Disasters Data Pathfinder
Natural disasters affect millions of people every year. According to the United Nations Office for Disaster Risk Reduction (UNDRR), there were roughly 7250 disasters between 1998 and 2017, killing over 1.3 million people. Of those, flooding and storms account for the greatest number of disasters, while earthquakes cause the largest number of deaths; close to 750,000 people died from earthquakes during that period. It is also important to note that climate change will likely increase frequency of extreme heat and other extreme weather events in the coming decades.
Exposure and vulnerability are important components in risk-management efforts and adaptation strategies. The presence of people, animals and ecosystems, environmental resources, infrastructure, or economic, social, and cultural assets in places and settings that could be adversely affected by a disaster is called exposure. Vulnerability is the propensity of a community to be adversely affected by a disaster, taking into consideration factors such as susceptibility to harm and lack of capacity to cope and adapt. Risk is determined by exposure and vulnerability to hazards.
Understanding the vulnerability and exposure of a community to a disaster aids in the mitigation, prevention, and management of the disaster, while also providing information to help with response and relief efforts. NASA provides several types of data that support disaster mitigation and response.
Disaster Data Pathfinders:
About the Data
NASA collaborates with other federal entities and international space organizations, including NOAA, USGS, the Japan Aerospace Exploration Agency (JAXA) and Ministry of Economy, Trade, and Industry (METI), and the European Space Agency (ESA), to provide information for understanding a number of phenomena that cause disasters, including flooding, droughts, cyclonic storms, earthquakes, volcanic eruptions, landslides, and extreme heat events. NASA also provides socioeconomic datasets to help assess the exposure and vulnerability of a community to one of these disasters.
The accuracy of NASA's Earth science data products has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. For more information on this process, view NASA's data maturity levels.
Datasets referenced in this pathfinder are from satellite and airborne sensors shown in the table below, including their spatial and temporal resolutions. Note that many satellites/platforms carry multiple sensors; the table below only lists the primary sensor used in collecting the specified measurement. When available, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides data to the public within three hours of satellite overpass, which allows for near real-time (NRT) monitoring and decision making (sensors from which select datasets are available in LANCE are marked with *).
Note: This is not an exhaustive list of datasets but rather only includes datasets from NASA's Earth Observing System Data and Information System (EOSDIS).
|Measurement||Satellite/Platform||Sensor||Spatial Resolution||Temporal Resolution|
|Active Fire and Thermal Anomalies, Cloud Top Temperature, Land Surface Temperature, Surface Reflectance, Sea Surface Temperature, Vegetation Indices||Terra and Aqua||Moderate Resolution Imaging Spectroradiometer (MODIS) *||250 m, 500 m, 1000 m, 5600 m||1-2 days|
|Active Fire/Thermal Anomalies, Land Surface Temperature, Nighttime Imagery, Sea Surface Temperature, Surface Reflectance, Vegetation Indices||NASA/NOAA Joint Polar Satellite System (JPSS) NOAA-20 satellite and Suomi NPP||Visible Infrared Imaging Radiometer Suite (VIIRS) *||500 m, 1000 m, 5600 m||daily|
|Clouds||NASA/NOAA Geostationary Operational Environmental Satellite-East (GOES-East) and GOES-West||Advanced Baseline Imager (ABI)||1 km||10 min|
|Clouds||Japan Meteorological Agency Himawari-8||Advanced Himawari Imager||1 km||10 min|
|Elevation/Topography||Space Shuttle||Shuttle Radar Topography Mission (SRTM)||30 m||Static|
|Freeze/Thaw, Soil Moisture||Soil Moisture Active Passive (SMAP)||Radar (active) and microwave radiometer (passive)||9 km, 36 km||3 days|
|Land Surface Backscatter||JAXA and METI Advanced Land Observing Satellite-1 (ALOS)||Phased Array type L-band Synthetic Aperture Radar (PALSAR)||10 m, 100 m|
|Land Surface Backscatter||ESA Sentinel-1 and -2||Synthetic Aperture Radar (SAR)||25 x 40 m, 5 x 5 m, and 5 x 20 m||12 days (using together 6 days)|
|Land Surface Backscatter||Uninhabited Aerial Vehicle
Note: data are available over specific areas
|Land Surface Temperature, Surface Reflectance||NASA/USGS Landsat 8||Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
|15, 30, 60 m||16 days|
|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|
|Relative Humidity||Aqua||Atmospheric Infrared Sounder (AIRS) *||1°||daily, monthly|
|Sulfur Dioxide||Aura||Ozone Monitoring Instrument (OMI) *||13 km x 24 km||daily|
|Sulfur Dioxide||ESA Sentinel-5P||TROPOspheric Monitoring Instrument (TROPOMI)||7 km x 3.5 km||daily|
|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|
|Surface Kinetic Temperature, Topography||Terra||Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)||15 m Very Near Infrared (VNIR), 30 m short-wave infrared (SWIR), 90 m thermal infrared (TIR)||Variable|
|Surface Reflectance||NASA/USGS Landsat 7||Enhanced Thematic Mapper (ETM)||15, 30, 60 m||16 days|
* sensors from which select datasets are available in LANCE
In addition to mission data, NASA has a series of models that use satellite- and ground-based observational data to produce high-quality fields of land surface states and fluxes. There are a few reasons model data may be preferred over remote sensing observations, including obtaining more complex data parameters, temporal coverage, spatial coverage and/or data completeness. Models are often used for projections and forecasts, but time is not the only dimension in which projections can be made. Models can also project in space, offering data where sensors are unavailable.
For instance, satellite observations of land surface temperatures can only be made where there is a clear view of the land. Clouds and dust can obscure views, and observations are further dependent on the type of land cover, so highly reflective areas, such as snow and urban areas, can be challenging to observe. A model, however, can bring in additional data from ground stations, or other sensors that measure different wavelengths, to fill those gaps.
NASA's Goddard Earth Observing System, Version 5 (GEOS-5) model assimilates data from a variety of observations for each Earth System component. GEOS-5 has a series of weather maps which can be used to produce a 240-hour/10-day forecast of parameters, such as precipitation, humidity, wind speed, and temperature.
The Land Data Assimilation System (LDAS) provides data within 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, such as runoff and evapotranspiration.
The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. Climate, for example, is typically measured over a 30-year period, and so MERRA-2 can be used to make quantitative points about changes in climate.
|Model Source||Data Parameter||Spatial Resolution||Temporal Resolution|
|GEOS-5||Land surface temperature, soil moisture, surface humidity, winds||0.3125° x 0.25°||NRT|
|LDAS||Land surface temperature, runoff, surface humidity, soil moisture||0.25° x 0.25° or higher||Monthly, daily, hourly|
|MERRA-2||Land surface temperature, surface humidity, winds, soil moisture||0.5° x 0.667°||Monthly, daily, hourly|
Use the Data
One of the important assessments is exposure and vulnerability of a community to a natural disaster. Exposure is the presence of people, animals and ecosystems, environmental resources, infrastructure, or economic, social, and cultural assets in places and settings that could be adversely affected by a disaster. Vulnerability is the propensity to be adversely affected by a disaster, taking into consideration factors such as susceptibility to harm and lack of capacity to cope and adapt. Risk, therefore, is determined not only by the hazards, but also by the exposure and vulnerability to these hazards. Understanding each of these components is critical to disaster risk management efforts and adaptation strategies.
- Earthdata Disasters Feature Articles
- Earth Observations Inform Cities’ Operations and Planning: Rio de Janeiro, Brazil, and Chicago, Illinois, are using NASA Earth observations to map, monitor, and forecast water and air quality, urban heat island effects, landslide risks, and more.
- Improving Hurricane Forecasts with Near Real-Time Imagery and Data
- Satellites Have Drastically Changed How We Forecast Hurricanes
- A Guide to Understanding Satellite Images of Hurricanes
- Cyclone Amphan Story Map
- Post Assessment of Hurricane Dorian in the Bahamas Story Map
- Revisiting Hurricane Dorian
- Earth Observatory's Severe Storms
- A Machine-Learning Assist to Predicting Hurricane Intensity
- NASA Hurricane and Typhoon Updates
- Hurricane Florence
Earthquakes and Volcanoes:
- Satellites Guide Relief to Earthquake Victims
- NASA's ARIA Team Helps in Puerto Rico Quake Response
- NASA Maps Surface Changes From California Quakes
- New NASA Radar Looks to Monitor Volcanoes and Earthquakes from Space
- Study Suggests Rainfall Triggered 2018 Kilauea Eruption
- The Afar Triangle: 15 Years of ASTER Imagery
- Puerto Rico Earthquake, January 2020
- Indonesia Earthquake and Tsunami, September 2018
- Earth Observatory's Earthquakes
- Earth Observatory's Volcanoes
- Greenland's Rapid Melt Will Mean More Flooding
- NOAA and NASA Prepare for the 2020 U.S. Flood Season
- NASA Helps California Get Ahead of Coastal Flooding
- Central US Floods in Late Spring 2019
- Midwest Flooding 2019
- Earth Observatory's Floods
- NASA's GPM Articles
Other NASA Assets of Interest
NASA's Socioeconomic Data and Applications Center (SEDAC) has several tools to aid in hazards assessment. The SEDAC Hazards Mapper enables users to visualize data and map layers related to socioeconomic, infrastructure, natural disasters, and environment and analyze potential impacts and exposure. The Hazards and Population Mapper (HazPop) is a free mobile application that enables users to easily display recent natural hazard data in relationship to population, major infrastructure, and satellite imagery. Users can visualize the location of active fires over the past 48 hours, earthquake alerts over the past seven days, and yesterday′s air pollution data measured from space, and can estimate the total population in proximity to the user′s current location or to a recent hazard event or other point of interest. HazPop is designed for use by disaster risk managers, humanitarian response organizations, public health professionals, journalists, and others needing a quick assessment of the population potentially exposed to a major hazard event or developing emergency.
NASA's Flooding Days Projection tool produces probabilistic projections of flood frequency in the future that provide information about the full range of possibilities for a given year, including the potential for the occasional—yet inevitable—severe years. The projections leverage the predictability inherent in certain contributions (e.g., tidal amplitude and climate-change-induced sea level rise) and use statistical methods to account for everything else.The Global Precipitation Measurement (GPM) Mission provides additional information for specific disaster-related applications focused on cyclones, landslides, and floods which use GPM data.
NASA's Applied Sciences Disasters Program promotes the use of Earth observations to improve the prediction of, preparation for, response to, and recovery from natural and technological disasters. The Disasters program is supported by NASA scientists representing many data products relevant to natural hazards, including floods, earthquakes, volcanoes, and landslides. This ensures that there is a robust connection between the researchers involved in developing hazard-relevant products and the end users who could benefit from them. Disaster applications and applied research on natural hazards support emergency preparedness leaders in developing mitigation approaches, such as early warning systems, and providing information and maps to disaster response and recovery teams. Users can access near real-time and event specific GIS products for free through the Disasters Mapping Portal. The Portal also contains story maps and dashboards used show what is possible when NASA products are combined with other data sources.
NASA's Short-Term Prediction Research and Transition Center (SPoRT) is a project that transitions experimental/quasi-operational observations and research capabilities to the operational weather community to improve short-term forecasts on a regional scale.
Deep Learning-based Hurricane Intensity Estimator is an experimental framework investigating the application of Artificial Intelligence technologies and cloud computing resources to provide an automated and accurate estimation of tropical cyclone intensity.
NASA's Jet Propulsion Laboratory Tropical Cyclone Information System is being actively developed to help scientists improve their understanding and forecasting of hurricanes. It has two components: a set of near real-time portals that integrate model forecast with multi-parameter satellite and airborne observations from a variety of instruments and platforms, providing interactive visualization and some on-line analysis tools that work with both observations and models, and a 12-year global archive of multi-satellite hurricane observations.GeoGateway is a web map-based science gateway that expands the utility of NASA's geodetic imaging data. GeoGateway provides tools for scientific discovery, field use, and disaster response using Interferometric SAR (InSAR) and GPS integrated with earthquake faults datasets, seismicity data, and models.
SARVIEWS Hazard Portal is a SAR-based hazard monitoring service funded by NASA's Applied Sciences program and based at NASA's Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC). It is a fully automatic processing system that produces value-added products in support of monitoring natural disasters. The SARVIEWS processor is implemented in the Amazon Cloud and utilizes modern processing technology to generate geocoded and fully terrain-corrected image time series, as well as interferometric SAR data over areas affected by natural disasters.
NASA has developed a landslide hazard site to provide information on landslide mapping and monitoring. The site features a viewer where landslide inventories are displayed as well as a global landslide modeling system (Landslide Hazard Assessment for Situational Awareness). There is also a Citizen Science site called Landslide Reporter where the community is able to log in and provide reports of landslides observed or identified in the media.
It is important to note that NASA enables the conduct of research activities. The agency does not do disaster operations. Many of the research capabilities developed at NASA, however, have been transitioned to operational agencies, such as NOAA, their National Hurricane Center, the USGS, and other agencies, many of which are included below.NOAA has numerous resources that can aid in disaster planning and response. The National Centers for Environmental Information (NCEI) track and evaluate climate events in the U.S. and globally that have great economic and societal impacts. NCEI is frequently called upon to provide summaries of global and U.S. temperature and precipitation trends, extremes, and comparisons in their historical perspective.
- U.S. Billion-Dollar Disaster Events Time Series
NOAA Climate Maps
Provides maximum, minimum, and mean; also get the difference from average and browse data going back and forth in month and year.
FEMA's Flood Map Service Center (MSC) is the official public source for flood hazard information produced in support of the National Flood Insurance Program. Use the MSC to find your official flood map, access a range of other flood hazard products, and take advantage of tools for better understanding flood risk.
Global Flood Monitoring System at the University of Maryland is a NASA-funded experimental system using real-time TMPA and IMERG precipitation information as input to a quasi-global (50°N - 50°S) hydrological runoff and routing model. The models output time-series graphs and visualizations of flood detection, streamflow, surface water storage, and inundation variables at 1 km resolution.
USGS National Landslide Hazards Program aims to reduce long-term losses from landslide hazards by improving our understanding of the causes of ground failure and suggesting mitigation strategies. They provide a web-based interactive map with a consistent set of landslide data from a variety of agencies at the local, state, and federal level.
USGS Earthquakes Map provides locations, date/time, and depth of recent earthquakes around the U.S., with filtering capabilities to include temporal options of one-day or the past 30 days and magnitude options of 2.5 or greater or 4.5 or greater.
The National Weather Service Heat Index provides a chart incorporating both temperature and relative humidity. It also has Heat Safety Tips and Resources for dealing with and preparing for excessive heat conditions.
The UNDRR put out the Sendai Framework for Disaster Risk Reduction, which promotes the "...substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries."
Benefits and Limitations of 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, 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. NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) supports users interested in monitoring a wide variety of natural and man-made phenomena in a timely manner.
With satellite data, assessments can be made regarding the land surface, precipitation events, ground movement and air temperature. In addition, incorporating satellite data with in-situ data into modeling programs makes for a more robust and integrated forecasting system.
- Spatial resolution: while lower resolution data provide a more global view, as with the Aqua/Terra MODIS measurements, the spatial resolution is too coarse for certain assessments. 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. 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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Published May 11, 2020
Page Last Updated: Aug 24, 2021 at 2:14 PM EDT