Vegetation Characteristics and Processes Data

Biological Diversity and Ecological
Forecasting Data Pathfinder

Vegetation is a primary component of terrestrial biodiversity, playing a critical role in the global energy budget and in many of our biogeochemical cycles. Maintaining species richness ensures the productivity and stability of ecosystem processes, making it critical to monitor vegetation health. Measurements include 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.

Photograph of a lush, green rainforest in the Pacific Northwest

Constant rains from storm after storm breed life in the rainforests of the Pacific Northwest. (Courtesy D. Stolz)

Vegetation Greenness

Vegetation Greenness

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)

Vegetation indices measure the amount of green vegetation over a given area and can be used to assess vegetation health. A commonly-used vegetation index is the Normalized Difference Vegetation Index (NDVI), which uses the difference between near-infrared (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, while higher NDVI values indicate greener vegetation, including forests, croplands, and wetlands. The enhanced vegetation index (EVI) is another widely used vegetation index that minimizes canopy-soil variations and improves sensitivity over dense vegetation conditions.

Vegetation products from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (on the Aqua and Terra satellites) and the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite can be accessed in various ways.

Research-quality data products can be accessed directly via Earthdata Search or the Land Processes Distributed Active Archive Center (LP DAAC) Data Pool; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

The LP DAAC Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) offers a simple and effective way to extract, transform, visualize, and download MODIS and VIIRS vegetation-related data products. AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest, and output data can be downloaded in csv (point), GeoTIFF (area) or NetCDF-4 (area) format. The Oak Ridge National Laboratory DAAC (ORNL DAAC) subsetting tools provide a means to simply and efficiently access and visualize MODIS and VIIRS vegetation-related data products as well.

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 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.

Data can be visualized in Worldview:

  • MODIS NDVI in Worldview
    This dataset has a spatial resolution of 250 m and a temporal resolution of eight days. 16-day and monthly data are also available within Worldview.
  • MODIS EVI in Worldview
    This dataset is monthly at 1 km spatial resolution. Rolling 8-day and 16-day data are also available within Worldview.

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Leaf Area Index / Fraction of Photosynthetically Active Radiation (FPAR)

Leaf Area Index / Fraction of Photosynthetically Active Radiation (FPAR)

Multi-year average of the leaf area index of the Amazon based on Terra MODIS data

Multi-year average of the leaf area index of the Amazon based on Terra MODIS data. Credit: Earth Observatory

Leaf area index (LAI) is the amount of leaf area in an ecosystem; more specifically, it is the one-sided green leaf area per unit ground area in broadleaf canopies and is one-half of the total needle surface area per unit ground area in coniferous canopies. FPAR is the fraction of photosynthetically active radiation (400-700 nm) absorbed by green vegetation. Both of these measurements are used for calculating surface photosynthesis, evapotranspiration, and net primary production, which in turn are used to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation.

The MODIS Level 4 LAI product is a 4-day composite data set with 500 meter pixel size. The product algorithm chooses the best pixel available from all acquisitions of the MODIS sensors located on both NASA’s Terra and Aqua satellites from within a 4-day period.

LP DAAC AppEEARS offers a simple and effective way to extract, transform, visualize, and download MODIS and VIIRS vegetation-related data products. AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest, and output data can be downloaded in csv (point), GeoTIFF (area) or NetCDF-4 (area) format.

ORNL DAAC subsetting tools also provide a means to simply and efficiently access and visualize MODIS and VIIRS LAI and FPAR data products.

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Gross/Net Primary Productivity

Gross/Net Primary Productivity

net primary productivity in August 2010, when the Northern Hemisphere reached its peak productivity. On land, areas where plants are growing most—and storing the most carbon—are dark green. Highly productive areas in the ocean, where the most phytoplankton are growing, are dark blue.

Net primary productivity in August 2010, when the Northern Hemisphere reached its peak productivity. On land, areas where plants are growing most—and storing the most carbon—are dark green. Highly productive areas in the ocean, where the most phytoplankton are growing, are dark blue. Credit: Earth Observatory

Gross primary productivity (GPP) is the total energy captured by vegetation. Net primary productivity (NPP) is how much carbon dioxide vegetation takes in during photosynthesis minus how much carbon dioxide the plant releases during respiration. Values typically range from 0 to 6.5 grams per square meter per day. A negative value indicates decomposition or that respiration overpowered carbon absorption, i.e., more carbon was released to the atmosphere than the plants took in. Monitoring GPP and NPP is important as they form the basis of most ecosystem food webs.

The MODIS Level 4 GPP and NPP products are available in yearly and 8-day temporal resolutions with 1 km or 500 m pixel size.

LP DAAC AppEEARS offers a simple and effective way to extract, transform, visualize, and download MODIS and VIIRS vegetation-related data products. AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest, and output data can be downloaded in csv (point), GeoTIFF (area) or NetCDF-4 (area) format.

ORNL DAAC subsetting tools also provide a means to simply and efficiently access and visualize MODIS and VIIRS GPP and NPP data products.

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Evapotranspiration

Evapotranspiration

The combination of evaporation from the land surface and transpiration from plants is evapotranspiration, abbreviated ET. This parameter approximates the consumptive use of a landscape’s plants.

The combination of evaporation from the land surface and transpiration from plants is evapotranspiration, abbreviated ET. This parameter approximates the consumptive use of a landscape’s plants. Image Credit: U.S. Geological Survey

Measurements of evapotranspiration (ET), the sum of evaporation from land surface and transpiration in vegetation, are extremely useful in monitoring and assessing water availability, drought conditions, and crop production. One of the challenges in acquiring ET data is that ET can’t be measured directly with satellite instruments as it is dependent on many other variables, such as land surface temperature, air temperature, and solar radiation. However, there are Level 4 data products (see data processing levels for more information) that incorporate daily meteorological reanalysis data with remote sensing data to arrive at estimations of ET. MODIS has such a product. Meteorological reanalysis data are assimilated products from historical atmospheric data from an extended period of time.

Research quality MODIS Level 4 ET products are available in yearly and 8-day temporal resolutions with 500 m pixel size.

NASA's Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) aboard the International Space Station (ISS) measures the temperature of plants to better understand how they respond to the stress of insufficient water availability. ECOSTRESS was launched in June 2018 and uses a multispectral thermal infrared radiometer to measure radiance, which is converted into surface temperature and emissivity. ECOSTRESS produces Level 3 ET data products according to the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm, using the surface temperature and emissivity as inputs (among other ancillary data inputs from other sources).

Research quality ECOSTRESS ET data products can be accessed directly via Earthdata Search or the LP DAAC Data Pool; datasets are available as HDF files but are, in some cases, customizable to GeoTIFF.

The LP DAAC AppEEARS offers a simple and effective way to extract, transform, visualize, and download MODIS and ECOSTRESS ET data products. AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest, and output data can be downloaded in csv (point), GeoTIFF (area) or NetCDF-4 (area) format.

ORNL DAAC subsetting tools also provide a means to simply and efficiently access and visualize MODIS ET data products.

The Land Data Assimilation System (LDAS) provides model-based ET data of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses measurements of precipitation, soil texture, topography, and leaf area index to model soil moisture and evapotranspiration. When calculating ET, there are biases around seasonality or local-specific effects but developers try to account for those and calibrate accordingly; estimates of ET are provided every day and integrated to get monthly, seasonal, or annual information within 2-12% error.

GLDAS 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.

  • GLDAS ET in Giovanni
    Data are available with a temporal resolution of 3-hourly, daily, and monthly.

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Evaporative Stress Index and Water Use Efficiency

Evaporative Stress Index and Water Use Efficiency

Level 4 Evaporative Stress Index PT-JPL, average from August 5, 2018 captured over California's Central Valley. High ESI is in shades of green and low ESI in shades of red.

Level 4 Evaporative Stress Index PT-JPL, average from August 5, 2018, captured over California's Central Valley. High ESI is in shades of green and low ESI in shades of red. Image Credit: LP DAAC

ECOSTRESS also produces Level 4 evaporative stress index (ESI) and water use efficiency (WUE) products. The ESI product is derived from the ratio of Level 3 actual ET to potential ET (PET), calculated as part of an algorithm. WUE is the ratio of carbon stored by plants to water evaporated by plants. This ratio is given as grams of carbon stored per kilogram of water evaporated, over the course of the day from sunrise to sunset, on the day when the ECOSTRESS granule is acquired. ESI applications include indicating agricultural drought and observing vegetation stress.

The LP DAAC AppEEARS offers a simple and effective way to extract, transform, visualize, and download ECOSTRESS L1-L4 data products.

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Forest Structure and Biomass

Forest Structure and Biomass

Ice, Cloud and land Elevation Satellite-2

NASA's Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory carries a single instrument, the Advanced Topographic Laser Altimeter System, or ATLAS. ATLAS measures the travel time of laser pulses to calculate the distance between the spacecraft and Earth’s surface. While other satellites such as Landsat and MODIS allow researchers to study the location and extent of forests, ICESat-2 will allow them to study forest height. Because ATLAS is sensitive enough to detect individual photons, and has such a rapid firing rate, the instrument will be able to detect both the forest floor and the tops of canopies in all but the densest woods and jungles.

Research quality ATLAS data products can be accessed directly via Earthdata Search:

Visualizations of ATLAS data can be accessed through OpenAltimetry. OpenAltimetry is a cyberinfrastructure platform for discovery, access, and visualization of data from the ICESat and ICESat-2 missions:

Global Ecosystem Dynamics Investigation

The Global Ecosystem Dynamics Investigation (GEDI) instrument makes precise measurements of forest canopy height, vertical canopy structure, and surface elevation, using high-resolution laser-ranging observations of the 3D structure of the Earth. GEDI data provide critical information on vegetation biomass, which is important to our understanding of how much carbon is stored by vegetation. In addition, these data provide characterization of habitat quality for many organisms. The instrument was deployed in December 2018 and is attached to the International Space Station (ISS).

GEDI data collection over South Carolina woodland; darker green shows where the leaves and branches are denser, lighter green shows less dense canopy.

GEDI data collection over South Carolina woodland; darker green shows where the leaves and branches are denser, lighter green shows less dense canopy. Credit: Joshua Stevens (NASA Earth Observatory), Bryan Blair (NASA Goddard Space Flight Center), Michelle Hofton and Ralph Dubayah (University of Maryland).


In January 2020, GEDI mission researchers released the first measurements of forests around the world.

Research quality GEDI data products can be accessed directly via Earthdata Search

In addition, LP DAAC has a web service, GEDI Finder, for locating GEDI orbits (files) that intersect an input bounding box. You can generate a means of downloading the data intersecting your region of interest.

Synthetic Aperture Radar

Synthetic aperture radar (SAR) datasets provide a unique perspective for monitoring forest changes. SAR instruments are able to penetrate cloud cover and work in both day and night conditions. In addition, SAR, operating in the microwave portion of the electromagnetic spectrum, captures different target parameters than optical sensors, therefore providing unique information that complements standard optical remote sensing methods. For information on SAR, view What is Synthetic Aperture Radar?

With optical sensors, the returned signal is an indication of the chlorophyll concentration of a leaf. Using microwave sensors, the returned signal is proportional to the size, shape, and water content of the leaf. Therefore, SAR is sensitive to forest structure and biomass (adapted from SAR Handbook, 2019).

Research quality (higher-level “standard”) SAR data products can be accessed via Earthdata Search or through NASA partner websites. For more information on choosing a dataset, refer to the Tools for Data Access and Visualization section.

  • Sentinel-1A and Sentinel-1B SAR data from Earthdata Search
    Note: Sentinel-1 operates at C-band and data are available in single or dual polarization. Another option for SAR data is the inclusion of phase information. Level 1 data are produced as single look complex (SLC), in which the phase information is preserved, or as ground-range detected (GRD), in which the phase information is lost. GRD data are appropriate for forest mapping and change detection.
  • Sentinel-1A and Sentinel-1B SAR data from Vertex
    Vertex is NASA's Alaska Satellite Facility DAAC (ASF DAAC) search tool, which allows for the subset and preview of numerous types of SAR data, including Sentinel-1.
  • UAVSAR from Earthdata Search
    Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) is an airborne SAR instrument operating at L-band. Missions over portions of North America, Greenland, Iceland, and North, Central, and South America were conducted from 2008 to present.
  • UAVSAR from Vertex
    Vertex is the ASF DAAC search tool, which allows for the subset and preview of numerous types of SAR data.
  • ALOS PALSAR from Earthdata Search
    From 2006-2011, the Phased Array type L-band Synthetic Aperture Radar (PALSAR), onboard the Advanced Land Observing Satellite-1 (ALOS) acquired data from multiple observation modes with varying polarizations and resolutions.
  • ALOS PALSAR from Vertex
    Vertex is the ASF DAAC search tool, which allows for the subset and preview of numerous types of SAR data. Radiometrically terrain corrected (RTC) data are available in GeoTIFF format.

To learn more about SAR and processing Level 1 data, view NASA's Applied Remote Sensing Training (ARSET) Introduction to SAR training or read the SERVIR SAR Handbook.

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Aboveground Biomass

Aboveground Biomass

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

Aboveground Biomass, Kalimantan Forests, Indonesia, 2014 from the Spatial Data Access Tool.

ORNL DAAC provides access to numerous aboveground biomass (AGB) datasets as well as vegetation and forest datasets from various regions around the world, some of which can be downloaded in GIS analysis ready formats.

Research quality AGB data products can be accessed directly via Earthdata Search or from ORNL DAAC.

Specifically, at ORNL DAAC there are new maps which combine remotely sensed biomass data for different land cover types into harmonized global maps of above and belowground biomass. For information, read Mapping Carbon Beyond Forests. For geospatial data, see the Spatial Data Access Tool (SDAT) in the Tools for Data Access and Visualization section.

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Ocean Color

Ocean Color

Ocean color remote sensing uses remote-sensing reflectance, which is based on the properties of the materials in the water. When light interacts with water, it can be absorbed or scattered. Light is absorbed by a combination of phytoplankton, non-algal properties, colored dissolved organic matter, and water itself. There is a specific Data Pathfinder pertinent to this topic. The Water Quality Data Pathfinder provides information on chlorophyll concentration and ocean color data.

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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)| Sentinel Toolbox

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, 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 the NASA 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 Worldview mapping application provides the capability to interactively browse over 900 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 Long Term Ecological Research Network 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)

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 data sets 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.

Sentinel Toolbox

Before choosing data, it’s important to determine which band meets your needs, as radar signals penetrate deeper as the sensor wavelength increases. This difference in penetration is due to the dielectric properties of a given medium, which dictate how much of the incoming radiation scatters at the surface, how much signal penetrates into the medium, and how much of the energy gets lost to the medium through absorption.

SAR signal penetration by sensor wavelength λ

SAR signal penetration by sensor wavelength λ. Credit: NASA SAR Handbook.

Note that for biomass estimation, L-band and P-band sensors are preferred over higher frequencies and smaller wavelengths for two reasons: 1) at these bands, the radar waves or energy can penetrate the tree canopy and scatter from larger woody components of the forest, and 2) the scattering from larger tree components, unlike leaves, are more stable temporally and remain highly coherent over the acquisition period in the case of repeated measurements for change detection or interferometric applications (adapted from SAR Handbook, 2019).

The C-band can be used for low-vegetation biomass such as grasslands, shrublands, sparse woodlands, young secondary regeneration, and low-density wetlands.

Another important parameter to take into consideration when choosing a dataset is the polarization, or the direction in which the signal is transmitted and/or received: horizontally or vertically. Dual polarization, for example, refers to two different signal directions, horizontal/vertical and vertical/horizontal (HV and VH). Knowing the polarization from which a SAR image was acquired is important, as signals at different polarizations interact differently with objects on the ground, affecting the recorded radar brightness in a specific polarization channel.

Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils).

Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils). Credit: NASA SAR Handbook.

SAR data are complex, requiring a certain level of processing skill.

The European Space Agency (ESA) Sentinel-1 Mission consists of two satellites, Sentinel-1A and Sentinel-1B, with synthetic aperture radar (SAR) instruments operating at a C-Band frequency. They orbit 180° apart, together imaging the entire Earth every six days. SAR is an active sensor which can penetrate cloud cover and vegetation canopy and can observe at night; therefore it is ideal for flood inundation mapping. It also provides information useful in detecting the movement of Earth material after an earthquake, volcanic eruption, or landslide. SAR data is very complex to process, but ESA has developed a Sentinel-1 Toolbox to aid with processing and analysing Sentinel-1 data. For more information on active sensors, see What is Remote Sensing?; for more information on SAR specifically, see What is Synthetic Aperture Radar?

Once you have downloaded the needed SAR data, it must be calibrated to account for distortion in the data. The objective in performing calibration is to create an image where the value of each pixel is directly related to the backscatter of the surface. So calibration takes into account radiometric distortion, signal loss as the wave propagates, saturation, and speckle. This process is critical for analyzing images quantitatively; it is also important for comparing images from different sensors, modalities, processors, and different acquisition dates.

Screenshot of the Sentinel-1 toolbox

Important note: DO NOT unzip the downloaded SAR file. Open the .zip file from within the Sentinel Toolbox. When you expand the Bands folder, you will see an amplitude and an intensity file for each polarization option. (The intensity band is a virtual one and is the square of the amplitude.) Open the amplitude file. Subset the data by zooming in to the area of interest and right-clicking on “Spatial Subset from View.”

Calibration is done by following these steps:

  1. Radiometric calibration is performed by selecting Radar/Radiometric/Calibration (leave parameters as default)
  2. Geometric correction is done next to fix the main geometric distortions, due to: Slant Range, Layover, Shadow, and Foreshortening. Terrain correction can be performed by selecting Radar/Geometric/Terrain Correction/ Range-Doppler Terrain Correction. This requires a digital elevation model (within the processing parameters, Shuttle Radar Topography Mission, or SRTM, is the default selection). You can also specify a map projection in the processing parameters.

Sentinel-1 Toolbox Geometric Correction

Another characteristic of SAR images that must be accounted for is speckle. Speckle is the grey level variation that occurs between adjacent resolution cells, creating a grainy texture. Within the Toolbox, speckle can be removed by selecting “Radar/Speckle Filtering/Single Product Speckle Filter,” and then choosing a type of filter; “Lee” is one of the most common.

Comparison of speckle in SAR imagery within Sentinel-1 Toolbox

Change Detection

One approach for monitoring change detection, caused by forest degradation or deforestation, is the log-ratio scaling method. You will need two images for which you have completed the steps above. The images must be from the same season. This is important for change detection operations as it avoids seasonal changes and focuses on true environmental changes in a change detection analysis.

Log-ratio image with the ArcMap Imagery basemap

The resulting log-ratio image over Huntsville, Alabama, was created from a pair of images acquired on 7/17/2009 and 9/04/2010, approximately one year apart. In the log-ratio image, unchanged features have intermediate gray tones (gray value around zero) while change features are either bright white or dark black. Black features indicate areas where radar brightness decreased while in white areas, the brightness has increased. Credit: ASF DAAC 2017

For further information on SAR change detection, see the ASF DAAC change detection recipe for QGIS or change detection recipe for ArcGIS. The SERVIR SAR Handbook also contains tutorials on change detection, developing time series and making RGB composites; these are provided as Python scripts in Jupyter notebooks.

Often-used color scheme for multi-dimensional false color SAR composites

Another option for change detection is to create an RGB composite. When creating RGB composites using SAR data, the example color-scheme is often used. Note that for forest applications in particular, it is always useful to assign cross-polarized (HV/VH) data to the green band as these data are more related to volume scattering of the canopies. Co-polarized data (VV or HH) are suited for the red band, where surface scattering components are more pronounced. When only dual-polarimetric data are available (HH/HV or VV/VH), a color SAR image is often constructed by assigning the ratio of co-polarized to cross-polarized data to the blue channel. For more information on this procedure, read the SAR Handbook Chapter 3.

Sentinel-1 C-band dual polarimetric VV and VH data: (a) VV, (b) VH, (c) VV/VH ratio, and (d) SAR false color composite with RGB = VV/VH/ratio channel assignment. Image acquired on May 31, 2018.

Sentinel-1 C-band dual polarimetric VV and VH data: (a) VV, (b) VH, (c) VV/VH ratio, and (d) SAR false color composite with RGB = VV/VH/ratio channel assignment. Image acquired on May 31, 2018. Credit: NASA SAR Handbook

Forest Biomass

There is strong synergy between ground and remote sensing measurements for quantifying Above Ground Biomass (AGB). However, before biomass estimation from SAR measurements can be acquired, SAR data must be processed such that pixel size, geometric attributes, and environmental effects are all normalized and radiometrically calibrated. In addition to the Sentinel toolbox, ASF DAAC also has a free software tool, MapReady, which accepts Level 1 detected SAR data, single look complex SAR data, and optical data. MapReady can terrain-correct, geocode, and save the data file in several common imagery formats including GeoTIFF.

The SAR Handbook’s APPENDIX D Mapping Forest Biomass with Radar Remote Sensing – Chapter 5 Training Module provides detailed instructions for mapping forest biomass.

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Published April 7, 2020

Page Last Updated: Aug 10, 2020 at 1:20 PM EDT