Additional Project Selections Made for FireSense Technology 2022 Solicitation
NASA Science Mission Directorate
Research Opportunities in Space and Earth Sciences –2022
NNH22ZDA001N-FIRET A.53 Technology Development for Support of Wildfire Science and Disaster Mitigation
UPDATED 08/02/2023 – NASA’s Science Mission Directorate, NASA Headquarters, Washington, DC has selected seven proposals (two of which were previously announced in May 2023) for the 2022 solicitation of the FireSense Technology Program (NNH22ZDA001N-FIRET, element A.53 of the ROSES-22 omnibus announcement).
FireSense Technology is a technology development program managed by the Earth Science Technology Office (ESTO) that seeks new, innovative Earth system observation capabilities to predict and manage wildfires and their impacts. These projects aim to develop new tools for monitoring extreme fire events and forecasting wildfire spread.
In total, 24 proposals were evaluated under the 2022 FireSense Technology Program solicitation. The seven awards have a total dollar value of approximately $14M over three years.
Kyle Hilburn from Colorado State University will synthesize innovative observation capabilities, including mobile Doppler RADAR observations, the northern California Doppler wind LIDAR network, high-resolution hyperspectral wind fields, and low latency satellite fire detections to develop the technology needed for utilizing these observations in coupled atmosphere-fire forecasting. The scope of this project is not limited to just these observations, but the technology developed will be applicable to many other types of observational capabilities that are established through FireSense, integrating novel observations of fuels, the fire state, and the atmosphere.
James Thompson from University of Texas, Austin will address the active-fire stage of wildfire management. The project will advance the spatiotemporal resolution and latency of novel multispectral thermal infrared (TIR) data acquired from a small Unmanned Aircraft System (UAS), increasing the accuracy of the detection and characterization of burn stages. Deriving the temperature and emissivity in 3D of both the solid and gas phases improves knowledge of maximum burn temperatures as well as heat and gas flux rates, which are important for modeling of fire behavior.
Fatemeh Afghah from Clemson University will develop an integrated AI-based formation and onboard computing method for a fleet of heterogeneous drones. The project will develop a hierarchical platform of multiple UAVs to provide long-term coverage of fires; develop low-computation real-time collaborative learning methods for fire detection and mapping onboard the drones; and, transmit the final fire map to stakeholders. The work will also develop an AI-based onboard fire spread modeling capability using deep learning methods, a Digital Twin Environment for fire management activities, and fire behavior modeling outputs so that fire management teams can interactively assess risks across active wildland fires.
Anjali Singh from Planet Labs will initiate concept studies to develop and demonstrate low-cost and scalable infrared sensing and other technologies for wildfire management. The work will conduct a study to mature a low cost mass-producible, space-based fire detection thermal Earth observation imager. This study will mature a space-based fire detection instrument as part of the active fire stage of the wildfire lifecycle. The study specifically focuses on validating the approach of leveraging SWIR and/or MWIR thermal cameras to perform real-time fire detection from space. By validating the instrument and sensing concept of operations, the study will investigate the case for a low-cost proliferated LEO constellation approach to wildfire detection, capable of providing a 15-minute detection cadence across the continental US.
Douglas Morton from Goddard Space Flight Center will develop a Compact Fire Imager (CFI), a push broom instrument with six spectral bands between the shortwave infrared (SWIR) and thermal infrared (TIR), including two channels in the mid-wave infrared (MWIR) specifically designed to detect and characterize flaming and smoldering fires. The CFI will deliver unsaturated multi-spectral measurements at high spatial resolution in a form factor that is compatible with the size, weight, and power (SWaP) constraints for NASA’s next-generation airborne platforms. CFI builds on the design and performance of the dual-band Compact Thermal Imager (CTI) that collected more than 15 million images from the International Space Station (ISS) in 2019, including thousands of fires.
Sreeja Nag from the Bay Area Environmental Research Institute, Inc. plans to develop and verify a space-based distributed observing system that will enhance the Weather Research and Forecasting Fire Spread Model (WRF-SFIRE) and other existing USGS fire danger products. Building off a previous ESTO project—Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions (D-SHIELD)—Nag’s team plans to use Global Navigation Satellite Systems- reflectometry (GNSS-R) data from currently operational Cyclone Global Navigation Satellite System (CYGNSS) and Spire Global satellites to make improved soil moisture measurements and to generate Burnt Area Maps (BAMs). These data products will significantly augment current fire prediction and provide new frameworks for wildfire observation and management.
The Pyro-atmosphere InfraRed Sounder (PIRS), to be developed by Sun Wong at the Jet Propulsion Laboratory (JPL), aims to provide 3-dimensional information about the state of the atmosphere during the pre- and active-fire stages of wildland fires. This airborne instrument would measure temperature and humidity with high spatial resolution, providing insights into how fires start and spread as well informing fire management response. Like Nag’s project, PIRS advances previous ESTO work and will achieve major size reductions and lower costs for instrument development and flight operations.
- AI-Enabled Drone Swarms for Fire Detection, Mapping, and Modeling
Fatemeh Afghah, Clemson University
- Technology Development to Integrate Innovative Observation Capabilities into Coupled Wildfire Models for Improved Active Fire Forecasting
Kyle Hilburn, Colorado State University
- The Airborne Compact Fire Imager (CFI) for Measurements Across the Entire Fire Lifecycle
Douglas Morton, Goddard Space Flight Center
- Distributed Spacecraft with Heuristic Intelligence to Monitor Wildfire Spread for Responsive Control
Sreeja Nag, Ames Research Center (Announced May 2023)
- Wildfire Thermal Detection Instrument Development
Anjali Singh, Planet Labs PBC
- UAS Thermal Infrared Spectroscopy will Improve Real Time Evaluation of Hazards and Environmental Impacts of Wildfires
James Thompson, University of Texas, Austin
- Pyro-Atmosphere Infrared Sounder: Monitoring Fire Weather Conditions with a Sub-Kilometer Spatial-Resolution Hyperspectral Infrared Sounder
Sun Wong, California Institute of Technology (Announced May 2023)
AI-Enabled Drone Swarms for Fire Detection, Mapping, and Modeling
Fatemeh Afghah, Clemson University
Tremendous scientific and technology advances have been made in the past decade toward detecting, mapping, and predicting behavior of wildfires, mainly due to or building on the expansion in airborne and spaceborne remote sensing. These advances have left important gaps, including the ability to monitor a wildfire’s minute by minute change and conditions in its near environment, to quickly yet accurately anticipate near-term dynamic fire behavior, and to integrate diverse sources of intelligence and predictions and test action scenarios in an accessible framework. Patrolling UAVs have been proposed to fill data gaps but have had limited use as one or a few independent radio-controlled drones controlled by a nearby human, where limited bandwidth communication requires most collected information to be stored onboard for later processing.
To both extend existing capabilities built on NASA data and address the limits of current UAS-based fire management technology, our objective is to develop integrated AI-based formation and onboard computing methods for a fleet of heterogeneous drones. We will accomplish this through proposal thrusts in coordinated fire detection and mapping, short term UAS-based fire spread modeling, and a synthesized data and modeling visualization environment (digital twin) to aid analysis and management of a complex, evolving wildfire and its environment. Thrust 1 will develop a hierarchical platform of multiple UAVs to provide long-term coverage of the fire using both high altitude platforms to serve as leaders, managing a fleet of small, low altitude UAS or fixed wings. The work will develop optimal coalitions to provide full fire coverage and optimally assign resources while maintaining space and communication coverage. Thrust 2 will develop low-computation real-time collaborative learning methods for fire detection and mapping onboard the drones to mosaic multi-vehicle data into a common image, distill the detected fire area, and only transmit the final fire map. Thrust 3 will develop AI-based onboard fire spread modeling using deep learning methods training from airborne data, satellite active fire data, and a library of 1-minute frequency model output from validated coupled weather-fire modeling fire event case studies. Rapid short-term predictions will anticipate alignments of fire environment conditions that enable rapid fire growth. Thrust 4 will develop a Digital Twin Environment – a 3D enhanced augmented reality environment that can integrate real-time data from aircraft and UAS assets, fire management data from SIT-209 reports, plans for fire management activities, and fire behavior modeling outputs into a semi-immersive geospatial platform so that fire management teams can spatially and interactively assess risks and opportunities across active wildland fires.
Our four thrusts build upon NASA-sponsored projects, data, and target key gaps encountered in previous work across observation, modeling, and communication. They address FireTech topic areas “enabling unprecedented measurements from multiple vantage points through model-directed, coordinated observations using autonomous tasking”, addressing computational challenges for modeling and for data acquisition, fusion, and processing in a real-time environment”, and facilitating machine learning and artificial intelligence to create new data products needed for wildfire management and for management of the constellation of observing platforms”. As new discoveries and technology, they will benefit from demonstration and testing in prescribed fires, managed wildfires, and capstone field tests at FireSense airborne field campaigns.
Technology Development to Integrate Innovative Observation Capabilities into Coupled Wildfire Models for Improved Active Fire Forecasting
Kyle Hilburn, Colorado State University
Accurate wildfire and smoke modeling is extremely sensitive to the initial conditions: how dry are the fuels, where are the strongest winds, where is fire burning, and how high is the smoke being lofted? How and where these elements of the combustion triangle come together has a dramatic influence on the subsequent fire spread and smoke production. Current observational capabilities lack the coverage, resolution, and timeliness to produce the firefighter-scale forecasts that are needed to significantly improve wildfire management. However, improved observations alone will not lead to improved forecasts because there is a significant technological challenge in connecting observations with models.
This proposal will synthesize innovative observation capabilities, including mobile Doppler RADAR observations, the northern California Doppler wind LIDAR network, extremely high-resolution hyperspectral wind fields, and low latency satellite fire detections to develop the technology needed for utilizing these observations in coupled atmosphere-fire forecasting. The scope of this project is not necessarily limited to just these observations, but the technology developed here will be applicable to many other types of observational capabilities that are established through FireSense, integrating novels observations of fuels, the fire state, and the atmosphere.
A major aspect of the technology development will be in the development of strategies for the cost-effective use of cloud computing and high-performance computing to obtain the lowest possible latency. We also recognize that physical models alone are unlikely to meet latency requirements, especially for providing probabilistic information from ensemble modeling techniques. Thus, the other major aspect of technology development is the use of machine learning to accelerate the physical model and data assimilation components of the coupled modeling system. Note that while WRF-SFIRE provides the specific application in this project, the machine learning development will be applicable to other modeling systems, and to data assimilation and ensemble modeling for fire and smoke forecasting in general. Thus, the proposed project is relevant to the mission objectives to address computational challenges for modeling and for data acquisition, fusion, and processing in a real-time environment” and to facilitate machine learning and artificial intelligence to create new data products needed for wildfire management”.
This project will demonstrate the integration of innovative observations into a coupled wildfire model with the purpose to support wildfire airborne field campaigns. These observations would be assimilated into the fire modeling framework to provide the best possible four-dimensional representation of the atmospheric environment, composition, and fire conditions. These fields will be useful for answering science questions related to campaign objectives, while the data from the campaign would be useful for evaluating fire and smoke models.
The Airborne Compact Fire Imager (CFI) for Measurements Across the Entire Fire Lifecycle
Douglas Morton, Goddard Space Flight Center
Fires continue to grow hotter, faster, and more destructive in a warmer world. There is an urgent need for new observations of extreme fires to understand and anticipate rapid changes in fire risk, to detect and track individual fire events, and to evaluate fire impacts on ecosystems and communities. Here, we propose to develop a new instrument, the Compact Fire Imager (CFI), which will deliver unsaturated multi-spectral measurements at high spatial resolution in a form factor that is compatible with the size, weight, and power (SWaP) constraints for NASA’s next-generation airborne platforms. Our team will also design and implement an onboard processing system to deliver fire products in near-real time for fire management a critical step to enable the autonomous operation of CFI on unmanned aerial systems (UAS), including high altitude long endurance (HALE) platforms under development to support NASA’s Wildland FireSense Project.
We propose to develop and deliver CFI, a pushbroom instrument with six spectral bands between the shortwave infrared (SWIR) and thermal infrared (TIR), including two channels in the mid-wave infrared (MWIR) specifically designed to detect and characterize flaming and smoldering fires. CFI builds on the design and performance of the dual-band Compact Thermal Imager (CTI) that collected more than 15 million images from the International Space Station (ISS) in 2019, including thousands of fires. CFI leverages the stability and proven performance of innovative Strained-Layer Superlattice (SLS) detector technology on CTI with four specific improvements for fire science and applications: 1) a larger format SLS detector array that improves cross-track resolution and swath width, 2) a custom butcher block filter that provides six specific bands for fire science and applications, 3) a custom optical design that leverages the latest infrared glass technology, and 4) an enhanced processor card that supports instrument operation and onboard fire detection using machine learning (ML) algorithms.
CFI’s six bands provide critical inputs for fire detection and characterization. Our team has a proven track record of algorithm development and data product delivery for active fires, burned area, and fire carbon emissions. New measurements from CFI will supply real-time data to fire managers and critical new insights regarding the fine-scale details of fire behavior and ecosystem impacts that cannot be captured with existing airborne or satellite sensors.
The proposed onboard processing approach leverages commercial off-the-shelf (COTS) components and existing software workflows developed by our team for routine instrument operation, including commanding and data handling. This allows our team to focus on the design and implementation of new ML algorithms for onboard processing that leverage CFI’s unique spectral channels and the spatial resolution, swath width, and repeat observations possible with airborne platforms. The baseline design includes a GPU to accelerate ML algorithms that draw upon existing packages such as TensorFlow lite to provide optimal performance while meeting the stringent SWaP constraints for the overall CFI design.
The proposed CFI instrument and onboard processing system primarily targets two research topics in A.53: 1) Enhance capabilities of existing science instruments needed for monitoring pre-fire, active-fire, and post-fire environments; 2) Reduce mass and power of instruments for accommodation by next-generation small spacecraft and aerial platforms,” with additional attention to the use of ML for onboard processing (Topic 5). Our team is uniquely qualified to develop and deliver the CFI instrument and analyze and interpret CFI data from airborne deployments as part of NASA’s Wildland FireSense Project. In addition, the proposed effort has broad support from US agency partners with operational roles for active fire management (USFS, NOAA) and post-fire assessment (USGS).
Distributed Spacecraft with Heuristic Intelligence to Monitor Wildfire Spread for Responsive Control
Sreeja Nag, Ames Research Center
We propose to develop and verify a space-based distributed observing system to improve wildfire response decisions by monitoring and forecasting fuel flammability and wildfire spread and providing on-demand fire danger and burnt area maps. The system will use Global Navigation Satellite System Reflectometry (GNSS-R) within an intelligent, adaptive sensing framework, that leverages mid-TRL tools like D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) and WRFx employing WRF-SFIRE (Weather Research and Forecasting Fire Spread Model). GNSS-R provides microwave data that can pierce through clouds/smoke/canopy, has a small form factor allowing for scalable constellations, and hence frequent data products during active fires than MODIS and VIIRS (12-24 hours). D-SHIELD is a software tool suite for optimal, ground-based, observation planning for a constellation of spaceborne instruments informed by dynamic scientific objectives (AIST-18). WRFx is an integrated fire and smoke decision support tool (AIST-21). Resultant data products will be used to enhance existing USGS fire danger products and the USGS-supported LANDFIRE fuel layers product.
We will develop/enhance five products using GNSS-R data from CYGNSS (7-satellite NASA mission) and Spire Global (commercial fleet) and assimilate into WRFx. GNSS-R has shown successful Soil Moisture (SM) retrievals for weather and flood forecasting, but active fire applications have been limited.
1/ Improved SM retrieval accuracy and resolution via an automated calibration strategy around active fire regions, enabled by new physics and machine learning based retrieval models
2/ Dynamic burnt-area maps (BAMs) from high-resolution Delay Doppler Maps; Will improve the ‘absence of fire’ component of WRFx,
3/ & 4/ Enhanced USGS Wildfire Fire Potential Index and Wildfire Large Fire Probability fire danger products using the SM from #1; will improve fire prediction
5/ High cadence LANDFIRE fuel layer products during active fires; will provide the fire simulator community access to updated fuel layer data.
#3, #4, #5 leverages the popularity of USGS and LANDFIRE products such that public can access GNSS-R data enhanced fire products without the need for development of new interfaces.
The WRFx system will be revised to process the new data products: SM, BAM. A new emissions module will be developed to replace the current simplistic one within WRF-SFIRE. The developed data products and new module is expected to improve WRFx fire prediction, which will inform observation planning and fire management via the 2 frameworks that we will develop:
A/ Observation Value Framework, to quantify observation priorities based on different dynamic objectives such as improving WRFx prediction quality, field campaigns in populous areas
B/ Fire Forecast Reporter to improve situational awareness and support ongoing wildfire field campaigns using improved fire growth expectations, smoke dispersion, visibility predictions
A critical component of the proposed system is the intelligence to dynamically task the observing (satellites) and planning (ground stations) assets, and synchronization between them. GNSS-R satellites can switch to a high-resolution data gathering mode and have a programmable uplink-downlink with customizable data priorities. We will significantly enhance D-SHIELD planner for wildfire applications using satellites like CYGNSS to capture a desired number of specular locations at resolutions and priorities informed by frameworks A-B. System responsiveness depends on runtime of computational components, number of satellites and ground stations. Agile technology beyond what is currently available will be simulated and the utility vs cost evaluated to inform future GNSS-R missions. Increased utility by adding responsively tasked GNSS-R data will be verified against nominal WRFx forecasts, and nominal USGS and LANDFIRE products
Wildfire Thermal Detection Instrument Development
Anjali Singh, Planet Labs PBC
Planet Labs PBC (Planet) in collaboration with NASA Ames and JPL proposes to conduct a study to mature a low cost mass producible space-based fire detection thermal Earth observation imager.
This study is to mature a space-based fire detection instrument as part of the active fire stage of the wildfire lifecycle. The study specifically focuses on validating the approach of leveraging a SWIR and/or MWIR thermal camera to perform real time fire detection from space. By validating the instrument and sensing concept of operations, the study will investigate the case for a low-cost proliferated LEO constellation approach to wildfire detection, capable of providing a 15-minute detection cadence across the continental US.
Planet believes this thermal imager could be deployed using our Superdove satellites which are a TRL9 platform, optimized to provide very high-performance while being mass producible at a very low cost. Leveraging a mature platform like this can enable operational systems to be deployed at a fraction of the cost and schedule of other approaches. An initial study done internally at Planet suggests that such a multi-spectral WIR/MWIR system would be capable of 30m resolution at nadir, and 70m at the highest off nadir angle. Using our current understanding of the requirements, we initially estimate being able to detect fires even as small as 10m2 with a system of this size on a 15-minute cadence for the continental United States. This proposed constellation will also utilize inter-satellite link technology being developed at Planet for real time communication as part of the NASA Communications Services Project (CSP), to allow stakeholders such as federal, state, and local agencies to see real time detections and near real time thermal imagery anywhere.
UAS Thermal Infrared Spectroscopy will Improve Real Time Evaluation of Hazards and Environmental Impacts of Wildfires
James Thompson, University of Texas, Austin
Our proposal primarily addresses the active-fire stage of wildfire management. We propose to advance the spatiotemporal resolution and latency of novel multispectral thermal infrared (TIR) data acquired from a small Unmanned Aircraft System (UAS), increasing the accuracy of the detection and characterization of burn stages. Deriving the temperature and emissivity in 3D of both the solid and gas phases improves knowledge of maximum burn temperatures, as well as, heat and gas flux rates, important for first-principle process-based modeling of fire behavior. Higher temporal resolution enables estimation of instantaneous fluxes to constrain fire and plume dynamics in near real-time. Unique to this study, the spectral emissivity data quantifies specific gas emissions (e.g., SO2, CO, and NH3) using established TIR gas retrieval algorithms and empirical formulas (e.g., Realmuto and Berk, 2016; Vasileva and Moiseenko, 2013), which coupled with accurate temperatures vastly improve estimates of burn intensity, oxygen levels, and pollutant concentrations. These characteristics are important for understanding the impacts of wildland and prescribed fires on vegetation and soils, and air quality, for regulatory and legislative policy consideration.
Technologically, this proposal centers on developing an affordable small UAS-based high-temperature multispectral TIR imaging system with high spatial (meter) and temporal (second) resolution. For the first time, the system will measure emissivity and unsaturated temperature up to maximum potential flame temperatures (<2100 K). A prototype of this system is developed and successfully tested by the PI over active volcanoes (PyMTI-UAS). This proposal improves the performance, reliability, and latency metrics of that system. The versatile small UAS (<2 kg and <20 W) is easily deployable during a wildfire event to rapidly quantify the heat and gas fluxes in 3D. A telemetry downlink is coupled with this system to allow the processing and distribution of data into fire management systems with low latency. Results are directly available to wildland fire commanders to aid in evaluating current fire behavior (thermal and gases). Previously developed temperature/gas sensors and ground-based multispectral TIR imaging systems will provide vicarious calibration and validation. This setup compliments the detection of wildfires using orbital data products by providing higher spatial and temporal resolution plus dynamic tasking. Though the TIR imaging system is useful for providing important information for all stages of wildfire management (e.g., land-use and biomass pre- and post- fire), the spectral data collected during the active and smoldering phase of wildland fires (full life and diurnal cycle) are most important for the thermal and gas flux determination objectives of this study. The final goal of the proposal develops acquisition-processing-analytical protocols and provides actionable results to stakeholders/incident commanders in near real-time.
This project partners with U.S. Fish & Wildlife Service, Balcones Canyonlands National Wildlife Refuge (Texas). The Refuge aids in the deployment of this system during prescribed burns, and ultimately to become operational during wildfires. This collaboration allows wildfires to be studied under different conditions, environments, and habitats, improving our synoptic understanding of burns. The proposal delivers an affordable high-impact system that can be used by numerous stakeholders, especially those with limited resources.
Pyro-Atmosphere Infrared Sounder: Monitoring Fire Weather Conditions with a Sub-Kilometer Spatial-Resolution Hyperspectral Infrared Sounder
Sun Wong, California Institute of Technology
Wildfire results in tremendous economic loss in the Continental United States, and the frequency of occurrence is increasing in a warming global climate. We propose to perform unprecedented measurements using a new hyperspectral infrared sounder (PIRS) on aircraft campaigns to demonstrate the capabilities of high spatially resolved temperature (T) and humidity (q) soundings on improvement in fire weather monitoring and forecasts during pre- and active-fire stages. We will obtain consultation and advices from Pacific Wildland Fire Sciences Laboratory, United States Department of Agriculture (USDA), to refine our instrument measurement requirements as well as data products for improvement of monitoring fire meteorology and atmospheric conditions for fire sciences and management.
Remote sensing datasets used for fire weather monitoring are mainly from imagers, e.g., MODIS or GOES, which provide 2-D surface information such as surface temperature and vegetation types. However, 3-D information of the state of the atmosphere is essential in fire weather monitoring and forecasting. For example, synoptic scale weather patterns are highly related to development of extreme fire events. Coupling of the planetary boundary (PBL) during extreme fire events with mid-tropospheric moisture advection may induce pyrocumulonimbus (pyroCb), causing fire suppression activity to be more difficult and unpredictable due to the turbulent atmosphere. Therefore, fire meteorology is not only the study of how meteorological conditions influence fire initiation and development, but also how the wildfire can alter atmospheric circulation and provide feedback to fire development. This information helps assist in fire management and public safety.
The Pyro-atmosphere InfraRed Sounder (PIRS) uses new grating spectrometer optics and detector technology developed under NASA’s Earth Science Technology Office (ESTO) to achieve a major size reduction in infrared sounding from an aircraft, achieving lower cost for the instrument development and flight operations. PIRS exists as a TRL 5 brassboard and is well suited to implementation in an aircraft. PIRS can perform three measurements with unprecedented flexible spatial resolution (~15-470 m) and wide swath (~20 km at 8.5 km atltitude) from an aircraft platform: (1) 3-D sounding of temperature (T) and specific humidity (q) in the atmosphere from the PBL to the upper troposphere, (2) Carbon Monoxide (CO) measurement for monitoring transport of polluted air, and (3) estimates of fire radiative power (FRP) in imaging mode at 15 m resolution.
We aim to achieve the following goals to help wildfire prediction and management:
(G1) Obtain atmospheric thermodynamic structures (T and q profiles) in ‘sounding’ mode with a wide swath (~20 km at 8.5 km flight altitude) and high spatial resolution (470 m horizontal and ~300-500 m vertical) and FRP in ‘imaging’ mode (15 m horizontal) to monitor the initiation and development of wildland fire events.
(G2) Map instantaneously the atmospheric thermodynamic conditions to the initiation of pyrocumulonimbus (pyroCb) for mitigation of hazards during fire suppression activities.
(G3) Measure the CO distribution, an indicator of air quality and smoke spread, and monitor its transport relative to the atmospheric thermodynamic conditions.