Naturally occurring wildfires can be nearly as impossible to prevent, and as difficult to control, as hurricanes, tornadoes, and floods. Along with their destructive power, they also are a vital component of forest growth, ecological succession, and soil nutrient enhancement. NASA provides datasets and tools for assessing and managing wildfires before, during, and after an event.
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Wildfires emitted 1.76 billion metric tonnes (equivalent to more than 1.9 billion tons) of carbon globally in 2021, according to data from the European Union's Copernicus Atmosphere Monitoring Service. Credit: U.S. Forest Service.
Wildfires are an essential process connecting terrestrial systems to the atmosphere and climate, and are an integral component of ecological succession, plant germination, and soil enhancement. Along with these beneficial aspects, they also emit vast quantities of carbon into the atmosphere along with aerosols and other particles that can impact health, restrict visibility, and contribute to global climate change.
This Data Pathfinder is designed to help guide you through the process of selecting and using datasets applicable to wildfires, with guidance on resolutions and direct links to the data sources. If you are new to remote sensing, the What is Remote Sensing? Backgrounder provides a good overview. In addition, NASA's Applied Remote Sensing Training Program (ARSET) provides numerous training modules, including Fundamentals of Remote Sensing.
If you have specific questions about how to use data, tools, or resources mentioned in this Data Pathfinder, please visit the Earthdata Forum. Here, you can interact with other data users and NASA subject matter experts on a variety of Earth science research and applications topics.
An Overview of Wildfire
The number, severity, and overall size of wildfires has increased, according to the U.S. Department of Agriculture, through contributing factors including extended drought, the build-up of fuels, past fire management strategies, invasive species targeting specific tree species, and the spread of residential communities into formerly natural areas. In 2021, 58,985 wildfires were reported across the U.S. that consumed 7,125,643 acres, according to the 2021 Annual Report by the National Interagency Coordination Center (NICC), which coordinates the mobilization of resources for wildland fire and other incidents throughout the U.S. But wildfires also have a human component. The NICC report notes that wildfires destroyed almost 6,000 structures in 2021, including 3,577 residences, 2,225 minor structures, and 237 commercial or mixed residential structures.
Data collected by sensors aboard orbiting satellites, carried aboard aircraft, or installed on the ground provide a wealth of data that can be used to assess conditions before a burn, track the movement of a wildfire in near real-time, and assess the environmental impact of an historic burn. NASA provides numerous datasets, tools, and other resources that can be used to investigate, track, and assess wildfires, all of which are available without restriction under NASA's open data policy.
Common Measurements at a Glance
Datasets referenced in this Data Pathfinder are from sensors shown in the table below and is not intended to be an exhaustive list of all sensors or missions producing data relevant to wildfires. Some of these datasets are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available generally within three hours of a satellite observation, which allows for near real-time (NRT) monitoring and decision making. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information.
Near Real-time (NRT) data are available within three hours of a satellite observation, which allows for monitoring and decision making during ongoing events.
Once a fire burns through an area, there are many potential impacts, such as loss of vegetation, landslide potential, runoff, and more.
Connection of Sustainable Development Goals to Wildfires
The Sustainable Development Goals (SDGs) are a collection of 17 interlinked global goals designed to be a blueprint for a sustainable future for all of Earth’s inhabitants. The SDGs are part of the 2030 Agenda for Sustainable Development, an international plan signed by all United Nations (UN) member states in 2015 and underpinned by the foundational components of People, Planet, and Prosperity.
The 17 SDGs in the Agenda are made up of 169 objectives that include specific social, economic, and environmental targets. These targets provide a blueprint for developing a more sustainable global future.
Data acquired remotely by sensors aboard satellites and aircraft or installed on the ground play a unique role in tracking the progress toward achieving the SDGs. These remotely sensed Earth observations provide consistent and continuous information on the state of Earth processes and their change over time. These data also are integral components of socioeconomic metrics that provide a measure of how humans co-exist with the environment and the stresses they encounter through natural and human-caused changes to the environment.
NASA Earth observation data are available without restriction to all data users, a policy that is being adopted by other international space agencies and one that reduces the cost of monitoring the SDGs and provides developing countries a means to acquire and utilize these data for other policy-making purposes.
NASA’s datasets are organized by topics that help users to locate, access, and apply relevant and complementary datasets for each SDG. The Wildfire Data Pathfinder addresses (but is not limited to) the following SDGs:
SDG
SDG Goals Relevant to Wildfires
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Goal 1. End poverty in all its forms everywhere
Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social, and environmental shocks and disasters
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Goal 2. End hunger, achieve food security and improved nutrition, and promote sustainable agriculture
Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production; that help maintain ecosystems; that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters; and that progressively improve land and soil quality
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Goal 11. Make cities and human settlements inclusive, safe, resilient, and sustainable
Target 11.5: By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations
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Goal 13. Take urgent action to combat climate change and its impacts
Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries
Target 13.2: Integrate climate change measures into national policies, strategies, and planning
Target 13.3: Improve education, awareness-raising, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning
The opportunities to connect NASA data to the SDGs are infinite; therefore, the datasets included in specific Data Pathfinders are not intended to be comprehensive. Additionally, NASA datasets are not official indicators for SDG monitoring and decision-making but are complementary.
This section provides links to tools and applications relevant to analyzing and visualizing wildfire data referenced in this Data Pathfinder. NASA's Earth Science Data Systems (ESDS) Program maintains many more resources for data analysis that may be helpful. Explore the full list on the NASA Earthdata Data Tools page.
Earthdata Search
Earthdata Search is a tool for searching for and discovering data collections from NASA's Earth Observing System Data and Information System (EOSDIS) collection as well as from U.S. and international agencies across Earth science disciplines. Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections. Note that an Earthdata Login is required to download data from Earthdata Search.
In the project area, for some datasets, you can customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML, or a GeoTIFF. You can also choose from a variety of projection options. Lastly, you can subset the data, obtaining only the bands that are needed.
HDF and NetCDF files can be viewed using NASA's Panoply data viewer. Along with viewing data, Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.
Giovanni is an online environment for the display and analysis of geophysical parameters. Note that an Earthdata Login is required to use Giovanni. While Giovanni provides many options for analysis, the following are the more popular ones:
Time-averaged maps: A simple way to observe the variability of data values over a region of interest
Map animations: A means to observe spatial patterns and detect unusual events over time
Area-averaged time series: Used to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step
Histogram plots: Used to display the distribution of values of a data variable in a selected region and time interval
For more detailed tutorials:
Giovanni How-To's on the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) YouTube channel
Data recipe for downloading a Giovanni map as NetCDF and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text
The NASA Worldview data visualization application provides the capability to interactively browse more than 1,000 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, which supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview now includes nine geostationary imagery layers from the GOES-East, GOES-West, and Himawari-8 geostationary satellites that are available at 10-minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the visualization of the differentiation between air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.
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Worldview Suomi NPP/VIIRS nighttime lights comparison image showing power outages caused by Hurricane Irma in September 2017. The right image (acquired September 1) shows the island before Hurricane Irma. The left image (acquired September 9) shows power outages across the island after the storm. Credit: NASA Worldview.Back to top of Tools section
AppEEARS
AppEEARS, available through NASA's Land Processes Distributed Active Archive Center (LP DAAC), offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available:
Point samples, for geographic coordinates
Area samples, for spatial areas via vector polygons
Performing Area Extractions
After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you will specify a series of parameters that are used to extract data for your area(s) of interest.
Spatial Subsetting
Define your region of interest in one of three ways:
Upload a vector polygon file in shapefile format (you can upload a single file with multiple features or multipart single features). The .shp, .shx, .dbf, or .prj files must be zipped into a file folder to upload.
Upload a vector polygon file in GeoJSON format (can upload a single file with multiple features or multipart single features).
Draw a polygon on the map by clicking on the Bounding box or Polygon icons (single feature only).
Select the date range for your time period of interest.
Specify the range of dates for which you wish to extract data by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting dates a start and end date in the calendar.
Adding Data Layers
Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching your query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.
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Selecting Output Options
Two output file formats are available:
GeoTIFF
NetCDF-4
If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF-4 is selected, outputs will be grouped into .nc files by product and by feature.
Interacting with Results
Once your request is completed: From the Explore Requests page, click the View icon in order to view and interact with your results. This will take you to the View Area Sample page.
The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AppEEARS ID (AID). If your feature contains attribute table information, you can view the feature attribute table data by clicking on the Information icon to the right of the Feature dropdown. To view statistics from different features or layers, select a different AID from the Feature dropdown or a different layer of interest from the Layer dropdown.
Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF-4 files.
The Soil Moisture Visualizer tool at NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) integrates in-situ, airborne, and remote sensing data from a variety of soil moisture datasets covering North America into an easy-to-use platform (read more about this tool at Soil Moisture Data Sets Become Fertile Ground for Applications). This integration helps to validate and calibrate the data, and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format.
To use the visualizer, select a dataset of interest under Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and data collection relative time frames. Upon selection of the parameter, the tool displays a time series with available datasets. All measurements are volumetric soil moisture. Surface soil moisture is the daily average of measurements at 0–5 cm depth, and root zone soil moisture (RZSM) is the daily average of measurements at 0–100 cm depth. The visualizer also provides data sources for download.
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The Soil Moisture Visualizer allows users to compare soil moisture measurements from multiple sources (figure legends, top left and bottom right) at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data from NASA’s Soil Moisture Active Passive (SMAP) Observatory are shown with data from in situ sensors across the 9-kilometer Equal-Area Scalable Earth (EASE) grid cell encompassing the Tonzi Ranch Fluxnet site in the Sierra Nevada foothills of California. Daily precipitation values for the site (purple spikes) are also provided for reference. Credit: ORNL DAAC.Back to top of Tools section
With the Global Subset Tool, you can request a subset for any location on Earth and receive this subset as GeoTIFF and in text format, including interactive time-series plots and more. Specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, you can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. Note: An Earthdata Login is required to request data.
With the Fixed Subsets Tool, you can download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as NEON, ForestGeo, PhenoCam, and LTER that are of relevance to the biodiversity community.
With the Web Service, you can retrieve subset data (in real-time) for any location(s), time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into users' workflow.
ORNL DAAC's SDAT is an Open Geospatial Consortium standards-based web application to visualize and download spatial data in various user-selected spatial/temporal extents, file formats, and projections. Several datasets including land cover, biophysical properties, elevation, and selected ORNL DAAC archived data are available through SDAT. KMZ files are also provided for data visualization in Google Earth.
Within SDAT, select a dataset of interest. Upon selection, the map service will open displaying the various measurements with the associated granule and a visualization of the selected granule.
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Canopy Height, Kalimantan Forests, Indonesia, 2014, from the Spatial Data Access Tool. Credit: ORNL DAAC.
You can then select your spatial extent, projection, and output format for downloading.
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Canopy Height, Kalimantan Forests, Indonesia, 2014, from the Spatial Data Access Tool with various output options. Credit: ORNL DAAC.Back to top of Tools section
Sentinel Toolbox
The ESA (European Space Agency) Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with synthetic aperture radar (SAR) instruments operating at a C-Band frequency. The satellites orbit 180° apart, and together image the entire Earth every six days. SAR is an active sensor that can penetrate cloud cover and vegetation canopy, and also observe at night. Therefore, it is ideal for flood inundation mapping. It also provides useful information to detect changes in land position after an earthquake, volcanic eruption, or landslide. SAR data are very complex to process; however, ESA has developed a Sentinel-1 Toolbox to aid with processing and analysis of Sentinel-1 data.
For more information on active sensors like SAR, see What is Remote Sensing?; for more information about SAR specifically, see What is SAR?.
Flood Inundation Mapping
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Effects of the SAR band on penetration of surfaces. The longer the wavelength, the deeper the penetration through most land types. Credit: The SAR Handbook, Dr. Franz Meyer.
Before choosing data, it's important to determine which SAR wavelength band meets your needs, as radar signals penetrate deeper as the sensor wavelength increases. This difference in penetration is due to the dielectric properties of a given medium, which dictate how much of the incoming radiation scatters at the surface, how much signal penetrates into the medium, and how much energy gets lost to the medium through absorption.
Another important parameter to consider when choosing a SAR dataset is the polarization, or the direction in which the signal is transmitted or received: horizontally or vertically. Dual polarization, for example, refers to two different signal directions: horizontal/vertical and vertical/horizontal (HV and VH). Knowing the polarization from which a SAR image was acquired is important, as signals at different polarizations interact differently with objects on the ground and affect the recorded radar brightness in a specific polarization channel.
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Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils). Credit: The SAR Handbook, Dr. Franz Meyer.
Once you have downloaded the needed SAR data, the data must be calibrated to account for distortion. The objective in performing calibration is to create an image where the value of each pixel is directly related to the backscatter of the surface. Calibration takes into account radiometric distortion, signal loss as the wave propagates, saturation, and speckle. This process is critical for analyzing images quantitatively; it is also important for comparing images from different sensors, modalities, processors, and acquisition dates.
Important note: DO NOT unzip the downloaded SAR file. Open the .zip file from within the Sentinel Toolbox. When you expand the Bands folder, you will see an amplitude and an intensity file for each polarization option. (The intensity band is a virtual one and is the square of the amplitude.) Open the amplitude file. Subset the data by zooming in to the area of interest and right-clicking on "Spatial Subset from View."
Calibration is done by following these steps:
Radiometric calibration is performed by selecting Radar/Radiometric/Calibration (leave parameters as default).
Geometric correction is done after radiometric calibration. This fixes geometric distortions due to slant range, layover, shadow, and foreshortening.
Terrain correction can be performed by selecting Radar/Geometric/Terrain Correction/ Range-Doppler Terrain Correction. This requires a digital elevation model (within the processing parameters, SRTM is the default selection). You also can specify a map projection in the processing parameters.
Speckle is another characteristic of SAR images that must be addressed. Speckle is the grey level variation that occurs between adjacent resolution cells, and creates a grainy texture. Within the Toolbox, speckle can be removed by selecting "Radar/Speckle Filtering/Single Product Speckle Filter" and then choosing a type of filter; "Lee" is one of the most common.
For further information on SAR flood inundation mapping, see the NASA Alaska Satellite Facility DAAC (ASF DAAC) flood inundation recipes for QGIS or ArcGIS.
EWX hompage showing precipitation in Africa for May 2022. Credit: USGS/FEWS NET.
The Early Warning eXplorer (EWX) Next Generation Viewer is an interactive web-based mapping application that helps users explore and visualize global geospatial data related to drought monitoring and famine early warning. Meteorological and agricultural drought hazard-related data are offered on a decadal to monthly basis. A guide produced by the Climate Hazards Center, University of California Santa Barbara, provides tutorials for using this tool.
RAPT homepage. RAPT enables users to combine data layers to create community maps to inform preparedness, response, and recovery strategies. Credit: FEMA National Integration Center.
The Resilience Analysis and Planning Tool (RAPT) created by the U.S. Federal Emergency Management Agency (FEMA) is a GIS web-based app that offers a variety of data (i.e., census data, infrastructure locations, and hazards, including real-time weather forecasts, historic disasters and estimated annualized frequency of hazard risks) that may complement the NASA data in this Data Pathfinder. These datasets presented in RAPT enable federal, state, local, tribal, and territorial emergency managers and other community leaders to examine and mitigate wildfire risks for community resiliency and environmental justice planning/analysis. RAPT provides a number of resources for users to get familiar with using the tool:
In determining whether or not to use remote sensing data, it is important to understand not only the benefits but also the limitations of these data.
The U.S. is fortunate to have numerous ground-based measurements for assessing a wide range of environmental variables, including water storage, precipitation, particulate matter, and more. However, this is not the case in other countries—and even in some of the more remote areas of the U.S. Data acquired by sensors aboard satellites provide local, regional, and global coverage and are useful for observing areas that are inaccessible. Rapid processing of raw satellite data also enable events to be monitored in near-real time, allowing for a faster response.
It is difficult for a single sensor to combine all desirable features into one instrument, and trade-offs are made by instrument designers. For example, to acquire observations with moderate to high spatial resolution (like the Operational Land Imager [OLI] aboard Landsat 8 or the OLI-2 aboard Landsat 9), a narrower swath is required. This, in turn, requires more time between observations of a given area. Finding a sensor with the spatio-temporal resolution capable of addressing your research, application, or decision-making needs is a crucial first step in using remotely sensed data.
Passive instruments (those that use energy reflected or emitted from Earth for measurements) are not able to penetrate cloud or vegetation cover, which can lead to data gaps or a decrease in data utility, such as the inability to detect a fire or sense the radiative power of small fires. This is not the case when using data from active sensors, which send out a signal and measure the intensity of the returned signal.
With satellite data, comparisons can be made using pre-event and post-event imagery, providing information on smoke and ash transport, burn severity, vegetation loss, and more. Incorporating satellite data with in-situ data (ground-based measurements) into modeling programs makes for an even more robust forecasting system.
