Right, So one of the biggest things we do
every year is have this symposium um to bring together
people on campus and keep interested people off campus to
just see what we're doing with regards to research with
some of the activities of faculty and students. So
I would, I really like to thank everybody for
joining us today, especially our two external speakers who
came all the way from nova to talk to us
today. And really that's all I have to say
, I was just here to welcome you and I'm
going to hand you over to john McGee, one
of the other organizers of this symposium. And he
has a couple of awards, you know, before
we get going, do you want this? And
might All right, can you be my award holder
pressure again? Mhm. Can everybody hear me?
All right. Great to turn this off. So
we have a couple of awards, would like to
hand out to some colleagues here on campus who have
, they've been here for a long time. They've
kind of been the steady Eddie at Virginia tech in
terms of supporting geospatial, uh, service, extending
their knowledge and providing us with lots of opportunities to
extend our work further. I think everybody in this
room, it's probably safe to say has benefited from
these two individuals. Um not only do they do
it almost without acknowledgement, but they do it with
a skip in their step and a smile on their
face. Um these are people who have really helped
to elevate Virginia text geospatial activity. So with that
I like to ask Bruce open house and Ed brooks
to come forward support. Oh, you're staying over
here. Okay, A camera. Thank you.
I also point out for those who have never,
never had the opportunity. That adds one of the
best ping pong players I've ever met. Um so
I'm randy Winn. And um just so you're aware
, for those of you that may not have been
to one of these, We've been at this for
16 years, This is the 16th annual, oh
jeez symposium. And thanks to our organizers this year
, I mean we are just rolling along really in
a, just a fantastic way, and part of
what is really a sea change this year is in
fact, um, this particular location and we've already
, with the awards we just received, we've uh
certainly it's really readily apparent to our broader community,
those of us that have been here a long time
, how important the library is for what we do
. And so we wanted to initiate this year's symposium
by having julie speer start us out, she's associate
Dean for research and informatics with university libraries and she's
just going to give us a few remarks that she
claims are just minutes in duration, but I told
her I'd probably have a intro to be comparable to
her remarks. But um the bottom line is that
she leads efforts to map and integrate the library's resources
, expertise and services to the university's research enterprise.
She's been involved in the development of digital library services
designed to provide preservation, visualization and open access strategies
for institutional and inter institutional digital research scholarship and cultural
heritage collections. Her efforts have been aimed at enabling
faculty and students to engage in open access and become
knowledgeable and successful and current and increasingly more open research
environments. And I just want to say that the
library has been our partner for a very long time
and with the efforts that she's going to tell you
about a little um it's getting better and better all
the time. Well, I'd like to start,
can you all hear me? I'd like to start
by thanking Randy for his introduction. Um And to
thank you all for coming and to welcome you to
the multipurpose room. The library's new multipurpose room,
if you've never been here before. The space was
actually recently renovated over the summer and fall and actually
opened for use in january. Um And it was
designed as a space that could be used by the
Virginia tech community to come together and to identify and
perhaps discover some common interests related to research, teaching
and learning across. It extends beyond traditional disciplinary and
departmental boundaries. Um And so that's exactly what's happening
here today. So we're really happy to see that
. Um I looking at the program and looking at
the posters earlier, I'm excited to see that we
have a lot of exciting research happening related to G
. I. S. Representation from multiple colleges across
the institution. Um And I personally am excited to
learn more about the G. I. S.
Related research is happening at Virginia Tech. Um I
think the programming committee has done a tremendous job and
I know it's made up of individuals across the university
, but I'd like to take just a second um
to recognize the work of the individuals in the library
. Um those that have contributed to the planning and
the implementation of the symposium, if that's okay.
Um So Bruce Open House and Ed brooks obviously thank
you and I'm I'm happy to see that their work
within the G. I. S. Community has
been recognized today. Um I'd also like to thank
Bruce pin sick, um scott frail in max officer
who I don't see here. Um As well as
I have my cheat sheet, so. Mhm.
Um Laurie lee Therese walter scott, Pennington and of
course Liz Mc boy. Um I'd like to thank
you all for coming again and I really hope you
enjoy your time here in the library. I'm excited
to learn more myself. I wasn't involved in the
planning this year for the program but all the folks
that I mentioned were and we thank you all for
coming. Thanks. Well, I'm going to introduce
our first keynote speaker and like dr thomas mentioned,
we're immensely grateful to our two keynotes for their travel
and for their time today I'm going to uh first
start with Dr Richard Bevilacqua um who is just going
to give you a little bit of his background.
He did his Master's and PhD and meteorology at Penn
State. His research interests lie in remote sensing of
the atmosphere. Atmospheric retrieval methodologies, middle atmosphere,
water vapor, stratospheric, polar ozone processes and photo
chemistry and dynamics of the stratosphere. He has more
than 25 years experience in the retrieval of atmospheric constituents
from ground based and space based limb viewing instruments.
He's been the P. I of both the polar
ozone and aerosol measurement two and three satellite based systems
and project scientist for the shuttle based millimeter wave atmospheric
sounder experiment Is the author or co author of more
than 110 refereed journal publications. He's spent his entire
scientific career at the Naval Research Laboratory and is currently
head of the remote sensing division there at the lab
. And we're extremely pleased today to have you speak
with us. Yeah, well thank you for that
kind introduction. I am very happy to be here
and and uh and and I've gratitude that the invitation
was extended to me. One of the reasons I'm
excited about doing this is that I love to talk
about remote sensing because I think this is such an
important time in that in this field. I think
that the explosion of technology in sensors, in satellites
, in computer processing and computer storage uh and many
other things have have led to opportunities in remote sensing
that even 10 years ago we wouldn't have even dreamed
about. Uh and so for that reason, it's
an exciting time and also that I'm really very excited
about about the opportunity to speak to it. Uh
I've entitled my talk with all due apologies to Julia
child. Uh the joy of remote sensing. Uh
because I because I really do feel that way.
One of the reasons is that, and I know
I don't have to say this, this group I
know from our conversation this morning that you all realize
that remote sensing is so interesting because it involves so
many different disciplines and it's never boring. There's always
a lot of interesting things to do and there's always
interesting problems to work on. So I'd like to
hopefully can convey that my excitement for remote sensing by
talking a little bit about just a couple of applications
of remote sensing in in and the Naval Research Laboratory
, uh some real world applications and how remote sensing
is applied. And in all cases my approach would
be to try to take it from sort of the
initial concepts in the back of the envelope, either
figuratively or literally in some cases, all the way
through two programs. And uh so before I do
that, I wanted to start by uh saying a
few words to give context for my talk about the
about the NRL because the NRL is is a great
lab, but it's not very, very well known
. It's kind of a national best kept secret.
So basically, let me just say just a few
words about it, basically, the the laboratory was
established in 1923. Uh so it was the first
oldest and first uh Department of Defense Research laboratory.
Uh It is the Navy's corporate laboratory. And that's
just a fancy way of saying that it is the
Navy's in house laboratory for basic research. That is
needed to spur the technology that it needs for its
for its development. And uh we have a bust
of thomas Edison In the in the front of art
lab because we owe our existence really can be traced
back to an op ed piece that Thomas Edison wrote
in the New York Times in 1916, right after
the sinking of the Lusitania, which which he said
that the nation needed a laboratory to advance, as
he called it. The technology of warfare. So
that really is the development of our laboratory can be
traced back to their. And I drew that pyramid
there because that's really the way the laboratory set up
. It has a very broad basic research program.
Uh, and uh, from that program, some
applied topics have evolved. And then from there transitions
that is transitions from from a very much research oriented
laboratory out to the, as we call it,
the big Navy. Um, so basically in the
basic research world, which is where I live primarily
, we consider ourselves part of the science community,
just like a university, just like in Virginia Tech
. We judge ourselves by the typical science metrics that
any psy institution judges itself, papers, uh,
citations, membership in committees, etcetera. And the
other foot very much in the operational community where we
work with the Navy and transitioning technology. So I
think what has worked with the laboratory very much is
having this having one ft squarely in the basic research
community and the other in in the in the more
applied community. And that that that formula is one
that has been since the beginning of the laboratory and
I think it's worked fairly well. One other point
I want to make is that is that for millennia
, the weather environment in general has been a very
important part of military history. Uh And therefore environmental
research is important part of the NRL mission. And
that's good because I consider myself an NRL scientist.
I do primarily environmental remote sensing. And also it's
good because that is one of the many areas of
laboratory works and where there's good overlap between the research
interests of Navy laboratory and those of the outside scientific
community. And that's the kind of areas I'd like
to work in. So with that background let me
just say a word or two about my division.
The remote sensing division. Basically I have the mission
set there and I won't read that for you.
But I just want to say two or three things
about it. First of all, we are the
remote sensing division but we are by no means the
only organization in the Navy that does remote sensing or
even the only organization in the NRL does remote sensing
. But I think what sets us apart is uh
is the fact that we treat remote sensing as not
only a tool to do science which is most people
do but also is a research area in in itself
, remote sensing is all about the measurement of signatures
and interpretation of those signatures in terms of a phenomenology
. So therefore it has really two basic parts is
both the technology of remote sensing which includes the sensors
, the image processing algorithms, retrieval processing algorithms,
et cetera. And also the other part is the
science of phenomenology that you're trying to sense with the
remote sensing and to work in this field. You
really have to have feed in both of those communities
. And what's important, I think for us is
that we have aspects of both within the same organization
. I'm no Virginia tax within your organization, feels
that that is important as well. So anyway,
that's all I wanted to say about uh about that
. Now. Just go to this eye chart with
this is just a one slide summary of the research
areas that our division is is uh is working in
at the moment and I won't go through these,
I won't even read them. But what I want
to say is that what I've decided to do is
pick two or three of these areas uh to highlight
and talk a bit about again to give an example
of the application of remote sensing to real world problems
. And again, the emphasis is going to be
from concepts all the way through two programs. And
also what hopefully is going to become clear is that
remote sensing is not sort of a linear thing?
You you you evolve something but then more research to
an operational status. But then more research questions come
and you're back in the research mode and sometimes you
go around that many times. So those are the
two points I'm hoping I'll get across by doing this
. So let me first start with the remote sensing
of winds over the ocean. Um you know,
obviously wins over the ocean are are important. First
of all, ocean in general, we don't have
a lot of meteorological observations there. So, remote
sensing of course, is very important there. And
uh when the course is one of the important ones
and clearly its significance for the Navy in terms of
ship routing etcetera, is very important. So that's
been an important Navy issue for a long time as
a way to to measure winds over the ocean.
So this is an example of an application of,
of passive microwave sensing. So, before I get
into the specifics of this and tell that story,
I wanted to just spend a few minutes giving a
few preliminary because some of you are not familiar with
uh with passive microwave sensing. Uh The first point
I want to make is that uh in uh microwave
Radiometer tree, we often make make the definition the
brightness temperature, the brightness temperatures given by that expression
where I is the intensity and and the other terms
of the plank function terms. And the reason why
we we we we write that definition is that in
the Raleigh jeans limit, uh that is where H
new is much less than Katie, where we are
for terrestrial temperatures at microwave frequencies uh that the T
. B reduces to uh the physical temperature of a
medium times the emissivity of the media. So that
makes things so the so the brightness temperature, which
is proportional to the radiant intensity, is just equal
to the temperature of the medium times the emissivity.
So that's an important point. Uh The next one
is polar imagery. Polar symmetry is important, remote
sensing in general, because the state of polarization of
electromagnetic field contains a lot of information about, about
the medium upon which that electromagnetic neck magnetic radiation has
has interacted with. Um So so that's very,
very important. And of course the the polarization state
of uh of electromagnetic field is described completely by the
Stokes vector. I'm not gonna talk in any detail
at all so relaxed about the Stokes vector. Uh
but I just want to say that all that that
is those contained the four components, the two primary
components. And then the correlation of those two components
from all the information about the polar metric state of
radiation. And that's again important concept in in uh
remote sensing. So let me go to wind speed
. Okay, so so again, wind speed has
been important issue for a long time. And in
1970 Jim Hollinger at the Naval Research Laboratory long before
I was was there back in 1970. Long before
I arrived, in fact, before most of you
in this room were even born, he did some
of the pioneering measurements relating the wind speed over the
ocean to the brightness temperature. Again, remember this
temple temperatures think of it as the intensity of the
radiation field in both vertical and horizontal polarization, that
is the two primary polarization. And you can see
two things. First of all, you see that
especially in the horizontal polarization. You see that as
you go up in frequency, there is dependence on
wind speed increases, It's fairly linear and the wind
speed increases and it's much larger for the horizontal and
the vertical polarization. Now that was very consistent with
the theory at the time, I won't go into
to why that is, but that was a very
important results. And these were very much pioneering measurements
that led to some aircraft measurements if we did over
the next five years or so. Which which led
to retrieve the model development of actually how to actually
retrieve this information to the wind speed from those measurements
. And from that actually came the S. S
. M. I. A special sensor, microwave
imager which has flown on all of the D.
M. S. P. The Department of Defense
Meteorological Satellite programs. Uh And it was made the
first global measurement of the of the of the wind
speed addition to several other other things like, say
, ice concentration. So so I think there's a
direct path from from the early tower measurements down to
that satellite. But the story doesn't end there.
What was discovered in about 1990 was that people began
to see that there was a wind speed era in
the S. S. M. My data which
was dependent on the wind direction. Okay. To
to a remote sensing person. What that means that
then is that there is dependence of that signal on
on the wind direction, which is of course is
also very important. Remember before with S. S
. M. I. All we have is to
win speak. So here was the first indication that
there was there's a wind direction dependence as well.
So that led to uh let us to do some
some further airplane measurements this time with a fully polar
metric Radiometer where we measured not only too hard to
to primary polarization, but the cross pole terms to
and this is the signatures we got. We got
basically the way we made these measurements, we flew
out to buoy. There's several buoys off the coast
of the United States. We flew out their bank
, the plane at about a 50 degree angle and
flew around the buoy. So you can get,
you can get the signal as a function of as
if the wind direction we're turning the wind directions not
turning, just the airplane is turning. And we
got those signals in all of those polarization. Now
there's two curves there. You know, if you
can see them. One is the actual data we
got and the other was the model development we have
done. So we have a very beautiful directional signal
there. But there are two issues that we need
to talk about. One is that you notice that
in the two primary polarization is that is the only
ones that get system I had or any other satellite
had. Uh you can see that there is a
nice wind direction dependence, but but it's a totally
even function. That means you have a complete ambiguity
going 45°.. Uh this way of line of sight
or 45° the other way of life that gives you
the same answer. So you never get rid of
that ambiguity. So that wouldn't work just in those
two polarization. But you add the cross pole terms
and you say those two are both odd functions about
the line of sight wind. So then you can
then you can actually retrieve that wind direction. The
other thing is that you remember from the, from
the other plot the wind directional signal was in the
order of 10° Kelvin or so 10 or 12° Kelvin
. Here is only one or two degrees kelvin.
So we need much more sensitive, we need fully
parametric Radiometer and we need a much more sensitive Radiometer
have ever flown before. And that led directly to
our wind sack experiment, which was actually developed the
concept, developed, built and tested at AT NRL
. And and once that was the first fully parametric
microwave Radiometer in space, it was launched to demonstrate
the measurement of the ocean vector winds over the ocean
from passive microwave Radiometer. And I want to talk
a little bit more about that because that's our,
that's our baby. We developed that. So basically
, uh let me just say, the only thing
I really want to say from this is that it
was launched again to demonstrate this capability and it succeeded
very much. Uh, the data now is is
operationally uh developed um, and used in models.
And I'll talk about that in the second here again
. Here. First though, is just the parameters
of the instrument. Other frequencies, it measures at
five different frequencies. It's fully metric at 10:18 and
37 GHz and then just the vertical and horizontal.
The other two polarization. And it's a big thing
, it's 10 ft tall. It's got a six
ft antenna. You need that big antenna because of
the low frequencies. Uh you need to have big
antenna to get the resolution you need. And so
that's what wins that looks like. Uh And um
here are the products that we get from that again
, all developed at NRL is the wind vector,
sea surface temperature, precipitation, water, which is
just the total amount of water vapor in a column
cloud, liquid water and the rain rate. So
those are the operational products. And as I said
before this data is now, it started off as
a research and development program, but now the data
is actually operationally assimilated and not only in the Navy's
weather model, but several weather models around the world
operational weather models and including the uh National Hurricane Center
in Miami. So it really was a, a
big success for us, but, but uh that's
not the end of that story either, because then
we have been looking for other applications. One important
one is soil moisture. We don't really have a
good global soil moisture product. And we've developed one
, I think the first, the first really global
soil moisture product from from from one side. And
you can see there's a, there's a plot of
that that was taken right after Katrina. You see
the signal of the wedding of the soil from from
Katrina. Uh So that was really very important.
However, the resolution is very coarse, it's 50
kilometers, which is not good enough for for assimilation
into weather models. Uh So, so what we're
doing current direction is trying to combine those with a
visible infrared measurement. And the goal is going to
be to get a one kilometer soil moisture product that
would be really unique and that's really needed in the
community. Um and other research issues include uh retrievals
in rain basically when we get into heavy rain,
uh, retrievals don't work. The wind speed retrievals
don't work. But you know, this is for
tropical cyclones. That's an important application here. And
of course it's raining in tropical cyclones. So you
really want to get close to those rain bands as
close as you can and that. So that's something
we're working on, is to get closer to the
to those rain bands. So that's an important area
of research. So, This is the inside of
the microwave story going all the way from from the
original measurements in 1970. All the way to the
current work, that is still part of of of
research. I wanted to change gears now to talk
about another area that's important to us and that is
uh remote sensing of ocean colour. Now, let
me just say one thing before I get started on
that and that, you know, one of the
most, I think remarkable things about nature is the
fact that water is essentially totally opaque to electromagnetic magnetic
radiation. Except for one band, that band is
divisible. Okay, the visible, of course,
is also the band where the solar uh spectrum peaks
and also where there's an atmospheric window. So those
, the juxtaposition of those three things have made uh
the oceans teeming with life instead of being a uh
vast wasteland, they would have been had it been
otherwise. Uh It also means that it makes the
job of a remote sensing pretty easily. Because if
you want to remote sense to remote sensing of the
ocean column, you have one choice. You've got
the visible slash near for advanced. That's the only
place you get penetration into the water. So that's
been known of course, for many, many years
. And the with regard to uh to in the
case one waters which is the first one there,
uh It's quite advanced case one waters are are what
we call open ocean waters. They're the optics is
very, very simple. It's totally controlled by the
by the biology, by photo plankton. And um
you can't see the bottom. And and our multi
sensor satellites, they've been operating for decades. Get
all the information that we need. Did I just
did I do that such a technical problem after all
, you just brush this and switched it. I
knew it was something I did. Of course it
was. Let's just blame the most distinguished person.
Um So let's uh let's proceed with ocean colour and
our and our apology. They don't usually let me
get this close to electronics. That's the problem.
So anyway, I was I was, what I
was saying was that in the case of the open
ocean waters, the optics is quite simple and that's
a pretty tractable and really solve problems. However,
in the coastal region, it's quite different. First
of all, you can see the bottom and the
bottom has variants, reflective reflectivity is depending on what
the bottom is made of. Also you have outflow
from from from the land regions which all these make
for a very complicated uh, optical signatures, which
is impossible to be involved with just a few bands
. So it's been learned for a long time.
It's been known that in order to really do something
in this area to really do remote sensing in in
the water column in the coastal region, you have
to go not to multispectral system, not just a
few bands, but to a hyper spectral system.
And over the last 20 years or so, the
Navy has pretty much made a transition from the blue
water of the open ocean to the to the white
waters of the coast. So the expert, our
interest in this field essentially for coastal remote sensing before
I get too far in that, I wanted to
say a word about hyper spectral sensing. For those
of you. I think most of you are familiar
with the term. But let me just, what
I just want to say is that uh I I
find that a unnecessarily hyper uh pretentious word for a
concept. It's really quite simple basically. What you
have is it's an image just what you would have
an image that you take with your digital uh camera
except that rather than three color bands and what you
which is what you have with your camera. You
have a complete continuous spectrum at each spectral pictures.
So that's the continuing, that's sort of the the
distinctive feature of hyper spectral and there's many ways to
implement it. The way we have typically implemented is
with very simple sensor concept consisting of three major components
telescope or some other four optic. Uh Some sort
of a spectrometer. Typically we use a grading to
disperse the light and then two D detectors, some
sort of a camera. And basically the way we
oriented most of our program has been airborne. Uh
So if we're flying in this direction we get the
cross the cross track. Especially uh that's one dimension
of the C. C. D. Or the
two detector and then the latest dispersed in this direction
parallel to the to the velocity vector. And then
you build up the other uh spatial dimension by by
the motion of the airplane. So that's how you
get your two D. Images that that's shown there
. So basically what I want to say is that
the image you would get, that's just three bands
. Just just an RgB RgB representation of it because
you can't really represent all those colors you have and
then a spectrum you get one of those for every
single pixel and that's the distinguishing feature of of that
. Okay, so so now let me let me
go on. We've had set a long long standing
program in that and it really is something that I
think is uh soup to nuts. Which is what
it has to be in terms of uh sensor development
. We have calibration labs, we do a lot
of flight work. We have to do ground truth
because you have to know what you're measuring is is
uh how how good the product is. And also
product extraction, the algorithms and having all those people
within the same organization has really been very good because
the people who do the algorithms and exploitation work and
turn to the to their colleagues and say, hey
, we would do a lot better if we had
a little bit more scattered light rejection in the blue
and then we can go work. So that's the
way we've worked. And we've had this program for
about 15 years and out of that has actually come
uh our our newest instrument which is the hyper spectral
imager for the coastal ocean. And it's shown there
that is sitting that was launched in 2009 and sitting
on the ISS. On the Japanese experiment module of
the ISS. and it's been there 2009 is still
operating. And here is the image at the bottom
one of our first images. The images are typically
40 to 590 kilometers. And that's a picture of
the bosphorous strait with the Istanbul the bottom of the
picture. And it was uh there's not a particular
reason I put it in there, except it's a
beautiful picture. So uh and that's one of our
first, so that's why it's there. Um so
we did quite a quite a lot of investment in
retrieval of data from these hyper spectral instruments. And
what we involved too is something very simple called our
coastal water spectral toolkit, which is basically a look
up table where we use our radio transfer model and
we calculate millions of spectra where we calculate where we
, where we vary the environmental parameters. And then
we do a comparison of the measured spectrum with those
with the spectrum. And then uh we use those
to find the constituents which are closest match. And
we say, oh that's the retrieval results out of
there. Now, the one thing that I'm kind
of going to kind of loss under the rug because
it's a It's actually very big areas. The atmospheric
correction, you're measuring the radiance is at space,
but you really want the radiance, the upwelling radiance
from from the ocean surface. That turns out the
ocean services really dark. About 90 of the photons
that you that you measure from space actually didn't come
from the ocean at all, but came from the
back scattering from the atmosphere. So in order to
do remote sensing the ocean column, you have to
subtract that out. And that's been a long standing
field current field of work. So, so that's
part of this, this tool kit as well as
our is our status of that. But that's something
we're still working on anyway. So that's the way
that that that process has worked. And here is
an example of that. This is uh an image
taken over release docking island Bahamas. We picked that
because that's a test site and we know a lot
about the, about the water property. Is there
one thing that's very difficult in this field is that
there is, it's very difficult to get ground truth
. The stocking island is one place where you can
get that. So that's what we use this again
. This is just the typical RGB image. You
can see immediately from the image that the water on
the right side of the island is deep and it's
dark, how dark it is uh compared to the
other side where it's shallow and you're seeing the bottom
. So we applied our retrieval results uh product to
that. And here we get the simultaneous retrieval of
of these products, the chlorophyll, the seed,
um which is the color, dissolved organic material,
the depth, that is the symmetry and also the
horizontal visibility. And again, again, these we
can do this for each, Each pixel on on
that on that spectrum. And it's got 512 pixels
by 512 pixels. So this is an example of
the work we're doing in that area. So let
me go on to to one last topic. And
that is uh the remote sensing of traffic ability.
For first, let me define traffic ability. Uh
Traffic ability is basically the ability uh to move people
or equipment through a through a region, um and
especially the ability of the substrate to to support the
those equipment. It turns out this has been a
long standing problem, uh for the, for the
Navy, uh because, you know, it's it's
first of all, it's very important for you always
have to move material and and people from one place
to another in the military and not knowing the substrate
is extremely important problem there and currently or before our
work, really, the way this information was routinely
obtained was sending combat engineer sometimes into harm's way to
make the necessary mechanical measurements of the soil strength.
That then could be folded into geotechnical measurements, which
could translate those mechanical measurements into traffic. The ability
of, uh, to support traffic, truck,
traffic, traffic or whatever. So that's typically the
way that's been done, but it's slow. Uh
and it's expensive and it really involves putting putting people
in sometimes into bad places. So, what we're
trying to look for is a is a remote sensing
solution uh to to the problem. So that's what
we have been working on. And the reason why
we thought we could perhaps do this was just sort
of typified by by this example here, that is
that is something that we all know. This example
of sand in the, in the part, in
the in the middle of that square is wet and
when SAm gets wet, it gets darker. So
there is definitely a signature and of course, sort
of moisture is a big part of soil strength.
There's really three things to soil strength, its moisture
, its composition, and it's the grain size.
So you can see that there's some clearly some information
which could be picked up by a uh, by
a radiant sensor for for this problem. And here
again, is the two uh spectrum one for the
for the undisturbed sand. One for the disturbed sand
. We just uh disturbed sandwiches shoveled. You can
see in the visible, you know, it's darker
throughout. And also because we've built up some different
minerals, you can see they're different spectral signatures as
well. So there this made some hope that there
would be a way to make these traffic ability measurements
using um using remote sensing assets. So what we
did is we decided for a very very brute force
approach to this. We went out and several coast
and we'll look at those in a second. And
we first made and distinguished on the left is all
the geotechnical measurements, the strength measurements. And also
we collected soil samples. Okay. And and on
the right side for each place where we did this
measurement. We tacked that measurement with the GPS location
. And we also made a radiance measurement at that
position. So we have the spectral information plus we
have all the soil strength information. Now we could
have gone to ways with this the hard way would
have been tried to retrieve soil moisture composition and density
which are the three things that go into the soil
strength from the radiance measurements. Uh We decided to
take an easier approach at least to get started here
. That is go directly from from the radiance measurements
to soil strength. So that's what we tried to
correlate. So we made measurements like this and we
did this correlation between the the strength measurements on the
left side and the spectral measurements on the right side
. And we did this. What a bunch of
different types of coasts are. Home area sort of
is the Virginia Coastal Reserve which is on the Delmarva
Peninsula. One of the the island actually owned by
the Nature Conservancy. That's where we make most of
our measurement. That's where we started. We also
made measurements in hawaii uh in Australia and also in
in Guam. So we made the measurements in these
places and our retrieval technique was really very very brute
. For sometimes you just got to do that.
Sometimes it's not always rocket science. So basically we
made these in situ mechanical measurements and these ratings measurements
. And we generated they look up table to relate
the two of them. And what we saw is
very nice correlation between those. So then we'd fly
over the same area. Getting that same spectral information
from an airplane where we can survey the big area
. And we used look up table to relate the
spectra that we've made that we measured with the strength
parameters. And that's how we were able to get
information about, about the traffic ability in that area
. And as simple as that sounds, that has
worked extremely well for us. And uh we uh
most uh most recently we worked with the Australians uh
in uh in in one of the major uh maneuver
area there between Australian and american forces in called Talisman
Saber Expedition. And what we did is we made
a series of measurements there. And what they wanted
was simple, there was a bearing strength which we
which we estimated from those from those remote sensing measurements
. But they weren't interested in something that technical.
They wanted a simple stoplight a go no go,
where could they go, where they could not go
. And that's the picture on on the right.
So we did that in both uh 1000 in 2012
, And also in 2013. And again, that's
worked extremely well for us, but that's just the
beginning of of where we are with this project.
Um Oh, I should also say that we're also
gonna make a measurement in the same way in in
in Korea and we'll do that in in May of
this year. But there's several more research areas that
we're gonna go into. First of all, we
noticed that remember, density is one of the important
things with soil strength. And we noticed that if
you uh that there is a strong dependence of uh
of the radiance that you receive with respect to uh
the view angle and that dependence is different for low
density stand and high density standard. Well, what's
more important is that, so there's definitely information there
. However, uh, the state of the art
in forward modeling in this area is extremely crude and
uh we don't really have a lot of information,
but what information we have says exactly the opposite.
It says, it says it should be the high
density should go, should go up, and the
low density should should go down. And we don't
really understand that. And we really want to do
that because because we really want to develop a ford
physically based forward model because we want to take it
beyond this simple look up table approach that we've had
because in order to do that, we do these
look up tables but we have to generate a different
one for each coastal type. We make the measurements
in uh what we'd like to do is if we
can actually do physically based modeling, where the retrieval
is based not on this look up table, but
from the physics, from the forward model, we
can make the algorithm much more general. So that's
our current current area of research and we've got a
lot of work to do because it said we can't
we can't reproduce that signature that we've we've we've received
there. So that's one area. Um The other
problem that we have to address with our with our
sensing here is the fact that uh Vienna visible slash
near infrared uh sensing uh is only sensitive to surface
. This does not penetrate the soil, so it's
only sensitive to surface parameters. So what it doesn't
give us is it doesn't give us any information about
a situation where you may have seen, where you
may have a crusted layer on top of the top
of of muck and they can just go right through
that and the soil is actually very, very,
very porous and not very strong at all. We
would never know that just fine from a vendor measurement
. So in order to do that, we have
to begin to incorporate other measurements which begin to sense
uh something about the subsurface soil properties. And basically
uh we've we've elected to use thermal which which the
thermal signature, you know, that we can measure
with the infrared in the Midway. And the long
wave IR turns out the thermal signature is very dependent
upon the soil moisture. So that's important. Also
, radar can penetrate, depending on the wetness of
the soil and the frequency of the radar, we
can get penetration. So what we need to do
is, so the other area we're working in is
to add on these these other modalities into into the
problem. So we can actually get a situation solution
that's more complete. Again, we've got a product
that is already valuable, but I think that there
are also very, very challenging areas ahead. And
this will be I think a research area for us
for for for for a very long time. So
if you would uh I'm going to wrap up here
and if you could permit me to wax philosophically for
just a minute or two, uh this picture that
I picked here, this is uh one of the
pictures that the Apollo Apollo astronauts took of the moon
. Uh and I like it. And what it
shows me is that the world is uh indeed it's
a very beautiful place. However, it is under
tremendous stress at the moment in ways that we don't
yet completely understand. And I think that um I
don't think it's going out on a limb to say
that trying to understand uh this dress uh is going
to play be an important part of national and international
uh research programs for for for many years to come
. And of course remote sensing is definitely have to
be part of that. And uh here are the
the areas which I've just in terms of environmental remote
sensing that I see are the things that uh that
we really need to do better than we can do
now. Some of these we already talked about this
morning, but uh soil moisture, we have not
uh we don't have a good solution yet for soil
moisture, um sea ice thickness, we really need
that. We have good information about the total concentration
of sea ice, but not the three dimensional structure
, not the thickness. And there's evidence that that
the sea ice is not only the extent of it
, the horizontal extent is beginning to change is changing
, but also the thickness of the ice is changing
. We don't have good measurements of that. And
and part of the problem is the fact that we
don't understand snow debt. So we cannot measure snow
depth on on on ice very well. So that's
another big area. Uh and the other one is
uh radio forcing by aerosols. We still don't really
understand that. That is the biggest uncertainty in terms
of all in all of the climate change area.
It's the RsL peace and we need to make some
more measurements in order to understand that. And then
um exchange between the patrol officer and lower stratosphere is
shown recently to be a very important part of climate
change. And the other part is other piece here
. That's important is that we don't have a good
characterization of the impact of climate change on the productivity
of the coastal ocean, the coastal ocean that that's
of course where the where the productivity of the ocean
is dominant. And we know that there's increasing acidification
of the ocean. And we know that adversely affecting
coral reefs, coral reefs are sort of uh,
just a hotbed of productivity. And but we don't
know exactly how and that's got to be important.
And so there's just a host of problems. And
, and so so that all these problems are really
gonna require, you know, the new generation of
, of bright young remote sensing scientists to to to
help us with. So that's why I'm happy to
be here. So thank you very much for my
yeah. With our A. V. Issues,
we ran a little bit late, but we have
time for a question or two if there's, are
you able to comment on if they army or if
they navy post to extend some to work with seconds
environments? Absolutely. It is important there. It
is definitely important there. We know we've been in
touch with our wind set product is very much a
research product, but they already used that in certain
areas. So yes, I think it's important.
We've concentrated on the coastal area, but that's by
no means the only thing. Only place where that's
important by no means. So you're absolutely right.
Yes. I'm intrigued by the prospect role imagery for
symmetry. How does that compare to them? The
light are the green light? Our methods yeah.
That that of course is is a very important area
. Obviously you're going to do better with with the
light are, but you can't, you can't measure
a bigger big enough area with the light are what
we found from from the few places where we've had
coincident measurements we found that uh were something like plus
or minus a meter or something like that. So
we still have some work to do uh and that's
only just a few areas. So we don't know
if that's a complete characterization because we haven't done enough
places. But that is a very good question.
And that's a current area of research because we really
want to uh what we'd like to use the lighter
actually trained algorithms to do a bit better because rather
than with the light, are you just get below
the plane? Yeah. You get a much more
horizontal swath, bigger horizontal swath with passive imaging.
So we'd like to do just that is to try
to understand that better. Okay, well, thank
you very much. Once again, I really appreciate
it. Yeah.