all right hello it's so exciting to be here at the grand opening of the earth
at night book which our team I'm the science PI of the black marble project
which is the data set that you see behind me and it's sort of that the
theme of the book this year which has really has been exciting for our team to
work on obviously this imagery is so sexy, um, it's beautiful to look at but it's
also I'm hoping that this talk gives you some idea of the the depth of the
scientific insights that it can also give us besides just being a bunch of
pretty pictures but actually I'm going to start by looking at the Blue Marble
which is the first slide that's going to come up here this is the next-generation
Blue Marble it came out like less than a decade ago and this is a composite of
the earth during the daytime and cloud free so we can see really interesting
signals with this blue marble things like changes in natural land systems
like the change in forest green up we can see changes in the ice caps etc but
we what we can't really see with the Blue Marble or what you can barely
perceive on this map is urban areas it's like a tiny little blip on the map 3% of
the global land area and yet it has huge implications on planetary health so if
you can flip the slide, urban areas even though they're only a tiny land area
they produce 80 percent of the global GDP they house more than half of
humanity and they also produce 76 percent of our carbon emissions so when
you're thinking about what happens in this tiny little and land area it's
actually going to be the site where the interventions of bringing about the UN
sustainable development goals have to happen it's places like this that house
millions of people who live in slums it's places like urban areas that are
the drivers of planetary changes that we're seeing
in the blue marble and so in some ways the black marble is like the yang to the
blue marbles Yin it is it is the human stat to our natural land system stat of
them of the MODIS so this is an imagery of black marble before the clouds are
are taken away black marble is derived from a sensor aboard Suomi NPP called
VIIRS, it's the VIIRS day/night band is what you're looking at and the that's
been out since 2012 and since then our team has been working really hard to put
out two products one is a top of atmosphere reflectance which looks like
this it has all the clouds and everything intact and then the second
which is coming out in the beginning of this year in just a couple of weeks is
going to be that black marble composite that you see it actually it'll be daily
but with the clouds removed with the moonlight removed with all of these
aberrations that Mar your understanding of urban this is Cairo removed so that's
a really exciting upcoming event for us, next slide, so this is not the first
night lights data set that's been out there it's the first scientific night
lights data set so before of years there was DMSPLOS which is a meteorological
satellite that NOAA used to try to understand clouds actually, so that's
been out since the 70s it's been digital since the 1990s this is the picture of
Cairo from DMSP and you can see that it has a pretty coarse spatial resolution
2.7 kilometers also with in Cairo you can't really see any differences between
the center of the city and the suburbs but it has been out for a long time so
it's good for that long-term record if you look now this is VIIRS it's almost
like putting on glasses and compared to DMSP you can really make out the
different heterogeneities within the urban area you also have a higher
spatial resolution at 750 meters and maybe what's most important is there's
calibration across time which means that from day to day we're getting
a signal that's stable and that's in part due to a lot of the algorithmic
algorithmic developments that we've put into place in the last year but this is
going to be useful for a whole new set of research questions that DMSP
couldn't even touch next
so but of course it doesn't look like that pretty
picture when it first comes out of the sensor it looks more like this here what
you're seeing is different swaths the moonlight reflecting off of the desert
isn't taken away and so the cities don't even pop out when moonlight is there so
a big physics challenge for us was to try to even understand how to understand
the physics of nighttime radiance reflectance etc if you go to the next
slide I don't want to get into the weeds too much but I do think it's important
to understand kind of what the algorithm is doing there are several sources of
light in addition to urban light which the urban light is shown in red here you
got the moon contribution you also have the reflectance off of the atmosphere
and off of the ground for example from day to day I was
looking at Texas once and it was in December and I didn't even think but in
one day the radiant shot up really high and I was like what could this be it was
because there is a freak snowstorm in Texas so of course the reflectance of
the ground really matters for how streetlights reflect off of off of the
ground in addition the vegetation occlusion of streetlights can really
dampen the signal so we have to remove all of these artifacts in order to get a
stable understanding of what what night lights are otherwise you think
something's happening when something really isn't happening next
right so
this is the this is the global composite from 2012 it's gonna scroll over and
show you 2016 we now have all the way to 2019 and we have it daily you can't
really see big changes here when it's like in this format so the next slide is
going to show you a colored version where the areas of the
world where there are new lights are shown in purple and where lights have
declined are shown in orange and I'm so clear regional patterns start to stick
out if you want to turn to the next slide you can see India Lower South East
Asia having lots more additional lights this is largely because of
electrification that has been happening there you can also see like Puerto Rico
a huge loss of lights from a lot of the storms that had happened there so some
of these regional patterns start to come out we we actually can't explain all of
them we don't we don't know because we're not experts in each area but
that's kind of what's exciting about this is it's like objective data that we
can talk to regional experts and really start to understand what could these
these different signals mean I mean ice and actually I think you can skip over
this one this one's just a slow version right so I'm gonna just sort of talk
about two different kind of areas of how we've used this data to look at new
science questions the first is to understand how planetary changes are
impacting urban areas and this has mostly been in the area of disasters
this is an image of cyclone Edye which hit Mozambique this past year actually
that area of the world was hit seven consecutive times this hurricane season
and not unlike many other places in the world there's been escalating incidences
of these storms and combined with the poor infrastructure and the low quality
housing in Mozambique even though this was only a category two storm when it
hit land hit landfall it killed over a thousand people so it had a huge impact
next slide so while this storm was happening black marble was one of the
only datasets that was available to really show the impact so we worked with
the World Bank and other recovery teams on the ground to try to help them map
and understand where the biggest outages had occurred and also where recovery was
happening so this picture on the right is Mozambique before cyclone Edye on
the left is 10 days after the the storm hit and you can see that the Manga
region the Micucci region they were all very, very hard hit by this storm and so
not just the before after but we can also progressively track how recovery
efforts are doing which is a good way to keep and understand whether the the
investments that are put into recovery are having an effect next slide it's not
just disasters like hurricanes and cyclones that we can monitor also
geopolitical conflict and disasters are another application where black marble
has proven useful these areas of the world and you can keep going are really
hard to get on the ground data because because they're involved in conflict
this this first picture is from 2012 and then 2016 and then changed on the far
left and this is the Syria and you can see areas that were totally demolished
by the conflict in Syria so this is like a spatial distribution of where battles
took out the electric infrastructure in the country during the the war next and
it's not just the spatial distribution that we get we also get the timing of
these events so this is um Aleppo Syria and you can see that drop that just
happened right there in 2012 that was the Battle of Aleppo which was a major
battle in Syria and in Aleppo that really it forced hundreds of thousands
of people to leave the city and so we can also see whether there whether
there's a recert whether there's people coming back whether the infrastructure
is um is repaired and as you can see in Aleppo there really hasn't been much
recovery unfortunately next at the same time we
can also see signals where many of these migrants are going so this is Zaatari
refugee camp in Jordan houses 30% of the Syrian refugees in
Gordon and you can see the increase in lighting over Zaatari as that camp grows
and this is not just a people signal it's mostly actually that the increase
in population in Zaatari was happening mostly in 2012-2013 this is the delayed
signal for when infrastructure was provided for those people that are still
living in the Zaatari refugee camp also an area where on the ground data has
been hard to get so next so the next area that I want to talk about is how we
use black marble to understand how urban areas are changing the planet not how
the impacts within urban areas and so this is you can you can turn the slide
so before we scrolled in and we saw that electrification signal in India you can
see it again here in close contact so this is 2012 2016 and the change here so
almost all of India is purple and that's because in 2012 over I think it was over
a million I don't quite remember the number but several towns still didn't
have electricity infrastructure in 2012 that was down to less than 20,000 towns
that didn't have electrification so there was huge progress made between
2012 and 2016 and that is reflected in the purple next a similar case is in
Ivory Coast there was a lot of infrastructure investment there in both
solar and hydro providing new electricity for that region these are a
bunch of different news articles that came out during that time period
tracking the power generation development next slide and then this is
a black marble understanding of how electrification has happened in three
cities in the Ivory Coast and where which which parts of the cities have
benefited from that investment so what's really nice about this data is you know
you know that the investment is going there but it's
it's a way for people to objectively track how successful that has been at
the end user level so one of the key sustainable development goals I think
it's SDG 7 is to ensure modern energy services for all and so this is the key
way that we're gonna have to be able to track that across places that have very
different levels of statistical data collection systems
next
ok so I wanted
to end by talking about a couple of future directions for our team a lot of
times in the remote sensing literature nightlights has been used as a proxy for
everything it's a proxy for GDP, it's a proxy for where people are, people use it
to map where urban areas are but we want to move a step beyond that instead of
thinking of it as a proxy we want to look at places where infrastructure
where lights might diverge from those other things for example where does
population grow but lights doesn't where does lights grow but population
doesn't where has land change happened without infrastructure development and
what are these different signals where those signals diverge what are those
mean so this ternary diagram is something
we've developed where we're looking at a global population data set, a global land
data set and a global infrastructure data set which is nightlights at the
same time and seeing what kind of classes are pulled out and what kind of
new information about urbanization is pulled out by using these three data
sets in tandem, orthogonally, as opposed to as a proxy of one another, ok next so
this is the ternary diagram that we've developed in this corner this is a when
population increases but the other two don't we call that a population dominant
class a land dominant class and an infrastructure dominant class we have in
the center we have our most common conceptualization of urbanization that's
where they all happen at once and then these other ones are what we're
referring to as skewed urbanization where two of the three growth signals
happen next
and so you can pull out different kinds of
development from that from taking these three together and these different types
of development have really different impacts on natural land systems in the
environment so for example vertical growth in cities is a really different
type of urbanization than growth in informal settlements so though these
have very different social implications and also very different like energy and
resource implications okay now so I'm just going to show you
some examples of what this classification pulls out so this first
one is this is the center okay then this first one this Center triangle right
here that's concurrent change so this is what we think of this is Noida India
before in 2012 and 2017 basically a whole city grew up out of nowhere this
is a very common signal to see in India where population growth happens
infrastructure growth happens and land change happens all at once and people
who have visited this area of the world understand this is what people usually
mean when they say urbanization next
these are three of the independent types
so this is actually where I live in Washington DC and NOMA this is the
previously developed area that started to grow vertically and so this is we saw
an increase in population here but no change in land and no change in
infrastructure because already there were streetlights already those places
were paved so they didn't get pulled out in an impervious signal but what was
really changing was a vertical change and so that was pulled out in the
population signal this is in Uttar Pradesh this is a electrification of a
road these red you can barely see it but these red circles are showing the shadow
of the street lamps that were picked up by the Google images so this is just an
electrification change no population change and no land change associated and
then this is just a land change where one undeveloped land was changed into
like a garden farming so these are three different signals that are pulled out as
urbanization depending on what what data set you're using but they're really
different processes and they have really different implications on sustainability
and next and then finally oh sorry and this is
also from the electrification this is the electrification one where I was
showing you the roads so we looked at this class across all of India and we
compare that to the statistical data from surveys and we found that actually
it mapped pretty well the electrification signals so if you pull
out just that that class it's a it's a pretty good indication of
electrification in terms of estimating the population in those pixels okay next
and so finally the three classes we haven't talked about next are these
three so this class is where population and infrastructure are changing but no
land change this is Broadmoor neighborhood in Detroit so here we
actually saw a decrease in population a decrease in lights but no change in land
and that's because when Detroit went into the Great Recession lots of people
moved out of this area the streetlights weren't even kept on because they lost
so much population but there's very little change in land you can see a few
houses here and there that have changed but it's not really picked up in a lot
of our land data so it was picked up in the other two this is a really important
land class that's pulled out by this this is the growth of informal
settlements in in Mumbai so here you could see that there was a change in
population and a change an infrastructure but the land data set
really didn't capture because the pixels were were large and there's not a ton of
I mean it's it's an infill land change and then here this is the final one this
is like a sports facility in California there was a huge change in lights
because they put a sports field there was a big change in land because they
also created this parking lot and this new development but no change in
population no one's living there so that kind of commercial development is also
pulled out by this urbanization signal which is really really interesting
because then we're getting it to land use not just lot land cover right okay
so next and so finally one other next step is
the fusion of our black marble data with different spatial resolution data so
this is black marble in its native form it's 500 meters this is overlaid on an
Open Street Map of University of Houston okay next one so when we downscale that
based on Landsat we get kind of a 30 meter which we've been calling the black
marble HD product and this is where we start to guess for each pixel what are
the uses within each pixel that we're using the light how do we distribute the
light within each pixel based on our Landsat there's a paper out about how we
do this but it starts to get us to more exact targets especially for those cases
where we're looking at disaster impacts of like where the outages might be
occurring and what are the uses that might be affected and then this last
image is something nobody's seen before and this is the 2 meter version with
digital Globes data so you can see how you can start to get really specific
about how lights are potentially distributed within each pixel and you
can have a very high-resolution understanding of the energy
infrastructure in a city I think over here it's gonna be a full-scale version
of Houston based on that data so a little bit of eye candy to leave you
with next so yeah I just wanted to wrap up by saying I think that black marble
is really exciting because it's making the case for how we start to bring
together this human system side with the natural system side that NASA has been
so focused on and really we can't understand one without the other because
now that we're in the Anthropocene it's the human systems that are driving a lot
of the natural system changes so we really have to understand the processes
within urban areas to understand why their impacts are the way they are so
this is a one step towards completing that picture thanks so much for
listening