The World as Seen by ERTS-1

In the summer of 1972, NASA and USGS launched the first satellite of their joint ‘Landsat’ program from Vandenberg Air Force Base. Originally named Earth Resources Technology Satellite (it would be renamed to Landsat 1 a couple years later), ERTS-1 was the first satellite to carry a Multi-Spectral Scanner. The earliest satellites studied things like radiation, weather, or communication. ERTS-1, by contrast, was designed to be all about observing the Earth.

There were two instruments onboard ERTS-1: a Return Beam Vidicon and the aforementioned Multi-Spectral Scanner. At the time of launch, the Return Beam Vidicon was presumed to be the primary sensor, with the Multi-Spectral Scanner along for the ride as an experimental instrument. By the time data was collected, it became clear that the Multi-Spectral Scanner (designed by Virginia Norwood and the Hughes Aircraft Company) was the higher-performing instrument.

So what did the Multi-Spectral Scanner do, exactly? It collected data on distinct lengths of electromagnetic energy reflected back from Earth in the form of spectral bands – specifically, it collected data on green, red, and two infrared bands. While the human eyes sees the world in what we call “true color,” ERTS-1’s scanner produced “false color” images using the spectral bands it was able to collect. When green, red, and infrared bands are collected, forests and other heavily vegetative areas often appear red (as they reflect more infrared light than green). Human eyes can’t discern infrared light (which has long wavelengths), but sensors can!

The above map was produced using Landsat 1 imagery during the winter of 1976-1977, when a cold freeze hit the Chesapeake Bay area. The image on the right shows the ice extent on February 7th, 1977, while the left image shows the next day, February 8th, 1977. A written key helps map reader decode the false-color image: open water is black/near black, evergreen vegetation is red, urban areas are light to dark green, and snow is white.

Side-by-side, these images communicate the differences in ice conditions over a 24-hour span. Maps like this were produced to help demonstrate Landsat 1’s new-found ability to document and communicate important geographic phenomena to the American public.

The earth data Landsat was collecting also allowed scientists to produce land cover maps which used the spectral signatures recorded in satellite imagery to classify land at new levels of detail.

This map, produced in 1980 by the Department of Agriculture, shows a detailed land cover classification for southwest Georgia. Green areas of the map represent forests, pastures are yellow, cultivated or exposed earth is orange, and wetlands and water are purple. Here, satellite imagery is combined with more traditional cartographic layers such as city names, transportation networks, and administrative boundaries to create new maps for the public.

The ability to identify and classify land cover through the use of spectral bands opened up a huge range of new forms of spatial analysis (often referred to as remote sensing):

In “Critical Erosion Areas of the Lafourche-Terrebonne Cooperative River Basin Study,” erosion areas were identified by Soil Conservation Service personnel using Landsat imagery from 1973 and 1981. This map demonstrates the use of imagery as analysis inputs to measure change over time (in this case, soil erosion), while the ultimate cartographic product no longer directly uses the imagery. Today, we are still using satellites to help us understand Louisiana’s changing coastline.

Landsat 1 stayed in orbit for five years and was deactivated in 1978. The Landsat program itself is ongoing, with Landsat 9 launched as recently as 2021. The Multi-Spectral Scanner lasted through the first five Landsat missions, though today Landsat uses what is called an Operational Land Imager, capable of collecting nine distinct spectral bands.

Incredible examples of Landsat imagery are available in the Library of Congress online exhibition “Earth as Art 3: A Landsat Perspective.”

Sources: NASA’s Landsat Science, Landsat I