USGS Public Lecture Series: Science Through Imagery

Welcome to USGS, and welcome to our first in a series of public lectures on science in in action.

Um and thank you for coming out tonight in uh in kind of rainy weather or staying on if you're employees here.

I'm just kind of curious, how many people here are USGS employees?

That's quite quite with you, so make sure you tell your friends and neighbors for our future series.

My name, for those of you I don't know, my name's Bill Werheiser, the eastern region director, and uh we're very proud to have John Jones here as our as our first speaker in this series.

Um the idea of these series is, you know, you can imagine we're pretty proud of of the work we do, and we'd like to share that work with the community at large and for folks who may not be know instantly what what John does other USGS employees.

So uh John is here.

He's a research geographer here in Reston, although this week he was out in West Virginia working really hectically, so thanks to John for coming back in on on uh this rainy night, and he'll be going back out tonight, I guess, to West Virginia.

So um John is an expert in satellite and air aircraft um imagery and those applications.

And I think he's got a real exciting talk tonight, and I see some show-and-tell things, so uh I think you're you're going to have a a good time and enjoy the talk.

So without further ado, I'll pass on to John.

Thank you, Bill.

Okay.

Swap this slide out.

Well, actually, let me see first.

Um I earned my doctorate 7 years 3 and 1/2 months ago.

And while I wouldn't expect anybody, anybody to call me Dr. Jones, I have to point out that still people do

That's

That's

That's

That's

That's

So I just wanted to leave this slide up here a little bit longer.

But taking that one down,

I'll bring this up.

Now, the title of my talk this evening is Knee-High to Bird's Eye: Multiscale Remote Sensing of Vegetation Dynamics, which is rather a mouthful.

Um if I do a really good job in the next hour, this will only make sense.

If I don't, at a minimum, what I am hoping I can do is give you a deeper appreciation for the information that's in remote sensing imagery.

And give you just a little slice of the work we do here at the Survey in using that technology.

That's sort of my guarantee.

So you can let me know how I do in this regard.

My teammates and I have a variety of projects in what the USGS considers the eastern United States, which is everything east of the Mississippi.

All of these projects have a few things in common.

They all use field-based and aircraft-based remote sensing technology for the purpose of seeing how the vegetation is changing across space or through time,

why that vegetation is changing, and then what impact those changes have on either water supply or habitat.

So what we're trying to get at ultimately is information that resource managers can use to improve the health of the environment or to conserve our resources.

I'm not going to go through all of these in detail tonight, because it's hard to do that in 45-50 minutes.

Instead, what I'm going to do is focus on a couple of these projects and give you a better idea of what I'm talking about.

So I'll start with a really basic introduction to remote sensing.

I'll focus on two projects in this list and then leave some time for discussion.

So what's remote sensing?

In the broadest sense, remote sensing is the collection of information about something without actually touching it.

That's the most basic definition I can give you of remote sensing.

So everybody in this room right now is using remote sensing system.

Light is coming out of that projector, it's hitting this screen, various different colors of light in certain patterns, they're reflecting off of this screen, going through the lens of your eye, hitting your retina, stimulating your rods and cones,

traveling through your optic nerve to your brain, your brain's putting those pieces of information together to formulate a picture and understand what's on the screen.

Remote sensing system.

I intend to use satellite-based systems, for example, where the source of light is the sun.

That light is being transmitted through the atmosphere, most of it, or some of it, I should say.

It's hitting targets on the ground, it's either being absorbed by those targets and then sent out as temperature or different wavelength, or it's being reflected back to a remote sensing satellite.

Sensors on it detect the different wavelengths of light in different patterns.

That's sent down to a computer here on the ground, and then we analyze that computer information.

We got to look visually at that data, or we can crunch through the numbers that the satellite's collecting to get information.

I get grease on my graphs, which you're going to get some graphs here today, okay?

This is a depiction of the variety of light energy

that's possible.

What we're seeing down here is tiny portions, see everything from gamma rays to X-rays to ultraviolet light.

This is why you're wearing sunscreen, right?

So you don't get a sunburn.

Into the visible wavelengths, infrared light, all the way up to microwave, radar, television, radio waves, and so forth.

So there are remote sensing instruments that are designed to measure various types of light energy or various ranges of light energy.

Notice what we can see is just a tiny sliver.

So one of the points I'm going to make to you tonight is that remote sensing extends our ability to see these different wavelengths of light and gather more information.

It's curious that this area of light is the type of light that the sun puts out the most of.

So we've sort of evolved to be sensitive to the maximum wavelength region from the sun.

So the instruments I'm going to talk to uh tonight are primarily in the visible and up into the infrared portions.

Here in the Eastern Geographic Science Center, we have instruments that measure other wavelengths.

Um and I'll talk a little bit more about some of those, but not all.

I do want to make it clear I'm not talking about all the remote sensing that the USGS, I'm focusing on a portion of the remote sensing that I do within my own project work.

Okay.

Sorry.

This picture is about 10 years old.

This is me when I had more hair and more of the hair I had was not white.

Um this instrument is ground it's is a ground-based remote sensing instrument.

Uh it's it's like an old aircraft carrier.

We keep refitting it, rearming it, improving it through time, and using it over and over.

This is actually the same instrument here.

The internals are all the same.

What's inside this box down here is still inside there, but we made I think some improvements on how you carry it, for example.

Now, why do I have a 10-year-old picture?

Because it's really hard to get people to go out with me in the places I take this instrument and take my picture.

So I'm stuck with this one from 10 years ago when somebody could stand on the side of the road and go click.

More often, what happens is

I get dropped off by a helicopter.

And on the day I took this picture, I had my instrument in a watertight cooler.

I landed in a helicopter.

I put the cooler on the pontoon.

I stepped onto the pontoon, and I looked at out, and all my co-workers were shinnied up in water.

I stepped off of the pontoon and caught it before my armpits went under.

I slipped.

We had landed over a nice solution hole.

The helicopter pilot he thought it was in a hysterical, didn't really realize that I had to get back in the helicopter to go back to our launch site.

So this instrument, believe it or not, can't get wet.

And when I called the folks who designed it and I said, "Hey, I've taken this down to the airport."

They said, "What?

You know this can't get wet, right?"

Which I didn't know.

I don't talk to them about this so often anymore.

But I have this mantra of protect the instrument is constantly in my head.

So the reference here is to the fact that they would stand there and watch me get nibbled on by things I couldn't see underneath the water, and while I'm balancing trying to make this instrument stay still, protect the instrument.

These are the kind of friendships you build when you work out.

So what kind of data does this instrument collect?

In this graph, what we're seeing on this line are the wavelengths of light.

So here's the blue, here's the green, here's the red, here's the infrared, and we go further along through the infrared.

On this axis, we're seeing reflectance, how much light's being reflected as a function of that type of light.

Here we're seeing data collected by this instrument for three different surfaces.

This blue one is water, and and more truthfully, it's it's a puddle on the airport at Griffin Airport southeast of Atlanta.

Um they're very nice down there in Atlanta.

They let you walk out on there there, they don't run you over to take the measurements.

This is some grass like you'd see in your yard.

Here's some soil.

Okay?

So we can get very detailed information about how much light's being reflected at very specific wavelengths using this instrument.

Now the satellite can't quite get as much information.

It's too expensive to put something like this up into space.

But I can use this

to see what sort of information the satellite can give me.

And I'm going to use it to show you what sort of information the satellite can give me.

So the satellite's going overhead, and for areas on the ground, it's collecting reflectance as a function of the type of light and it's storing that.

So I end up with an image that shows how much light is reflecting at each point on the ground.

I can tell the computer to take the blue light that the satellite has sensed and display it as blue on the computer.

Take the green light the satellite has sensed and display that as green light on the computer.

Take the red light the satellite has sensed and display that as red on the computer.

And this is what I get.

So this is a satellite image from the Landsat satellite systems, taken from about 400 km in space.

And this is the Blue Ridge running here.

This is Route 50 coming eastward toward the District of Columbia.

This is the Shenandoah River.

So you can see where the bridge is crossing the river.

Um let's say right in about here is Paris, Virginia.

Middleburg would be somewhere around here.

Front Royal would be somewhere down here.

So this looks a lot like the image.

How many of you use Google Earth?

Yeah, this is a lot like the sort of imagery you can see in Google Earth, yes?

And you know, okay, green is vegetation.

You know, this is probably trees because it's so green and mottled-looking and so forth, right?

This is also what you sort of see looking out of an airplane window.

You know, some of those people were the sort of men I met after the flight, because I've spent the whole flight staring out the window, right?

But I said to you already, these satellites can measure light we can't see.

So instead of just showing a true-color composite,

let's still keep the blue light displayed as blue so the water looks kind of blue, but let's take the green light and uh the green display on the computer and use it to show the red light.

Sorry.

And now let's get a little fancier and take the infrared light we can't see and display it as red.

What happens?

Sorry.

Sorry.

Sorry.

Just keep me awake.

The vegetation is red.

What is up with that?

Well,

not only is it red, but it's showing all sorts of variation that we had a much harder time seeing in that true color picture.

There's a couple of reasons for that, and some of them I'll get to in a minute.

We can see a lot more difference in the agricultural fields that are out here

because of this piece piece of information.

In that infrared band, there are changes in vegetation that occur as a function of the health of the vegetation and the amount of vegetation that we can't see with our eyes, but the satellite can see.

So we can see stress, for example, in vegetation with the satellite remote sensing then we would before we would when we're standing on the ground.

Let's go a little further.

Let's move the blue up to the red.

Let's use the infrared as green.

So now what color is our vegetation going to appear?

It's a lot of vegetation.

Green.

Thank goodness, right?

You don't want me screaming at you again.

Now let's get really fancy.

Let's take the red and move it down here to a portion of the uh light spectrum where the reflectance is affected by the amount of water in the leaves or the amount of water in the surface of the soil.

And this is what it looks like.

So now, sorry, you guys are seeing, right?

So now I can still see a lot of that variation in the vegetation, but I'm also picking up even more variation in those agricultural fields, how much moisture's in the vegetation or the soil.

I can really see the river out here much more easily.

And see Route 50. Here's Route 17. Let me down here.

So, you can see quite a difference, right?

So, we've really leveraged the information that we can gain about the Earth's surface by using these other portions of the spectrum.

Uh now, the reason why we're looking at this satellite image is because Sky Meadows State Park, West Virginia, is right in here.

This is actually the farmhouse right about here.

And Blue Ridge Middle School out in Loudoun County is participating in a National Park Service program called Trails in Every Classroom, Trails to the Classroom.

And so, this very day they went for a hike.

They drove down the road here and up to the farmhouse.

They walked down this gravel road and walked all the way up to the Appalachian Trail.

A bunch of brave teachers and 120 kids did this, sixth graders.

And I went out last Friday.

How did that go, by the way?

Good.

I went out last Friday to talk to them about how the AT appears from space and how where they were going to be walking is in this greater area that affects what they're going to see.

And the night before that presentation, I went to this website at home and I downloaded all of this imagery.

So, since the start of January, all of the imagery that's been collected by this satellite and another satellite and one of its predecessors from 1973 on to present around the globe is now available for no fee to anybody with an internet connection.

In 1986, I was a graduate student at the University of Maryland and I had to win a $5,000 grant to get one satellite image for my thesis.

And now, this is one of those boy, I had it so bad when I was a kid, you know.

No, my point is we're really excited because now any graduate student anywhere in the world with a connection to the internet can download more data than they ever want to process at no additional fee.

There's taxes involved, tax money, and people's effort, but you don't have to pay to download it.

So, we're very, very excited about that, and I wanted to call your attention to that.

So,

so far we've been relying on our vision to interpret this imagery, taking light that we can't see with our eyes, putting it into light we can see with our eyes effectively, and allowing us to interpret what we're seeing on the screen.

But to the computer, these are all just numbers.

So, let's talk a little bit about what we can do analyzing these numbers to gain even more information.

So, we're back to a graph similar to the one I showed earlier.

Here's wavelength, we're just not going as far out uh in the spectrum with this particular image.

Here's reflectance still.

Here's our vegetation curve.

That's showing how the vegetation reflects.

So, what type of light do you think is around here?

Green.

Only guys have to keep saying green, and we'll be good to go.

Green light right here.

So, vegetation appears green to us because it's reflecting the most when it's healthy in the green portion of light.

These boxes that you're seeing here are actually uh the wavelengths or the types of light that a Department of Commerce satellite senses.

That satellite was put up actually to map ocean temperature for climate modeling and monitoring and things of that sort.

But they also in their wisdom put two uh sensors on it that collect information in the red and the infrared.

And folks who worked with the satellite data that I just showed you thought, aha, we can capitalize on this system and get information about the vegetation.

The reason why it's attractive is because this particular instrument makes a measurement every day.

Now, it makes measurements over larger areas, which is good.

But each individual measurement also covers a larger area.

So, there's trade-offs that we're always going through in remote sensing.

How many pieces of light do we get?

How small an area do we resolve so we can pick out individual houses or maybe just a football field?

How often do we collect those data?

We have to balance all these things because of cost primarily.

And so, the reason why I use all these different instruments in my projects is because I'm trying to gain the best information from each one as I pull those pieces of information together.

So, this portion sorry this portion of the spectrum here uh this type of light, red light, how much reflectance occurs here is dependent upon how much the vegetation's growing essentially.

You know, if you think back to your biology in school and you talk about photosynthesis and you talk about chlorophyll, this is a chlorophyll absorption This is a chlorophyll absorption feature.

All I'm saying here is that if there's a lot of healthy vegetation, less and less light will be reflected back to the satellite here because the plants are using it.

This portion out here is affected by the number of layers of leaves.

The more leaves there are there, the more light gets sent back to the satellite.

Okay?

So, here with this soil, it's not absorbing has no chlorophyll or very low, it's not absorbing much light.

It doesn't have many layers of leaves on it.

It's not reflecting a lot of light.

But this place has vegetation with layers of leaves.

So, if I look at the difference between this and this, that gives me some idea of how much vegetation is in that spot on the ground.

And we can use the computer to take every measurement that satellite makes and calculate numbers that we can relate to the amount of vegetation that's on the ground.

So, so in this particular equation, if I end up with a one, it means I've got lots of healthy vegetation.

And if I end up with something like a zero or below zero, it means I don't have vegetation or anything like bare soil and water.

Here are some maps from that instrument.

You can see the entire United States in each in in each image.

And the colors that are being shown here are ranging from zero all basically or water, all the way on up to about 0.65. And the darker green something gets, the more vegetation is there, the healthier the vegetation is.

So, if we look up in this corner, you can see that in April of 1995, the southeast and sort of central uh Mississippi Valley are the only places where the weather's been warm enough for the vegetation to be growing in any great amount.

If we go into May, however, we can see that the leaves and everything are out and it's just cranking away here in Virginia.

All right?

Some parts of New England, not so much, not yet.

We go into June and really the whole east and up into the cen- center of the country, the Pacific Northwest, all these areas have some vegetation growing or agriculture taking place.

Now, watch what happens when we start getting deeper into the summer.

See down here in the coastal plain in Florida, we're seeing the variations in that green that are a function of rainfall.

So, now suddenly we have some idea on how well the vegetation is doing as we get variations in temperature and in rainfall through the year.

Scientists in the survey use this sort of data to try and monitor for drought.

And maybe agencies that calculate how much food will be available for international aid aware of the problems as they're occurring or before they occur.

But I use these data for something a little different.

Different type of graph.

In this case, down here we're seeing time of year.

So, here's January and we're moving through the year all the way to December.

Here is that number between one and zero for vegetation health, vigor, and growth, right?

So, in the winter we bounce along here, this is a dot every two weeks, and we bounce along hovering around zero as things are too cold for the leaves to start to grow.

But once things get warm enough, boom, this number starts to rise.

And in the absence of limiting water, it'll go all the way up to some peak.

Just keep watering it, watering it, giving it lots of sunshine, it'll get to this level of production and growth that it just stays at and cranks along in until the sun starts to set a little earlier in the day,

right?

You get a little less light energy or it has a little less water, and then boom, the leaves will start to change and fall off the trees until it's bare again.

So, we've gone now from, oh look, we can see how healthy the vegetation is from one place to another, to look, we can see how healthy the vegetation is through time from one place to another.

So, what would I use this for?

Well, we have a project in the Shenandoah National Park.

And the primary scientific question we're asking at this point in that project is can we see evidence of climate change in Shenandoah National Park?

Our objectives are to track those vegetation changes over large areas for many years.

We want to explore why that change is occurring, correlate that with weather, correlate it with other factors that would affect vegetation.

And then, this is where I was really interested in getting involved in this project actually, we want to relate those changes in vegetation to water flow and habitat conditions for all the streams that get their water from the Shenandoah National Park or the Blue Ridge.

So, here's a satellite image, that Landsat satellite image again.

Actually, uh this is similar to the one that I showed you earlier.

We were looking right about up here in that tiny little spot.

This is Massanutten Mountain.

I impress all my friends because I look at a satellite image and go, oh, I know Blue Ridge is right there.

And I'm just looking for Massanutten Mountain.

I know I know.

It's right there.

Okay?

So, there's the outline of the park and Skyline Drive.

Here's the familiar graph.

All right?

I said to you, we can we can make estimates of when the leaves come out, how quickly things green up, what the maximum amount of greenness is, how quickly things change color and the leaves drop off.

This is called, by the way, phenology.

Phenology is the study of the timing of biological events as they relate to climate.

So, when birds migrate, you study that in relation to climate, that's phenology.

Okay?

In my case, it's when the leaves come out and so forth that I'm interested in.

Now, let's just take one variable.

If we total all the area under this curve, we get some idea of the overall growth and productiv- productivity of a spot on the ground in the course of a year.

All right?

What if we total that up for every spot on the ground for every year for which we have a measurement?

And then we look to see whether that is increasing or decreasing through time.

And this is what we get.

So, this map right here is Massanutten Mountain.

There's Loudoun County, Virginia.

Uh and of course, the state boundaries, right?

Just keep that in mind.

Everywhere you see green here, we're seeing an increase in the total amount of vegetation across the course of the year in general.

Like an increasing trend through time.

Everywhere you see these reds and yellows, like down here in the coastal plain, we're seeing a decrease through time.

So, some scientists use this to monitor the health of rangelands, for example.

We have folks in the USGS who do that.

But as I said, I'm really interested in what impacts these changes have on the streams that flow out of here.

So, you know, we know vegetation needs water to grow.

If it has warmer temperatures and more carbon dioxide and it's growing more and the water doesn't change, then that is really important if you're a fish in the stream in Shenandoah.

Because it means less water's coming to you.

So, the idea then is to look for these changes and try and relate them to their causes and their impacts.

Now, what could cause changes here besides changes in climate?

Any guesses?

You needn't phrase your answer in the form of a question.

Is it people?

People?

Yeah, how would people do that?

Well, cultivating the land, building houses in different places, development.

Absolutely.

And that's one of the reasons why the answer was people.

Which is that could you hear her?

Cultivating the land, building houses, absolutely.

Uh that's why we're working in the national park.

That's the real benefit of a national park because nobody's cutting the trees down or building houses in the national park.

But other things are defoliating those trees.

Gypsy moths, right?

You've seen evidence of that you hike on the Appalachian Trail.

Or other pests like woolly adelgid or fire, for example.

So, it's entirely possible there could have been a fire here.

And we're just seeing this increase, you know, the first few years it was recovering from the fire and now it's really come back.

So, we have to go through a lot of effort pulling in other information to try and tease out the places that are changing maybe because of climate as opposed to some other cause.

So, to help with that, we've also put out a series of weather stations throughout the park.

And we use a very scientific approach to decide where we put them.

We put them 200 m or more away from any sensitive features or the the uh Appalachian Trail.

We camouflage them so they're hard to see.

And they look something like this.

Now, this is not an official USGS photo.

We do not recommend that you do this at home.

I wasn't there for this particular work, and I have a feeling Sharon is standing here saying, boy, is John going to be really mad when he found out we're climbing on his weather station.

But the fact of the matter is, I've lugged a 10-ft ladder out to some of these sites and I climbed on the weather station.

And the reason we have to climb on the weather station, well, I'll get to that in I'll get to that in a minute.

We have all sorts of things we need to deal with in the park.

So, we have instruments on here that measure, for example, the amount of light that's made it through the canopy.

The amount of light that reaches the ground and is coming back up.

So, here's another remote sensing instrument, right?

We measure things like the temperature and the humidity, wind direction.

We have things planted in the ground so we know what the temperature of the soil is because it's really the soil temperature that plants really care about.

When the soil gets warm enough, they start moving fluid through their their roots and up into the tree and they start to grow.

So, I said there are a lot of challenges in this.

One of them is animals.

Actually, one of them is a particular animal.

is a bear that's sat in a tree eating some apples at Sydney Cunningham, sort of the backbone of keeping these systems up, was about to repair weather station that some bear had decided to climb on,

play on, and eat.

It was repaired for two or three days before some bear decided to climb on it, chew on it, and play.

So, we have a real problem trying to keep these instruments up and running in the park.

What are some other challenges?

People.

Um so, if you see a system like this out in the woods, please please don't mess with it, okay?

Don't use it for geocaching.

Thank you.

This is actually Jasper playing with another instrument we use in the field.

This is a camera with a fish eye lens and what we actually do is point it up at the sky and take a picture when there is no leaves on the trees.

Then we go back out later, point in the sky in the same way and take a picture when there are leaves on the trees.

And we use that to determine how many leaves were there at that particular time.

So, we have a measurement against the one we're making from 400 km or 900 km in space.

We have another camera out there called the phenocam.

So, here I am with a National Park Service collaborator putting this instrument out on National Park on the edge of the cliff.

It's a Chief Sidell.

And it collects images like these every 15 minutes.

And we have in fact put this up on a website so you can go up on the website and check the conditions in the park.

And we've animated for example, the fall leaf sequence.

So, we have a specific scientific purpose for this camera.

We know where that is on the ground and where it is in our satellite images.

So, as we go along calculating that value, the condition of the leaves and we're saying based on our analysis, the leaves started turning brown on this date, right?

We have something on the ground we can use to check on it.

So far so good?

Yeah.

So, this project is certainly a working project.

One of the things I had pointed out is right now we have 18 years of measurement.

And when you're a scientist, you never have enough data, but 18 years isn't a whole lot of data to start drawing lines through and drawing conclusions from.

So, we're constantly working on expanding that database.

One of the things we're doing is adding that that 30-40 year record from that other instrument.

We're trying to use those two instruments together.

But, I think this is a really good example of local one of the utility of remote sensing to measure what can't be seen over large areas and through time.

Okay?

Let me give you another one.

This is South Florida.

The Florida Everglades.

Here.

So, here's Lake Okeechobee and historically water has flowed from Lake Okeechobee down through the Everglades and out into the bay.

Up here, you're seeing all the sugar cane and other agricultural areas that have been put in place up here.

Wherever you see a line here, it's a canal or a levee that's used to convey water from here on out into the ocean.

So, that either there's enough water to drink here or this area doesn't get flooded when there's something like a hurricane.

So, all of these levees and canals were put in place and this by the way is Tamiami Trail, one that runs from Miami to Tampa.

And as a result of these features, that flow of water has been interrupted and the chemistry of the water has been changed.

So, wherever you see these orange colors, for example, that's where nutrients are leaking out of the canal and changing the composition of vegetation in those locations.

So, right now a 20-year $7 billion effort is underway to restore the health of the Florida Everglades by getting the flow of water to have the same sort of timing and the same sort of quantity that it has had historically.

And as things like sea level rise occur, send sufficient water down here so that we don't have a lot of salt water coming back up into the interior.

of the state.

South Florida is really really flat.

I don't know if you know that.

But, our measurements, which is a whole another seminar, right?

Show that the average slope here is about an inch and a half per mile.

So, it's dropping about an inch and a half per mile.

The only way I can give you a sense of that is to turn the camera on an angle before I take a picture.

So, the water is all kind of going like this at a very slow rate.

It's going so slowly that one of the things that really affects where the water flows is vegetation.

So, my role in this project is to help build models that show us where the water will flow by incorporating into them vegetation using remote sensing.

So, I mentioned Tamiami Trail.

That's here.

Here's a canal that runs down.

We have devices when I say we, there's a lot of collaborators on this project, right?

And we're measuring the amount of water that flows through holes underneath the road and we're measuring the amount of water that comes out of these little streams at the bottom end.

And with those two pieces of information, we're trying to build a computer model that will move water through here very realistically.

Okay?

Now, my collaborators began to look at how this vegetation affects flow.

So, they went out in the field and started making measurements.

They also built a flume.

They built a big bathtub basically and they grew sawgrass in it and they tilted it on a lift and they measured how quickly the water moves through different vegetation.

Well, I took a slightly different approach.

I went out in the field with some friends, the ones who are still willing to go out in the field with me, and I use another remote sensing instrument like this.

This is called a spectroradiometer.

So, this actually has a little computer inside of it and all along here are light sensors.

And what I do with this instrument is I hold it up above the vegetation and I press a button after I've told it what type of vegetation I'm looking for.

And it actually runs a little calculation and tells me how much light has come in at that place.

Then, actually it's more like, right Greg?

Right?

And I go oh while Greg listens for the gators and the moccasin, right?

And I press the button again and it sees how much light was intercepted or kept from getting to the sensor by the canopy or vegetation and it tells me an estimate of how much vegetation is there.

So, unlike my collaborators who have to cut everything, weigh it, or put it on a table and measure how large the blades of every grass uh stalk are, I make a measurement,

make a measurement, and then move to the next spot, make a measurement without actually affecting the vegetation, right?

So, I collect data like that and I look at that vegetation index from imagery.

So, I have an air photo that I can calculate that number from and I compare it against my measurements of how much vegetation is there and I develop some relationships that I then take from aerial photographs on up to satellites.

The other thing I do is I get on airboats and I travel around with my computer in a box with my satellite image on the computer and with GPS,

global positioning system, connected to that laptop.

So, as I move around in that airboat, I can literally see the image and change where I am on the image that's been collected from 400 km in space.

Now, anybody here old enough to remember the cartoon show Ed, Edd, and Eddy?

Yeah, a few of you.

This guy's name is Ed.

And this guy's name is Eddy.

And I told them if I ever show up in the morning and there's another Ed in this boat, I am not getting on.

So, one day we're out there and R.

Frank, another collaborator says to us, "Boy, it'd be really cool if we could put an instrument in a spot like this.

And we know we need big areas of the same sort of vegetation if we're going to get a proper measurement.

We don't want to be on the edge of one type of vegetation and another making measurements because we don't know where our measurements are really coming from."

So, I looked in this box and I said, "You know, if we go about 10 km in this direction, we're going to go from really dense sawgrass into a huge patch of this sort of rush of sparse sawgrass mix."

And I stuck my head back in the box and we started flying on the airboat and I watched us go along, you know, and I saw us go from one area into another and I put my hand up like this.

The boat came to a stop.

I looked at everybody and they all stood there and we were in a patch just a data.

We're in this huge patch of sparse rush and sawgrass mix.

And always the scientist R.

Frank says to me, "Do that again."

So, we spend an entire day going around and showing that yeah, what I'm seeing on the on the screen here, what I'm calculating as being the density and type of vegetation is really what's there.

And what we produce is a map like this.

I'm not so concerned you look at the classes.

Uh the point to get from this map is we have groups of vegetation that are grouped together by their their density and their shape so that we can uh alter our model and the flow of water within our model as a function of those classes.

Okay?

I always have to have an alligator picture in there.

I get people get upset.

This was a guy who was waiting by one of our sites.

I was going to get out and make one of those measurements.

The guy driving the airboat said, "You don't need to get out.

Don't don't know me.

When they bellow at you, it's a really cheap thrill."

Okay, so here's our model.

Remember Tamiami Trail and the uh canal boundary along the edge?

Here's the ocean on this side.

What we're seeing in colors is the calculated depth of the water.

So, we start out by flooding the place and we start running the model.

And the model starts using how much light's coming in, what the temperature is, what vegetation is there, to start drying the system out.

These arrows show the direction in which water is flowing.

The length of the arrows, it be nice to change them their length according to how fast the water is moving, but the difference is so great across this that a bunch of them become dots.

So, we can't really show the speed.

But, what's going along what's going on along here?

Anybody?

Tides.

Tides.

Tides.

We took a model that was developed for the Chesapeake Bay, which is not an inland model, because it handles tides very well and we adapted it to work here in the Everglades because we wanted to get that mix of salt water along the coast in our model.

Okay?

Some of the things we found out, uh water flows uphill in the Everglades because of wind.

So, if the wind is strong enough and the vegetation isn't thick enough, it'll actually change the direction of flow of the water.

The other thing we found out was before we put vegetation in this model, when they just had incoming water and outgoing water, they had to really make a belief about how much water evaporated off the surface.

They had to put completely outrageous numbers in there for the amount of water that was taken up into the sky because otherwise, too much came out of the model.

So, when we put that vegetation in there and slowed the water down, suddenly they could change those evaporation numbers in the model into something that we were actually measuring on the ground.

Why do we need a model like this?

Because again, the Corps wants to do things like punch bigger holes underneath this road or raise the road.

And the question is, how much of that road do you need to raise to get the types of flows that we want here?

If we raise it this much, how much flow will we get?

Okay?

Oh, something went wrong.

What do you want to see here?

You want to see it dry out from here.

That's what you want to see.

Okay, I'll try this button.

All right, so as a result of this, remote sensing is allowing us to include vegetation into these Everglades water flow models.

We're getting much better models.

They're much more like the real world, which means we have a lot more confidence or faith that these models are going to be showing us what might happen if we start changing things.

That's really the reason why we want to build these models.

What I'm working on right now is taking those changes to vegetation through time and putting them into this model.

Fire is a very big thing down in the Florida Everglades.

Lightning strikes hit that sawgrass and they burn large areas of it.

Right now, we don't account for that.

So, a large area of sawgrass could be burned during a time that we're simulating and we don't account for it.

So, we want to use remote sensing through time to take care of that issue.

This is my reminder just to point out to you that there are other types of instruments that we use.

Some of them are active systems, meaning we don't rely on the sun's energy.

We actually send the energy off from the instrument itself.

One of the types of data that I'm really excited about using more and more is called lidar.

So, we have an airplane that has lasers shooting the lasers down toward the ground as it flies along.

And it's recording the amount of time it takes for that light the amount of time it takes for the light to go out hit the surface and come back.

So, that's how precise a clock we have and a global positioning system we have.

And when we know how long it takes for the light to go from the plane to the top of the vegetation or to the ground, and we know very precisely where the plane is, we can figure out how high that tree is and how high that ground is from the center of the earth.

Okay?

We get data like this.

So out here is how we used to use remote sensing to look at the elevation surface.

We used to use aerial photographs and some other types of processing.

This is what an image looks like from one of those lidar systems.

You can actually see the channels of the stream running through here.

You can actually see the footprints of the houses being built.

All the roads.

We can see the level of the ponds that are collecting storm water that runs off of these buildings.

So the experiment for me is to look at the vegetation.

But the original purpose for these systems was to look at the ground.

So my research is teasing out the vegetation, which everybody else wants to throw out.

45 minutes.

Thank you.

Of course, we have to check these things on the ground.

So here I am with Dr. Hogan.

We have what a couple of years ago was the state of the art for surveying instruments.

It's the total station.

And we've got some other friends and colleagues running around in the woods, and we're spending a day collecting about 300 points on the ground very accurately, so we can see how good a job we're doing at finding the ground in this imagery.

Last Thursday, right in the center of this room, I set up this instrument.

It collects millions of points with the same accuracy as this in about 10 minutes.

Uh there's good and bad to that, right?

Now it only takes me 10 million 10 minutes to get millions of points.

Now I have millions of points I have to process.

So for me, a lot of the effort is to figure out how this particular system works and how I can use it to look at the vegetation and other features that I'm getting from that airborne imagery.

Exciting thing for me is remote sensing is constantly evolving.

The challenge for me is remote sensing is constantly evolving.

The point is we want to take these various systems and bring that information together so we can see how the surface of the earth is changing through time and over space.

So there's a great deal more to remote sensing imagery than just beautiful pictures.

I hope I've conveyed that to you.

Remote sensing helps us measure what can be seen, track the health of our resources, and understand how and why that health may be changing.

To do this well, I truly believe you have to be on the ground making measurements so you understand the system.

You have to be on the ground making measurements, controlled measurements, so that you can tease out of these various different satellites and airborne systems as much information as possible.

Here are some websites that I can put up later on if you're interested in looking for additional information.

Uh Dr. Stronge and Menzie Al have agreed to hang out by our hilts spec instrument over here.

They've got some plants and some there's a geologist in the building who carted some rocks over here and said I could show you some rocks.

So they're sitting on the table.

So there's a couple of rocks over there.

Behind the slide projection, we have that that lidar system set up.

I don't think it's activated and running.

So if somebody's really interested in that, we can look at doing that.

Uh I'm more than willing to entertain some questions for a while and hang around for a while after.

So I have to bring up the final slide that shows you uh our schedule for our main talks.

And then I will also at the same time entertain questions.

Thank you, John.

Any questions for or John?

I I think you can all see why he was chosen as our first speaker.

This was a fascinating fascinating talk.

Now okay, so here's the quiz.

John mentioned phenology earlier in his talk.

Hmm.

Who remembers what what that was?

Okay.

Well

Can't Sue help me out here?

The good news is we're going to hear all about it at our next talk.

And and why it's important, why we should care about it, and what you can do to help phenology.

And that talk is uh what's the date of it?

It's always

May 6th.

Yeah, it's always the first Wednesday of every month at 7:00 p.m.

I know Jake uh Jake runs the National Phenology Network, and actually the Shenandoah National Park project is part of that network.

We are the mid-Atlantic subregion for that network.

We're working on a workshop, writing up a workshop.

We're putting a workshop together this summer for scientists and citizen volunteers to make measurements in the field along the Blue Ridge.

Uh we're hoping to hold that in the park.

So we'll try and get some announcements out on that.

So I can tell you Jake is excellent.

An excellent speaker.

I recommend you come there so somebody can tell you what phenology is and you'll remember that.

You could do me a favor and if you're just being shy, you know, he says, "Does anybody know what phenology is?"

You all raise your hand and tell him.

I'd appreciate it.

Other questions or just come on up and yes.

Um I know on your NDVI

Mhm.

Mhm.

There was a point where the I think soil there was I think a peak in where it was reflecting some amount of light and that peak I think was where the uh grass I think lost it.

Absolutely.

didn't have any

Yes.

That is an excellent question.

So there are places where the reflectance two different materials will be exactly the same.

So when we're trying to distinguish or tell what what is soil and what is vegetation, we can't make a measurement there.

Okay?

So that's part of the power of these remote sensing systems.

When we're making measurements in all these different points, we know aha, if we want to tell the difference between soil and vegetation, we don't want to use this part, this type of light, because they can look a lot the same.

If we really want to see the water, we want to look here and here because the water looks really different than the land than the vegetation and the soil.

So that's part of that art and the science.

It's part of part of the science as well.

And so we're looking for those combinations of the light that we look at, right?

And the the target we're after.

One of the things I often tell people when they come to talk to me, "Can I use remote sensing to do this and that?"

The point I make to them is, "Can you separate the target, what you're interested in, from the background?"

If you can't do that, remote sensing won't work for you.

So you have to find the technology that allows you to do that.

So that's an excellent question.

That he wasn't a plant, by the way.

Anything else?

Well, you're welcome to come on up afterward, visit with me, visit with Dr. Stronge, visit with Mark and Ben, and I thank you all for coming.

Thank you.