Northern Hemisphere
Northern Hemisphere Footprints
The Northern Hemisphere Foothprints image is a visualization of the GoLIVE Landsat 8 World Reference System Version 2 (WRS-2) descending node (daytime) footprints over the northern hemisphere.
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Southern Hemisphere
Southern Hemisphere Footprints
The Southern Hemisphere Footprints is a visualization of the GoLIVE Landsat 8 path/row footprints over the southern hemisphere.
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Globe with GoLIVE footprints
Global Footprints
The Global Footprints image is a visualization of the GoLIVE Landsat 8 footprints over the entire globe.
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Southeast Alaska glaciers
Ice Velocity in Southeastern Alaska
This visualization, based on an analysis by GoLIVE investigators, shows the velocity of ice in southeastern Alaska near Malaspina and Hubbard glaciers.
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Northern Hemisphere
Southern Hemisphere
Globe with GoLIVE footprints
Southeast Alaska glaciers

Overview

The Global Land Ice Velocity Extraction from Landsat (GoLIVE) project was created by the National Snow and Ice Data Center (NSIDC) under NASA funding. GoLIVE is a processing and staging system for near-real-time global ice velocity data derived from Landsat 8 panchromatic imagery.

The system performs repeat image feature tracking using newly developed Python Correlation (PyCorr) software applied to image pairs covering all glaciers > 5km2 as well as both ice sheets. GoLIVE runs on the University of Colorado’s supercomputer and Peta Library storage system to process ~10,000 image pairs per hour. The data are provided in Network Common Data Format (NetCDF) as geolocated grids of x and y velocity components at 300 m spacing with accompanying error and quality parameters. GoLIVE NetCDF files contain multiple variables including unmasked and masked ice velocity vectors, the magnitudes of those vectors, and several data quality variables.

Gallery

Flow speed visualization
Figure 1. GoLIVE NetCDF files contain multiple variables including unmasked and masked ice velocity vectors, the magnitudes of those vectors, and several data quality variables. This image is a visualization of scalar flow speed, for example, the velocity magnitude variable vv_masked of the relatively fast moving (~2.4 meters/day) and wide (~22 km at its narrowest point) Byrd Glacier (orange and red) and the slower moving and narrower Mulock Glacier (pale blue) where the two glaciers exit the Transantarctic Mountains and flow into the Ross Ice Shelf.

Several other smaller glaciers are also visible. The thin gray steaks in the visualization are masked regions where the GoLIVE quality parameters indicated that the there was poor correlation between the image pairs used to calculate the velocity field, possibly due to clouds or blowing snow.  Note that the x-y grid lines shown here are spaced ~54 km apart and that a single polar stereographic projection is used to define the grid used for all Antarctic velocities.

The two Landsat 8 images used for this and the next two visualizations shown in Figures 2 and 3 were acquired on October 28 and November 29, 2016.

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Peak correlation visualization
Figure 2. This image is a visualization of the peak correlation strength, the variable corr, representing the confidence of the calculated vector displacement for the same Landsat 8 image pair as in Figure 1. The corr variable is one of the GoLIVE data quality parameters.

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Correlation strength visualization
Figure 3.  This image is a visualization of the difference between the highest and second highest correlation strength values, the variable del_corr, which represents the accuracy of the calculated vector displacement. The del_corr variable is another one of the GoLIVE data quality parameters. Note that the dark blue streaks in this image contain del_corr values less than 0.15, and have therefore been masked in Figure 1 vv_masked, where they are set to a gray color. The dark blue streaks also seem to correlate with low corr values in Figure 2.

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Landsat 8 flyover visualization
Figure 4. From 2013 to 2016, Landsat 8 collected thousands of images from Antarctica alone. The globes in this visualization show how many times Landsat 8 passed over a given icy parcel in 2015 alone. As many as 150 to 200 images were collected over the brightest yellow and green areas, while purple areas have just a handful of useful images because of frequent cloud cover and fewer orbital passes. Due to the nature of the satellite’s polar orbit, when there is sunlight, areas in the far north and south can be imaged more frequently.

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Antarctica
Figure 5. The imaging system on Landsat 8 is far more sensitive than previous Landsat sensors, distinguishing far more subtle differences in shading and surface texture. The GoLIVE team wrote software that allows researchers to follow these subtle features, like bumps or dune-like patterns on ice surfaces. By comparing images of the same location on different dates, researchers can track individual features and determine the speed of the surface flow.

The question is:  "How sensitive are these ice sheets to changes in the atmosphere and the ocean?” said Alex Gardner of JPL. “We could wait and see, or we could look to the past to help inform what is most likely to happen in the future.” Gardner has been looking closely at Antarctica, with ice velocities represented in this visualization. He is working to combine the new Landsat 8 ice-flow data with prior maps of the continent’s glacier flow in the hopes of understanding decadal changes across the entirety of the ice sheet. Almost 2,000 cubic kilometers of ice flows into the Southern Ocean from Antarctica each year.

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