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Wildfires Data Pathfinder

New to using NASA Earth science data? This pathfinder is designed to help guide you through the process of selecting and using applicable datasets, with guidance on resolutions and direct links to the data sources.

After getting started here, there are numerous NASA resources that can help develop your skills further. If you are new to remote sensing, check out What is Remote Sensing? or view the Applied Remote Sensing Training on Fundamentals of Remote Sensing.

A prescribed fire is applied to a Pinus nigra stand in Portugal. (Courtesy P. Fernandes)
A prescribed fire is applied to a Pinus nigra stand in Portugal. (Courtesy P. Fernandes)

According to the Fourth National Climate Assessment, climate change has made its appearance, afflicting the world with extreme weather conditions, including increased heat waves, rising sea levels, and increasing devastation from wildfires, to name just a few. NASA is interested in all of these issues, particularly for fires because of their environmental, societal, and economic impacts. How are changes in climate boosting the increase in fire activity across the globe and what can be done to mitigate the impacts?

About the Data

About the Data

A combination of ground- and satellite-based data provides a unique view of the globe to better understand active fires and hotspots, thermal anomalies, and aerosol transport of smoke and particulate matter. In addition, climatological and vegetative measurements help scientists, researchers, and decision makers prepare for risk and response to events as well as aiding in forecasting events and assessing the post-event impacts.

NASA, in cooperation with its partner agencies and organizations, has a series of instruments that provide data to assist with forecasting, monitoring and assessment. NASA's Earth science data products are validated, meaning the accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.

Scientists, researchers, land managers, and decision makers are using wildfire data in numerous ways (to see how the data are being used, check out the Data User Profiles section of the Wildfire Articles page). Wildfire data can be used to forecast events. By acquiring data on precipitation, soil moisture, drought severity, topography, land surface temperatures, and vegetation density and extent, land and/or wildlife managers can perform pre-fire mapping to indicate potential areas of risk. Near real-time wildfire data, exploring the estimated extent of total area burned and fire radiative power, can be used to assess risk in a given area and develop a more efficient strategy for response. Wildfire data can be used in post-fire mapping, incorporating total burned area, burn severity, and vegetation regrowth. Below is a list of datasets found within this pathfinder. Datasets are alphabetized by sensor.

Measurement

Satellite

Sensor

Spatial Resolution

Temporal Resolution

Smoke Plumes

NASA/NOAA Geostationary Operational Environmental Satellite-East (GOES-East) and GOES-West

Advanced Baseline Imager (ABI)

1 km

10 min

Smoke Plumes

Japan Meteorological Agency Himawari-8

Advanced Himawari Imager

1 km

10 min

Carbon Monoxide, Sulfur Dioxide

Aqua

Atmospheric Infrared Sounder (AIRS) Level 2 and 3 products

1° x 1°

daily, 8-day, monthly

Precipitation, Soil Moisture

Global Change Observation Mission – Water 1

Advanced Microwave Scatter Radiometer-2 (AMSR2)

2 km

daily

Elevation, Land Surface Temperature

Terra

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

15 m, 30 m, 90 m

Variable
Land Surface Temperature, Evapotranspiration, Evaporative Stress Index International Space Station
Note: data are available in areas of 51.6° S to 51.6° N
Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) 70 m ~ 1-7 days

Aerosol Optical Depth, Active Fire and Thermal Anomalies, Vegetation Indices, Land Surface Temperature, Land Surface Reflectance

Terra and Aqua

Moderate Resolution Imaging Spectroradiometer (MODIS)

250 m, 500 m, 1000 m, 5600 m

1-2 days

Land Surface Reflectance

Landsat 8

Operational Land Imager (OLI)

15, 30, 60m

16 days

Aerosol Index, Sulfur Dioxide

Aura

Ozone Monitoring Instrument (OMI)

13 km x 24 km

daily

Aerosol Index, Sulfur Dioxide

Joint NASA/NOAA Suomi-National Polar-orbiting Partnership (Suomi NPP)

Ozone Mapping and Profiler Suite (OMPS)

50 km x 50 km

101 minutes, daily

Soil Moisture

Soil Moisture Active Passive (SMAP)

Radar (active) and a radiometer (passive)

9 km, 36 km

1 day

Elevation

Space Shuttle

Shuttle Radar Topography Mission (SRTM)

30m

Precipitation

Integrated multi-satellite data

TRMM Multi-satellite Precipitation Algorithm (TMPA) and Integrated Multi-satellitE Retrievals for GPM (IMERG)

0.1° x 0.1° or 0.25° x 0.25°

half hourly, daily, monthly

Active Fire and Thermal Anomalies, Vegetation Indices, Land Surface Temperature, Land Surface Reflectance

Suomi NPP

Visible Infrared Imaging Radiometer Suite (VIIRS)

375 m and 750 m

1-2 days

Active Fire and Thermal Anomalies, Land Surface Reflectance

NOAA-20

VIIRS

375 m and 750 m

1-2 days

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Find and Use Risk and Response Data - Near Real-time

Find and Use Risk and Response Data - Near Real-time

Aerosols/Smoke Plumes | Active Fires and Thermal Hotspots | Lightning

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public within three hours of satellite observation, which allows for near real-time (NRT) monitoring and decision making. Specifically for fires, both the MODIS instrument on board the Terra and Aqua satellites and the VIIRS instrument on board the joint NOAA/NASA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and the NOAA-20 satellite provide fire information on hotspots/fires and thermal anomalies, and smoke plume movement via true color reflectance imagery.

NRT Aerosols/Smoke Plumes

Photo of Camp Fire, 2018 from NNNN data.

Terra MODIS Corrected Reflectance imagery of Camp Fire, 2018.

Aerosols and suspended particles in the air that are made up of smoke and dust from burning fires can be tracked and measured by instruments aboard NASA Earth observing satellites. NASA's Worldview application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and download the underlying data. Many of the imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now."

Using Moderate Resolution Imaging Spectroradiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS) imagery, you can follow a smoke plume and its movement through time. You can also assess the aerosol optical depth (which can be converted to particulate matter with specific algorithms), carbon monoxide, and sulfur dioxide at the fire location, where and when available. Worldview also allows you to capture a snapshot, create an animated gif, compare imagery over time or compare two different imagery layers for the same date.

For a tutorial see Getting Started with NASA Worldview.

Smoke Plumes in NRT:

Surface reflectance data from the MODIS and VIIRS instruments provides a means to measure the spatial distribution and intensity of smoke plumes. In addition, the red visible imagery layer of the geostationary satellites, primarily used to monitor the evolution of clouds, can also be used to identify smoke plume movement. The Geostationary Operational Environmental Satellites (GOES) -East satellite is centered on 75.2 degrees W, covering the Conterminous US, Canada, Central and South America (so most of the Atlantic Ocean). GOES-West is centered on 137.2 degrees W, covering most of the Pacific Ocean, the U.S., most of Canada, Central, the western half of South America, and parts of Australasia. The Himawari-8 satellite is centered on 140.7 degrees E, covering most of the Pacific Ocean, a portion of Eastern Asia, and parts of Australasia. Data are acquired every 10 minutes and are available on a rolling 30-day window. Note: The Red Visible imagery is only viewable during the day time. Change the time in the timeline if the imagery is black over your area of interest.

Surface Reflectance
  • MODIS Corrected Reflectance in Worldview
    The MODIS Corrected Reflectance imagery is available only as near real-time imagery. The MODIS Corrected Reflectance algorithm utilizes MODIS Level 1B data (the calibrated, geolocated radiances). It is not a standard, research quality product. The purpose of this algorithm is to provide natural-looking images by removing gross atmospheric effects, such as Rayleigh scattering, from MODIS visible bands 1-7.
  • VIIRS Corrected Reflectance in Worldview
    The VIIRS Corrected Reflectance imagery is available only as near real-time imagery.
Geostationary Imagery

Types of aerosol data provided in NRT:

Aerosol Index (AI)

Aerosols absorb and scatter incoming sunlight, which reduces visibility and increases the optical depth. Satellite-derived AI products are useful for identifying and tracking the long-range transport of smoke from wildfires or biomass burning events. Currently there are two near real-time Aerosol Index data products available, one from Aura Ozone Monitoring Instrument (OMI) and the other from the Joint NASA/NOAA Suomi-National Polar-orbiting Partnership (Suomi NPP) Ozone Mapping and Profiler Suite (OMPS). AI indicates the presence of ultraviolet (UV)-absorbing particles in the air (aerosols); the higher the AI, the higher the concentration in the atmosphere. For both satellites, the spatial resolution is 2 km and the temporal resolution is daily.

  • OMPS AI in Worldview
    AI from OMPS includes a newer product, PyroCumuloNimbus (pyroCb), which makes it easier to track the extent and spread of pyroCb and other high-aerosol events. Typically the AI signal remains below 5.0 for most smoke and dust events, the OMPS AI product with an AI range of 0.0 to 5.0 satisfies the needs of most users. However, the AI signal for pyroCb events, which are both dense and high in the atmosphere, easily can be much larger than 5.0. In fact, the highest AI value ever observed (55.0) occurred during a pyroCb event in Canada, August 2017.

Aerosol Optical Depth (AOD)

Screenshot showing 2 world maps displaying burning fires and a second map showing average monthly aerosol amounts.

Locations of burning fires (above) compared to average monthly aerosol optical depth.

AOD indicates the level at which particles in the air (aerosols) prevent light from traveling through the atmosphere. From an observer on the ground, an AOD of less than 0.1 is "clean" - characteristic of clear blue sky, bright sun and maximum visibility. As AOD increases to 0.5, 1.0, and greater than 3.0, aerosols become so dense that the sun is obscured.

  • MODIS AOD in Worldview
    Aqua and Terra's MODIS Combined Value-Added Aerosol Optical Depth layer is a value-added layer based on MODIS Level 2 aerosol products. The layer can give a quick, synoptic view of the level of aerosols in the atmosphere.
  • MODIS Deep Blue AOD in Worldview
    Deep Blue AOD layer is useful for studying aerosol optical depth over land surfaces. This layer is created from the Deep Blue algorithm.
  • AERONET
    Ground-based AOD measurements are available online at the Aerosol Robotic Network (AERONET).
Trace Gases from Fires

In addition to the particulate matter, numerous trace gases are found in the atmosphere during and after a fire event. These trace gases, like carbon monoxide (CO) and sulfur dioxide (SO2), are harmful pollutants that can impact public health. Data for trace gases are available from a variety of different satellites. For CO, the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite provides the best global coverage at 2 km resolution and twice daily measurements (day and night). AIRS also provides measurements of SO2. Two other instruments, OMI and OMPS (descriptions above) provide information on SO2 at the lower troposphere, middle troposphere, and upper troposphere/stratosphere layers.

  • AIRS L2 CO (Day/Night) in Worldview
    Carbon monoxide in units of parts per billion by volume at the 500 hPa pressure level, approximately 5500 meters (18,000 feet) above sea level. AIRS Level 2 data are nominally 45 km/pixel at the equator and the data has been resampled into a 32 km/pixel visualization.
  • AIRS SO2 in Worldview
    Indicates sulfur dioxide column amounts in the atmosphere, measured in Dobson Units (DU).
  • OMI/OMPS SO2 in Worldview
    Indicates the column density of sulfur dioxide at different layers of the atmosphere and is measured in Dobson Units (DU). OMI data are available from 2005-present and OMPS from 2012-present.

NRT Active Fires and Thermal Hotspots

There are some differences between the fire datasets that must be considered. Each of these instruments has a different spatial and temporal resolution. MODIS data are at 1km and are acquired daily (Terra satellite passes over in the morning and Aqua in the afternoon), whereas VIIRS are at 375m and are acquired daily, with improved nighttime performance over MODIS. The NOAA-20 satellite follows the same orbit as Suomi NPP but lags behind by about 50 minutes.

Ground observation of active fires compared to output display for MODIS sensor.

Ground observations versus output display for MODIS sensor.

The thermal anomalies/fire NRT data are basically a snapshot in time, showing what is occurring at the moment the data was acquired. It is determined by a contextual algorithm that utilizes the infrared or thermal radiation of the fires. Each MODIS active fire represents the center of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel (see figure to right). As VIIRS has a higher resolution, it can pick up fires that MODIS overlooks, especially those covering relatively small areas.

It is important to note that the NRT products are not considered research quality because predicted geolocation is used. Research quality data, which are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science, are available with an approximate two to three months lag.

There are two primary ways of exploring NRT fire data through the Fire Information for Resource Management System (FIRMS): through an interactive map or by direct download of the NRT data. The interactive map provides NRT and the full archive of global MODIS and VIIRS fire locations. It also enables users to view the MODIS Terra/Aqua Global Burned Area data product (with an approximate 4-month lag between the date of burn and burned area data product in FIRMS).

Note: In partnership with the U.S. Forest Service, NASA will release a new version of FIRMS for the U.S. and Canada, late 2020.

Active fire data are available for download for any area of interest, in NRT and from the full archive. Within Worldview, fire and thermal anomalies are provided as vector layers, which have attribute information that can be examined when a vector feature is clicked. For example, when a point is clicked on in the layer, a table of attributes will appear including latitude, longitude, brightness temperature, and fire radiative power.

Research quality data products can be accessed via Earthdata Search.

For a tutorial on using FIRMS data, see the webinar Discover NASA's FIRMS on NASA's Earthdata YouTube channel. For more in depth information about FIRMS read the FIRMS Frequently Asked Questions.

Lightning

Animation of the number of lightning flashes from the International Space Station (ISS) Lightning Imaging Sensor (LIS). Animation, from the Worldview Application, includes data from August 16-20, 2020; these lightning strikes have caused some of the largest California fires.

Animation of the number of lightning flashes from the International Space Station (ISS) Lightning Imaging Sensor (LIS). Animation, from the Worldview Application, includes data from August 16-20, 2020; these lightning strikes began fires that are becoming some of the largest California fires in history.

The International Space Station (ISS) Lightning Imaging Sensor (LIS) is a space-based lightning sensor aboard the ISS. The ISS LIS instrument records the time of occurrence of a lightning event, measures the radiant energy and estimates the location during both day and night conditions with high detection efficiency.








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Find and Use Fire Forecasting Data

Find and Use Fire Forecasting Data

Screenshot of Normalized Difference Vegatation Index of King Fire area of burn.

False-color image of Normalized Difference Vegetation Index (NDVI) data of King Fire area, September 2013 (left) and Nov 2014. (ORNL DAAC)

Many factors contribute to a fire, its intensity and its severity, including changing weather patterns, land cover, vegetative health, drought conditions, and changing land use or land management. As such, it's important to monitor contributing factors in order to predict the formation of a fire and how it will move through the environment. NASA has several datasets for making these predictions.

Vegetation Greenness

Vegetation indices have been developed to measure the amount of green vegetation over a given area and can be used to assess vegetation health. One commonly used vegetation index is the Normalized Difference Vegetation Index (NDVI), which takes the difference between NIR and red reflectance divided by their sum. NDVI values range from -1 to 1. Low values of NDVI generally correspond to barren areas of rock, sand, exposed soils, or snow. Increasing NDVI values indicate greener vegetation, including things like forests, croplands, and wetlands. Aqua and Terra's MODIS and Suomi NPP's VIIRS vegetation data products can be accessed via the following ways:

Research quality data products can be accessed directly via Earthdata Search; datasets are available as HDF files, which can be opened in Panoply.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NRT imagery can be accessed in Worldview.

Precipitation

Near real-time IMERG Early Run Half-Hourly Image, acquired on November 12, 2019.

Near real-time IMERG Early Run Half-Hourly Image, acquired on May 7, 2020. Credit: NASA.

NASA's Precipitation Measurement Missions (PMM) provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

The products from these missions are available individually or have been integrated with a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes. TRMM has been integrated into the TRMM Multi-satellite Precipitation Algorithm (TMPA) and GPM into the Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG's multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours for flash flood events, Late = 12 hours for crop forecasting, and Final = 3 months for research with the incorporation of rain gauge data).

Research quality data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late and Final precipitation data on the half hour or one day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.
  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at three hours and one day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NASA, in collaboration with other agencies, has also developed models of precipitation, incorporating satellite information with ground-based data when available. These models are part of the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates. In addition to IMERG and LDAS, the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument collects data that indicates the rate at which precipitation is falling on the surface of the ocean.

Near real-time data can be accessed from Worldview:

Daymet is a collection of gridded estimates of daily weather parameters. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved in a variety of ways, including: Earthdata Search; an Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) API; ORNL DAAC tools; and through the Land Processes DAAC (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS).

Land Surface Temperature

Satellite images show the relationship between the characteristics of a landscape, and day and night surface skin temperature. Heavily forested areas remain relatively cool throughout the day, while barren and arid areas can be tens of degrees warmer. These images were acquired in the early morning and afternoon of July 6, 2011.

Satellite images show the relationship between the characteristics of a landscape and day and night surface skin temperature. Heavily forested areas remain relatively cool throughout the day, while barren and arid areas can be tens of degrees warmer. These images were acquired in the early morning and afternoon of July 6, 2011. Credit: NASA Earth Observatory

Land surface temperature is useful for monitoring changes in weather and climate patterns and used in wildfire risk assessment.

Research quality land surface temperature data products can be accessed directly from Earthdata Search or the LP DAAC Data Pool; MODIS and ASTER data are available as HDF and VIIRS and ECOSTRESS are available as HDF5:

To quickly extract a subset of ECOSTRESS, MODIS, or VIIRS data for your region of interest, use the LP DAAC AppEEARS tool or the ORNL DAAC subsetting tools.

Landsat data from USGS's Earth Explorer are available via Earthdata Search. Note that you will need a USGS login to download the data.

Data can be visualized in Worldview:

Soil Moisture

Soil moisture is important in forecasting fire events as the dryness of the soil contributes to the overall fire potential in the area. Satellite data can provide a synoptic view of soil moisture across the globe. Although ground- based measurements are at a higher resolution, the data are often sparse and have limited coverage. The preferred measurement should be chosen based upon your needs.

New Smap Image 3 - 8 - 16
SMAP's rotating golden antenna functions like a satellite dish to focus radio waves from Earth's surface into a collector on the spacecraft. Image: NASA JPL/Caltech
NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top 5cm of the soil globally every three days, at a resolution of 10-40km. The AMSR2 instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides a NRT product, which is a daily measurement of surface soil moisture.

NASA, in collaboration with other agencies, has also developed models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of the LDAS.

Research quality data products can be accessed via Earthdata Search; MODIS and VIIRS datasets are available as HDF files and can be opened using Panoply, but are also customizable to GeoTIFF:

The ORNL DAAC Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. See the Tools for Data Access and Visualization section for additional information.

AppEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NRT imagery can be accessed via Worldview:

Topography

Knowing the topography of an area is important so that fire managers and emergency management professionals can anticipate areas of risk, including direction and speed of wind, landslide potential and runoff.

An ASTER GDEM image of Mt. Raung and the surrounding area.

An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

A method for delineating topography is the Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The ASTER Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover, however. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

Winds

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides surface wind data beginning in 1980 and runs a few weeks behind real time.

MERRA-2 data products can be accessed via Earthdata Search:

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) Wind Speed Weather Map of North America during the 2020 Lightning Complex fires in California.

NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) Wind Speed Weather Map of North America during the 2020 Lightning Complex fires in California. Credit: NASA

The NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System, Version 5 (GEOS-5) has a series of weather maps that can be used to predict parameters such as wind speed up to 240 hours out, to understand the movement of a smoke plume over time.

  • GEOS-5 Weather Maps
    Within the viewer, select the parameter or field of interest, the area of interest, and indicate the forecast time and the forecast lead hour. Selecting “Animate” shows the forecast for the given parameter over the time period indicated. Note that it may take time to load the images to animate. For wind speed near the surface, select 850 as your level (note; 850 hPa is approximately 5000 ft or 1500 m above sea level).

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Find and Use Post-Fire Impact Data

Find and Use Post-Fire Impact Data

The regional impacts of wildfires include not only burned area, but also changes in runoff patterns and landslide potential. While some areas allow for ground-based measurements of these post-fire impacts, areas that are remote or that have rugged terrain can make ground-based measurements impractical. Remote sensing data provide a means to extend our knowledge in these areas.

Total burned area

A combined MODIS burned area product is acquired by employing 500m MODIS Surface Reflectance imagery coupled with 1 km MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data.

Screenshot showing burned area in Earthdata FIRMS.

  • Burned Area Data from Earthdata Search
    Terra and Aqua combined Burned Area data product is a monthly, global gridded 500m product containing per-pixel burned-area and quality information.
  • MODIS Land Surface Reflectance from Earthdata Search
    Adding the Terra/MODIS Bands 7-2-1 layer provides more context; this layer is useful for distinguishing burn scars where burned vegetation shows as red and vegetation is bright green.
  • MODIS Corrected Reflectance Bands 7-2-1 in Worldview
    Worldview Tour provides stories to help you learn more about the visualization tool, the satellite imagery we provide and events occurring around the world. This story takes you on a tour of the Camp Fire event, November 8, 2018 north of Sacramento, California. An example of MODIS Corrected Reflectance, bands 7-2-1, is shown in slide 2.

Burn Severity

 Illustration showing fire intensity versus burn severity (Source: U.S. Forest Service).

Illustration of fire intensity versus burn severity (Source: U.S. Forest Service).

Burn severity is the effect of fire on ecosystem properties, often defined by the degree of mortality of vegetation (relating to soil heating, consumption of litter). Using satellite imagery from Landsat or MODIS allows for pre and post-fire comparisons and a change detection approach. One of the most effective ways to discriminate is by generating a normalized burn ratio. To calculate this, using Landsat data, see the Applied Sciences Remote Sensing Training on Introduction to Remote Sensing for Wildfire Applications.

Vegetation Greenness

NDVI provides a means to assess vegetation health in a given area. Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8). Aqua and Terra's MODIS and VIIRS NDVI data can be accessed via the following:

Research quality data products can be accessed via Earthdata Search; datasets are available as HDF files which can be opened using Panoply.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NRT imagery can be accessed in Worldview.

Precipitation

Near real-time IMERG Early Run Half-Hourly Image, acquired on November 12, 2019.

Near real-time IMERG Early Run Half-Hourly Image, acquired on May 7, 2020. Credit: NASA.

NASA's Precipitation Measurement Missions (PMM) provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. The follow-on mission, GPM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

The products from these missions are available individually or have been integrated with a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes. TRMM has been integrated into the TRMM Multi-satellite Precipitation Algorithm (TMPA) and GPM into the Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG's multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours for flash flood events, Late = 12 hours for crop forecasting, and Final = 3 months for research with the incorporation of rain gauge data).

Research quality data products can be accessed via Earthdata Search:

  • IMERG from Earthdata Search
    Early, Late and Final precipitation data on the half hour or one day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.
  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day or NRT and accumulated rainfall at three hours and one day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

  • IMERG from Giovanni
    Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF. Data are only available from 2000.
  • TMPA from Giovanni
    Select a map plot, date range and region, and determine your variable and then plot the data. Data can be downloaded as GeoTIFF.

NASA, in collaboration with other agencies, has also developed models of precipitation, incorporating satellite information with ground-based data when available. These models are part of the LDAS, which takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates. In addition to IMERG and LDAS, the AMSR2 instrument collects data that indicates the rate at which precipitation is falling on the surface of the ocean.

Near real-time data can be accessed from Worldview:

Daymet is a collection of gridded estimates of daily weather parameters. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved in a variety of ways, including: Earthdata Search; an ORNL DAAC API; ORNL DAAC tools; and through the LP DAAC AppEEARS.

Topography

Knowing the topography of an area is important so that fire managers and emergency management professionals can anticipate areas of risk, including direction and speed of wind, landslide potential and runoff.

An ASTER GDEM image of Mt. Raung and the surrounding area.

An ASTER GDEM image of Mt. Raung and the surrounding area. Image Credit: Land Processes Distributed Active Archive Center

A method for delineating topography is the Shuttle Radar Topography Mission (SRTM). SRTM provides a digital elevation model of all land between 60 degrees north and 56 degrees south, about 80% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane. The ASTER Global Digital Elevation Model (GDEM) coverage spans from 83 degrees north latitude to 83 degrees south, encompassing 99% of Earth's landmass. The spatial resolution is 30 m in the horizontal plane.

On average, compared to geodetic points over the U.S., SRTM data has a lower root mean square error (RMSE); RMSE is a commonly used method to express vertical accuracy of elevation datasets. Digital elevation model data accuracy is typically very sensitive to vegetation cover, however. ASTER tends to perform better over certain landcover types.

February 2020, LP DAAC released a new data product, NASADEM, available at 1 arc second resolution. NASADEM extends the legacy of the SRTM by improving the DEM height accuracy and data coverage as well as providing additional SRTM radar-related data products. The improvements were achieved by reprocessing the original SRTM radar signal data and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

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Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite | Spatial Data Access Tool (SDAT)

Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the 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.

Screenshot of the Search Earthdata site.


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.

Earthdata Search customization tools diagram.

Panoply

HDF and NetCDF files can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.

Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are the more popular ones.

  • Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  • Map animations are a means to observe spatial patterns and detect unusual events over time.
  • Area-averaged time series are 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 are 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 NASA's 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.

Worldview

NASA's EOSDIS Worldview visualization application provides the capability to interactively browse 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, essentially showing the entire Earth as it looks "right now." This 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 GOES-East, GOES-West and Himawari-8 available at ten 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.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. The post-hurricane image on the left shows widespread outages around San Juan, including key hospital and transportation infrastructure.

AppEEARS

AppEEARS, from 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 and 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

You can define your region of interest in 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.

Extracting an area in AppEEARS

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.

If GeoTIFF is selected, you must select a projection

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 and/or a different layer of interest from the Layer dropdown.

Interpreting Results in AppEEARS

Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or NetCDF-4 files.

Soil Moisture Visualizer

ORNL DAAC has developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of different soil moisture datasets over North America. The visualization tool incorporates in-situ, airborne, and remotely-sensed data into one easy-to-use platform. 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. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide.

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 relative time frames of data collection. 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. Lastly it provides data sources for download.

ORNL DAAC Soil Moisture Visualizer

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.

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several MODIS and VIIRS Subset Tools for subsetting data.

  • With the Global Subset Tool, you can request a subset for any location on earth, provided as GeoTiff and in text format, including interactive time-series plots and more. Users 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. You will need an Earthdata Login to request data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 3000+ 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.

Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

Spatial Data Access Tool (SDAT)

The ORNL DAAC's SDAT is an Open Geospatial Consortium (OGC) 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.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

You can then select your spatial extent, projection, and output format for downloading.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

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Other NASA Assets of Interest

Other NASA Assets of Interest

Earthdata has tutorials for utilizing the monthly burned area and thermal anomalies/fire data from MODIS Terra/Aqua. Tutorials are also provided from the Earthdata YouTube channel.

ORNL DAAC data include maps of fire extent and severity, estimates of carbon emissions from fires, and other measurements.

NASA's Atmospheric Science Data Center (ASDC) Studying the 2019-2020 Australian Bushfires Using NASA Data story map guides users through the factors leading up to the 2019-2020 Australian bushfires disaster, the effect this event has had on air quality and global atmospheric composition, and the science behind researching the tie between disasters and public health.

Daily Global Fire Emissions, including PM2.5, CO, CO2, Carbon, Methane, and Dry Matter, from the Visible Infrared Imaging Radiometer Suite (VIIRS).

Daily Global Fire Emissions, including PM2.5, CO, CO2, Carbon, Methane, and Dry Matter, from the Visible Infrared Imaging Radiometer Suite (VIIRS). Credit: NASA Applied Sciences Program.

NASA's Applied Sciences Disasters Program Wildland Fires provides information on active fire assessment, disaster mitigation, and smoke transport. In addition, they have information on the latest fires around the globe.

Global Fire Emissions is captured daily using the VIIRS instrument. The service contains 6 bands, each defined by a different renderer (raster function template) for particulate matter 2.5 (PM2.5), CO, CO2, Carbon, Methane, and Dry Matter.

NASA's Global Fire Weather Database (GFWED) integrates different weather factors influencing the likelihood of a vegetation fire starting and spreading. It is based on the Fire Weather Index (FWI) System, the most widely used fire weather system in the world. GFWED is comprised of eight different sets of FWI calculations, all using temperature, relative humidity, wind speed and snow depth estimates from the NASA Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA-2).

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External Fire-Related Resources

Screenshot of the Global Wildfire Information System's Current Situation Viewer The Global Wildfire Information System is a joint initiative of the Group on Earth Observations and the Copernicus Programs. The Global Wildfire Information System (GWIS) aims to bring together existing information sources, including NASA's MODIS and VIIRS data, to provide a comprehensive view and evaluation of fire regimes and fire effects at a global level.

  • GWIS Current Situation Viewer
    Viewer provides a fire danger forecast as well as a rapid damage assessment, which includes active fires, burnt area, and fire emissions (sulfur dioxide, nitrogen oxides, and particulate matter). The viewer also allows for some basic statistical analysis.

The Fire and Smoke Map displays information on ground level air quality monitors recording fine particulates (PM2.5) from smoke and other sources

The Fire and Smoke Map displays information on ground level air quality monitors recording fine particulates (PM2.5) from smoke and other sources.

The Fire and Smoke Map is a collaborative effort between the U.S. Forest Service led Interagency Wildland Fire Air Quality Response Program (IWFAQRP) and the U.S. Environmental Protection Agency. The map displays information on ground level air quality monitors recording fine particulates (PM2.5) from smoke and other sources, as well as information on fires, smoke plume locations, and special statements about smoke issued by various sources.

NOAA also maintains several fire and smoke products from the geostationary orbiting satellites, which typically have a much coarser spatial resolution but more frequent observations.

Firecast is a tool by Conservation International, Firecast uses satellite observations to track ecosystem disturbances such as fires, fire risk conditions, deforestation, and protected area encroachment, and delivers this time-sensitive information to decision makers through email alerts, maps, and reports.

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Benefits and Limitations of using Remote Sensing Data

Benefits and Limitations of using Remote Sensing Data

In determining whether or not to use remote sensing data, it is important to understand not only the benefits but also the limitations of the data. Benefits of using satellite data include:

  • Filling in data gaps: the United States is fortunate to have numerous ground-based measurements for assessing 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 United States. Satellite data provide local, regional, and global spatial coverage and are also useful for observing areas that are inaccessible.

  • Monitoring in near-real time: some satellite information is available 3-5 hours after observation, allowing for a faster response.


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 so much more. Incorporating satellite data with in-situ data (ground-based measurements) into modeling programs makes for a more robust forecasting system.

  • Spatial resolution: while satellite-derived fire data can provide a more global view, the coarser spatial resolution makes it difficult to detect small fires or field level events.This is not the case for instruments at higher resolutions, like those on Landsat.

  • Temporal resolution: Many satellites only pass over the same spot on Earth every 1-2 days and sometimes as seldom as every 16+ days. This is the satellite's return period.

  • Spectral Resolution: Passive instruments (those that use energy being 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, for example, the inability to see the fire or the size and radiative power of small fires. This is not the case when using data from microwave or thermal sensors (active sensors).

It is difficult to combine all of the desirable features into one remote sensor; to acquire observations with moderate to high spatial resolution (like Landsat) a narrower swath is required, which in turn requires more time between observations of a given area resulting in a lower temporal resolution. Researchers have to make trade-offs. Finding a sensor with the spatio-temporal resolution capable of addressing your research, application, or decision-making process needs is a crucial first step to getting started with using remote sensing data.

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Page Last Updated: Sep 9, 2020 at 6:11 PM EDT