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Biological Diversity and Ecological Forecasting Data Pathfinder

Biological Diversity and Ecological Forecasting Campaign logo , which contains images of a wolf, a leaf, a bird and aquatic vegetation

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 NASA's Applied Remote Sensing Training (ARSET) on Fundamentals of Remote Sensing.

An ecological data revolution is underway. Ecologists have gained unprecedented insights into Earth’s many biological systems, from determining populations of species to tracking and observing interactions between ecological communities and even using DNA from the environment (eDNA) to identify invasive or rare species. This revolution is driven by the integration and harmonization of multiple data sources at multiple spatial scales.

Traditional field observations provide data on organisms and their environment at the local level. NASA satellite-based remote sensing data complement field data with environmental variables (such as temperature and precipitation) and vegetation data (such as canopy height and evapotranspiration).

This Biological Diversity and Ecological Forecasting Data Pathfinder is divided into four parts, each of which contains data that provide information to assess Essential Biodiversity Variables and Sustainable Development Goals. Click on the links below to find data specific to the section:

  • Find Vegetation Characteristics and Processes Data: includes vegetation condition (e.g., greenness, water stress), vegetation types, canopy height, vertical structure of forests, habitat structure, delineation and conservation of protected areas, phenology, and biomass measurements of groups and individuals.
  • Find Biodiversity-related Spectroscopy Data: includes direct detection of species presence, species distributions and abundances, prevalence of invasive species, and agricultural and aquacultural expansions.
  • Find Human Impacts Data: includes habitat fragmentation due to agriculture, deforestation, urbanization, and other land cover changes; effects of nighttime lights on predator/prey interactions; and environmental stressors brought about by climate change.
  • Find Species Distribution Modeling Data: includes the indirect detection of species distribution based on correlative models using in-situ species presence/absence data and remotely sensed climatological or geological variables (e.g., precipitation, elevation, land/sea surface temperature, changes in land use/land cover, ocean color, and many more).

About the Data

About the Data

NASA collaborates with U.S. federal entities and international space organizations to provide data used in understanding a number of biological phenomena, including vegetation characteristics and change, biodiversity, the impacts of human activities on the natural environment, and habitat suitability. Some of these phenomena can be detected directly through remote Earth observation. For instance, forest loss can be tracked by comparing satellite imagery across time, and the distribution of ecosystems and species can directly be detected by the color signatures that characterize them. Species-specific detection is challenging with multispectral data due to the coarse spatial resolution; however, with hyperspectral data (having a high spectral resolution), a species' unique spectral fingerprint can provide species-specific information for vegetation mapping and individual species identification.

Other biological phenomena are detected indirectly through models, which often use remotely sensed and ground-based observations of environmental variables as inputs. For example, species distribution or movement can be modeled by comparing known occurrence locations or movement records to the surrounding physical characteristics of the landscape. These analyses can help forecast where species are likely to be distributed in the future.

NASA’s Earth science data products are validated, meaning their accuracy has been assessed and verified over a widely distributed set of locations and time periods using ground-truth and validation efforts. All NASA Earth observing data are freely and openly available to all users.

Datasets referenced in this pathfinder are from sensors shown in the table below. Some of these datasets are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE provides data generally within three hours of a satellite observation, which allows for near real-time (NRT) monitoring and decision making. If latency is not a primary concern, users are encouraged to use standard science products, which are created using the best available ancillary, calibration, and ephemeris information.

Asterisk (*) indicates sensors from which select NRT datasets are available through LANCE. Please note that this list includes only datasets that are part of NASA's Earth Observing System Data and Information System (EOSDIS) collection and is not meant to be an exhaustive list.



Spatial Resolution

Temporal Resolution


International Space Station
Note: data are available in areas of 51.6° S to 51.6° N
Global Ecosystem Dynamics Investigation (GEDI) 25 m diameter, 1 km grid N/A Canopy Height, Vertical Canopy Structure, Surface Elevation
Space Shuttle Shuttle Radar Topography Mission (SRTM) 30 m, 90 m Static Elevation
Japan Aerospace Exploration Agency (JAXA) and Japan's Ministry of Economy Trade and Industry (METI) Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 10 m, 100 m Forest Structure
ESA Sentinel-1 Synthetic Aperture Radar (SAR) 25 x 40 m, 5 x 5 m, and 5 x 20 m 12 days (using together 6 days) Forest Structure
Uninhabited Aerial Vehicle
Note: data are available over specific areas
Uninhabited Aerial Vehicle SAR (UAVSAR) 1.8 m Non-cyclic Forest Structure
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 Land Surface Temperature, Evapotranspiration, Evaporative Stress Index, Water Use Efficiency
Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 15 m, 30 m, 90 m Variable Land Surface Temperature, Surface Reflectance, Elevation
Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) * 250 m, 500 m, 1000 m, 5600 m 1-2 days Land Surface Temperature, Surface Reflectance, Vegetation Indices, Leaf Area Index/Fraction of Photosynthetically Active Radiation, Gross/Net Primary Productivity, Evapotranspiration, Land Cover Dynamics, Vegetation Continuous Fields, Sea Surface Temperature
Note: data are available over specific areas
Airborne Visible InfraRed Imaging Spectrometer (AVIRIS)
AVIRIS - Next Generation (AVIRIS-NG)
Varies, but typically 3-20 m Variable or N/A Leaf Surface Indices
JAXA Global Change Observation Mission 1st - Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) * Precipitation Rate: imagery resolution is 2 km, sensor resolution is 5 km
Soil Water Equivalent (SWE): 25 km
Precipitation rate: daily
SWE: daily, 5-day, monthly
Integrated multi-satellite data Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Algorithm (TMPA)
Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG)
0.1° x 0.1° or 0.25° x 0.25° Half-hourly, daily, monthly Precipitation
Soil Moisture Active Passive (SMAP) Radar (active) - no longer functional
Microwave radiometer (passive)
9 km, 36 km 1 day Soil Moisture, Sea Surface Salinity
Earth Observing-1 (EO-1) Hyperion 30 m Variable Spectroscopy
Uninhabited Aerial Vehicle Portable Remote Imaging SpectroMeter (PRISM) ~30 cm Variable Spectroscopy
NASA/USGS Landsat 8 Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
15, 30, 60 m 16 days Surface Reflectance
NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) * 500 m, 1000 m, 5600 m Daily Surface Reflectance, Land Surface Temperature, Vegetation Indices, Nighttime Imagery, Sea Surface Temperature
Ice, Cloud and land Elevation Satellite-2 (ICESat-2) Advanced Topographic Laser Altimeter System (ATLAS) 3 m accuracy at 1 km spatial resolution 91 days Vegetation Height
* sensors from which select datasets are available in LANCE
Note: this is not an exhaustive list but rather only includes datasets with NASA's Earth Observing System Data and Information System (EOSDIS)

In addition to mission data, NASA has a series of models that use satellite- and ground-based data to produce high-quality fields of land surface states and fluxes. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is a NASA atmospheric reanalysis that uses Goddard Earth Observing System Model, Version 5 (GEOS-5) with its Atmospheric Data Assimilation System (ADAS). The MERRA project focuses on historical climate analyses for a broad range of weather and climate time scales and places the NASA suite of observations in a climate context.

Model Source

Data Parameter

Spatial Resolution

Temporal Resolution

Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Emissivity, Evaporation, Humidity, Leaf Area Index, Precipitation Rate, Runoff, Temperature, Snow Cover, Soil Moisture, Soil Temperature, etc. 0.5° x 0.625° Diurnal, monthly

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Use the Data

Use the Data

Ecological variables can be measured using multiscale remote sensing, modeling, and advanced analytical techniques. The type of instrument (active versus passive) and whether it operates in the visible, infrared, thermal infrared, and microwave portions of the electromagnetic spectrum allows for discerning different variables.

Ecological variables can be measured using multiscale remote sensing, modeling, and advanced analytical techniques. The type of instrument (active versus passive) and whether it operates in the visible, infrared, thermal infrared, and microwave portions of the electromagnetic spectrum allows for discerning different variables.

Scientists, researchers, land managers, and decision makers use remote sensing data in numerous ways. Satellite imagery, coupled with ground-based data, aids in our understanding of species distribution, biodiversity, ecosystem services, conservation, and much more. NASA Earth science observations are transforming our approach to some of these critical issues. For examples of how people are using these data, see NASA's Land Processes Distributed Active Archive Center (LP DAAC) Data in Action articles or the Earthdata Biodiversity Feature Articles, which includes related Data User Profiles.

Ecosystems respond to changes in land use and climate with changes in species abundance and distribution, as well as altered ecosystem services, such as nutrient recycling and water storage. Understanding potential changes and how they might impact ecosystems provides the ability to detect the negative and often detrimental effects of drought, invasive species, reduced biodiversity, fire susceptibility, disease vectors, and other changes. Increasing knowledge of how ecosystems change under current conditions also helps to model ecosystem and habitat changes under different climate scenarios.

“Conservation biologists rely on estimates of species richness (i.e., the number of species in a particular place) as they race to determine areas in which to spend limited resources in an age of rapid biodiversity decline. Scientifically sound environmental management requires frequent and spatially detailed assessments of species numbers and distributions. Such information can be prohibitively expensive to collect directly. Measuring the distribution and status of biodiversity remotely, with airborne or satellite sensors, would seem an ideal way to gather these crucial data” (Turner, et al, 2003).

Case Studies

Many ecological questions are answered using remote sensing and ground-based data. NASA-funded projects within each of the four sections of this data pathfinder provide more details on how NASA data are contributing to ecological research.

Vegetation characteristics and processes
Direct detection of biodiversity

Researchers have used very high resolution satellite imagery to directly detect animal populations:

Human impacts

Researchers are using the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite's Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band to detect biological activities or changes in behavior due to artificial light around urban environments.

Indirect detection of biodiversity

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Other NASA Assets of Interest

Other NASA Assets of Interest

NASA’s Applied Sciences Biological Diversity and Ecological Forecasting Program supports basic research that advances the understanding of how and why biological diversity is changing. ARSET also provides a number of webinars on land management and conservation. Webinars are conducted in a several different languages.

NASA’s Terrestrial Ecology Program researches Earth's carbon cycle and ecosystems using space-based observations. The focus is on land-based ecosystems, changes in their structure and functioning, and their roles in supporting human life and maintaining Earth's habitability.

Photo of volunteer installing an AudioMoth recording device.

Image credit: Soundscapes to Landscapes.

Soundscapes to Landscapes is a project of NASA's Earth Science Data Systems (ESDS) Citizen Science for Earth Systems Program (CSESP), which combines bioacoustic data collected by citizen scientists with satellite and environmental data to monitor bird diversity in Sonoma County, California.

Floating Forests is another CSESP project that trains citizen scientists to trace patches of kelp on Landsat imagery. So far, more than 20 years of imagery has been processed. The results are helping to uncover the history of giant kelp forests around the globe and the environmental factors that affect them.

The Oceanographic In-situ data Interoperability Project (OIIP) is a collaboration among NASA's Jet Propulsion Laboratory, the University Corporation for Atmospheric Research/Unidata, and the Large Pelagics Research Center at the University of Massachusetts, Boston. The goal of the project is to address interoperability and data challenges associated with the integration of oceanographic in-situ datasets and satellite Earth observations using field campaign measurements and marine animal electronic tagging data as test cases.

WhaleWatch is a NASA-funded project coordinated by NOAA Fisheries' West Coast Region to help reduce human impacts on whales by providing near real-time information on where whales have been observed and where they might be most at risk from threats such as ship strikes, entanglements, and loud underwater sounds.

NASA Airborne Data

The NASA Airborne Data Management Group (ADMG) has built a NASA airborne and field investigation inventory, called the Catalog of Archived Suborbital Earth science Investigations (CASEI). CASEI is a unique inventory that provides intensively curated information about the context, research motivation, funding, and details of non-satellite instruments and platforms. Information about important events and observations are included, along with links to relevant data products, all in a single, intuitive, and highly interconnected web user interface. Campaigns related to region-specific biodiversity are:

  • The Arctic-Boreal Vulnerability Experiment (ABoVE) is a NASA-led project, which studies Arctic and boreal ecosystems to understand their vulnerability and resilience to environmental change. ABoVE began in the spring of 2017 and has completed three deployments.
  • Delta-X campaign gathers remotely-sensed and in situ data to study changes occurring to the water, vegetation, and sediment along the Atchafalaya and Terrebonne Basins along the Mississippi River Delta. The data will be assimilated into a model to forecast the resilience and vulnerability of the delta given the rising seas; the framework for the model can be used for forecasting change in other delta systems around the world.
  • The AfriSAR campaign began as a European Space Agency (ESA)-led project that started in 2015 and focused on using airborne and field measurements to better characterize forest structure and evaluate biomass retrieval algorithms. The following year, NASA partnered with ESA, the Gabonese Agency for Spatial Studies and Observation, and the German Space Center to lead the second and final deployment, which took place in the African tropical forests of Libreville, Gabon, from February 3-25, 2016.
  • The Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) focused on quantifying Arctic storage and fluxes of carbon and how these relate to climate change. CARVE had three deployments over Alaska during the boreal spring, summer, and fall of 2011-2015. Airborne observations of gas concentrations, water vapor, and other parameters were supplemented by ground sites that included flux towers.
  • The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) campaign focused on observations of root zone soil moisture (RZSM) and net ecosystem exchange (NEE) of carbon dioxide over a variety of North American biomes across several seasons. AirMOSS completed 34 deployments from 2012-2015, over parts of Canada, the U.S., and Costa Rica.
  • The Coral Reef Airborne Laboratory (CORAL) uses the Portable Remote Imaging Spectrometer (PRISM) instrument to extensively assess coral reef condition. The CORAL mission combined a variety of in situ data, deployed under six sub-campaigns, to identify reef composition and study primary production near the Mariana Islands in Palau, portions of the Great Barrier Reef, and the U.S. Hawaiian Islands. NASA's Ocean Biology DAAC (OB.DAAC) provides a CORAL data browser for easy access to these data.
  • The Southern African Regional Research Initiative (SAFARI 2000) was an International project that strove to make connections between land and atmosphere interactions and processes. SAFARI 2000 took place from 1999-2001 during the wet and dry seasons of Southern Africa.
  • The Boreal Ecosystem-Atmosphere Study focused principally on radiation, biogeochemical, and chemistry interactions between the boreal forest and troposphere through winter, summer, and transitional seasons. This large field effort included multiple seasonal campaigns spread over two deployments in 1994 and 1996 over forested portions of Canada’s Manitoba and Saskatchewan provinces.
  • The Oregon Transect Ecosystem Research project was a NASA-funded campaign designed to provide scientists with a better understanding of ecosystem functions in coniferous forests. Data collection occurred in Oregon’s coniferous forests during the summer of 1989 and concluded in 1991.

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External Resources

External Resources

The Landscape Fire and Resource Management Planning Tools (LandFire), is a shared program between the wildland fire management programs of the U.S. Forest Service and the U.S. Department of the Interior, providing landscape-scale geospatial products that describe vegetation, wildland fuel, and fire regimes across the U.S. and its territories.

Global Forest Watch provides data and tools for monitoring forests, specifically tree loss and gain, and biodiversity hotspots.

The United Nations Biodiversity Lab is an online platform that allows policymakers and other partners to access global data layers, upload and manipulate their own datasets, and query multiple datasets to provide key information on the Aichi Biodiversity Targets and nature-based Sustainable Development Goals. The core mission of the UN Biodiversity Lab is three-fold: to build spatial literacy to enable better decisions, to use spatial data as a vehicle for improved transparency and accountability, and to apply insights from spatial data across sectors to deliver on the Convention on Biological Diversity and the 2030 Agenda for Sustainable Development. ARSET has an introductory webinar, Using the UN Biodiversity Lab to Support National Conservation and Sustainable Development Goals, for additional information on using the UN Biodiversity Lab.

Trends.Earth provides maps that monitor land degradation as it applies to Sustainable Development Goal (SDG) 15.3.1. SDG target 15.3 states: “By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world.”

Aerial photo of a blue whale.

WhaleWatch uses satellite data to predict where blue whales might be migrating. (Courtesy of NOAA.)

The Tagging of Pelagic Predators program is an international collaboration that allows users to interact with tracking data and oceanographic datasets to observe marine megafauna, understand factors influencing animal behavior in the ocean, and use sensor data from animal tags to aid in climate models and a better understanding of ocean ecosystems.

Pelagic Habitat Analysis Module is a GIS software tool for fisheries managers, scientists, and researchers to examine and predict pelagic ocean biota habitat; it uses biota presence/absence or abundance data combined with environmental data (satellite imagery, bathymetry, survey cruises, and ocean circulation models).

NOAA TurtleWatch is a map providing up-to-date information about the thermal habitat of loggerhead sea turtles in the Pacific Ocean north of the Hawaiian Islands.

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Benefits and Limitations of Remote Sensing Data

Benefits and Limitations of Remote Sensing Data

When deciding to use remote sensing data, it is important to consider both the benefits and the limitations of the data.

Benefits of using satellite data include:

  • Supplementing ground-based data: ground-based data are more comprehensive on a local scale, providing direct observations of ecological phenomena. However, airborne or satellite data are far more extensive, with millions of measurements over regional and global scales, providing more complete spatial coverage.
  • Temporal resolution: the temporal resolution of satellite data ranges from hours to weeks. Many satellites pass over the same spot on Earth every one-two days and sometimes as seldom as every 16+ days. Data have been collected over increasingly long periods of time, from the 1970s to the present, whereas many ground-based biodiversity studies often use data sampled at a single point in time.
  • Monitoring in near-real time: some satellite information is available 3-5 hours after observation, allowing for a faster response than ground-based observations.

Limitations specific to using satellite data in ecological assessments:

  • Spatial resolution: while lower resolution data provide a more global view, as with Aqua/Terra MODIS measurements, the spatial resolution is too coarse for certain assessments. Most satellite-based data are not at a fine enough resolution to distinguish individual organisms and their movements; for example, using most satellite-based data, scientists can determine the presence/absence of an algal bloom, yet the particular species of algae cannot be determined. This is not the case for instruments at higher resolutions, like those on Landsat or from airborne missions.
  • Spectral resolution: passive instruments (those that use the 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 active instruments like microwave sensors.

A limitation with ground-based data:

  • Species occurrence: data are in analog format only; important specimens remain unidentified; not all records have been assigned geographic coordinates; data records include errors and inconsistencies; and data are not openly shared. Spatial distribution and re-visits are also very limited.

It is not possible to combine all desirable features into one remote sensor; to acquire observations with 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 April 7, 2020

Page Last Updated: Feb 28, 2022 at 9:22 PM EST