A paper that I co-authored was just published (#OpenAccess) a few minutes ago in Nature 🎉 https://www.nature.com/articles/s41586-026-10260-w
-
A paper that I co-authored was just published (#OpenAccess) a few minutes ago in Nature
https://www.nature.com/articles/s41586-026-10260-wHere's a short thread about what we did and what we learned
#LightPollution #Energy #ALAN #RemoteSensing #NightLightRemoteSensing #EarthObservation #VIIRS_DNB
You might have seen previous studies (including several from me) showing that lighting on the country and continental scale is increasing. Globally, the rate of increase is about 2% per year.
Those studies looked at (very) large areas, and used monthly or annual composite images. The new study analyzes change at the spatial scale of individual pixels (~0.5 square km) and at the temporal scale of individual nights.
-
You might have seen previous studies (including several from me) showing that lighting on the country and continental scale is increasing. Globally, the rate of increase is about 2% per year.
Those studies looked at (very) large areas, and used monthly or annual composite images. The new study analyzes change at the spatial scale of individual pixels (~0.5 square km) and at the temporal scale of individual nights.
That's like... A whole lot of work. A mindbogglingly large amount of work.
There were 1.16 million daily (NASA Black Marble) *images*. Now think about how many pixels make up each image... 🤯
-
That's like... A whole lot of work. A mindbogglingly large amount of work.
There were 1.16 million daily (NASA Black Marble) *images*. Now think about how many pixels make up each image... 🤯
For each pixel, the lead author Tian Li from the University of Connecticut fits a trend line (solid line left) and generates a prediction (dotted line left).
When something changes, the observations stop agreeing with the prediction (middle). If the change persists for at least 14 consecutive observations, then a break point in the trend is assigned. From that point on, a new model predicts future changes. And so on and so forth (right) for the following years.
-
For each pixel, the lead author Tian Li from the University of Connecticut fits a trend line (solid line left) and generates a prediction (dotted line left).
When something changes, the observations stop agreeing with the prediction (middle). If the change persists for at least 14 consecutive observations, then a break point in the trend is assigned. From that point on, a new model predicts future changes. And so on and so forth (right) for the following years.
This is where looking at the daily data (rather than monthly composites) becomes a REALLY BIG DEAL.
Nighttime lights data are really noisy. Part of the noise comes about because the satellite can view a scene from different directions. When that happens, it's viewing LITERALLY DIFFERENT LIGHTS, as in the aerial photos here.
-
This is where looking at the daily data (rather than monthly composites) becomes a REALLY BIG DEAL.
Nighttime lights data are really noisy. Part of the noise comes about because the satellite can view a scene from different directions. When that happens, it's viewing LITERALLY DIFFERENT LIGHTS, as in the aerial photos here.
For that reason, in the new analysis the data is broken up and fitted separately depending on the viewing zenith angle (angle from straight down). This helps deal with the fact that city centers are typically brightest when viewed from above (and dimmer from the side), while the opposite is true of residential neighborhoods.
-
For that reason, in the new analysis the data is broken up and fitted separately depending on the viewing zenith angle (angle from straight down). This helps deal with the fact that city centers are typically brightest when viewed from above (and dimmer from the side), while the opposite is true of residential neighborhoods.
@skyglowberlin very interesting, and as an amateur astronomer, depressing. Thanks
-
For that reason, in the new analysis the data is broken up and fitted separately depending on the viewing zenith angle (angle from straight down). This helps deal with the fact that city centers are typically brightest when viewed from above (and dimmer from the side), while the opposite is true of residential neighborhoods.
You're probably now saying "enough of what you did I want results!"
So here's the main finding: yes, Earth is getting brighter on average. But it's certainly not getting brighter everywhere - there are lots of places where light emissions are decreasing!
Here's the "gradually changing" areas in Berlin, for example. Within the city, there are places and neighborhoods that are brightening, and others that are darkening!
-
You're probably now saying "enough of what you did I want results!"
So here's the main finding: yes, Earth is getting brighter on average. But it's certainly not getting brighter everywhere - there are lots of places where light emissions are decreasing!
Here's the "gradually changing" areas in Berlin, for example. Within the city, there are places and neighborhoods that are brightening, and others that are darkening!
And here's Paris.
Out of wealthy countries that aren't in crisis, France stands out for really dramatic reductions in total light emissions. Partly this is due to their light pollution law, and partly it's because so many communities in France now turn off their streetlights late at night when there's no one on the street to see them.
-
And here's Paris.
Out of wealthy countries that aren't in crisis, France stands out for really dramatic reductions in total light emissions. Partly this is due to their light pollution law, and partly it's because so many communities in France now turn off their streetlights late at night when there's no one on the street to see them.
In 2022, the light emissions (during 1-4 am) in France were only 33% of what they were in 2014!
-
For that reason, in the new analysis the data is broken up and fitted separately depending on the viewing zenith angle (angle from straight down). This helps deal with the fact that city centers are typically brightest when viewed from above (and dimmer from the side), while the opposite is true of residential neighborhoods.
@skyglowberlin This is fascinating, thank you for getting into the weeds a bit. I'm so used to just treating satellite imagery as though it's from the zenith; I'm accustomed to season mattering, and sun angle in daytime images, but I just hadn't thought about this aspect of the challenge in your kind of work.
-
In 2022, the light emissions (during 1-4 am) in France were only 33% of what they were in 2014!
Chinese cities, on the other hand, are brightening incredibly rapidly. The total light emission from China grew by 56% during 2014-2022. (Reminder: we're only measured well after midnight. We don't know what's going on during the early parts of the night.)
Almost 30% of the increase in total light emissions for the entire world took place in China.
-
Chinese cities, on the other hand, are brightening incredibly rapidly. The total light emission from China grew by 56% during 2014-2022. (Reminder: we're only measured well after midnight. We don't know what's going on during the early parts of the night.)
Almost 30% of the increase in total light emissions for the entire world took place in China.
By the way - you can look at the data yourself! The team from UConn built a viewer that allows you to see abrupt (left) and gradual (right) changes, as for Houston, USA below: https://ee-downloading.projects.earthengine.app/view/alan-change
That's what I made the images in the thread with. #RemoteSensing #LightPollution #GIS #GEE
-
By the way - you can look at the data yourself! The team from UConn built a viewer that allows you to see abrupt (left) and gradual (right) changes, as for Houston, USA below: https://ee-downloading.projects.earthengine.app/view/alan-change
That's what I made the images in the thread with. #RemoteSensing #LightPollution #GIS #GEE
Long story short - it's a really cool analysis, it's a very cool paper, and you should read it!
I'm very grateful to Tian Li and Zhe Zhu from #UConn and Zhuosen Wang from NASA for involving me and my (former @GFZ) colleague Theres Kuester in the work. And also very grateful to @GFZ, #ESA, and especially @ruhr-uni-bochum.de for making it possible for me to work in this field!
-
@skyglowberlin This is fascinating, thank you for getting into the weeds a bit. I'm so used to just treating satellite imagery as though it's from the zenith; I'm accustomed to season mattering, and sun angle in daytime images, but I just hadn't thought about this aspect of the challenge in your kind of work.
@eldang It has an effect on daytime imagery as well, but it's an extra special giant mess during the night.
-
Long story short - it's a really cool analysis, it's a very cool paper, and you should read it!
I'm very grateful to Tian Li and Zhe Zhu from #UConn and Zhuosen Wang from NASA for involving me and my (former @GFZ) colleague Theres Kuester in the work. And also very grateful to @GFZ, #ESA, and especially @ruhr-uni-bochum.de for making it possible for me to work in this field!
Oh, wait, I forgot to say something important...
Remember when I said that the satellite pixels cover half a square kilometer? That really limits our ability to understand exactly what it is that is changing.
I'm part of a group that will propose a nighttime light satellite with unprecedented sensitivity to #ESA for consideration as their #EarthExplorer 13 mission. It would allow us to understand the nature of these changes far better (e.g. who exactly is responsible for the changes? Are whole areas changing gradually, or are we seeing the impact of specific buildings or parking lots?)
So, please cross your fingers for us, and if you would be interested in using such data, please reach out
-
A paper that I co-authored was just published (#OpenAccess) a few minutes ago in Nature
https://www.nature.com/articles/s41586-026-10260-wHere's a short thread about what we did and what we learned
#LightPollution #Energy #ALAN #RemoteSensing #NightLightRemoteSensing #EarthObservation #VIIRS_DNB
@skyglowberlin
Wow, front page! Congrats! And thanks for sharing here -
@skyglowberlin
Wow, front page! Congrats! And thanks for sharing here@notsoloud Thank you
-
M mjack@mastodon.bsd.cafe shared this topic
