Human Impacts Data

Biological Diversity and Ecological
Forecasting Data Pathfinder

There are many causes of biodiversity loss, including deforestation, agricultural development, urbanization, pollution, and climate change. Understanding the ways in which humans are interacting with the environment, and how resulting changes impact Earth’s systems is important to preserving biodiversity.



Surface Reflectance

Surface Reflectance

Landsat image acquired on July 10, 2019 shows forest-clearing hotspots from the establishment of oil palm plantations in Peru.

Landsat image acquired on July 10, 2019 shows forest-clearing hotspots from the establishment of oil palm plantations in Peru.

Surface reflectance is useful for measuring the greenness of vegetation, which can then be used to determine phenological transition dates including start of season, peak period, and end of season. Moderate resolution instruments that are primarily used for this measurement include the Moderate Resolution Imaging Spectroradiometer (MODIS), copies of which are flying on both the Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS), flying on the joint NASA/NOAA Suomi National Polar-orbiting Partnership satellite. MODIS reflectance products are available at 250 m, 500 m, 1000 m, and 5600 m spatial resolution. VIIRS reflectance products are available at 500 m and 1000 m spatial resolution. MODIS data are acquired every one to two days, whereas the wider swath width of VIIRS allows for daily global coverage.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), another high-resolution instrument, acquires visible and near-infrared (VNIR) reflectance data at 15 m resolution and SWIR (through 2009) reflectance data at 30 m resolution. Note that ASTER is a tasked sensor, meaning that it only acquires data when it is directed to do so over specific targets, making its temporal resolution variable depending on your target region of interest. ASTER Surface Reflectance products are processed on-demand and so must be requested with additional parameters. Note that there is a limit to 2000 granules per order.

Research quality surface reflectance data products can be accessed directly via Earthdata Search or the Land Processes Distributed Active Archive Center (LP DAAC) Data Pool; MODIS, VIIRS, and ASTER are available as HDF files, but are also customizable to GeoTIFF:

LP DAAC also provides a tool called the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). AppEEARS offers a simple and efficient way to access, transform, and visualize geospatial data from a variety of federal data archives. MODIS and VIIRS surface reflectance data are available in AρρEEARS, as well as the USGS Landsat Analysis Ready Data (ARD) surface reflectance product. Other products useful for biodiversity applications available in AppEEARS include Daymet daily weather parameters, Soil Moisture Active Passive (SMAP) products, and MODIS snow cover products.

The Oak Ridge National Laboratory DAAC (ORNL DAAC) also provides tools for on-demand subsetting of MODIS and VIIRS land data. In particular, the Subsets API allows users to retrieve custom subsets, analytics and visualization of MODIS and VIIRS data products.

For higher resolution, the Landsat 7 Enhanced Thematic Mapper (ETM+) sensor and the Landsat 8 Operational Land Imager (OLI) instrument, acquires data at 30 m spatial resolution in VNIR every 16 days (or less as you move away from the equator). Landsat 8 was developed as a collaboration between NASA and the USGS. USGS now leads satellite operations and data archiving at the Earth Resources Observation and Science (EROS) center.

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

Data can be visualized in Worldview:

  • MODIS True Color in Worldview
    Note that Worldview does have a corrected reflectance product but it is not a standard, research quality product. The purpose of this algorithm is to provide natural-looking images by removing gross atmospheric effects, such as Rayleigh scattering, from MODIS visible bands 1-7.

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Nighttime Lights

Nighttime Lights

map shows where the intensity of light decreased (orange), increased (purple), and stayed the same (white) between 2012 and 2016 in the Midwest.

The map shows where the intensity of light decreased (orange), increased (purple), and stayed the same (white) between 2012 and 2016 in the Midwest. Credit: NASA Earth Observatory

Nighttime lights can disrupt the natural behaviors of wildlife; hatching turtles will move toward an artificial light source rather than the natural light provided by the moon and nocturnal animals become less active as nighttime light levels increase. VIIRS (flying on the Suomi NPP satellite) nighttime imagery layer shows the Earth’s surface and atmosphere using a sensor designed to capture low-light emission sources, under varying illumination conditions, which can aid in our understanding of how nighttime lights affect animal behavior.





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Socioeconomic Data

Socioeconomic Data

The Socioeconomic Data and Applications Center (SEDAC) has various datasets that aid in the understanding of human impact on the Earth’s surface. Datasets and maps are grouped thematically under such topics as biodiversity, conservation and protected areas, agriculture, land use and land cover change, marine and coastal regions, population distribution and change, and urbanization.

SEDAC has several map-based tools to assess human impact on the environment. POPGRID Viewer enables direct comparison of different population datasets based on different data sources and methodologies.

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Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AρρEEARS | 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 NASA's Goddard Earth Sciences Data and Information Services Center (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

The Application for Extracting and Exploring Analysis Ready Samples (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 the Long Term Ecological Research 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 are 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 Alaska Satellite Facility DAAC (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