcations to the images are cropping
Several data processing algorithms have been proposed to process the video feed from
the UAV and swiftly detect the presence of active wildﬁres, intensity of the ﬁre and rate
Sustainability 2022,14, 12270 10 of 21
of spread. Lin et al., employed a Kalman ﬁlter-based approach that uses only the UAV
position and temperature recorded at each sampling point to identify a wildﬁre and predict
the ﬁre-spread behavior [
]. The performance of this model with multiple vehicles was
observed to be comparable to the results obtained with the FARSITE benchmarks, with less
computational cost. Bushnaq et al., proposed a combined UAV–IoT system as a more cost-
effective solution than satellite surveillance. In a ﬁre event, the ground-based IoT devices
in the vicinity of the wildﬁre generate alarms to the UAVs nearby, which then verify the ﬁre
probability to avoid misdetections [
]. The authors designed an algorithm to optimize the
IoT density and number of UAVs to minimize the cost of resources while also maximizing
the ﬁre detection probability. Lewicki and Liu designed a Fire Perception Box (FPB) with
RGB/IR cameras and an ARM microcontroller that can be installed on UAVs in a plug-and-
play manner [
]. At a suspected ﬁre scene, the RGB image is ﬁrst fed to a convolutional
neural network (CNN) classiﬁer to calculate an RGB ﬁre score, followed by the IR image
being fed to its corresponding pipeline to calculate an IR ﬁre score. Both scores are combined
to establish the presence of ﬁre, and subsequently ﬁnd the ﬁre localization hotspots and
RGB + IR heatmaps. Researchers have also demonstrated a large-scale YOLOv3- and
YOLOv5-based deep-learning network for ﬁre identiﬁcation from images and video feeds
captured by UAVs [
]. After training the YOLO network on annotated ﬁre images, the
researchers were able to obtain high ﬁre detection accuracies in real-time analysis of UAV
video feeds. The only disadvantage of this method is that the YOLO model needs to be
implemented on a ground-based high-performance computer, therefore the performance
of this system is contingent on an uninterrupted data transmission between base station
and UAVs. Besides these methods, other deep-learning methods such as recurrent neural
networks (RNN), long short-term memory neural network (LSTM), generative adversarial
network (GAN), deep belief network (DBN), etc., have also been shown to have good
accuracy with identifying wildﬁres [65,66].
Another active avenue of research is building efﬁcient algorithms for coordinating
the communication between multiple UAVs and controlling their ﬂight patterns, with the
aim of optimizing coverage of the area within the expected ﬂight endurance period. The
methods vary from classic linear controllers such as proportional–integral–derivative (PID),
and linear quadratic regulators to complicated non-linear implementations, such as
neural networks, genetic algorithms, etc. [
]. La et al., designed a control framework
by incorporating the following multiple objectives into the cost function: deployment of
UAVs to the ﬁrst location of ﬁre detection, avoiding in-ﬂight collision with other UAVs
by maintaining a minimum separation distance, maximizing coverage of the wildﬁre and
tracking progression of the ﬁre front [
]. Mawanza et al., discussed a real-time cooperative
and adaptive ﬁre monitoring method for multiple UAVs [
]. They trained a radial basis
function neural network (RBFNN) to counteract system uncertainties, aerodynamic drag,
etc. and a non-singular fast terminal sliding mode control (NFTSMC) function to dynami-
cally track the ﬁre. A similar evolutionary multi-objective algorithm was also formulated
for dynamically tracking ﬁre boundary with minimal resources, and the algorithm was
able to achieve 100% ﬁre coverage with 15 UAVs in the FARSITE-simulated model [
There have been numerous examples of detailed control strategies for quadrotors, but the
detailed review of these methods is beyond the scope of this review paper. The reader is
encouraged to review the papers by Kim et al. [
] and Kangunde et al. [
] for a study of
UAVs have limited ﬂight endurance, as the size of the power source on board is
constrained by the payload capacity of the UAV. To extend the ﬂight time of UAVs, solar
panels have been carefully integrated into the design. Airbus’ Zephyr and Titan Aerospace’s
Solara set the record for longest ﬂight times, with several days of autonomous ﬂight. Both
are equipped with solar panels and are intended to be used as high-altitude long-endurance
(HALE) UAVs for atmospheric monitoring [
]. HALEs ﬂy at altitudes of about
and cost millions of dollars. In contrast, solar-powered low-altitude low-endurance (LALE)
UAVs have been able to achieve maximum ﬂight times of a few days at an altitude of
Sustainability 2022,14, 12270 11 of 21
only <10,000 ft, with the record set by ETH Zurich’s AtlantikSolar at 81 h of continuous
]. In theory, the ﬂight time increases proportionally with ﬂight altitude, explaining
the stark difference in maximum ﬂight times achieved in HALEs vs LALEs. However,
HALEs have expansive wingspans and require a runway, which make them impractical
for many applications. Using an ATmega328 microcontroller-based photovoltaic power
management system, researchers were able to achieve a continuous surveillance of about
6 h using a swarm of six LALE UAVs [
]. XSun’s SolarXOne claims to be able to ﬂy
continuously for 12 h, powered by solar cells [
]. It can support a payload of 5 kg, and
provides customizable options including RGB or IR cameras, and other sensors which
can be used to detect wildﬁres. Zhao et al., proposed a thoroughly detailed method for
designing a solar-powered, hand-launched UAV considering payload capacity, expected
solar irradiance, aerodynamic efﬁciency considerations at proposed ﬂight altitude and
ﬂight control [
]. Using their model, they were able to achieve ﬂight endurance durations
of several hours at an altitude of 15,000 ft.
The biggest advantage of UAVs is ﬂexibility in repositioning, which allows dynamic
readjustment of areas surveyed as deemed necessary. UAVs can also be programmed
to provide high temporal and spatial resolution over areas determined to be at high
risk. Unlike static nodes or camera networks, UAVs’ usage often extends past the initial
detection, as they can follow the spreading patterns of ﬁres from a safe distance. Using a
limited number of UAVs to scan an area through multiple passes can be more efﬁcient than
installing a vast sensor node network. They are less expensive to implement than satellites
and may also be more capable of identifying smaller wildﬁres than the latter technology.
Most UAVs still need some form of human involvement in and throughout their
operation. The major hurdle for this technology is distance and duration of ﬂight. Despite
the advancements in assimilating solar panels into the device, the ﬂight times are practically
limited to a few days for hand-launched LALEs. Therefore, this technology is likely to
be unsuitable when uninterrupted surveillance is needed in wildﬁre-prone areas. The
technology is also relatively new; therefore, the costs of operating and maintaining a UAV
ﬂeet can add up exponentially. UAVs are also mired in frequent software and hardware
issues that have impeded their adoption as a widespread ﬁreﬁghting solution.
2.2.3. Stationary Camera Networks
Camera networks consist of advanced and feature-rich, interconnected cameras that
monitor a vast area for ﬁres (Figure 5). Early camera networks consisted of live camera
images or videos streamed to a control room, where an operator would manually scan the
feeds for signs of ﬁre. In modern camera networks, the cameras are still the main drivers
of the system, but are often partnered with other systems, such as AI and communication
servers, to fully optimize the camera to the desired area. Some may also begin as a single,
standalone camera with the option of expansion through additional cameras. They can
be stationed both in urban and remote areas and combat ﬁres through early detection
and prediction. While stationary, the cameras are usually free to pan in wide or complete
angles, signiﬁcantly increasing coverage zone per camera and limiting blind spots.
In addition to video feed, the cameras may also include different types of IR and thermal
to enhance detection ability by broadening the features captured. The integration of AI
enables them to be autonomous, allowing them to succeed the bulkier, man-powered and
more intrusive watch towers [
]. This allows the cameras to not only capture images of
the surroundings, but to independently identify if the captures are indicative of a ﬁre. The
inclusion of internet access grants the system the ability to communicate, allowing them to
broadcast their feeds to a network which directly notiﬁes speciﬁed users of notable events.
For example, a camera may capture a column of smoke an abnormality that its AI program-
ming classiﬁes as a probable ﬁre. It then relays the information to a central processor, the
Sustainability 2022,14, 12270 12 of 21
component that notiﬁes authorities through an app [
]. A widely implemented example
of camera networks is ALERTWildﬁre, a collaborative effort between The University of
Nevada (Reno), University of California San Diego and the University of Oregon [
Hundreds of ALERTWildﬁre cameras have been installed in the southwest and west United
States and assisted with real-time monitoring of ﬁres. In 2021, Paciﬁc Gas and Electric
(PG&E) installed AI-equipped ALERTWildﬁre cameras in northern and central California,
in collaboration with the AI company Alchera [
]. A San Francisco company founded in
2019, Pano AI, also uses AI on HD camera video feeds to automatically detect wildﬁres and
reduce the response time to ﬁre events [
]. IQ-Firewatch, a Portuguese company, employs
AI as well, but also provides customization and scalability to serve a variety of settings [
Their cameras, commercially sold, are optionally attached with sensors like monochrome,
RGB, near-IR, or thermal IR and can smoothly work with cameras added in the future. Its
method of wildﬁre prevention revolves around capturing high-quality raw images with
cameras and processing them in real-time using smoke detecting algorithms [
ﬁres at the earliest signs of smoke may be sufﬁcient in many cases, but IQ-Firewatch and
similar systems directly compete with ground level technologies such as sensor nodes,
which may detect ﬁres even before they emit visible amounts of smoke due to their sen-
sors and proximity to the ﬁre [
]. Furthermore, while camera systems are not directly
constrained by the need to conserve power, they are tethered to the nearest power source,
requiring users to consider optimal placements. By the same token, they beneﬁt from stable
power, eliminating problems with power sources such as batteries, allowing them to utilize
more power-intensive tasks such as real-time data processing.
Typical application of camera networks paired with AI. (Images sourced from Creative Com-
mons, following the guidelines on Attribution 3.0 Unported, CC By 3.0 [
]. The only modiﬁcations
to the images are cropping and resizing).
Although each designer’s techniques are trade secrets and often undisclosed, popular
methods often involve smoke detection and machine learning, including the usual feature
extraction from raw captures and applying various classiﬁers. Jie Shi et al., have reviewed
such algorithms and listed their common extracted features: color, motion, texture, shape
and energy [
]. Each provides unique attributes of a smoke image that enhances the
classiﬁcation process. The next steps involve choosing classiﬁers, which can be of many typ-
ical sorts such as: k-nearest-neighbor, neural networks, support vector machines, etc. [
The models programmed in the cameras are trained and tested before deployment. The
decoupling of the capturing and processing provides the advantage of continuous improve-
ment and integration as researchers can update the models and analyze past performance
without having to alter any of the physical components or interrupt its ongoing operation.
For example, by basing their project on cloud computation [
], Darko Stipaniˇcev et al.,
have continued to release improvements to their iForestFire semi-automatic monitoring
system after its commercial deployment and operations in various regions.
Many techniques have successfully utilized smoke detection in early wildﬁre detec-
tion prototypes; yet, researchers continue to reﬁne and improve the process. A common
Sustainability 2022,14, 12270 13 of 21
weakness in smoke detection and image-based detection is deterioration of image quality
at night and by the presence of haze. Haze is deﬁned as an “an obscurity occurring due to
smoke, dust, or other particles suspended in the atmosphere” [
]. This causes images and
colors to become fuzzy or blurry, decreasing contrast between elements of the image and
therefore increasing the difﬁculty of identifying smoke. Some examples of haze are fog and
air pollution. To address this, Darko Stipaniˇcev et al., utilized a de-hazing technique known
as “dark channel prior”. It reconstructs haze-reduced images with equations generated,
with the knowledge that (1) most haze-free non-sky patches of an image have low or
zero intensity in at least one color channel, and (2), that the intensity of these patches is
increased by the presence of haze [
]. Like haze, the lack of light signiﬁcantly diminishes
the usability of captures taken at night. Inadequate lighting prevents distinguishing of
colors and behaves like noise, often causing ﬁre misclassiﬁcation with other light sources
such as car lights or lanterns. To combat this, Ahmet Agirman et al., extract smoke temporal
features and motion in addition to spatial features, considering factors such as ﬂickering
and ﬂaring [
]. With the extra information, their process uses two machine-learning
stages: the GoogLeNet CNN ﬁrst extracts spatial features to then pass onto a bidirectional
long short-term memory network (BLSTM), which learns the temporal relationships in
the data [
]. The models’ decisions are then congregated and ultimately decided through
majority voting .
Camera networks are largely autonomous and positioned farther away from the
site of potential ﬁres, unlike sensor networks and some UAVs. Therefore, there are little
opportunities for them to receive any sort of damage from ﬁres. Although considerations
still need to be taken in the camera’s placement, it is simpliﬁed by the longer range and
more straightforward operation when compared to sensor nodes, whose low coverage and
various short-range sensors necessitate more planning.
The relatively large coverage of a stationary camera partially alleviates, but does
not eliminate, the constraint of a required continuous power source, as video feeds or
frequent captures are power consuming. Consequently, it may be impossible to use these
systems in an extremely remote area. The setup of the camera towers also needs an initial
investment of resources and manpower for building the towers, and, therefore, might not
be economically feasible in many geographical locations. Due to this positioning of the
camera, any sensors considered for inclusion in the camera system may also need to have
similar range or function as the cameras, limiting possible additions. For example, it would
be ineffective to outﬁt cameras with temperature or gas sensors, as cameras are designed to
be distant from the target area.
2.2.4. Satellite Surveillance
NASA and NOAA were two of the ﬁrst organizations to observe wildﬁres using an
extensive network of polar orbiting (Terra, Aqua) and geostationary (GOES) satellites.
Polar satellites scan the entire Earth a few times each day and can monitor the entire planet
for ﬁres (Figure 6). However, each consecutive scan over the same geolocation is spaced
several hours apart and, therefore, the temporal sampling rate for such satellites is low. On
the other hand, geostationary satellites provide much higher temporal data for a speciﬁc
area but cannot monitor other global environmental developments. The instruments on
board these satellites monitor several different kinds of data, some of which are actively
analyzed to detect/observe wildﬁres, global transport of pollutants and long-term climate
impacts of ﬁre (Table 1, [
]). Processing this data for wildﬁre anomalies, especially small
ﬁres, poses a challenge due to the lower spatial resolution of satellite images. In addition,
smoke can easily appear identical to clouds, as shown in Figure 6of the Camp Fire, imaged
by moderate resolution imaging spectroradiometer (MODIS) on Terra.
Sustainability 2022,14, 12270 14 of 21
MODIS image of the Camp Fire in northern California on Nov 14, 2018 [
]. Image by
NASA Earth Observatory.
Most environment observational satellites have visible and infrared detectors on board.
Among the infrared bands, the 3.8/4
m (middle infrared) and 10.8/11
infrared) wavelengths are used to identify active ﬁres (Table 2). The relative brightness
temperatures of ﬁre pixels on these two bands are different from each other, with the 4
) registering a signiﬁcantly increased radiance for high-temperature sources
than the 11
m channel (T
). The difference in brightness (
) of pixels between
these two channels is compared with a pre-determined threshold to detect ﬁres and map
affected areas. This simple and intuitive approach was ﬁrst proposed by Matson and
Dozier in 1981 on AVHRR data [
]. Additional IR channels are often used to improve
detection, by helping reject false alarms caused by clouds, bright surfaces, sun glints,
water vapor, etc. [
]. A similar approach was applied to data from MODIS, ASTER
and GOES, with many iterations proposing improved sensitivity to smaller and lower-
temperature ﬁres [
]. One of the major challenges of the earlier algorithms was that
the threshold levels for comparing
T were empirically determined, and therefore varied
across different kinds of biomes on Earth. In a modiﬁed approach (version 4),
Giglio et al.,
proposed subsequent steps after the basic absolute threshold test has identiﬁed a ﬁre pixel
by comparing T
to a certain numeric value [
]. In the following step, the pixels
neighboring the pixel under consideration are used to estimate the radiance of the ﬁre pixel
in the absence of a ﬁre. The pixel window is started at 3
3 and gradually increased until
sufﬁcient ground-based, non-ﬁre pixels without clouds have been registered to compute
the background signature. If this step is successful, a third step uses this background
signature to calculate threshold values that hold contextual relevance to the speciﬁc area
T is now compared to this threshold for identifying a tentative ﬁre. In
the last step, further tests using another set of different threshold formulae help in rejecting
Sustainability 2022,14, 12270 15 of 21
false alarms due to sun glints, deserts and coastal boundaries, water bodies, clouds and
other sources of abrupt radiance transition.
In a radically alternate approach compared to the above method, deep-learning tech-
niques were proposed for active ﬁre identiﬁcation. To facilitate this, Pereira et al., generated
a publicly available dataset of image patches from Landsat-8 satellite covering global ﬁre
events in 2021 [
]. The database also included annotated ﬁre pixels and is available on
GitHub. The Landsat-8 was launched in 2013 and provides data in visible, SWIR, NIR
and thermal IR spectrums (Table 2). The Operational Land Imagers (OLI) channels 1, 5, 6
and 7 are typically used for ﬁre detection [
]. Using the Pereira dataset [
] for training,
Rostami et al., developed a convolutional neural network (CNN)-based architecture called
“MultiScale-Net” for ﬁnding ﬁres [
]. To accommodate for ﬁres of different sizes and
patterns, the CNN used multiple sized kernels for feature extraction and changing dilation
rates for the dilated convolution layer. SWIR bands and blue channel data were used as
inputs to the CNN.
As an attempt to make satellite technology accessible to the general academic commu-
nity, the CubeSat project was started in 1999 by a collaborative effort between California
Polytechnic state University (San Luis Obispo) and Stanford University. The objective of
this project was to boost research exploration and invention at affordable budgets. Cube-
Sats have a standardized design and are launched as part of the payload on other satellite
]. Gangestad et al., used the images from 1U AeroCube-4 equipped with
three 2 MP cameras to demonstrate CubeSats as a viable option for wildﬁre detection [
However, downloading the images to a ground station usually takes hours. To mitigate
this challenge, Azami et al., proposed k-nearest and CNN deep-learning (DL) models for
image classiﬁcation implemented on a Raspberry Pi [
]. The runtime for this RPi unit
was optimized to minimize the power consumption and was integrated on to a KITSUNE
6U CubeSAT. The KITSUNE uses a Sony IMX342 color sensor that generates the images
fed into the DL model for classiﬁcation. Using this setup, the group was able to achieve
classiﬁcation accuracy of above 95% using networks like ShallowNet and LeNet [
Another recent example is Orora Technologies, which speciﬁcally designs 3U CubeSats
furnished with IR cameras for ﬁnding and monitoring wildﬁres from space [
claim to be currently protecting 302,588,843 hectares of forests spread over six continents.
The Orora CubeSats have onboard GPUs to assist with wildﬁre detection without needing
to download the data to the ground station for analysis.
Satellites can survey large expanses of land in a single sweep and provide an expansive
perspective of ﬁre behavior. They are also built to last for several decades and serve multi-
ple purposes beyond ﬁre detection. For example, the NASA and NOAA meteorological
satellites also track oceanic currents, upper atmospheric climate changes, drought risks
and patterns, etc. They are adept at generating a comprehensive view of the biosphere
and environmental processes, of which wildﬁres are only a contributing factor. The exten-
sive data generated are available for public use and have spurred many discoveries and
breakthroughs in many diverse disciplines.
Aside from the cost aspects, the obvious bottleneck of wildﬁre observation using
satellite technology is optimizing the area covered with the frequency of data collection.
The high-ﬂying altitudes limit the resolving power of ﬁres smaller than a pixel in the images,
which can make identiﬁcation of young ﬁres extremely difﬁcult. Dense clouds and smoke
cover can severely reduce ground visibility to even IR and, therefore, adversely impact their
utility for tracking active ﬁres under such conditions. Until the end of the 20th century, this
technology was available only to a few niche organizations, due to high mission cost and
limited capacity for onboard systems. Although CubeSats project have largely addressed
this issue, launching a CubeSat network is still expensive compared to other methods
discussed in this paper. Currently, the cheapest option is Interorbital Systems (IOS) TubeSat
Sustainability 2022,14, 12270 16 of 21
satellite kits, at about USD 16,000 (academic pricing) and USD 32,000 (commercial pricing)
for a 1U kit, including launch. The price for a single 3U kit with inclusive launch services is
USD 36,480 (academic pricing) and USD 72,960 (commercial pricing) .
NOAA and NASA satellites that help in wildﬁre detection, tracking and for studying its
global impacts on climate and pollution.
Instrument Notes Launch Date
Spectrometer (AVIRIS) 
Optical sensor images 224 spectral bands between 400–2500 nm
•Assesses fuel availability and condition, and stage of the ﬁre
•Carried by ER-2 jet, WB-57, Twin Otter International’s
Advanced Very High-Resolution
•Measures data in six channels in 0.6 µm–12 µm range
•Records land surface temperature, ﬁre
temperature/expanse/radiative power 1978–1994
Advanced Spaceborne Thermal
Emission and Reﬂection
Radiometer (ASTER) 
•Records images in 14 wavelengths between visible to thermal
IR, including VNIR, SWIR and TIR bands
•Captures high resolution images and surface temperature
•Helps track smokes and active ﬁres
•Carried by Terra
18 December 1999
Moderate Resolution Imaging
Spectroradiometer (MODIS) 
•Records 36 spectral bands data between 0.4 µm–14.4 µm
•Monitors ﬁres and hot spots through visible and IR imaging
•Carried by Terra and Aqua
Dec 1999 (Terra);
May 2002 (Aqua)
SpectroRadiometer (MISR) 
•Nine cameras gather RGB-IR data from different angles
•tracks smoke plume and concentration of smoke particulates
•Carried by Terra
18 December 1999
Measurement of Pollution in the
Troposphere (MOPITT) 
•Measures IR radiation between 4.7 µm–2.4 µm wavelengths
•tracks emission and movement of carbon monoxide globally,
and emission contributions from ﬁres
•Carried by Terra
18 December 1999
Atmospheric Infrared Sounder
•Tracks global movement of greenhouse gases, including
emission from ﬁres, and IR mapping of Earth’s surface
•Helps understand impact on weather patterns, climate change
and forecast droughts
•Carried by Aqua
4 May 2002
Cloud Aerosol Lidar with
•Uses 532 nm and 1064 nm LIDAR
•Tracks smoke plumes and aerosol concentrations, focusing on
•Carried on CALIPSO
•CALIPSO also has a 645 nm wideﬁeld camera, infrared
m) to assist in data collection
28 April 2006
Sustainability 2022,14, 12270 17 of 21
Table 2. Cont.
Instrument Notes Launch Date
Visible Infrared Imaging
Radiometer Suite (VIIRS) 
•Spans visible and infrared wavelengths, covers 22 channels
between 0.41 µm–12.01 µm
•Similar to MODIS but gathers data at higher resolution. Also
measures aerosol, among other parameters
•Carried by Suomi National Polar-Orbiting Partnership
28 October 2011
Hyperspectual Thermal Emission
Spectrometer (HyTES) 
•Covers 256 spectral channels between 7.5 µm–12 µm
•Determines ﬁre temperature, tracks gas plumes of methane,
hydrogen sulﬁde, ammonia, sulphur dioxide, nitrogen dioxide
•Intended to support the HyspIRI mission that will track
Landsat 8 
•The ﬁrst Landsat was launched in 1978
•The Landsat-8 has an Operational Land Imager (OLI) and
thermal infrared sensors
•Provides data in visible, near-IR, SW-IR and thermal IR
ECOsystem Spaceborne Thermal
Radiometer Experiment on Space
Station (ECOSTRESS) 
•Measures six spectral bands data in 8.29 µm–12.09 µm range
•Helps estimate drought levels by measuring transpiration and
water stress in critical areas
•A potential ﬁre identiﬁcation product is currently being tested
29 June 2018
In this review paper, we emphasized the impact of wildﬁres on human life and
global ecosystems, and the current climate research indicates worsening ﬁre seasons in
the future. Advanced technologies like satellites, drones, ground-based sensor nodes and
camera systems have been proposed to supplement traditional ﬁreﬁghting techniques.
We discussed the stages and current state of research in these four main methodologies,
speciﬁcally focusing on detecting wildﬁres at early stages, predicting hot spots at high risk
and monitoring the spread. The paper highlights the technical aspects of the front end of
the device, along with the algorithms used in the back end for processing the data and
identifying an actual ﬁre. Each category has its own shortcomings and limitations, but also
excels at its own niche applications, and under different circumstances one technology will
outperform the other. For example, satellites have the capability of scanning a considerable
area in a short period of time, which is either impossible or time-consuming using the other
methods. However, satellites have very low resolving power and typically sweep the same
area once in several hours, making them unsuitable for real-time ﬁre detection. Stationary
camera networks address these issues but are immobile and need to be tethered to a power
source. UAVs are characterized by ﬂexible ﬂight paths that can be used to adapt to the
changes in ﬁre patterns and survey different areas as required. However, they are also
power hungry, and are inadequate for long-term continuous monitoring. In comparison
to the above, sensor nodes are the most low-power units that can be placed to monitor
very remote locations continuously, are relatively inexpensive to produce on a large scale
and do not need intensive data processing like those for analyzing images from UAVs,
satellites or cameras. On the other hand, nodes face the highest risk of damage due to their
proximity to ﬁres on the ground. Ultimately, none of these methods can fully substitute
the usefulness of the others, but all four can be uniﬁed for a robust ﬁrewatch system that
together can exhaustively detect wildﬁres around the clock. The shortcomings and gaps
in research in each area were underlined, which we hope will be addressed in the future
through rigorous research.
Sustainability 2022,14, 12270 18 of 21
Funding: This research received no external funding.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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