Three thick layers of cake and frosting sat atop Jeff Schmaltz’s kitchen counter. The programmer had completed a 3-D model of a GIBS tile pyramid; it was his entry into a collegial science bake-off at NASA’s Goddard Space Flight Center. But there was more to this cake than flour and eggs and sugar.
This tile pyramid cake shows a view of the world with Antarctica represented as the largest continent on the map. Credit: Susan Schmaltz.
If you have ever browsed Earth science imagery and data using the online tool Worldview, then you have also used GIBS, Global Imagery Browse Services. GIBS is like a gear behind a clock face, a mechanism that keeps the hands moving. Schmaltz and his colleagues rely on it daily as they assemble images of our dynamic planet. (Worldview is a free and publicly available Earth science browser used by scientists and non-scientists, including the NASA Earth Observatory team.)
How It Works
GIBS ingests and organizes satellite data to create a global mosaic. Then, it chops down the data into digestible bits—like that image tile pyramid that Schmaltz recreated with cake—so that users can quickly view Earth as seen from space.
Zoomed out in a broad view, you see just the top tile, the whole Earth in low resolution (like the top layer of the cake). Zoomed in, you see one tile covering a smaller region of the earth but in more detail (like a square from the bottom layer of the cake). On an interface like Worldview, which allows users to scroll and view daily images from the entire surface of Earth, an architecture like GIBS is necessary to keep the site running quickly.
“It’s very fast, and there’s not a lot of computing going on,” Schmaltz said. GIBS does the same thing that Google Maps does: it summons only the data the user requests. By dealing in tiles, the program can serve many people at once without getting bogged down.
GIBS uses tiles (512 x 512 pixels) to speed up data processing. Credit: The Open Geospatial Consortium (OGC).
Way Back When
Not long after NASA launched the Terra satellite in late 1999, the U.S. experienced a record fire season: A record 8.4 million acres burned in the year 2000. At the time, it could take weeks for data from Terra’s MODIS instrument to be processed into images. Scientists hoped that a quicker turnaround might translate into a more informed response to fires. As result, NASA created a near-real time fire pixel product.
Seventeen years later, scientists can visit Worldview to see roughly 150 near-real time data sets from different satellites and sensors as the clouds and snow cover change each day. Air pollution, vegetation cover, dust, smoke are just a few of the data layers users can view.
Credit: NASA.
P.S. To make Jeff’s satellite cake, follow his grandmother’s recipe below:
Ingredients:
Directions:
Mix dry ingredients together. Measure oil, water, egg yolks, and vanilla into a measuring cup and mix; then add to dry ingredients and beat until smooth.
Beat 2 egg whites + ¼ tsp. cream of tartar until stiff. Fold into batter. Slowly mix in grated chocolate.
Bake in ungreased 8×8 pan at 350 degrees for 20-25 minutes. Check with toothpick when done. Cool on a rack. Goes best with chocolate frosting. (Schmaltz uses the recipe on the side of a Hershey’s can.) Alternately, you can top the cake with an edible print of a satellite image.
Credit: Adam Voiland.
Recently, we published a data visualization showing tropospheric NO2 over the Indian Ocean. The effort got us to thinking about how we try to present data in a way that’s easy to interpret while staying true to the science.
The visualization below of satellite measurements of NO2 in the atmosphere revealed the location of shipping lanes in the Indian Ocean. Ships tend to pass consistently along the same paths — through the Red Sea, across the Arabian Sea, across the southern end of the Bay of Bengal, through the Malacca Straits — to major ports in eastern Asia. On any given day, the exhaust fumes from a few ships do not provide a dramatic signal. But by making a long-term average (2005 through 2012) of data, the small day-to-day fluctuations add up to a discernible signal.
One of the other things we did in building this visualization was to mask the land surfaces with light grey, in order to emphasize the NO2 over the oceans. But what happens if we take off that gray blanket over the land masses?
Oh my! Pretty much anywhere there are people, there’s a saturated pool of NO2. All of Europe looks like a putrid mass of polluted air, as does eastern China, the cities of the Middle East, the Himalayan regions of India and Pakistan. In fact, pretty much anywhere there are significant human populations, there is NO2 running right off the scale! You can still see the ship tracks, but it’s the deep, over-saturated brown-orange that grabs your attention.
If you want to show concentrations over land, you need a breath of fresh air, like this:
This is a better way to show NO2 emissions over land. Distinct signals show up around industrialized cities in Europe, the Middle East, and southern Asia, as well as fire emissions in equatorial Africa. Eastern China is still a saturated mess, as are some of the major industrial areas elsewhere in China. Heavy industrialization and an increase in automobiles for transportation has resulted in levels of atmospheric pollution in China not seen since the 1940s to 60s in the U.S. and Europe.
But this third map scarcely shows the NO2 emissions over the sea, and the ship track signals are hardly discernible, even though we are still using the same exact set of data in all three visualizations. So what is going on?
Look carefully at the color palette, or scale bar, below each map describing how different colors reflect different concentrations of NO2. The high end of the scale has been changed; in fact, it has been multiplied by a factor of ten in the last version. When compared to land-based sources of pollution, ship tracks are quite faint. As much as ships contribute to NO2 pollution, they can’t compare to land-based sources.
That makes sense, if you think about it. If a single ship emitted the same amount of NO2 each day as a small coal-fired power plant, you would expect the signals to match. But the ship is not sitting still; it is moving back and forth across thousands of miles of open ocean and its emissions are thinned out over long distances and time. It is only when there are hundreds of similar ships traveling along the same route that the signal begins to build; and even then, the emissions are still spread across a vast area in a way that land-based sources are not.
So for our story on ship tracks, we made the visualization with tight limits on the NO2 concentration in order to bring out the signal from the noise. Had we not masked out the land sources, the ship tracks would have been lost.
