Shining a light on the global spread of cities

Reference article: Goldblatt, R., M.F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A.K. Khandelwal, M.-H. Cheng and R.C. Balling Jr. 2018. Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment 205: 253–275. DOI: 10.1016/j.rse.2017.11.026

Mapping our urban planet

As people across the world migrate toward cities, urban areas are steadily expanding. Adding an area about the size of Belgium from 1970–2000, this ongoing mass migration is creating new urban ecosystems out of the forests, farmlands, and other landscapes that preceded them. A new study by Goldblatt and their colleagues demonstrate how we can better keep tabs on the spread of urban areas using a novel combination of satellite data.

A bird’s-eye view, 700 kilometers up
A program in operation since 1972, Landsat earth observing satellites collect reflected light to determine its surface characteristics and land cover. (Credit: USGS)

Satellite observations of the Earth’s surface can be a powerful tool for routinely monitoring human and natural processes, like for example detecting Amazon deforestation. Earth orbiting satellites, like the Landsat series in operation since 1972, make images of the planet surface by collecting reflected light of different wavelengths while passing overhead. The mix of wavelengths in each image pixel (its spectrum) contains information on that little piece of land because the light had to interact with the surface before being reflected back upwards to the sensor. An entire science of remote sensing has grown up around interpreting what these spectral mixes tell us about the land, air, and ocean, and researchers stay busy thinking up new sensor designs to fly on future missions.

Crunching the numbers

The usual approach to mapping different types of land cover is to feed satellite data through a computer program (often in the realm of “machine learning”) that compares the spectral signature of each pixel to a kind of template, a set of example pixels of the land cover of interest. This “training data” has to first be produced by a human researcher using their eyeballs to identify areas that represent each targeted class of land cover. The computer program then attempts to assign each pixel a cover type based on its statistical similarity to the training data it was given. The researchers then often have to go back and test the accuracy of their map against a set of “validation” data, another independent collection of human-classified pixels.

All this human intervention means training and validation data tend to be rare and expensive. However, such specific training and validation data are still frequently necessary because classification tends to works well only for limited geographical areas and times, making accurate global land cover mapping difficult. And mapping cities can prove even more tricky, given the vagaries around the definition of “urban” land, and the complex mix of cover found in many built-up areas.

Use a robot to train a robot
NASA image acquired April 18 - October 23, 2012rrThis new image of the Earth at night is a composite assembled from data acquired by the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite over nine days in April 2012 and thirteen days in October 2012. It took 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth’s land surface and islands.rrThe nighttime view of Earth in visible light was made possible by the “day-night band” of the Visible Infrared Imaging Radiometer Suite. VIIRS detects light in a range of wavelengths from green to near-infrared and uses filtering techniques to observe dim signals such as gas flares, auroras, wildfires, city lights, and reflected moonlight. In this case, auroras, fires, and other stray light have been removed to emphasize the city lights. Named for satellite meteorology pioneer Verner Suomi, NPP flies over any given point on Earth’s surface twice each day at roughly 1:30 a.m. and 1:30 p.m. The spacecraft flies 824 kilometers (512 miles) above the surface in a polar orbit, circling the planet about 14 times a day. Suomi NPP sends its data once per orbit to a ground station in Svalbard, Norway, and continuously to local direct broadcast users distributed around the world. The mission is managed by NASA with operational support from NOAA and its Joint Polar Satellite System, which manages the satellite's ground system.rrNASA Earth Observatory image by Robert Simmon, using Suomi NPP VIIRS data provided courtesy of Chris Elvidge (NOAA National Geophysical Data Center). Suomi NPP is the result of a partnership between NASA, NOAA, and the Department of Defense. Caption by Mike Carlowicz.rrInstrument: Suomi NPP - VIIRS rrCredit: NASA Earth ObservatoryrrClick here to view all of the  Earth at Night 2012 images rrClic
The glow of cities at night, as seen from orbit. (Credit: NASA Earth Observatory)

To streamline satellite mapping of urban areas, Goldblatt and colleagues turned to another signature of our cities – the nighttime glow of our electric lights. Testing their approach on the countries of Mexico, India, and the U.S., the researchers combined 30-meter-resolution Landsat data with lower-resolution data available from a constellation of military weather satellites that can detect the glow of urban lights at night (as well as gas flares and squid boats). Since these nighttime lights give an independent sense of where “urban” land is likely to be, the researchers could then tell a computer to sample these pixels to form its own training data for classification, rather than asking a human to do the work. They then used several other machine learning techniques to allow the computer to optimize and tweak parameters to get the best final fit to another independent set of validation data.

Cities like Sao Paolo in Brazil have expanded rapidly over the past few decades as urban populations increase. (Credit: Pixabay, Joelfotos)

The result of their work is an automated process for mapping cities that is both more accurate and in higher spatial resolution than other satellite-based maps – demonstrating a technique that can be potentially applied across the globe and requiring much less intensive human labor. Improvements in image processing as demonstrated in the study may allow for a better and more frequently updated picture of the global extent of cities that can be useful in a host of fields from urban planning, to climate modeling, to disaster response.

The next time you find yourself looking out across the city lights, imagine that the glow you see might be quietly helping computers and satellites map all the places humans have come to live across the planet.

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Andrew Trlica

Andrew Trlica

Ph.D. candidate in BU’s Department of Earth & Environment. My interest centers on landscapes defined by the human presence, focusing especially on how humanity’s choices connect our landscapes to the causes and consequences of climate change. My research has dealt with the urban carbon cycle and urban forest, the urban heat island, agricultural practices and soil quality, and disturbed land reclamation. I'm also interested in improving communication between science, policy, and the broader public. Twitter/Insta: @places_we_made

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