(gentle intro music)
- 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.
Because when we're producing a lot of data
in an automated fashion we want a really
computationally efficient algorithm.
The main downfall of the Triangular Irregular Network
is it doesn't exploit redundancy
in the LiDAR data to improve your accuracy.
You can imagine if you had multiple points
within a single grid cell, it's not averaging
those to reduce the error.
You're just pretty much getting the
elevation point that's closest to the center.
We'll talk more about that later in the week
during the MCU lessons.
As I mentioned these are the main products
that come from the LiDAR.
You have the Unclassified point cloud.
That's the L1 product as well
as the Classified point cloud, that's an L1 product.
And then the L3 products which are the rasters.
You get a Digital Terrain Model,
Digital Surface Model, Slope and Aspect,
Canopy hight model, and then the Slant range waveform.
I'm just going to briefly go
through each one of these products.
You've seen this picture already.
It's the L1 product.
It's in LAS 1.3 format and available by flight line.
And then we also have a Classified Point Cloud.
We give this zone in tiles.
1 kilometer by 1 kilometer tiles,
and we perform the classification with
a commercial software product called ASPRS
which basically just goes through
and looks at the geometry of the points
and determines whether the points are
ground of vegetation based on their structure
and how they are orientated to one another.
Then we further classify those also
into building, noise, and unclassified points.
And then we also colorize the point cloud.
We take the high resolution camera imagery
and apply the Calgard GB Colors
to the point that overlaps on that image
so you can kind of get a full color
3D model of each of our sites with these.
This is part of the Classified Point Cloud.
Next we have the Digital Surface Model
This is one of our L3 Raster products
Basically we just use that Triangular Irregular Network
interpolation algorithm with all of the points.
This is vegetation, building, all the points included
we're interpolating between all those
and getting that raster elevation.
Again creating 1 meter spatial resolution
and then we create a digital terrain model.
You saw in the classification we had a couple slides ago,
we remove all of the vegetation points,
that are points that are classified as vegetation
and then we move all of the vegetation points,
and then just interpolate just the ground points.
That gives us just the ground surface
and I think I mentioned on the first day
that LiDAR is really one of the only
technologies that's able to do this
to classify those ground versus vegetation points
and then just get the ground
so you get an idea what the surface looks like.
This is just a little animation that shows
the difference between ground, the DTM, and the DSM
removing those vegetation points.
Then we also create Slope and Aspect Rasters
from the digital terrain model.
This is also L3 product.
The slope is measured in degrees.
Basically just the slope of the terrain
while the aspect is the direction of the steepest slope.
These are also measured in degrees
between 0 and 360 degrees.
Both of these come from the Horn algorithm.
Which is the same algorithm that's used
in a lot of popular remote sensing packages
like ESRI, or QGIS, and ENVI to calculate
slope and aspect.
This is also produced at 1 meter
and given in 1 kilometer by 1 kilometer tiles.
Finally we have the Canopy Hight Model.
Also an L3 product.
A common issue in creating Canopy Height Models
from live hours you get data pits.
These are areas that don't go all the way
down to the ground in the center of the canopy.
Which sort of biases your estimates
that you might be gathering from the Canopy Height Model.
We use an algorithm from this paper here
that takes care of those date pits.
If you want more information on that you can go here.
And then the final product is
that Full Waveform LiDAR product.
You can see that these were points that were
taken over Canopy Height Model
and if you look at one of these individual points
you have this outgoing waveform here
and then a whole bunch of time passes,
but a whole bunch of time in LiDAR is like 300 nanoseconds
and then you get this return pulse here.
And so Keith is going to give us
his presentation on Waveform LiDAR.