After nearly 20 years, it’s time for me to leave NASA and do something radically different—help Planet Labs develop world-class satellite imagery. OK, not that radically different. In any case, now is as good a time as ever to point to some of my favorite visualizations. If you’d like like to get in touch with me, drop me a note on Twitter (@rsimmon) or contact the Earth Observatory team—they’ll know where I am.
Thanks to everyone on the Earth Observatory team that I’ve worked with over the years. Among them: David Herring, Kevin Ward, Mike Carlowicz, Paul Przyborski, Rebecca Lindsey, Holli Riebeek, Adam Voiland, Jesse Allen, Reto Stöckli, Goran Halusa, and John Weier.
Ten: The Blue Marble
Let me get this out of the way: this was the first NASA image of an entire hemisphere of the Earth made with full-color data since the Apollo 17 crew returned from the Moon. It’s certainly striking, but once you look carefully a number of flaws start to appear. Flaws that I may or may not have pointed out when I described my design process. (I’ll also reiterate Reto Stöckli’s invaluable work building the land and cloud textures, which were the hard parts.)
Nine: Global Net Primary Productivity
Although these two datasets show the exact same property (net primary productivity, a measure of the amount of carbon the biosphere draws out of the atmosphere) they’re measured in different ways, and deserve to be differentiated. By using palettes with different hues but an identical range of lightness and saturation, they are directly comparable but remain distinct from one another.
Eight: The Landsat Long Swath
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Shortly after launch, Landsat 8 collected what was probably the single largest satellite image ever made. Roughly 12,000 pixels wide by 600,000 pixels tall the image combined 56 individual Landsat scenes into a single strip from Siberia to South Africa.
Seven: LIDAR
Back when I used to be competent at 3D, I made this illustration showing how a pulse of laser light can measure the structure of a forest canopy.
Six: Air Quality over 16 Megacities
It may just be that I’m enamored with Alberto Cairo, but I’m growing increasingly fond of slope graphs. They occasionally tell stories more clearly than more conventional graph types.
Five: an Erupting Volcano from the International Space Station
When Sarychev Volcano blasted a column of ash high above the Kuril Islands an astronaut captured not one, but a whole sequence of photographs of the plume. Make sure you look for the pyroclastic flows coursing down the side of the volcano. (The eruption did not blast a hole in the clouds by the way, that’s a result of interactions between wind, clouds, and island topography.)
Four: Faults in Xinjiang
I’ve probably worked on more than 1,000 Landsat images over the course of my career. This scene of offset folds in Xinjian China is the best.
Three: Amazonia
I think this map of vegetation in South America was the first original color palette I really got right.
Two: The Original Earth at Night
For some reason the color and contrast work better in this version (originally published in 2000 to accompany the story Bright Lights, Big City) than any of my attempted remakes. This includes the 2012 Black Marble, which was made with much better data.
One: Seeing Equinoxes and Solstices from Space
[youtube FmCJqykN2J0]
I’m generally skeptical of animation in data visualization, but for some things motion is the story. I think this applies to the apparent motion of the sun over the course of a year, alternately lighting the North and South Poles. (Apologies for the poor quality of the YouTube compression. Make sure you check out the HD version.)
Finally: Thanks to the NASA family, and to all of you who’ve expressed appreciation for my pictures over the years.
This post was originally published by the Society for News Design for the Malofiej Infographic World Summit.
Palettes aren’t the only important decision when visualizing data with color: you also need to consider scaling. Not only is the choice of start and end points (the lowest and highest values) critical, but the way intermediate values are stretched between them.
Note: these tips apply to scaling of smoothly varying, continuous palettes. For discrete palettes divided into distinct areas (countries or election districts, for example, technically called a choropleth map), read John Nelson’s authoritative post, Telling the Truth.
For most data simple linear scaling is appropriate. Each step in the data is represented by an equal step in the color palette. Choice is limited to the endpoints: the maximum and minimum values to be displayed. It’s important to include as much contrast as possible, while preventing high and low values from saturating (also called clipping). There should be detail in the entire range of data, like a properly exposed photograph.
These maps of sea surface temperature (averaged from July 2002 through January 2014) demonstrate the importance of appropriately choosing the range of data in a map. The top image varies from -5˚ to 45˚ Celsius, a few degrees wider than the bounds of the data. Overall it lacks contrast, making it hard to see patterns. The lower image ranges from 0˚ to 28˚ Celsius, eliminating details in areas with very low or very high temperatures. (NASA/MODIS.)
Ocean chlorophyll (a measure of plant life in the oceans) ranges from hundredths of a milligram per cubic meter to tens of milligrams per cubic meter, more than 3 orders of magnitude. Both of these maps use the almost same endpoints from near 0 (it’s impossible to start a logarithmic scale at exactly 0) to 11. Plotted linearly, the data show a simple pattern: narrow bands of chlorophyll along coastlines, and none in mid-ocean. A logarithmic base-10 scale reveals complex structures throughout the oceans, in both coastal and deep water. (NASA/MODIS.)
Some visualization applications support logarithmic scaling. If not, you’ll need to apply a little math to the data (for example calculate the square root or base 10 logarithm) before plotting the transformed data.
Appropriate decisions while scaling data are a complement to good use of color: they will aid in interpretation and minimize misunderstanding. Choose a minimum and maximum that reveal as much detail as possible, without saturating high or low values. If the data varies over a very wide range, consider a logarithmic scale. This may help patterns remain visible over the entire range of data.
(Repost of an article on the Exelis Vis Imagery Speaks blog.)
One of the most interesting new capabilities of the NOAA/NASA/DoD Suomi-NPP satellite is the Day-Night Band. These detectors, part of the Visible Infrared Imaging Radiometer Suite (VIIRS), are sensitive enough to image Earth’s surface by starlight. The Day Night Band is both higher resolution and up to 250 times more sensitive than its ancestor, the DMSP Operational Linescan System (OLS).
Applications of the Day Night Band include monitoring warm, low-level clouds, urban lights, gas flares, and wildfires. Long-term composites reveal global patterns of infrastructure development and energy use.
Over shorter times scales (Suomi-NPP completes an orbit every 100 minutes or so) multiple Day Night Band scenes stitched together show a snapshot of the Earth at night, like this view of South America, including the 14 Brazilian World Cup cities.
Marit Jentoft-Nilsen and I used a number of software tools to read, stitch, project, and visualize the data, starting with a handful of HDF5 files. VIIRS data is aggregated into granules, each acquired over 5 minutes. These files are distributed, archived, and distributed by NOAA’s CLASS (the Comprehensive Large Array-data Stewardship System). To deal with the unique projection of VIIRS, I used ENVI’s Reproject GLT with Bowtie Correction function to import the data. (If you’re unfamiliar with VIIRS data, now’s a good time to read the Beginner’s Guide to VIIRS Imagery Data (PDF) by Curtis Seaman of CIRA/Colorado State University.)
So far so good. Of course the data is in Watts per square meter per steradian, and the useful range is something around 0.0000000005 to 0.0000000500. With several orders of magnitude of valid data, any linear scale that maintained detail in cities left dim light sources and the surrounding landscape black. And any scaling that showed faint details left cities completely blown out.
To make the data more manageable, show detail in dark and bright areas, and allow export to Photoshop I did a quick band math calculation: UINT(SQRT((b1+1.5E-9)*4E15)*(SQRT((b1+1.5E-9)*4E15) lt 65535) + (SQRT((b1+1.5E-9)*4E15) ge 65535)*65535)
It looks a bit complicated, but it’s not too bad. It adds an offset to account for some spurious negative values; multiplies by a large constant to fit the data into the 65,536 values allowed in a 2-byte integer file; calculates the square root to improve contrast, sets any values above 65,535 to 65,535; then converts from floating point to unsigned integer. This data can be saved as a 16-bit TIFF readable by just about any image processing program, while maintaining more flexibility than an 8-bit file would.
The final steps were to bring the TIFF into Photoshop, tweak the contrast with levels and curves adjustments to bring out as much detail as possible, add coastlines and labels, and export for the web. The result: Brazil at Night published by the NASA Earth Observatory on the eve of the World Cup.
