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MALE SPEAKER: Our guest today is Phil Christensen.
He's a professor of geology at Arizona State University, and
my former boss.
He runs a research center that does daily operations for
several instruments on board Mars Spacecraft, and he's just
sort of an all-around smart guy.
So he's here to talk to us today about remote sensing.
PHILLIP CHRISTENSEN: Greetings.
It's a pleasure to be here.
As Michael said, I'm at ASU.
I'm a geologist initially, then became a geophysicist,
got interested in Mars.
And unfortunately to date, have then had to become
interested in remote sensing, because that's the only way we
have of studying Mars.
So I've spent most of my last 20 years or so building a
variety of instruments.
I thought I'd start off by sort of summarizing the last
20 years of my life--
that's sort of a frightening statement to say these four
machines summarize what you've done for the last 20 years.
But these are instruments about yea big, mostly
infrared, we send all of them to Mars.
So what I like to do today is talk a little bit about just a
quick introduction to sort of the physics of remote sensing
so we're all on the same point, and then show a few
examples, and then just talk about some applications.
I'm reasonably well-aware of what you guys are doing in
terms of remote sensing with visible, and
Google Earth, et cetera.
What I want to try to do today, at least at an
introductory level, is discuss some of the other aspects of
remote sensing: other wavelengths, the importance of
time, et cetera, to see what other
information one could get.
And if nothing more, just sort of set the stage a little bit
to get you thinking about, OK, maybe we could do that for the
earth, or maybe there might be something useful there, or
maybe there might be an end result that could be
incorporated into the things that you're doing.
Pardon me, but not knowing the audience really well, I
thought I'd just give a little bit of overview about what do
I think remote sensing is, and some of the key
physics behind it.
And just to summarize, in the simplest case, remote sensing
is just using images.
I mean, everyone in this room has been doing remote sensing
since you were born.
Your eyes are probably the single best remote sensing
instruments ever designed.
Your ears aren't bad either.
And so in the simplest case, we are collecting visible
light, spatial patterns, temporal variability, and
using that information to construct a three-dimensional
model of the world around us.
The next level is to take full use of the
electromagnetic spectrum.
Our eyes evolved to focus on the light put out from the
sun, but there's a whole spectrum of electromagnetic
energy that goes beyond visible.
I want to talk a little bit about that.
And then to me what remote sensing really is about is
extracting quantitative information from all of that
information.
Not just the patterns that we see, but quantitative,
compositional, physical information about the nature
of the surface, whatever planet it's on.
Just going back to high school real quick, I'll remind
everybody visible light, about a half a micron.
I'm going to mostly today be talking about near infrared
and thermal infrared, which have wavelengths of about 10
microns, but obviously you can do everything from gamma ray
and x-ray remote sensing, all the way out into radar and
microwave. So there's this--
whatever that is--
nine orders of magnitude of variation in the wavelength of
light, and to say that we use, sort of, from 0.3 to 0.7
microns out of that nine orders of magnitude, it's a
pretty limited piece of the electromagnetic spectrum that
most of us use.
And I'll be talking a little bit today about both reflected
and emitted light.
Again, most visible light sensors use the sun as a
source, and they look at the reflected or scattered light
that comes off of surfaces.
We can have other sources of energy.
We can illuminate the scene with microwaves or radar, and
we can certainly look at just energy that's emitted.
Every object which is not at absolute
zero is emitting energy.
All of you are emitting a lot of energy right now, and we
can sense that and make use of that emitted part of the
spectrum as well.
I think I'll get through the word slides here pretty quick,
and then get down to looking at some images.
Just again, as a quick review, remote sensing is typically
broken up into two broad categories.
One is active remote sensing, where you're actually
providing a source.
That source traditionally has been radar or lidar So you are
illuminating the scene.
That has some real advantages because you can control the
wavelength.
You can control the illumination angle.
You can control how much power you put out.
You can control both the observing and illumination
geometries.
You can work when it's night, you can work
when it's cloud covered.
So active systems have a lot of benefits.
Obviously they take a lot more energy.
You have to actually
illuminate the source yourself.
The other part of remote sensing is the passive side,
and this has traditionally been one of the largest parts
of remote sensing.
I think you can break it up, again, into three basic
categories where the physics of what's going on is
fundamentally different.
You can look at very high-energy interactions, and
that would include things like gamma rays and x-rays.
Here on the earth, we're shielded by our atmosphere.
Not very many gamma rays and x-rays get to the ground.
But on airless bodies like Mars, there's a lot of cosmic
rays, a lot of high-energy sources that get to the
surface, excite the atom at the nucleus level, generate
gamma rays and x-rays, which we then observe.
We can tell, literally, what elements are on the surface by
looking at the spectrum of gamma rays that come off.
So there, the interaction is going on at the nuclear level.
What I'll call moderate energy is
ultraviolet invisible photons.
Those have enough energy to excite or interact with the
electrons that are surrounding the nucleus.
So slightly lower energy, but there, as we'll see in a
minute, we're actually exciting electrons to
different energy levels.
And then the final one is sort of the low energy, infrared,
and even out into the microwave. Now the energy of
the photon barely has enough energy to cause these bound
atoms within a structure to vibrate.
So each one of those then, based on the energy of the
electromagnetic wave that's hitting the material interacts
at a different level.
And each one of these-- x-ray, UV vis, infrared--
is providing a unique piece of information about the atomic
structure of the material that it's interacting with.
And I'll spend a couple minutes talking about that.
OK, some basic physics to remind everyone.
Light, of course, is an oscillating electromagnetic
wave that has a wavelength, a velocity--
the velocity of light--
and a frequency.
And those are very closely related.
Velocity equals wavelength times frequency, OK?
And frequency, as we'll see in a minute, is important.
The frequency of the light depends a lot on how it is
going to interact with the material.
I think one of the most useful equations to keep in mind is e
equals h nu.
The energy that a photon or a wave contains is directly
proportional to its frequency.
So that gets back to what I was just saying in the last
point, that short wave, high-frequency photons carry a
lot of energy.
Long wave, low-frequency photons
carry much less energy.
And therefore, they interact at the atomic level very
differently.
The other thing to keep in mind is electromagnetic waves,
as the name implies, are oscillating electric and
magnetic fields.
And so if light is an oscillating electric field,
and I interact with something that has charged particles,
either electrons or nuclei, that electric field can
interact with those charged particles.
And it's the nature of that interaction, then, that causes
light to be absorbed.
It's the reason why things have color.
It's the reason why water heats up in a microwave. It's
the process by which light interacts with material.
It's the fact that I've got oscillating electric fields
interacting with charged particles, and those charged
particles are going to move when they're bathed by that
electric field.
You convert the energy in the wave into the kinetic motions
of the material.
OK, just a couple more points here.
Similarly, at the same time that these electric fields are
oscillating, the atoms in any crystal structure are also
oscillating.
They're bound in there by the forces that are holding those
atoms together, but they're certainly not balls on sticks
like you see in a chemistry class.
A much better model would be balls on springs.
So I've always been trying to find somebody would make a
beautiful model of a complex molecule where you have these
little masses, and they are bound together by springs.
All those masses are free to move around, OK?
And just like in any system of masses and springs, the
motions of that system are going to have a resonant
frequencies.
They are going to vary with the mass of the atoms, and
they're going to vary with the strength of the bonds.
So in any given crystal structure, you've got these
masses and they're held together by these bonds.
Every one of those pairs of atoms has a specific resonant
or harmonic frequency that it's going to naturally want
to vibrate at, OK?
And that's then sort of the key to how light is going to
interact with this material.
Because what happens then is if I have this oscillating
electromagnetic wave that's coming along, if the frequency
of this oscillation--
say, some frequency, nu 2--
matches the frequency of this harmonic resonance frequency
within the material, then this electric field will excite
these charged particles, cause them to move, and in the
process, reduce or subtract energy from that wave so that
when the wave comes out the other side,
it's reduced in amplitude.
So this is just a fancy way of saying, this light is absorbed
by this material.
But the thing that makes it important here is if I have a
wave that's at some other frequency, that's different
from this resonant frequency of the material, then that
wave tends to pass through the material
more or less unchanged.
There isn't a strong interaction, and so the light
can come out.
So in this particular process, then, you can imagine that if
I took a spectrum of white light and illuminated a
surface, the waves that come out the other side--
the spectrum that comes out the other side--
I'm going to be missing those frequencies that correspond to
these natural vibrational
frequencies within the crystal.
Let me come back to that.
So that's two pieces of basic background.
The third piece, if we're going to talk in particular, I
want to focus now on sort of infrared spectroscopy.
I want to talk about the emitted
part of remote sensing.
And to do that, we need to talk
about the source function.
What energy is being emitted by a material?
I'm sure you're all, at least at some level, familiar with
the concept of a black body curve.
Planck, back in the early 1900's, derived this equation
from first principles of physics using the quantum
nature of the energy structure of materials, and was able to
predict, then, what we observe.
That is that the amount of energy emitted by material
varies with wavelength.
So here, I'm showing wavelength in microns and
temperatures--
0 degrees centigrade, 20 degrees centigrade.
So this blue curve is basically a spectrum of the
light that you're giving off.
All of you are emitting.
All of you are at about 20 degrees C, maybe 30 degrees C.
And you are emitting energy, and the energy that you are
emitting is peaked at about 10 or 12 microns.
If I were to heat you up to 6,000 degrees C, what would
your peak wavelength be if you were 6,000 degrees C?
AUDIENCE: Somewhere in the visible.
PHILLIP CHRISTENSEN: Somewhere in the visible.
The surface of the sun is 6,000 degrees, and these exact
same curves hold just as well for those temperatures as our
temperatures.
And so if you plug in 6,000 degrees C, two things happen.
One is, there's a lot more energy emitted, and the
wavelength at which it peaks shifts to shorter and shorter
wavelengths.
So the sun is peaked down at about half a micron, which is
exactly why our eyes have evolved to be most sensitive
to light at about a half a micron.
As we'll see in a minute, the world is a whole lot prettier
place in the infrared than it is in the visible, at least
the natural world is, rocks and minerals are, As a
geologist, I would be much better suited with infrared
eyes, able to look at the world, than with these crummy
visible ones that I'm stuck with.
But the fortunate thing is we can build detectors that allow
us to see in the infrared.
OK, so materials are emitting energy.
And in an ideal world, they're doing it as predicted by
Planck from his famous equation, and the energy
coming off of an ideal surface would look
something like that.
But in Planck's world, things weren't bound together.
There were no bound atoms. Everything was just free to
move around.
It was not bound to anything else.
When I start creating a crystal structure where I have
atoms bound together, then we have these harmonic motions,
these preferred frequencies, and now the light that's being
emitted no longer follows perfectly Planck's function.
We start to see these absorption bands which are due
to the vibrational motions of the material.
The upper curve shows--
the black line is the idealized Planck function.
The red curve is a measured spectrum of a mineral, in this
particular case, just a nice, simple quartz crystal.
And then, typically what spectroscopists do is they
take the ratio of those two, create something called the
emissivity.
The beauty of the emissivity is it's independent
temperature.
If I just measure the straight radiance, then those curves
are moving up and down with temperature, the shape of
those curves is changing slightly.
But the ratio of the energy emitted from a national
service to an ideal surface stays the same.
So this emissivity doesn't change whether I'm measuring
it at 20 below 0 or at 500 degrees Centigrade.
This emissivity of quartz won't change.
OK, so to quickly come back to our vibrational model, what's
happening is--
if I can explain this sort of quickly and simply--
deep inside the material, I've got all these motions, and
it's generating this Planck-like
distribution of energy.
As that energy tries to escape from that
crystal, it's being absorbed--
preferentially absorbed--
at frequencies or wavelengths that correspond to these
harmonic frequencies.
So this particular frequency that corresponds to a
wavelength of say, 20 microns, that's one of these harmonic
frequencies.
So the energy that's trying to get out is
preferentially absorbed.
The atom likes that energy.
It vibrates at that energy.
It takes the energy out of the wave. And so what we see
coming out of the surface, there's a deficit of energy at
that particular frequency.
So the point of all this is that these infrared spectra,
which we can quantify remarkably well, you can get
right down to the basic physics of vibrational modes
and all this other kind of stuff, there's is beautiful
quantitative basis of all this.
But for remote sensing, what we really care about is that
because every known substance on the planet has a unique
crystal structure, it will therefore have unique
vibrational modes, and it will therefore produce a unique
infrared spectrum.
So I can make a list of minerals that mean something
to a geologist: quartz, clay, olivine, calcite, this is what
they make concrete out of.
Each one of these minerals, then, has a very unique and
diagnostic infrared spectrum that's based on its internal
crystal structure.
It turns out that a lot of what we know about the
chemistry and the crystal structure of materials has
come over the last hundred years from studying their
infrared spectra.
And it turns out, you can get incredibly detailed with this.
It's one thing to say, this mineral is sort of different
from that one.
But these are examples of minerals that I don't expect
you to know, but these are classes of minerals which are
very, very similar, but if I start replacing, say, the iron
atoms with magnesium atoms, that changes the fundamental
frequencies because the mass of those atoms is changing,
the frequency of the system is changing, and these absorption
bands shift around.
So it's a very diagnostic tool down to very precise levels of
what the crystal structure of a material is.
And the beauty of an infrared spectrum from a remote sensing
point of view is I can measure from orbit around Mars the
light that's given off, measure the spectrum, and
determine what the composition of the surface is.
I don't have to touch it.
I don't have to put it into an instrument.
I don't have to do anything to that surface.
I can just measure its infrared spectrum, and get a
very unique identification of what mineral's on the surface.
And we've done that.
And I'll show some examples a little bit.
Although I've spent the last 25 years studying Mars, and I
think I only have one slide in here on Mars.
So for better or worse, if you wanted to hear about Mars,
you're going to be disappointed.
OK, the other the thing that you get from an infrared
measurement, which I think is extremely interesting, is this
idea of temperature.
The spectral information is telling as composition, but
the total magnitude of the energy emitted is telling as
temperature.
The hotter something is, the more it emits.
OK, that's fine, but we can take that information and use
it in a very quantitative way.
You can develop very sophisticated models that
predict the temperature of a surface as a function of time
of day, based on the physical properties of that surface:
its grain size, its conductivity, its sub-surface
layering, whether it's a metallic material.
You can put all that stuff into a model and predict how
the temperature will vary with the time of day.
So for example, large, rocky materials will hold their heat
really well at night.
They've stored a lot of heat during the day, and then that
heat comes back out at night.
So rocky materials will stay warm at night.
Very fine grain sand and dust materials were not able to
conduct heat into the interior, were not able to
store any heat, so at night, that upper layer cools off
very rapidly and there's no stored heat to come back out
and warm the surface.
So we get these diurnal curves that very significantly on
whether a material is rocky or fine grain.
So for example, here's a place on Mars that here, it's very
warm at night, and here, it's relatively
cool during the day.
We can go into these thermal models, measure those
temperatures, and say, OK, this is actually bedrock.
This is pure, solid rock sitting on
the surface of Mars.
This stuff that was relatively cold at night and heated up
during the day, that's say, millimeter sized gravel.
So you can actually get very quantitative about this, and
be able to pin down the particle size of surfaces.
I can tell whether it's millimeter gravel or two
millimeter diameter gravel, based on these very precise
temperature measurements.
OK, so from the infrared, then, we get composition.
We also get some indication of the physical nature.
And then finally, I wanted to come back.
So that was the vibrational spectroscopy,
the low energy end.
If you remember back, I was talking about the moderate
energy, UV-visible light.
That's where the photons have enough energy to actually
interact with the electrons.
And if you think, if you remember back to energy level
diagram of hydrogen or sodium, the electrons in the cloud
around an atom are at a ground state, they can be excited to
an excited state.
If that energy to go from a ground state to the next level
up is equal to the energy of a photon, then what happens?
That photon excites the electron.
Oftentimes, there'll be a collision within the atom.
Instead of the electron just jumping back down again and
giving off a photon, before it can do that,
another atom will collide.
That energy that was stored in the higher energy state is
converted to kinetic energy of the atoms.
And so again, if I took a flashlight, shined it in.
If I had light whose energy corresponds to one of these
energy states, it'll excite the electron, and what comes
out the other side will be--
I won't see that photon coming out the other side.
This is the basic concept of why objects have color.
This is what's going on in dyes and going on in the
things that you see with your eyes.
It's photons exciting electrons and being removed
from the beam.
OK, and just some quick examples.
This is down in the visible light.
So this is half-micron, one micron, two microns.
Our eyes end up cut off here at about 0.7 microns.
These absorption bands are due to electrons being excited
within atoms within the material.
So a lot of remote sensing, near infrared remote sensing
that's done, has to do with these electronic transitions
that are going on, where photons are being absorbed and
used to excite electrons.
The problem with electronic spectroscopy is there's only a
very small number of elements where this process actually
occurs at these kinds of wavelengths.
And specifically, it's the transition metals.
I know I was crummy in chemistry in high school, I
can never remember what the transition metals are, but
iron is one.
Copper is a good example.
So basically, this kind of spectroscopy works in nature
in iron-bearing minerals.
OK, well if you think about driving around in the deserts,
most of the color that you see is yellows, and oranges, and
browns, and butterscotches, and tans.
Almost all of that is due to iron-bearing
minerals, or iron stains.
And those stains, those iron-bearing minerals, have
colors because of these electronic transitions.
If a mineral doesn't have iron in it, like quartz, for
example, that's why it's colorless, because there are
no elements in there that can interact with the photons and
have energy levels at the appropriate levels to absorb
the visible light.
So it works great for iron-bearing minerals, but the
world isn't necessarily covered with
iron-bearing minerals.
All right.
So that's the end of the physics lesson.
Let's look at some real examples.
And again, what I'm going to try to focus on for the next
little bit is things that you're not used to.
I mean, things that don't have to do with looking at the
world in visible light.
This is a Landsat image.
Landsat is a space-borne imager.
It has seven bands.
You can use bands one, two, and three, and construct a
natural, normal light image that you would recognize.
This is an image of a delta, I think in the Amazon region,
constructed of some of the other bands, think the
wavelengths that your eye doesn't see.
But I would still say, this is pretty close to what humans
have been doing all along, looking at the world.
OK, so that's sort of what the world would look like.
I want to show another example.
This is Phoenix.
This is a more or less normal Landsat scene.
It's not quite red, green, blue.
It's green, red, near infrared.
I think many of you are familiar with this.
This is a false color image.
We've taken each of the wavelengths and
shifted it down one.
So green is displayed in the red gun of this projection;
red, and green, and near infrared in red.
As a result, vegetation, which is very bright, very
reflective, in near infrared light--
because it interacts really well, the
electrons are easily excited--
vegetation shows up bright red.
One of the things, though, that you can do if you want to
emphasize certain things and bring out certain
compositional information, you can actually make ratios of
those bands.
So instead of just making a color image, I can take two
bands and ratio them, and so this is an example of taking
one of the bands, where vegetation very reflective,
and one where it's not, making a ratio of that.
And so here, any vegetation is very clearly identified as
being in this ratio.
You also see other things that show up, and my point is not
to dwell on this, but to simply say, you see certain
patterns and certain information if you look at
just a, quote, "normal" color image.
You see other information and other patterns if you start
ratioing the images or in some other way trying to extract
information from them.
So for example, clear differences, say, right here
between urbanized and non-urbanized actually can
show up better in some of these ratios than they do in
the original, just in the color image.
This is an example of what I said about what the world
would look like if you had infrared eyes.
I've taken this set of spectra--
and you can fly a spectrometer, but they're big,
and expensive, and complicated, and they're hard
to get to work.
I can also build a camera, an infrared camera, that's
relatively straightforward and only has three filters.
And if I put one of those filters at 10 microns, one at
9, and one at 8, and took an image, and then I displayed
the emitted energy at 10 microns as red, 9 as green,
and 8 as blue.
Then for example, if you look at quartz, it's emitting a lot
in what then becomes the red band, not much in green, and
not much in blue.
So in a false color infrared image, that
quartz would look red.
Similarly, if I take a mineral that's bright at this
wavelength and is absorbing at these two other wavelengths
and display it, it's going to look blue.
So if I flew an infrared camera, three-band infrared
camera, across the desert, instead of--
you fly along, you look out the window, the world is brown
and gray and tan--
in the infrared, this is literally what the world would
look like if you had infrared eyes.
And to a geologist, or someone interested in the composition,
the nature, the makeup of the surface, this is
what you would see.
And this is basically a geologic map of this mountain
range without having to do anything at all.
I mean, geologists spend years detailed mapping to try to
find all these different mineral deposits, and rock
outcrops, and different types of material.
This infrared image is essentially giving you that
information instantaneously.
I can see quartz-rich sands, and I can see volcanic rocks,
and I can see gypsum-bearing salts, et cetera.
It's a tremendously powerful tool for mapping the
composition of the earth's surface.
This is an example of the surface temperature in
Scottsdale, Arizona.
Bright is warm, and in this case, pretty darn warm.
We flew this in August. Those surface temperatures are
probably 140 degrees Fahrenheit.
Cool, dark is cool, and it's pretty darn cool.
The cool temperatures are probably about 80 degrees
Fahrenheit.
So where would you want to have your house?
Next to the 140 degree baking hot asphalt and dirt, or next
to the 80 degree, nice cool lake and golf course?
Well, OK.
But the point is, these infrared images actually
provide a tremendous amount of information about temperature
which feeds into all sorts of other important things.
In Phoenix, we're trying to actually use these to measure
lake temperatures, and swimming pool temperatures,
and calculate evaporation rates, and how that's
affecting the humidity.
And there's all kinds of environmental impact of all of
these crazy golf courses and all these lakes that show up
extremely well just by looking at the temperature effect of
those things.
And I'll just touch on this.
This is a really detailed spectrum, again,
from about 20 microns--
sorry about the units-- down to about 5 microns.
So it's an infrared spectrum, extremely high resolution,
looking at gases in the atmosphere-- in this case, the
atmosphere of Mars.
Even down, if you look at that box, you can build
spectrometers that have that kind of resolution.
This is how we're monitoring trace gases in atmospheres.
So there's a tremendous amount of information that you can
get about the composition of the atmospheric gas.
When I was talking with Michael a little bit, one of
the topics that came up about what can you do with remote
sensing from a practical point of view, a lot of the time,
you're dealing with a scene that was taken looking through
the atmosphere, and you're trying to separate the
atmosphere from the surface.
If you're just trying to make a beautiful map of the ground,
you want to remove the atmosphere.
If you're an atmospheric scientist, you want to remove
the surface.
So I used to call this atmospheric correction, but
the atmospheric guys got annoyed about that.
So now I try to say, we're separating the two.
There are basically two ways to go.
You can do very sophisticated, radiative transfer modeling,
and there are packages that do that, where you put up a
radiosonde, and you measure the temperature, and the
pressure, and the water vapor, and all the stuff, and it goes
into incredibly complicated model.
And you try to remove that signal from the ground.
Or you can do scene-based approximations, which in the
real world the only practical way to go.
And there's a bunch of ways to do that.
And I'll just show a quick illustration.
This is an infrared scene, one of these beautiful, three-band
infrared color scenes.
And this particular scanner is a line scanner that looks out
in both directions.
So looking straight down, we're looking through a
certain amount of atmosphere.
Off to the side of the image, we're looking through almost
twice as much atmosphere.
And as a result, you see this band of
yellowing down the side.
This is an example of this type of problem.
A really simple way around that is to say, I'm going to
assume that, statistically, the ground is sort of randomly
distributed.
So what I've done here is I've just taken a column average of
the entire scene, and those three lines, then, represent--
a single line, that's the average of all the
lines in the image.
You can see the effect of the atmosphere with this
absorption that's going on over on the edge.
I could try to model that, or I could be really simplistic
and say, I'm just going to take this signature and
subtract it from every line in the image.
So this is just a simple scene-based example, and you
can see what just something even that simple has done.
This material, which is yellow in the original data, suddenly
looks like it's supposed to do in the scene-corrected data.
So there's a lot of very simple ways that people have
devised over the years in a practical sense to separate
the tremendous amount of information
that's in these images.
OK, let me just quickly show--
I'm not a radar expert, but I didn't want to talk about
remote sensing and not mention radar.
There's a tremendous amount of information in radar as well.
Here what you're seeing is all of this bright material is
basically man made, metal-rich material.
This is a scene from a radar experiment that was flown in
the shuttle, again, over Scottsdale.
And you see natural desert, watered fields, but this
incredible signature from the metal in the city.
With radar, you can transmit these electric waves.
You can transmit them this way and that way, and which way
you transmit them has a huge effect on what happens.
So for example, the main difference between those two
scenes is whether or not the wave was transmitted
vertically or horizontally.
In the first case, it's vertical.
The electric field and the wave was oscillating
vertically, and so where I had electric power lines, there
was very little interaction.
In the second case, the wave was oscillating horizontally.
Where I have electric power lines, I get a very strong
interaction.
So one of the beauties of radar is you
can actually tune--
you can design your experiment so that you can pick up
different things.
And this is just an example of three different radar images
combined into a color image with a fantastic amount of
information in there.
OK.
Let me just quickly go over a couple of applications.
This one I showed an example of.
This is, again, an infrared multi-spectral image of some
mountain range in the Southwest. Remarkably, very
few people use this type of information, even today, to
try to do compositional mapping of the world.
This is the type of instruments that
we've flown to Mars.
We have better maps of Mars than we do of the earth of
trying to look for rock types and minerals on the planet.
One of the things you can do, in addition to make pretty
images, you can actually look at the spectral signature.
You have all this information.
You can take that spectral information and try to
classify the scene.
So for example, this is a Landsat image of Phoenix.
This is taking that spectral information and classifying
the scene--
I'm going to zoom in on-- this is the airport.
So you can make this--
again, it's an automated tool.
You define what the rules are: what each one of your classes,
what it consists of, what type of spectral signature it has.
And so we're looking at cultivated, grass, vegetation,
commercial, compacted soils, water, asphalt, concrete.
So you can make these classification images of a
city that then you've extracted a lot of information
about that city, and you've done a lot more than just make
a pretty picture of the city.
I've actually got information that I can use to study, for
example, study the city over time, see how desert's being
converted to agriculture, agriculture into
urban use, et cetera.
OK, the other thing that you can do with remote sensing of
cities in particular is look at temporal changes.
This is an example of Beijing from a very early 1978,
relatively low-resolution version of a Landsat
instrument.
This sort of ugly gray color here is most of the urbanized
part of Beijing.
In this false color image, fields and vegetation are
showing up red.
And it doesn't take much to sort of note the difference
between 1978 and 2004.
In that same area two years ago, there's virtually no
agriculture or vegetation left in this region.
AUDIENCE: --seasonal difference there, too.
PHILLIP CHRISTENSEN: In this particular case, there is a
seasonal difference.
In an ideal world, you certainly wouldn't want that.
But one of the problems--
I was talking earlier--
it's remarkably hard to go back very far, even from the
satellite data.
Oftentimes, we only had one image a year
that was even acquired.
But you're right.
And that certainly complicates the whole situation.
And particularly in China, where their cities are
evolving quickly, it's remarkable how
much change is occurring.
Shanghai was a relatively small city surrounded by
vegetation 30 years ago.
Today, it's grown dramatically.
I think I have one more example, Hong Kong.
You can use remote sensing in the time domain as well to
really look for temporal changes
in the earth's surface.
You hear a lot about things like deforestation and rain
forest, but actually some of the most dramatic changes are
occurring around cities as they are growing
spectacularly.
From the night time temperature, you can study
things like urban heat islands.
I'm assuming you've heard of this.
This is a night time temperature image of Phoenix,
and a couple things are apparent.
First of all, where are the warmest parts of the city?
Well they're the roads, the buildings, that huge bright
blob up there is the airport.
It's pretty easy to see where the man made
sources of heat are.
What's interesting, though, and it sort of surprised a lot
of people is how warm the natural surfaces are as well.
So sometimes, if you converted this mountain into houses, you
might, in that particular case, actually lower the
temperature.
So studying heat island developments in cities is
complicated and these types of infrared data
are extremely useful.
This, for example, is the Gila River, which
doesn't flow at all.
There's no water in that river at all.
It's all underground, but there's enough underground
moisture flowing through this quote, unquote dry riverbed
that the evaporation from that moisture is
cooling off the surface.
So the point here is there's tremendous amount of
information in these infrared images.
Another quick example.
This is a handheld infrared camera looking out at Tempe
Town Lake, a small little man made lake just
real close to ASU.
And I wanted to use this to illustrate a--
well, I guess I won't.
I'll have to come back to it.
So even simple--
you don't have the fancy aircraft or satellite infrared
imagery, just handheld infrared cameras now are
becoming inexpensive enough that you can begin to put
these in strategic places and monitor things like water
temperature, and surface temperature, and road
temperatures, et cetera.
One of the points that I was going to make is oftentimes--
for those of you who are trying to do any type of scene
classification, or automated scene identification--
one of the things that we have real trouble with is telling
water from asphalt.
They're both really, really dark.
And in fact, there's cases of automated rovers that drive
out onto a lake because it's smooth, it's dark, it looks
like a perfectly nice surface to drive on.
The one place that water and asphalt really differ from is
in their temperature.
So if you had a temperature information as well, then
suddenly identifying two dark things is trivially easy.
So one of the things that we're trying to do is identify
water based on temperature.
You can also do things like monitor volcanic eruptions.
This is a satellite image in the visible, with the
superimposed infrared image.
It's an active eruption taking place in Kamchatka from space.
And so, for volcanic hazards and volcanic eruptions,
obviously, the infrared's a powerful tool.
Weather gets a lot of attention, as well it should,
but there's other things that go on in the atmosphere
besides just rainfall and cold fronts.
This is an example, and this is the
Western coast of Africa.
That's Gibraltar and that's Spain, for scale.
So this is a huge piece of Africa and part of Europe.
And this is a big dust plume that's blowing off of the
Sahara Desert.
These dust plumes are not uncommon, and oftentimes, they
reach all the way across to North America.
You can easily detect Saharan dust in the air in New York
City, and from space with remote sensing, you can
certainly track these things.
You can see them coming.
On the other side the world, you see the
same sort of effect.
This is, again, dust being blown off of the Chinese
continent and headed east. So there's a tremendous amount of
things that go on in the atmosphere
besides just weather.
So again, just to plant seeds of things: pollution,
aerosols, volcanic ash, volcanic dust. These are
additional things that you could monitor from space.
Just a couple more applications, some things that
we're doing that I wanted to mention.
As part of a NASA activity a couple years ago, a few years
back, I started up a thing called 100 Cities Project,
whose goal was to try to monitor 100
cities around the world.
And in doing that, we were trying to collect all kinds of
information, not just satellite data, not just
remote sensing data, but socio-economic data,
geographic data, land use data, zoning data.
And what we're finding is when you start combining those
other levels of information with the imaging data, then
suddenly economists, and sociologists, and all kinds of
folks get really interested in these data sets.
And so for things like Google Earth that have this beautiful
imagery, what we're trying to do is look at other pieces of
information that you can add to that, whether it's things
like the depth to the water table, well logs, traffic
accidents, air pollution, air quality,
thunderstorm tracks, whatever.
There's a tremendous amount of information, much of which you
can get remotely, that you can add to the image data.
And this is just an example where we've built up this
system that's trying to use some of the satellite data, in
our case, temperature data as well, to add to the existing
visible imagery.
One other thing that I just wanted to mention.
This is something that Michael, actually, was working
on back when he was at ASU.
This is an example of six or seven global data sets that we
have for Mars.
We have a total of 57 of these, and everything from
mineral type, to rock type, to where dust storms have
occurred, to changes in surface albedo, to nighttime
temperature, to elevation data.
What you find is when you put this 56 data sets together,
then suddenly people are doing very sophisticated research on
these global data sets.
And I don't think any of the ones that are in our set of 56
are classic visible remote sensing.
They're almost all thermal, radar,
topography, laser, et cetera.
So I think this is an example where on Mars, we're probably
doing more sophisticated combinations of remote sensing
data than we're actually able to do here on the earth.
And I think you could, easily, develop these very similar
types of data sets here for the earth.
I wanted to show one other example.
This is an example of a infrared spectrometer.
So it's that full spectral resolution that I showed for
the Martian atmosphere.
You can actually develop these, now, where they're
imaging systems, where each one of those pixels is a full
2,000 point spectrum.
So for each time step, for each pixel, I can then take
that full spectrum and identify, in this particular
case, gases that are being released.
So for pollution monitoring, for looking at what's coming
out of smokestacks, for what's coming out of cities, this
next generation of remarkably sophisticated imaging,
hyper-spectral instruments, it's a tool and a technique
which I think is going to have tremendous potential and get a
lot of use.
Only within the last few years have we been able to build
instruments that are capable of doing this, build computer
systems that are capable of processing these data.
But I think it's an extremely exciting way of looking at the
world in much more complex terms than just with RGB.
I think I'm going to skip this one and just close with a
couple of other thoughts.
I just came back from a--
I spent last week at a NASA conference with folks who are
looking at what astronauts are going to do when
they go to the moon.
And without getting into the detailed politics of that, in
NASA's mind we're going to send astronauts to the moon,
and the scientific community was then tasked with, what are
they going to do when they get there?
You could easily argue that you have that problem
backwards, but.
So I was tasked with, what could you do observing the
earth from the moon?
And my first thought was, that's really stupid.
We have 25 earth-orbiting satellites, and we have
geostationary weather satellites, and we're looking
at the earth just fine from the earth.
But it turns out that there are actually a few interesting
things that you can do.
For example, this is the earth from geostationary orbit.
This is the classic weather satellite view.
It turns out, those things are pretty far away.
They're 30,000 kilometers up, but they're still close enough
that you don't come close to seeing the entire earth.
So for example, you can't see the polar regions at all.
Up at the top, you can see Alaska and the Aleutian
Islands are just about disappearing over the limb of
the planet.
Viewed from the moon, that's what the earth would look like
at that same time.
And so there's two particular things that are of real
interest. And you may know this, but the polar routes
from North America to Asia always go over the Aleutian
Islands, and oftentimes they go over in the polar night.
And the Aleutian Islands are the single most volcanically
active place on the earth.
And at any given time, there are a dozen or so volcanoes
that are erupting.
Twice, on two separate occasions, 747s have flown
into ash clouds unknowingly and had all four engines stop.
In both cases, those engines were started before that
airplane crashed.
But this band of islands in the Aleutians, it's a
dangerous place to go.
So one of the things that people talked about is putting
sensors up that are constantly staring at that chain of
islands, monitoring volcanic eruptions as a heads up for
jets not flying through them.
The other thing you can do-- clearly, the poles are
extremely interesting from a climate change point of view,
and we don't have good, continuous coverage of the
poles So there are actually things one could do from the
moon looking back at the earth.
And it turns out, with a modest sized telescope, you
can actually see--
you can get 500 meter per pixel imagery of the earth
from the moon.
These are some examples.
I won't go into them, but these are some of the examples
of the infrared mapping that we're doing on Mars.
And again, I think it's a sad statement to say that we
probably have better data on the composition and the
physical nature of the Martian surface than we do the earth,
simply because we've flown some more sophisticated
instruments to Mars than we've flown to the earth.
Yeah, right.
AUDIENCE: There are also less nasty plants on Mars
[INAUDIBLE]
PHILLIP CHRISTENSEN: Yeah, there's definitely fewer nasty
plants on Mars than there are here on the earth.
And as a geologist, I do think that's a good thing, but not
everybody does.
OK, and so I think, that just a summary then.
Remote sensing across the electromagnetic spectrum and
through time, I think, has a lot of potential for providing
a lot of quantitative information about the world
that people are going to want more and more access to, and
are going to, I think, be able to come up with more and more
applications for how they actually might use that data.
And I'll certainly stop there, and I apologize for droning on
like a lecturer.
I will certainly take questions.
[APPLAUSE]
Yeah.
AUDIENCE: When you were discussing emissivity, you
described a process that I think corresponds to
incandescence.
Is there anything useful in fluorescence?
PHILLIP CHRISTENSEN: The comment was, in describing
emissivity, I was describing something that was similar to
fluorescence, and that certainly is the case.
You get these waves, and they excite things, and you can be
excited and then cascade back down, or you
can be excited and--
and fluorescence is one of those processes where waves
are absorbed, electrons are excited, and then as that
electron comes back down to its original ground state, it
can give off photons.
If it comes down in a series of steps, than it actually
gives off photons whose wavelength is different than
what it absorbed, hence, fluorescence.
And yes, there's a lot of information in that.
Typically, you illuminate in the ultraviolet and materials
then emit or fluoresce at visible wavelengths.
But there's a lot of information in that.
I find scorpions in our backyard because they
fluoresce at night, and so you can find them easier.
But that's a good example of another way of
looking at the spectrum.
Yeah.
AUDIENCE: What fraction of the earth's land surface can you
map, get geological maps from, because there are
not too many plants?
PHILLIP CHRISTENSEN: The comment was, what fraction of
the earth's surface can you map geologically, without the
interference of plants?
Well, Southern California, I can do fine.
Arizona, I can do fine.
Rainforest, no, Northeast forest, no.
It turns out, even if you have like 20% of the ground covered
with plants, or 30%, 40%, you can still get a really nice
signature through it.
One of the interesting facts is that huge parts--
from a human perspective--
large parts of the population of the earth live in
relatively arid places.
So monitoring those, mapping those is really useful.
The other thing, from a climate point of view, the
arid places on the planet are the ones that are undergoing
the most rapid change.
We can detect changes in vegetation.
So you can detect desertification, if you will,
in arid regions.
The polar regions are extremely interesting, looking
at alpine glaciers and seeing how they're retreating.
So there's a lot of really interesting processes that you
can map that are going on in places that don't have a lot
of vegetation.
--on Mars, you could do it on the earth.
AUDIENCE: Which implies that there is no material that is
identical to another material.
In the infrared spectrum, the difference in the ultraviolet
is invisible.
PHILLIP CHRISTENSEN: Yes.
I guess the point I was trying to make, and I didn't make it
very well was that every material, by definition, has a
unique crystal structure.
It's made up of a unique set of atoms that are bound
together in a unique way.
AUDIENCE: So this is not valid for
characterizing amorphous material?
PHILLIP CHRISTENSEN: You can identify that it's an
amorphous material, but things like glasses, for example,
don't have nearly as complex an infrared spectrum as, say,
a crystalline material.
So any crystal, mineral, plant, man made, whatever--
it's got a crystal structure, it will have a absolutely
unique infrared spectrum.
And if you have enough--
and enough depends on what you're looking for--
but for geologic materials, for biologic materials,
several hundred spectral bands is plenty to be able to
identify those uniquely.
All right.
Thanks very much.