Evidence from historical wildfire trends predicts increasing fire severity with “six of California’s seven largest wildfires [erupting] in the past year” ^[1]. The causes and conditions that have led to the increased fire activity are numerous (e.g., drought conditions, electric grid equipment failure, etc.), but the consequences are clear: staggering economic losses, property and ecological damage, and loss of life. One example is the California Camp Fire of 2018, which resulted in over USD 16 billion in damages over two weeks and a loss of 86 lives ^[2]. A large number of these fires occur in what is known as the wildland–urban interface (WUI), the zone of transition between wildland and human development ^[3]. Fires that start in this WUI zone have the potential to go undetected for a longer period of time than ignitions in urban areas due to a reduced population density in these zones, reduced coverage of security cameras, and the potential for ignitions to start out of line-of-sight from residences or businesses. This potential delay in ignition detection can give wildfires an opportunity to grow in size and unpredictability, which reduces the ease of containment. Under such circumstances, the importance and need for remote fire detection capabilities in these zones have become paramount.

Low-cost, low-power distributed sensor networks have become a promising avenue in achieving early wildfire detection capability [4,5,6,7,8,9]^{[4][5][6][7][8][9]}. If implemented in practice, such a system would supply fire fighting forces with an early warning of freshly ignited fires, knowledge of localized geography-specific environmental details, and real-time situational awareness of how the fire is expanding or moving. However, these systems need to be tested to prove their detection capabilities across a variety of parameters, and for that, we need to test them in live fire situations, which is most feasible under the quasi-controlled setting of a controlled burn.

Controlled burns are a burning method used to control vegetation in a given area for esthetic, restorative, precautionary, and/or training purposes. These burns can be used to create diverse habitats for plants and animals, reduce fuels, thereby preventing more destructive fires, and as training exercises for departments to better understand the nature of wildfires and how to effectively deal with them. The study deployed sensors at the Marin County Fire Department’s controlled burn in Novato, CA in May 2021 for fuel reduction purposes ^[10]. While controlled burns are of a lower intensity than uncontrolled wildfires, adapting sensor systems to data collected from these events will ensure that the sensors are sensitive enough to detect early fire starts in wildfire situations.

Many fire-detection systems employ camera-based techniques because they are the most intuitive sensing modality for operators to understand and can monitor a large area of land with just one unit. Camera systems can use pan-tilt-zoom (PTZ) cameras or multiple static cameras where the images are stitched together so that a single unit can have 360-degree visibility. Because these systems can easily employ high-resolution cameras, they often use machine learning techniques to identify specific characteristics of a wildfire (e.g., contrast, flicker, temporal patterns, etc.) and give operators automated alarms coupled with snapshots for further validation [13]^[11]. However, this means that the system needs to have line-of-sight to the target area, which may not be possible in every installation, and this type of system either needs to be coupled with local detection units or several units need to be distributed to limit blind spots. An additional concern with these types of systems is the power requirements of capturing, processing, and potentially delivering a real-time video feed to remote observers or edge computation nodes. The power requirements of such systems can be prohibitive for certain remote installations that do not have access to the electrical grid and prohibit large energy harvesting installations (e.g., photovoltaics). Finally, an obvious concern with camera techniques is privacy since, ideally, the system would have visibility of a large area of land, which also means that it can collect visual information on non-wildfire-related human activity that has the potential of being exploited.

Temperature fluctuations from a wildfire result from conduction, convection, and radiation, but for early detection sensing purposes, convection and radiative heating are the most relevant [20]^[12]. Radiative heating and convective heating during a wildfire demonstrate very different response curves, with radiative heating increasing monotonically as a fire approaches, and convective heating demonstrating significant increases in variance, but not necessarily in mean temperature, as cooling air can be drawn toward fire [21]^[13]. Researchers from Bilkent University confirmed this when they performed several fire experiments where they placed several COTS sensor units at varying distances from a fire source and measured the temperature [22]^[14]. They noticed that the sensor units that were placed downwind saw greater temperature spikes due to convective heat transfer from the combustion. For an early detection system, however, the fire ideally has not yet reached the level at which it is causing significant convective heat fluctuations due to fire-generated wind currents.

For a smoldering fire, the peak electromagnetic emission is within the infrared domain, 1–5 µm [13]^[11]. Cheap, single-pixel infrared detectors exist (i.e., thermopiles) that can be tailored to specific wavelengths with additional optical filters that can correspond to ranges indicative of a wildfire. One problem with this approach is that the exhaust of a wildfire is composed of a variety of matter that can attenuate any incident infrared radiation from the fire [23]^[15]. However, if the attenuation bands of the constituent parts of wildfire exhaust are known, a multiband system that can look at several wavelengths can be created to estimate the wildfire temperature or look specifically for the constituent attenuation bands to obtained an estimate of the amount of smoke being exhausted within the sensor’s FOV. By knowing that one of the attenuation bands for CO₂ is around 4.3 µm [24]^[16], a system can be created to look at a reference band (e.g., 4.0 µm) and the CO₂ attenuation band to create an alarm for the presence of a large concentration of CO₂.

4. Humidity

Humidity

Relative humidity can increase during wildfires due to the release of water vapor during combustion. Water vapor is released from wildfires through two processes: the chemical output of hydrocarbon combustion, and the evaporation of water from woody biomass due to conductive heat [21,25,26]^[13][17][18]. Both mechanisms together cause detectable spikes in humidity during the initial phases of combustion. Byram [27]^[19] developed a simplified oxidation reaction for generalized biomass (C₆H₉O₄) as follows: 4CHO + 25O + [0.322MHO + 94N]→18H + 24CO + [0.322MHO + 94N] + 11.6 × 10 J Moisture percentage of the fuel is represented by M (included in the brackets with atmospheric nitrogen as inert components of the reaction). As a simplified formula, it does not include the range of additional combustion emissions from wildfires beyond the basic oxidation reaction assuming complete combustion (note no CO in oxidation products due to this assumption). With this formula, Byram posits that for fuel moisture levels below 57%, the majority of water vapor from a wildfire is due to the combustion chemistry of the biomass hydrocarbons. Humidity sensors have been widely employed in multi-sensor wildfire detection systems, but most studies have not specified the relative importance of humidity sensors in overall system accuracy [28,29]^[20][21]. Nonetheless, capacitive humidity sensors are quite common in multi-sensor wildfire detection systems being developed in academia and the commercial sector, most likely because of the clear role of moisture in biomass combustion, as noted above.

Wind

Wind sensors (anemometers) are commonly used by firefighters responding to large wildfires due to the risks associated with sudden changes in wind direction for active firefighting [30]^[22]. These anemometers are most often used to predict fire front movement on live wildfire incidents, and not to detect new spot fires or engage in the early detection of a new wildfire. Wind sensors have likewise been less often used in academic research aimed at the early detection of wildfires [29,31]^[21][23]. They are commonly used in fire prediction and forecasting models due to the key role wind speed plays in fire risk [32,33]^[24][25]. Heat fluctuations from wildfires can influence wind currents in close proximity to the fire, but it is unclear at what stage of fire these winds are first evident. If they are evident at feasibly detectable levels at the earliest stages of fire, this could potentially be relevant for early detection in very remote areas or during times of significant atmospheric occlusion of other sensors. Basic wind sensors typically detect wind via anemometers. Another way that the wind intensity, direction, and changes can be detected is via a spatially distributed system detecting the evolving levels of relative humidity, temperature, PM, and gases over time. Local wind dynamics can potentially be inferred from the spatially distributed temperature, humidity, smoke, or other sensors detecting localized variations driven by fire with sufficient time intervals over a short period of time to detect granular changes [34]^[26]. The downside is that it is hard to differentiate the temperature and humidity fluctuations driven by close proximity to fire (radiant heat and water vapor release) from those driven by wind (wind-carried heat convection and increased humidity). This approach would therefore most likely be applicable beyond a minimum distance from the fire.

Particulate Matter

During combustion, an extensive variety of particulate matter (PM) is released, alongside gases [35,36]^[27][28]. The relative proportions of these quantities vary depending on the fuel source and environmental characteristics [13]^[11]. Burning biomass generates a higher proportion of PM 2.5 compared with coarser particulate matter [37]^[29]. One mass emission estimate ratio (grams of emission/kilogram of fuel burned) puts PM 2.5 at 10.3, while particulates between 2.5 µm and 10 µm are at 1.9 and anything larger is estimated at 3.8 [38]^[30]. These numbers vary depending on the type of fuel being burned (e.g., biomass, hydrocarbons, etc.) ^[8]. Some fine particulate components such as levoglucosan (1,6-anhydro-‚-D-glucopyranose) have been demonstrated to be a decent biomarker for biomass combustion emissions, and thus holds potential for sensors appropriately tuned to its detection [39]^[31]. A distributed fire detection system that uses a particulate counter as one of its modalities also serves the dual purpose of characterizing the air quality across a geographical region, which is an important feature for complex microclimate environments such as the San Francisco Bay Area, where the air quality can vary greatly across the metropolitan area. These systems have become low cost, are increasingly widespread through many WUI areas, and have been shown to be strongly correlative with PM reference instruments when correction equations for each package are implemented [40]^[32]. PurpleAir is one company offering low-cost particulate counters for consumers and is making that data publicly accessible for anyone to view ^[5]. This is a valuable offering for people wanting to monitor their local air quality and for informing computational models of wildfire particulate diffusion.

Gas

The top three exhaust constituents from wildfire by mass are CO₂ (71.44%), water (20.97%), and CO (5.52%) [38]^[30]. NOx is also generated in significant quantities. Therefore, having CO₂, CO, and NOx sensors could potentially increase the detection capabilities of any fire detection system. In a wildland fire setting, one general limitation will be the diffusion rate of these gases across an area and, in the case of sensors that require direct contact with the gas, the location of the sensor in relation to the fire and wind direction. Additionally, for CO₂, the additive concentration of CO₂ from the ignition source must be substantial enough to be detectable against high normal concentrations of CO₂ in the atmosphere and confounding sources of CO₂. NOx and CO do not suffer from this last drawback, as background concentrations in the atmosphere are very low, particularly in remote wildland contexts where early detection would be the most difficult. The most common sensors employed for CO detection are non-dispersive IR absorption (NDIR), electrochemical, and metal-oxide-based sensors. Although MOS sensors are widely used for gas sensing and have been used in other fire detection system prototypes, they are less ideal for use in distributed low-power systems because of the power requirements of the built-in heater module [41]^[33]. Alternative non-dispersive infrared (NDIR) sensing techniques exist where the attenuation at specific electromagnetic wavelengths is measured, which can be correlated to the attenuation of a specific gas, therefore indicating the concentration of a particular gas [42]^[34]. This method is far superior due to its simpler architecture (i.e., an emitter and receiver diode pair) and lower power requirements. GCxGC is also used to identify the compounds within wildfire smoke. Seventy-two gas phase and 240 particle phase compounds were analyzed using GcxGC to explore the profile of wildfire smoke [43]^[35]. Diterpenoids were found to be the most abundant organic particles detected in the wildfire smoke samples. Furthermore, monoterpenes in the gas phase were higher in the wildlife smoke samples compared to the lab smoke samples, which means that they can be used to identify wildfire. Organic aerosols (OAs) and brown carbon (BrC) are also present in wildfire smoke and can be used to quantify primary and secondary biomass burning. Phenolic compounds and their oxidation products are also large contributors to brown carbon (BrC) ABs405 in wildfire plumes [44]^[36], which can be identified with an aerosol mass spectrometer. OAs can be quantified using a photoacoustic absorption spectrometer. Fourier transform infrared spectroscopy (FTIR) can also be used to identify trace gas emissions from burning biofuels [45]^[37]. FTIR is especially strong in measuring both organic and inorganic compounds and providing information on the distribution of emitted carbon. Some understudied emissions from wildfires such as polycyclic aromatic hydrocarbons, intermediate-volatile compounds, and alkyl amines require more research given their toxicity and the increasing exposure of populations to biomass smoke [46]^[38]. Knowing the emission profile of wildfire smoke is important and can aid in the future modeling of wildfires or exposure assessments.

Sound

Wildfires have a specific sound associated with them that can indicate not only the presence of a fire but also the type of fire it is. Khamukhin and Bertoldo [47]^[39] attempted to create a system that can classify two types of forest fires: crown and surface. Crown fires occur when surface fires spread and ignite the forest canopy, leading to strong turbulent air vortices that result in an increased rate of combustion. Surface fires tend to have a low rate of spread (0.5 m/min) while crown fires tend to be very volatile, extremely dangerous, and can have rates of spread in excess of 200 m/min. Khamukhin and Bertoldo [48]^[40] analyzed several open-source wildfire recordings and noticed that the frequency response of a surface fire resembled that of the red noise spectrum while that of a crown fire was more distinct, with a Gaussian distribution centered around 350 Hz. Therefore, for a microphone array placed in the wild, it is possible to classify and triangulate certain fire types to estimate severe wildfire regions. Thompson et al. [49]^[41] performed an acoustic analysis of firebrands on a crown fire in Alberta, Canada. This research is especially pertinent because prior studies on firebrands focus solely on structure fires. The audio files from eleven cameras recording the fire were extracted and analyzed, and they found that the in-fire cameras had a low false negative rate of 15% and an even lower false positive rate of 1%. They developed a spatial estimate for the spread of firebrands and concluded that there was the highest amount of firebrands 75 m away from the source, with a concentration of 640 firebrands per kg of tree fuel consumed. This research encourages the re-examination of past studies that have well-documented audio tracks of fires to further observe firebrand distribution. Yedinak et al. [50]^[42] performed research on the effect of vegetation on the acoustic signature of fires and found that the moisture content of vegetation had an effect on the acoustic pattern of the wildfire. Higher moisture content led to a lower amplitude and duration of the acoustic signature. Furthermore, Yedinak et al. found that the type of vegetation burned affected the acoustic signature of the fire, with grass combustion having a higher duration but smaller amplitude than burning twigs. The acoustic information can help identify what kind of vegetation was burned in a wildfire.

Radio Frequency Interference

Wildfire-induced radio interference is an important active area of research since to enable any wireless distributed sensing network to work in the wild, there needs to be a stable communication network to rely on. The research on characterizing RF interference is sparse, but Boan [51]^[43] showed evidence that the extreme heat of a small diesel-fueled fire can cause RF attenuation below 600 MHz and an amplification above 600 MHz. He theorized that this is likely due to the refractive effects of the fire, which can work in favor or against the signal, depending on the frequency. However, Li et al. [52]^[44] saw no appreciable RF attenuation effects of a diesel-fueled flame on frequencies between 350 MHz and 400 MHz. They did find that the smoke resulting from the flame attenuated more at 300 MHz than at 400 MHz, and that this attenuation varied depending on what type of fuel was used to start the fire. More research in this area is warranted but there is supporting evidence that a system measuring the RF attenuation between a network of nodes can work to estimate the presence and spatial characteristics of a wildfire.

Multi Sensor Systems

Because of the strengths and weaknesses of the various sensor modalities, many academic and commercial systems employ multiple sensors on the same device. This can contribute to increased precision and recall, at the expense of additional processing power and time. It can also serve to build in predictive components to a detection system, where the sensor data reach combined thresholds determining a high fire risk. For instance, when the relative humidity is less than 30%, temperatures are in excess of 30 °C, and wind speeds are higher than 30 km/h, the fire risk is heightened [53]^[45]. This 30–30–30 rule is often used in the deployment of smart sensors to create a multi-tier alarm system that provides alerts on the varying risk and severity of a wildfire ^[7]. Landis [54]^[46] explored various multimodal sensors capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO₂), and ozone (O₃). The most effective device had an accuracy greater than 80%. Another aspect of this research was describing the importance of using federal reference method (FRM) instruments to evaluate the device’s performance in detecting biomass smoke. Current FRMs for measuring PM 2.5 are not well-characterized and Landis observed that the 1-h FRM correction factor is a function of burn condition. Therefore, this research supports that PM 2.5 can be used to detect wildfires.