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An approach to research, recommend and evaluate alternative architectures and technologies in support of enhanced monitoring and response operations associated with the Libby, Montana Superfund Site and other high hazard wildland fire environments is presented.
Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...
Multispectral satellite imagery has been demonstrated to accurately detect wildfires over a variety of land cover types at coarse- and medium-resolution (i.e., 1km to 30m), typically by detecting burned area after the fire has caused substantial damage. We developed an algorithm to map active fire (i.e., flame) in 2-meter resolution WorldView time-series imagery and monitor fire trajectory over time. Our results depict robust mapping across scenes collected from different fires over grassy and forested land cover, identifying fires as small as 4m 2 throughout the image. Operational deployment at scale may provide valuable near-real time maps of active fire for responders to leverage for timely, targeted mitigation efforts. This white paper describes the current capability specifications and indicates requirements for scaling.
Abstract. The eastern North Atlantic (ENA) is a region dominated by pristine marine environment and subtropical marine boundary layer clouds. Under unperturbed atmospheric conditions, the regional aerosol regime in the ENA varies seasonally due to different seasonal surface-ocean biogenic emissions, removal processes, and meteorological regimes. However, during periods when the marine boundary layer aerosol in the ENA is impacted by particles transported from continental sources, aerosol properties within the marine boundary layer change significantly, affecting the concentration of cloud condensation nuclei (CCN). Here, we investigate the impact of long-range transported continental aerosol on the regional aerosol regime in the ENA using data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility on Graciosa Island in 2017 during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign. We develop an algorithm that integrates number concentrations of particles with optical particle dry diameter (Dp) between 100 and 1000 nm, single scattering albedo, and black carbon concentration to identify multiday events (with duration >24 consecutive hours) of long-range continental aerosol transport in the ENA. In 2017, we detected nine multiday events of long-range transported particles that correspond to ∼ 7.5 % of the year. For each event, we perform HYSPLIT 10 d backward trajectories analysis, and we evaluate CALIPSO aerosol products to assess, respectively, the origins and compositions of aerosol particles arriving at the ENA site. Subsequently, we group the events into three categories, (1) mixture of dust and marine aerosols, (2) mixture of marine and polluted continental aerosols from industrialized areas, and (3) biomass burning aerosol from North America and Canada, and we evaluate their influence on aerosol population and cloud condensation nuclei in terms of potential activation fraction and concentrations at supersaturation of 0.1 % and 0.2 %. The arrival of plumes dominated by the mixture of dust and marine aerosol in the ENA in the winter caused significant increases in baseline Ntot. Simultaneously, the baseline particle size modes and CCN potential activation fraction remained almost unvaried, while cloud condensation nuclei concentrations increased proportionally to Ntot. Events dominated by a mixture of marine and polluted continental aerosols in spring, fall, and winter led to a statistically significant increase in baseline Ntot, a shift towards larger particular sizes, a higher CCN potential activation fractions, and cloud condensation nuclei concentrations of >170 % and up to 240 % higher than during baseline regime. Finally, the transported aerosol plumes characterized by elevated concentration of biomass burning aerosol from continental wildfires detected in the summertime did not statistically contribute to increase baseline aerosol particle concentrations in the ENA. However, particle diameters were larger than under baseline conditions, and CCN potential activation fractions were >75 % higher. Consequentially, cloud concentration nuclei concentrations increased by ∼ 115 % during the period affected by the biomass burning events. Our results suggest that, through the year, multiday events of long-range continental aerosol transport periodically affect the ENA and represent a significant source of CCN in the marine boundary layer. Based on our analysis, in 2017, the multiday aerosol plume transport dominated by a mixture of dust and marine aerosol, a mixture of marine and polluted continental aerosols, and biomass burning aerosols caused increases in the NCCN baseline regime of, respectively, 6.6 %, 8 %, and 7.4 % at SS 0.1 % (and, respectively, 6.5 %, 8.2 %, and 7.3 % at SS 0.2 %) in the ENA.
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High Impedance Faults (HIFs) occur when energized power lines come into contact with high impedance ground surfaces, such as tree branches and grassland. HIFs have the potential to cause arcing, leading to vegetation ignition and the initiation of wildfires. The challenge in detecting HIFs comes from the high impedance of the partially conductive materials in contact with the power lines. They create a fault current of low magnitude and traditional protective devices struggle to detect such faults. This paper proposes a novel HIF detection algorithm based upon the analyzed arcing signatures associated with HIFs. The algorithm is evaluated using the Australian Public Bushfire Safety Program (PBSP) dataset. For comparative analysis, a state-of-the-art commercial HIF detection product is also evaluated using the same dataset. The proposed algorithm demonstrates higher detection accuracy over the commercial products with fewer false flags and undetected faults.
Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.
Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.
This paper deals with detection and temperature analysis and of wildfires using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.
Here we report in the spring 2021, Nepal underwent a record wildfire season in which active fires were detected at a rate 10 times greater than the 2002–2020 average. Prior to these major wildfire events, the country experienced a prolonged precipitation deficit and extreme drought during the post-monsoon period (starting in October 2020). An analysis using observational, reanalysis, and climate model ensemble data indicates that both climate variability and climate change-induced severe drought conditions were at play. Further analysis of climate model outputs suggests the likely reoccurrence of drought conditions, thus favoring active wildfire seasons in Nepal throughout the twenty-first century. While the inter-model uncertainty is large and direct modeling of wildfire spread and suppression has not been completed, the demonstrated relationship between a drought index (the standardized precipitation and evapotranspiration index) and subsequent fire activity may offer actionable opportunities for forest managers to employ the monitoring and projection of climate anomalies at sub-seasonal to decadal timescales to inform their management strategies for Nepal’s wildlands.
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.
Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km h−1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (https://doi.org/10.5281/zenodo.8327264, Liu et al., 2023).
The frequency and intensity of natural disasters (i.e. wildfires, storms, floods) has increased over recent decades. Extreme weather can often be linked to climate change, and human population expansion and urbanization have led to a growing risk. In particular floods due to large amounts of rainfall are of rising severity and are causing loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Expanding urbanization along rivers and creeks often includes opening flood plains for building construction and river straightening and dredging speeding up the flow of water. In a flood event, rapid response is essential which requires knowledge which buildings are susceptible to flooding and which roads are still accessible. To this aim, SpaceNet 8 is the first remote sensing machine learning training dataset combining building footprint detection, road network extraction, and flood detection covering 850km 2, including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.
Electric utilities in California have historically been linked to up to 10% of wildfires. To mitigate this risk, Southern California Edison has invested significantly in wildfire prevention strategies, including undergrounding cables and enhancing equipment inspections. This article explores a novel approach to fire prevention by detecting anomalies in the distribution system that may indicate potential fire hazards. The focus is on identifying arcing conditions through high-resolution point-on-wave (POW) measurements. Arcing, a precursor to fires, is challenging to detect due to its subtle transients and complex system topology. The article discusses the use of advanced signal processing and machine learning techniques, such as spectral correlation function and discrete wavelet transform, to extract features from POW data and accurately identify arcing events. The study demonstrates a high accuracy rate in detecting arcing, paving the way for improved fire prevention measures in electric distribution systems.
Wildfires are projected to increase in severity and frequency due to climate change, and the electric grid is both a cause of wildfires and is vulnerable to wildfires. Equipment from the electric grid accounts for 10% of fires burned in California and 3% of fires nationally. Recent catastrophic wildfires, such as the Lahaina Fire, Camp Fire, Marshall Fire, and Smokehouse Creek fires, were all started by electrical equipment and show how devastating these events can be because they threaten lives and structures. Vegetation structure, weather and winds, climate and vegetation response, land use, and human activities all impact the likelihood of severe wildfires. We explore the relationship between the built environment, electric grid infrastructure specifically, and its role in causing catastrophic wildfires to find lessons learned for increasing resilience. Electric grid utility companies currently employ multiple methods to mitigate fire, including (1) early detection, (2) grid hardening, (3) vegetation management, and (4) pre-emptive shutoffs. Utility companies need to consider the conditions for wildfire and the impact that each mitigation strategy has on drivers of wildfire behavior, as a single solution will not be adequate. Utility companies need to work with stakeholders to develop a holistic strategy to reduce ignition likelihood and spread likelihood to reduce catastrophic wildfires and improve resiliency.
Summertime wildfire activity is increasing in boreal forest and tundra ecosystems in the Northern Hemisphere. However, the impact of long range transport and deposition of wildfire aerosols on biogeochemical cycles in the Arctic Ocean is unknown. Here, we use satellite-based ocean color data, atmospheric modeling and back trajectory analysis to investigate the transport and fate of aerosols emitted from Siberian wildfires in summer 2014 and their potential impact on phytoplankton dynamics in the Arctic Ocean. We detect large phytoplankton blooms near the North Pole (up to 82°N in the eastern Eurasian Basin). Our analysis indicates that these blooms were induced by the northward plume transport and deposition of nutrient-bearing wildfire aerosols. We estimate that these highly stratified surface waters received large amounts of wildfire-derived nitrogen, which alleviated nutrient stress in the phytoplankton community and triggered an unusually large bloom event. Our findings suggest that changes in wildfire activity may strongly influence summertime productivity in the Arctic Ocean.
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Muon flux, the rate of muons reaching Earth from the atmosphere, is impacted by various factors such as air pressure, temperature, and humidity. However, the influence of concentrations of PM2.5 (particulate matter with diameters 2.5 mm or smaller) on muon detection rates remains unexplored. During the summer of 2023, smoke from Canadian wildfires (containing significant amounts of particulate matter) blew over regions in the Northern US, introducing huge fluctuations in PM2.5 concentrations, thus inspiring this experiment to investigate the correlation of PM2.5 concentrations and muon rates. To investigate the correlation, muon collision rates were measured and analyzed alongside PM2.5 concentration data over the periods of both light and heavy smoke. Other confounding variables, including temperature, humidity, and atmospheric pressure, were also considered. The results reveal a statistically significant inverse correlation between muon flux and PM2.5 concentrations, indicating that particulate matter has an impact on the rate of muons reaching the earth’s surface.