A Gentle Introduction to GDAL Part 6.1: Visualizing Data
You may be wondering what I mean by “data visualization with GDAL”? After all, aren’t satellites images and maps — prominently featured in previous installments — both data? Well, yes. But there are differences between types of data that I think it’s worth expanding on, before diving into examples of using GDAL to create thematic maps (maps “used to emphasize the the spatial pattern of one or more geographic attributes” from Thematic Cartography and Geographic Visualization). If you want, you can just skip to the tutorials, but if you’re curious, hang around for a bit.
Images vs. Data
The first Earth observations returned from space — everything from Corona spy satellites to the first pictures from a weather satellites to photographs of the Earth from Apollo astronauts — were more on the “image” side of the data/image divide than the “data” side. All of these examples were recorded with, broadcast by, and stored on analog equipment (seriously, the early TIROS data is stored in a series of oversized books), with the variations in brightness representing relative, not absolute, differences in intensity.The information these pictures contained was more qualitative than quantitative, with the types of analysis that could be performed more suited to words than equations. These distinctions aren’t absolute. It’s certainly possible to make qualitative imagery into quantitative data with operations like counting aircraft or tracing the outline of a glacier. But for the most part, early satellite data is best thought of as a photograph (and some data were photographs, with the film returned to Earth).
This changed in the 1970s as satellite remote sensing moved into the digital age, spearheaded by Landsat’s Multispectral Scanner (MSS). (Keep in mind there’s nothing inherently superior about digital vs. analog — an analog HDTV signal is higher quality than a digital DVD. “Digital” in this usage just means measurements broken up into discrete values. Digital data can be more easily stored and replicated than analog data, and computers — which require digital inputs — enable revolutionary types of analysis.) The MSS was originally intended to be complementary to Landsat 1’s principal instrument — the TV-like Return Beam Vidicon (RBV). However, users soon realized the MSS was superior to the RBV. Scenes from the MSS were more uniform, more precisely aligned with features on the surface of the Earth, were more consistent from image to image, and contained two near infrared bands (which measured light in wavelengths slightly longer than what human eyes can see) that proved critical for mapping vegetation.
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