Towards a whole-system framework for wildfire monitoring using Earth observations.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole-system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre-fire vegetation inventories, (2) active-fire monitoring, (3) post-fire assessment, and (4) multi-scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co-development of information products using cloud computing that benefits from open-access Earth observation and other geospatial data layers.

Atmospheric Mercury Isotope Shifts in Response to Mercury Emissions from Underground Coal Fires.

Pollutant emissions from coal fires have caused serious concerns in major coal-producing countries. Great efforts have been devoted to suppressing them in China, notably at the notorious Wuda Coalfield in Inner Mongolia. Recent surveys revealed that while fires in this coalfield have been nearly extinguished near the surface, they persist underground. However, the impacts of Hg volatilized from underground coal fires remain unclear. Here, we measured concentrations and isotope compositions of atmospheric Hg in both gaseous and particulate phases at an urban site near the Wuda Coalfield. The atmospheric Hg displayed strong seasonality in terms of both Hg concentrations (5-7-fold higher in fall than in winter) and isotope compositions. Combining characteristic isotope compositions of potential Hg sources and air mass trajectories, we conclude that underground coal fires were still emitting large amounts of Hg into the atmosphere that have been transported to the adjacent urban area in the prevailing downwind direction. The other local anthropogenic Hg emissions were only evident in the urban atmosphere when the arriving air masses did not pass directly through the coalfield. Our study demonstrates that atmospheric Hg isotope measurement is a useful tool for detecting concealed underground coal fires.

Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

Thermal Infrared Sensing for Near Real-Time Data-Driven Fire Detection and Monitoring Systems.

With the increasing interest in leveraging mobile robotics for fire detection and monitoring arises the need to design recognition technology systems for these extreme environments. This work focuses on evaluating the sensing capabilities and image processing pipeline of thermal imaging sensors for fire detection applications, paving the way for the development of autonomous systems for early warning and monitoring of fire events. The contributions of this work are threefold. First, we overview image processing algorithms used in thermal imaging regarding data compression and image enhancement. Second, we present a method for data-driven thermal imaging analysis designed for fire situation awareness in robotic perception. A study is undertaken to test the behavior of the thermal cameras in controlled fire scenarios, followed by an in-depth analysis of the experimental data, which reveals the inner workings of these sensors. Third, we discuss key takeaways for the integration of thermal cameras in robotic perception pipelines for autonomous unmanned aerial vehicle (UAV)-based fire surveillance.

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

The Capabilities of Dedicated Small Satellite Infrared Missions for the Quantitative Characterization of Wildfires.

The main objective of this paper was to demonstrate the capability of dedicated small satellite infrared sensors with cooled quantum detectors, such as those successfully utilized three times in Germany's pioneering BIRD and FireBIRD small satellite infrared missions, in the quantitative characterization of high-temperature events such as wildfires. The Bi-spectral Infrared Detection (BIRD) mission was launched in October 2001. The space segment of FireBIRD consists of the small satellites Technologie Erprobungs-Träger (TET-1), launched in July 2012, and Bi-spectral InfraRed Optical System (BIROS), launched in June 2016. These missions also significantly improved the scientific understanding of space-borne fire monitoring with regard to climate change. The selected examples compare the evaluation of quantitative characteristics using data from BIRD or FireBIRD and from the operational polar orbiting IR sensor systems MODIS, SLSTR and VIIRS. Data from the geostationary satellite "Himawari-8" were compared with FireBIRD data, obtained simultaneously. The geostationary Meteosat Third Generation-Imager (MTG-I) is foreseen to be launched at the end of 2022. In its application to fire, the MTG-I's Flexible Combined Imager (FCI) will provide related spectral bands at ground sampling distances (GSD) of 3.8 µm and 10.5 µm at the sub-satellite point (SSP) of 1 km or 2 km, depending on the used FCI imaging mode. BIRD wildfire data, obtained over Africa and Portugal, were used to simulate the fire detection and monitoring capability of MTG-I/FCI. A new quality of fire monitoring is predicted, if the 1 km resolution wildfire data from MTG-1/FCI are used together with the co-located fire data acquired by the polar orbiting Visible Infrared Imaging Radiometer Suite (VIIRS), and possibly prospective FireBIRD-type compact IR sensors flying on several small satellites in various low Earth orbits (LEOs).