- My name's Tristan Goulden.

I'm a remote sensing scientist

with the Airborne Observation Platform

and I'm going to do a talk this afternoon on

Introductin to LiDAR.

Primarily on discrete LiDAR because Keith Cross

is going to be following me talking

about the waveform LiDAR.

LiDAR is a active remote sensing system

which means that it has its own energy source.

And so the main subsystem of a LiDAR is the laser

and we'll use the laser for is to generate

a pulse of energy that comes out of the sensor

which is pointed out the bottom of the aircraft,

travels down to the ground, reflects off targets

on the ground, and then returns back to the aircraft.

And so, we're actually able to measure the time

it takes for that laser pulse to go down,

reflect, and come back.

Based on that two way travel time we can

calculate a range from the aircraft down to the ground.

Also have a GPS sensor on the roof of the aircraft

to get the aircraft position,

an inertia measurement unit inside

the aircraft to get orientation,

roll, pitch, and yaw off the aircraft

and then a scanning mirror which directs

the laser pulse within a squad beneath the aircraft.

When you combine all of these subsystems together

you can actually coordinate points on the ground

based on all the observations from these subsystems.

What makes LiDAR really unique

is that it is able to achieve a really accurate

and dense raw sample of the terrane.

It's able to do that mostly from the Laser Ranger

which today's rangers can operate at about 500 kHz.

This means that the system is capable of sending

out 500,000 pulses per second.

Each pulse is capable of getting multiple returns.

It's possible that some of the energy

is going to reflect off the top of vegetation

then, energy will continue down through

the vegetation and might reflect off the understory,

then hopefully will make it down to the ground

reflect off the ground and we can get multiple returns

from each pulse.

That means we're able to get,

for sending out 500,000 pulses per second,

a multiple of that many points every second.

It's an incredible amount of data.

I just want to briefly introduce the difference

between discrete and full waveform.

I won't get into a lot of details

cause Keith is going to talk about that in a minute.

But basically there's kind of two flavors

of the observations we get from the LiDAR.

The discrete gives us points only.

When we get that reflection off of an object,

we that return range and we calculate

just a single coordinate.

From multiple returns we could get three or four

individual three dimensional coordinates.

With the Optech Gemini when that signal actually comes back

its split and part of the signal goes to

a waveform digitizer and that records the entire

return energy signature.

This energy signature will include the outgoing impulse here

and then some time will pass and then as we're going

through vegetation you're getting this humps here

where we're getting additional energy returns

from the object.

And so in the discrete LiDAR we're going to cut off

the timing at each one of these humps,

here, here, and here and give us three individual points.

But with the waveform LiDAR we get this full return

signal and you're able to do more

advance analysis on the structure

of the vegetation with that signal.

The NEON LiDAR currently we are operating two

Optech Gemini systems which are slightly older technology.

We purchased these in 2011 and 2012.

In the future we will be also doing surveys

with the Riegl Q780 which is a more contemporary system.

Right now we run our obtech at a

Pulse Repetition Frequency of 100 kHz.

We chose this frequency because it's the highest we can go

and still maintain the accuracy that we want.

If we go any higher than that,

it's capable of going up to a 166 kHz,

but there is a large degradation in accuracy.

We fly at 1,000 meters.

We have 30% overlap in our flight lines.

That gives us 2-4 pulse per squared meter.

In the overlapped areas we get 4 pulse per squared meter

but in the non overlapped areas we get 2.

It's capable of recording up to 4 overturns per pulse

on the discrete LiDAR.

And so that's theoretically we could achieve

400,000 points per second but generally you'll never get

4 returns on every pulse.

In order to position all of the LiDAR data

we also have to determine a trajectory.

So this is the information that the GPS IMU collects.

As part of that we set up GPS base stations

in the local vicinity of our survey areas.

I think somebody asked about this yesterday.

Generally we try to exploit the CORS Network

as much as we can.

And these are GPS base stations that are set out

across the United States and are run by the local

governments or the federal government.

We use those CORS stations.

There's kind of stationary GPS sites with really accurate

coordinates and we use those to differentially correct

the GPS trajectory.

We go to the site and there's COR station

and we see that the distribution of COR stations

doesn't give us sort of less than a 20 kilometer

baseline between that reference station and the aircraft

then we go ahead and we set up our own GPS base station

to correct the airborne trajectory.

For the most part, unless we're transiting from

the airport to the site, we'll never have base stations

that are more than 20 kilometers from the aircraft.

We do this because we're aiming for errors in the GPS

trajectory to be between 5 cm and 8 cm.

In order to do that we really need those local

GPS base stations that are close to the

airborne trajectory.

We try to achieve errors of .005 degrees in pitch and roll

and .008 degrees in yaw.

This is just a picture of the IMU that's located

inside the aircraft to get the roll, pitch, and yaw

as well as the GPS antenna.

One of the reasons we're able to get

these really accurate trajectories

is because GPS IMU are really complimentary technologies.

The IMU is able to achieve really fast positioning

but it's prone to drift over time.

Where GPS gives us really good position

every second or so but we can't get a position

in between those two GPS observations.

The GPS operates at 1 hertz.

The plane's traveling at 50 meters per second.

That means we're only getting 1 GPS

observation every 50 meters.

A lot could happen to the plane in 50 meters.

That's where the IMU takes over.

It's operating at 200 hertz.

It takes care of the positioning in between

those two GPS observations.

We get a good position 200 times per second.

I should mention that as you're going in between

the two GPS observations the IMU is prone to drift

but it gets corrected every time

you get to a new GPS observation.

So really it only needs to do

its positioning for one second.

And this is just an example of some results

of a trajectory.

This was done at Smithsonian Environmental Research Center.

This is the upper left hand side

you can see the software that we use

to process the trajectory and then

the results of the trajectory in Google Earth

where you can really see each one of the flight lines

that we flew going up and down the site.

We actually also worked up statistics

for all of our trajectories from our 2016 flights

and we'll probably do the same for the 2017 flights.

Just so we can get an impression

of the type of quality we're getting on those trajectories.

You can see generally we try to keep

our roll below 20 degrees so that we maintain

locked to all the satellites.

You can see that for the most part

our roll was always between 20 and -20.

Generally we always had above 6 satellites

but more like 8 or 9.

Our PDOP was generally below 4 which is quite good.

This is the distance to the nearest base station.

You can see that for most of the time

we're below 20 kilometers.

There is sometimes where we get up a little bit higher

but that's generally during transits

between the site and the local airport.

Once we have that trajectory we're able to

mix that with the range and scanning information

that's collected by the LiDAR sensor

to produce the point cloud.

This is an example of our L1 product

which is point clouds produced in LAS format.

LAS is a standard binary format

for exchange of LiDAR point clouds.

This is an example from the San Joaquin Experimental Range

You can see all the individual points

that were collected by the LiDAR

and even the structure of the vegetation

you can make on those individual points.

That's just our L1 product.

All the L3 products that we produce are rasters

opposed to point clouds

Instead of all those individual coordinates

we have a grid of points.

We actually have to convert those points

into a raster product.

You can imagine if we observe this area of land

with the LiDAR we might get a

sampling like this of all the LiDAR points.

But what we really want to create our raster product

is the elevation at each one of these grid points.

Basically all of the points overlaying

where we want those grid notes.

What we do is we look in the area surrounding

a particular gride note and then we use

an interpolation method to calculate

what the elevation of that grid note might be.

And of course we can create that at any size

And here at NEON we create

these rasters at 1 meter resolution.

There's lots of different interpolation methods

that you could use.

I encourage you to go out there

and research the different ones that are available.

At NEON we use what's called a

Triangular Irregular Network.

Which basically means we're just creating

linear connections between all the points

and forming triangles between all the points.

If you think about it you can kind of lay that grid

underneath this Triangular Irregular Network

that's connecting all those points.

Just interpolate the elevation from the plane

of the triangle that overlaps the raster cell

and pull that elevation down and

assign it to that raster cell.

That's how we get the elevation from the LiDAR Point Cloud.

All the points here are LiDAR observations

and we interpolate in between them,

pull the elevation from the triangular plane,

and assign that elevation to the raster grid.

One the advantages of this is that

obviously it honors the location of the true data point.

You're never interpolating down or filtering

a lot of observations in creating a new

elevation from what you observed,

and it's computationally efficient.

This is the main reason that we want to use it.