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A model of the production of wildfire ignitions and damages is developed and used to determine wildland activity-regulation decisions, which minimize total expected cost-plus-loss due to wildfires. In this context, the implications of various policy decisions are considered. The resulting decision rules take a form that makes it possible for existing wildfire management agencies to readily adopt them upon collection of the required data.
With the surge in wildfire frequency and severity, the risk to firefighters, communities, and forests has escalated dramatically. Climate change and increased amounts of fuel have intensified these challenges, making wildfire management more important than ever. Prescribed burns, a controlled manner of burning land, are a crucial strategy for wildfire prevention, ecosystem management, and forest health. Nonetheless, traditional methods of implementing prescribed burns are labor-intensive, slow, and risk-laden due to human involvement. Our innovative approach leverages autonomous drone swarms to revolutionize prescribed burning methods. These advanced drones collaborate to ignite fires strategically, gather real-time data, and suppress sections of the fire as needed. By minimizing human proximity to the flames, our solution has the potential to significantly enhance operational efficiency and safety.
The increase in the wildfires were observed globally in accordance with global warming, and to real- time monitoring of wildfire risk in broad scale is demanded for wildfire management to prevent the spread of wildfires. Scientists invented a lot of indices to assess the wildfire risk. Vapor Pressure Deficit (VPD) is one of the most important meteorological components for those indices. Compared to other components of fire weather indices, VPD can change quickly from lower risk to higher risk even in sub-hourly. Therefore, real-time fire risk monitoring requires high-resolution and high- temporal VPD spatial map. Here, we developed VPD estimation method using the GOES Advanced Baseline Imager (ABI) data. Unlike the polar-orbital satellite data, the ABI can observe target region every 10 minutes, so that we can estimate VPD for fire weather in real-time. The method used to estimate VPD is same with the algorithm of NASA Earth Exchange Gridded Daily Meteorology (NEX- GDM), which estimate meteorological variables from ground weather observation and spatial variables based on random forest (RF). We calculated RF importance to select bands of ABI as input of the model. To validate our results, we compared the spatial pattern of our VPD data with the Real- Time Mesoscale Analysis (RTMA) data over the conterminous USA. We sought possibility of applying our method to the region where no real-time high-resolution weather data is available, such as South America. The developed method can produce real-time high-resolution high-frequent VPD data in the continental scale. The derived data from GOES ABI could contribute to improve the fire weather monitoring and lead to prevent wildfires.
The risk of severe wildfires in Texas has been related to weather phenomena such as climate change and recent urban expansion into wild land areas. During recent years, Texas wild land areas have experienced sequences of wet and dry years that have contributed to increased wildfire risk and frequency. To prevent and contain wildfires, the Texas Forest Service (TFS) is tasked with evaluating and reducing potential fire risk to better manage and distribute resources. This task is made more difficult due to the vast and varied landscape of Texas. The TFS assesses fire risk by understanding vegetative fuel types and fuel loads. To better assist the TFS, NASA Earth observations, including Landsat and Moderate Resolution Imaging Specrtoradiometer (MODIS) data, were analyzed to produce maps of vegetation type and specific vegetation phenology as it related to potential wildfire fuel loads. Fuel maps from 2010-2011 and 2014-2015 fire seasons, created by the Texas Disasters I project, were used and provided alternating, complementary map indicators of wildfire risk in Texas. The TFS will utilize the end products and capabilities to evaluate and better understand wildfire risk across Texas.
From Intro: A study was initiated in the Santa Monica Mountains to investigate the use of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)for providing improved maps of chaparral coupled with direct estimates of canopy attributes (eg. biomass, leaf area, fuel load)...Analysis focused on AVIRIS data collected on October 19, 1994.
Fire significantly impacts both human-altered and wild landscapes on national and global scales. Though often devastating, wildland fires naturally reduce fuels, preventing larger wildfires. However, their smoke can harm human health locally and globally. To understand its impact on air quality and health, quantifying the emissions released into the atmosphere is crucial. To accurately model fire emissions, understanding fuel characteristics and burning conditions is crucial, as they vary with the fuelbed type and fire weather. For example, savanna fires have lower carbon loading and intensity but spread quickly, while boreal forest fires burn longer and release more carbon due to higher loading. Hotter, drier conditions increase fuel consumption and smoke plume height. Fire weather and available fuel are the key drivers of emissions.
In recent years, the pan-Arctic region has experienced increasingly extreme fire seasons. Fires in the northern high latitudes are driven by current and future climate change, lightning, fuel conditions, and human activity. In this context, conceptualizing and parameterizing current and future Arctic fire regimes will be important for fire and land management as well as understanding current and predicting future fire emissions. The objectives of this review were driven by policy questions identified by the Arctic Monitoring and Assessment Programme (AMAP) Working Group and posed to its Expert Group on Short-Lived Climate Forcers. This review synthesizes current understanding of the changing Arctic and boreal fire regimes, particularly as fire activity and its response to future climate change in the pan-Arctic have consequences for Arctic Council states aiming to mitigate and adapt to climate change in the north. The conclusions from our synthesis are the following. (1) Current and future Arctic fires, and the adjacent boreal region, are driven by natural (i.e. lightning) and human-caused ignition sources, including fires caused by timber and energy extraction, prescribed burning for landscape management, and tourism activities. Little is published in the scientific literature about cultural burning by Indigenous populations across the pan-Arctic, and questions remain on the source of ignitions above 70_ N in Arctic Russia. (2) Climate change is expected to make Arctic fires more likely by increasing the likelihood of extreme fire weather, increased lightning activity, and drier vegetative and ground fuel conditions. (3) To some extent, shifting agricultural land use and forest transitions from forest–steppe to steppe, tundra to taiga, and coniferous to deciduous in a warmer climate may increase and decrease open biomass burning, depending on land use in addition to climate-driven biome shifts. However, at the country and landscape scales, these relationships are not well established. (4) Current black carbon and PM2:5 emissions from wildfires above 50 and 65_ N are larger than emissions from heanthropogenic sectors of residential combustion, transportation, and flaring. Wildfire emissions have increased from 2010 to 2020, particularly above 60_ N, with 56% of black carbon emissions above 65_ N in 2020 attributed to open biomass burning – indicating how extreme the 2020 wildfire season was and how severe future Arctic wildfire seasons can potentially be. (5) What works in the boreal zones to prevent and fight wildfires may not work in the Arctic. Fire management will need to adapt to a changing climate, economic development, the Indigenous and local communities, and fragile northern ecosystems, including permafrost and peatlands. (6) Factors contributing to the uncertainty of predicting and quantifying future Arctic fire regimes include underestimation of Arctic fires by satellite systems, lack of agreement between Earth observations and official statistics, and still needed refinements of location, conditions, and previous fire return intervals on peat and permafrost landscapes. This review highlights that much research is needed in order to understand the local and regional impacts of the changing Arctic fire regime on emissions and the global climate, ecosystems, and pan-Arctic communities.
A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning
Topics include: Coherent Frequency Reference System for the NASA Deep Space Network; Diamond Heat-Spreader for Submillimeter-Wave Frequency Multipliers; 180-GHz I-Q Second Harmonic Resistive Mixer MMIC; Ultra-Low-Noise W-Band MMIC Detector Modules; 338-GHz Semiconductor Amplifier Module; Power Amplifier Module with 734-mW Continuous Wave Output Power; Multiple Differential-Amplifier MMICs Embedded in Waveguides; Rapid Corner Detection Using FPGAs; Special Component Designs for Differential-Amplifier MMICs; Multi-Stage System for Automatic Target Recognition; Single-Receiver GPS Phase Bias Resolution; Ultra-Wideband Angle-of-Arrival Tracking Systems; Update on Waveguide-Embedded Differential MMIC Amplifiers; Automation Framework for Flight Dynamics Products Generation; Product Operations Status Summary Metrics; Mars Terrain Generation; Application-Controlled Parallel Asynchronous Input/Output Utility; Planetary Image Geometry Library; Propulsion Design With Freeform Fabrication (PDFF); Economical Fabrication of Thick-Section Ceramic Matrix Composites; Process for Making a Noble Metal on Tin Oxide Catalyst; Stacked Corrugated Horn Rings; Refinements in an Mg/MgH2/H2O-Based Hydrogen Generator; Continuous/Batch Mg/MgH2/H2O-Based Hydrogen Generator; Strain System for the Motion Base Shuttle Mission Simulator; Ko Displacement Theory for Structural Shape Predictions; Pyrotechnic Actuator for Retracting Tubes Between MSL Subsystems; Surface-Enhanced X-Ray Fluorescence; Infrared Sensor on Unmanned Aircraft Transmits Time-Critical Wildfire Data; and Slopes To Prevent Trapping of Bubbles in Microfluidic Channels.
Wildfires in Chile in the last decade were the worst on record, destroying homes and livelihoods, polluting the air, and displacing whole towns. To predict locations where wildfires were likely to start, the Corporación Nacional Forestal (CONAF) created a wildfire risk model within ArcGIS Pro and Google Earth Engine (GEE) that utilized the NOAA Global Forecast System (GFS) and the NASA Shuttle Radar Topography Mission (STRM) 90-meter datasets. The previous CONAF model was very resource-heavy and time-intensive to run. NASA DEVELOP, in partnership with CONAF, automated the previous model and transferred it fully into GEE where all Earth observation datasets could be used without downloading. The new model substantially reduced the runtime. The final model was used to create a near real-time wildfire monitoring application as well as fire severity maps. The end products will be used by CONAF for wildfire prediction and management to prevent more destruction in the future.
Wildfires have increasingly become major threat to US towns and cities, with California alone having its most destructive wildfire seasons to date in 2017 and 2018 and 2020 already becoming one of the worst fire years on record, causing the large-scale evacuations, the destruction of towns and property, and a significant release of hazardous smoke over the entire west coast. Thus, the U.S. Forest Service recently spent over 50% of its budget on wildfire management – funds which could otherwise be directed to science that could support fire prevention. Aerial support plays a major role in fighting wildfires. Airtankers, helicopters, and other aerial assets support the construction of fire-lines, gather data, and transport crews and equipment. However, presently, aerial firefighting operations rely on relatively unsophisticated technologies for communications, such as over-the-air radios, which limit the ability of pilots and management to relay fire data and coordinate operations. Aerial operations in and around fires are additionally high-risk activities due to the variable, dangerous conditions and complex, technically difficult maneuvers which must be performed to, for example, conduct a retardant drop. Thus, between 2000 and 2013, of the 298 wildland firefighter fatalities, 26% were related to aerial operations. As a result, there is significant potential to both improve the performance and resiliency of wildfire response while reducing risk to human operators.
Unmanned vehicles can be useful in emergency situations for many applications such as surveillance, access to harsh environments, and delivery of supplies. However, efforts to use drones in these situations has not been well-coordinated. This project is looking at multiple aspects of the problem with sub-teams addressing: establishing a method and database network to identify and communicate resources for deployment during major incidents and disasters; identifying how drones and other aviation assets can be useful in wildfire-specific emergencies, including mitigation and prevention planning; and considering safety, rules, and behavior of autonomous vehicles.
The United States Department of Agriculture (USDA) describes wildland fires as, “a force of nature that can be nearly as impossible to prevent, and as difficult to control, as hurricanes, tornadoes and floods.” Existing challenges in managing wildland fires often put first responders’ lives at risk. The emergence of drones and their capabilities to supplement human efforts could alleviate some, if not all, of those risks that first responders face during wildfire management efforts. However, the process of adding drones to wildfire response has come with its own challenges as well. NASA’s System-Wide Safety Project is working towards overcoming these challenges to enable routine transfer of risk from responders to aviation assets. The concept of operations and model-based systems engineering (MBSE) effort for this shift is underway. To inform and to validate the MBSE effort, we delivered a questionnaire to wildland firefighting experts on the hazards they currently face. This questionnaire has given us insight and a better understanding of the challenges related to the use of drones from a first responder’s point of view. We are using this information to better address responders’ concerns, develop a safety management system, and eliminate the roadblocks that prevent the use of drones in wildfire management.
Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.
Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wildfire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry (a Marin-based fire service workforce development program) and the Marin County Fire Department to develop models to assist with fire management. Using data from Sentinel-2A, PlanetScope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), our team developed a number of input data layers for three different models to evaluate wildfire severity. One model performed a suitability analysis with weights based on scientific literature; another model utilized a U-Net Convolutional Neural Network trained on previous fires in Marin and neighboring Sonoma County to predict the difference normalized burn severity; and the third inputted data layers into the FlamMap tool that outputs risk categories. We compared model outputs and performed a weighted overlay analysis to identify specific locations where a fireline could be constructed to interrupt the progress of an active fire. These tools will assist partners in preparing for and managing active wildfire situations.
The Advanced Capabilities for Emergency Response Operations (ACERO) project is expected to develop, integrate, demonstrate, and transition to operations, evolving aviation technologies from NASA and industry to identify, monitor, and suppress wildland fires as a means to enhance safety, improve operational efficiency, and prevent economic loss. Some of the concepts ACERO explores are: wildfire airspace mangement to improve safety and resource utilization during a disaster, applying new aviation technologies to improve safety and demonstrate new mission-support capabilities, second-shift operations to enable progress towards 24/7 aerial response, and the integration of capabilities across NASA's Aeronautics Research, Science, and Space Technology mission directorates to enable expanded and scalable operations. Realizing this vision is expected to bring positive impact to such areas as: consistency of technology adoption through a unified concept of operations, airspace coordination and deconfliction that integrates portable solutions and enables diverse operations, remote sensing and data fusion for modeling and predictions that support in-time decision making, aircraft technologies for hazard avoidance and state management, and increased response capabilities through persistent communications and surveillance. ACERO continues the research begun under the Scalable Traffic Management for Emergency Response Operations (STEReO) activity, which brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA’s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an informational overview of the ACERO and STEReO projects to the Council of Western State Foresters (CWSF) Western State Fire Managers (WSFM) Committee.
Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.
Wildfires, exacerbated by extreme weather events and land use, threaten to change the Amazon from a net carbon sink to a net carbon source. Here, we develop and apply a coupled ecosystem-fire model to quantify how greenhouse gas–driven drying and warming would affect wildfires and associated CO2 emissions in the southern Brazilian Amazon. Regional climate projections suggest that Amazon fire regimes will intensify under both low- and high-emission scenarios. Our results indicate that projected climatic changes will double the area burned by wildfires, affecting up to 16% of the region’s forests by 2050. Although these fires could emit as much as 17.0 Pg of CO2 equivalent to the atmosphere, avoiding new deforestation could cut total net fire emissions in half and help prevent fires from escaping into protected areas and indigenous lands. Aggressive efforts to eliminate ignition sources and suppress wildfires will be critical to conserve southern Amazon forests.