Biological Diversity and Ecological Forecasting Data Pathfinder
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 to 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.
The 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
NASA, in collaboration with other organizations, has various instruments that 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), the unique spectral fingerprint each species has 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 via several ground-truth and validation efforts. These 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 Earth observing systems (LANCE). LANCE provides data to the public within three hours of satellite overpass, which allows for near real-time (NRT) monitoring and decision making. If latency is not a primary concern, users are encouraged to use the standard science products, which are created using the best available ancillary, calibration, and ephemeris information.
|Advanced Land Observing Satellite-1 (ALOS)||Phased Array type L-band Synthetic Aperture Radar (PALSAR)||10 m, 100 m||Forest Structure|
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|
|Earth Observing-1 (EO-1)||Hyperion||30 m||Variable||Spectroscopy|
|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
|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|
|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|
|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|
|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||NA||Canopy Height, Vertical Canopy Structure, Surface Elevation|
|Landsat 8||Operational Land Imager (OLI)
Thermal Infrared Sensor (TIRS)
|15, 30, 60 m||16 days||Surface Reflectance|
|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|
|Soil Moisture Active Passive (SMAP)||Radar (active) - no longer functional
Microwave radiometer (passive)
|9 km, 36 km||1 day||Soil Moisture, Sea Surface Salinity|
|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|
|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|
|Uninhabited Aerial Vehicle
Note: data are available over specific areas
|Synthetic Aperture Radar (UAVSAR)||1.8 m||Non-cyclic||Forest Structure|
|Uninhabited Aerial Vehicle||Portable Remote Imaging SpectroMeter (PRISM)||~30 cm||Variable||Spectroscopy|
|Space Shuttle||Shuttle Radar Topography Mission (SRTM)||30 m, 90 m||Static||Elevation|
* 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 observational 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.
|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|
Use the Data
Scientists, researchers, land managers, decision makers, and others 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 have used the data, visit the Land Processes Distributed Active Archive Center (LP DAAC) Data in Action, or read our 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 us 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).
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 are below and provide more details on how NASA data are contributing to ecological research.
Vegetation characteristics and processes
- Detecting Invisible Plant Stress
- Third Dimension of Forests
- Protection of Forests using Synthetic Aperture Radar
Direct detection of biodiversity
Researchers have used very high resolution satellite imagery to directly detect animal populations:
Researchers are using the NASA/NOAA Joint Partnership Suomi National Polar-orbiting Partnership (NPP) 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
- Houston, We Have a Penguin (Penguins detected by guano)
- Tracking giant gerbil mounds from herbivory damage
- Spotting the Spotted Owl: 30 Years of Habitat Change
- Data on the Half Shell (detecting conditions that favor an oyster virus outbreak)
- Temperatures Predict Bird Biodiversity
- NASA Fosters Innovative Ways to Understand Biodiversity
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. NASA's Applied Remote Sensing Training Program (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.
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.
COral Reef Airborne Laboratory (CORAL) is a mission to determine the relationship between coral reef condition and biogeophysical forcing parameters.
The Oceanographic In-situ data Interoperability Project (OIIP) is a collaboration among JPL, UCAR/Unidata, and the Large Pelagics Research Center (LPRC) 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.
Landfire, or the Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Forest Service and U.S. Department of the Interior, providing landscape-scale geospatial products that describe vegetation, wildland fuel, and fire regimes across the United States 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 U.N. 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. The Applied Remote Sensing Training Program (ARSET) also has an introductory webinar, Using the U.N. Biodiversity Lab to Support National Conservation and Sustainable Development Goals, for additional information on using the U.N. 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.”The Tagging of Pelagic Predators (TOPP) 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 (PHAM) 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.
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.
Published April 7, 2020
Page Last Updated: Oct 30, 2020 at 12:28 PM EDT