- Hi, welcome, everybody.
Welcome to our first talk
in the technology and innovation track.
I'd like to introduce Adam Zylka
a research specialist
with the University of Vermont's
Spacial Analysis Laboratory.
The presentation is titled "Monitoring Forest Health
"With UAS ("drone") Technologies".
And welcome, Adam.
- Great, thanks (indistinct)
and thanks everyone, good morning.
So I'm Adam Zylka.
I'm the UAS team lead
here at the Spatial Analysis Lab at UVM.
A lot of this work also was done by employees
and student employees at the lab
including Maddy Zimmerman, Lauren Cresanti
and Kelly Schulze.
So the issue we saw in Vermont
which there was another presentation yesterday
maybe some of you attended that
was this spring, we saw issues
with Lymantria dispar dispar
formerly known as gypsy moth caterpillars.
And so, these are an invasive moth species
introduced to North America in the late 1860s.
But in their larval stage
they feed on vegetation and foliage
and sort of the extremeness of this
leads to massive defoliation
and degradation of the tree canopy.
And so, since we're geospatial people
at the Spatial Analysis Lab, of course
we were interested based on the impact of the outbreak
this last summer
on what kind of technologies we could use
to sort of monitor the impacts to the forest
and to monitor the regrowth
and recovery of these impacted areas.
And so, this spring, we had an outbreak
the first really major infestation since 1991.
Usually, these moth larva are regulated by a fungus
but that fungus needs sort of wet weather conditions, right?
Spring rains and things
to really to be around.
And so, the dry weather we had this spring
across the northeast
sort of minimized the growth of this fungus.
And as a result, we saw this big outbreak
in the moth population.
Of course, I think across the state and the region
we saw some impacts like this.
But in particular we noticed
areas in Addison County here in Vermont
were hit particularly hard.
And so, for sort of our data collection
we focused on this parcel of land
called Little Hogback Community Forest in Monkton.
Maybe some of you folks have been out there
but it's about 115 acres.
Sort of jointly owned by the 16 shareholders.
The Vermont Land Trust holds a conservation easement there
and they kind of share the remaining rights for timber
and sugaring and things as an LLC with the members.
And so, the community forest and the trails
what they enter on the left in the picture here.
And sort of on the right
is just one image of sort of this impact of defoliation
that we saw here in early June.
Our main data collection area
focused sort of at the southern portion of the site.
And we'll sort of go through the next steps.
And so, in terms of technology, right?
When we see issues in the forest
or sort of other environmental issues
or things like disaster response.
In the last 10 years
we've had really rapid development
of drone technology in particular.
And so, what's really great about drones
or unoccupied aircraft systems, UAS
there's many names for these
is that they're really fast
and they're a super cost-effective way to obtain imagery
or sort of other sorts of remotely sensed data
compared to traditional methods, right?
And so, they're very small
they're lightweight and deployable, they're safe.
You maybe aren't able to access some sites by foot
to go and do some field work and scouting.
They're repeatable, so we can go out with a drone
and collect data over the same area every week
or every month or a few times a year
or year over year really easily.
And then we can develop
really rapidly these GIS-ready products
to use in analysis.
And so, that's what's really special
about these technologies.
Obviously, there's a lot of people
who have concerns about drones with privacy
and safety and things
and that's very important as well to consider.
We won't get too deep into the weeds
in sort of the technology portion.
But I think it's interesting to highlight
the types of drone technology that we have at the lab
and we're using for this.
And so, primarily to collect data at the site
we were using a DJI M300.
And so, DJI is sort of the market leader in drones
across the country and globally, right?
So pretty much anytime you see someone flying a drone
it's a pretty good chance it's made by this company.
This is sort of a commercial quad-copter.
It has four rotors.
It's able to carry pretty heavy payloads.
So up to five or six pounds of payload weight
and it has interchangeability for the sensors.
We also used the DJI Mavic 2 Pro which is a bit smaller.
More of a consumer grade type of thing.
And this can collect imagery and video in real time
which is also valuable.
But primarily for this, for our data collection
we went out with a LiDAR sensor.
So this is a Yellowscan Mapper LiDAR sensor.
Basically, the idea with LiDAR, we'll talk about shortly
and then also a MicaSense RedEdge Dual Camera.
So this is a 10-band multi-spectral sensor.
So this gets us from the blue, red, green
into red edge and near-infrared bands
that our eyes can't see.
For those folks who maybe are not familiar with LiDAR.
The key here is that it's an active sensing technology.
So, the sensor actually shoots out a pulse of laser light.
This bounces off objects and it returns back to the sensor.
And so, that's how we can determine elevation data, right?
By the time these pulses take to come back to the sensor.
What's really great about LiDAR technology
is that we can actually get multiple returns
from each laser pulse back.
And so, that allows us to get through vegetation
or through tree canopy
and actually see elevations
of different layers of vegetation.
With the multi-spectral imagery
so here, we can use passive sensors
to capture information about wavelengths of light
that are outside our visible spectrum as humans.
And so, in particular, when we're looking at vegetation
and plant health and forest health
where we might be interested in looking at the red edge
and near-infrared bands of light, right?
And so, really healthy vegetation
has a lot more chlorophyll.
This will reflect more near-infrared light
and green light compared to other way wavelengths.
And so, that's why for us plants look green
when they're healthy
but they absorb more red and blue light.
And so, by comparing differences
in reflectance for the red edge or the near-infrared bands
this can give us a pretty good sense
of sort of is a plant healthy or is it stressed?
How is it doing in general?
For our field work, we went out with the M300.
We collected flights over a few times series.
And so, here is Maddy who's a UAS technician in our lab
flying with our 10-band multi-spectral camera.
And an image of the drone, the LiDAR setup
flying there as well.
We don't need fancy technology necessarily with drones
to get really useful information.
Even just images or video
kind of giving an aerial overview can be really powerful.
So here we see an image of the forest
and sort of that defoliation in middle of June.
Just a week later, right?
We can see really the heavy, heavy impacts
of this defoliation, right?
By early July, you see a lot of the leaves
are sort of starting to bounce back and grow again.
And then by August, we're in pretty good shape
in terms of having our sort of full canopy back, right?
And the fall down in this area was a bit disappointing
with the belief change.
It went pretty quick
but just kind of to give a sense of what it was in October.
What we think is sort of the most interesting here
is looking at mapping data and orthorectified imagery
from these sensors that we use.
And so here, we're looking at NAIP imagery, right?
Just traditional sort of aerial imagery, right?
We can see that section in the forest in the middle.
We expect it to have quite a bit of canopy cover
during the summer.
But here's what our results looked like in July
from our imagery collection.
And so, you can even see straight through down to the ground
in a lot of places.
You can see the trails and old logging roads and things.
And that's just a really powerful snapshot
to see sort of the impact of this.
When we compare that to August
so we can see that yeah, that the trees
have done a good job.
We have a lot more leaves and canopy coverage, right?
Compared to month to month.
If we take a closer look at some of these datasets
this is from July.
You can see the logging roads
sort of cutting through on the right-hand side.
You can see a lot of the branches and leaves and things
that we might be missing here.
And again, comparing that same section to August
it's quite a difference in terms of growth and recovery.
We can also visualize these datasets
using different bands
than are sort of red, green, blue, true color.
And so, this one, we're looking at the near-infrared band
and the red and green bands instead, right?
And so, here in this case
sort of the darker colors
indicate perhaps a higher chlorophyll content in the leaves.
And so, sort of down in the southern section
we can see that those trees were not impacted
by the moth larva as much.
They're looking quite a bit healthier
compared to sort of the light pink
and other shades of vegetation up north
up on the hill there.
And again, if we compare this to August
the canopy itself is looking much better and healthier.
Just another quick look, a little bit closer here.
And it's really interesting.
You can see sort of differences in specific trees
in terms of this analog for chlorophyll content, right?
Some trees did a lot better than others
or maybe some are in the process of regrowth here in July.
And then we look in August
and we're in much better shape once again.
Next, we'll take a little bit of a look at the LiDAR data.
And so, this is our initial point cloud
that we got from the LiDAR sensor, right?
So this is all just 3D points in space as elevation data.
You can see the sort of those colored lines
above the data set on the right-hand side.
That's actually our flight path trajectory of the drone
to be able to capture this data.
And so, that's pretty typical
that you fly sort of parallel flight lines
and then you're merging all these strips
together using software.
This is the actual point cloud from July.
And so, you can see it's colored by elevation.
So that southern section of the areas is quite low.
As we move up the hills
where the tops of the trees turn red.
Our density for the point cloud to give a sense here
is about 150 points per square meter on the ground.
If we compare this to like the Vermont State LiDAR
that's about 10 points per square meter.
So, quite a magnitude higher density in these datasets.
We can also use the point cloud data
to do some additional analysis
and create some sort of extra files here.
And so, if we just rasterize the top surfaces of everything
this creates a digital surface model or DSM here.
And so, that's just showing us sort of the top-down view
all of the above ground objects like vegetation, right?
Obviously, you can see the tree canopy here.
There's some gaps in the canopy as well.
And compare that to August
where we can see really well
how the foliage has sort of filled in those areas, right?
The other thing we can do with point cloud data
is actually classify this
into the points that are above the ground
or points that are the ground.
And so here, we're looking at the same data.
Anything in red is sort of above ground.
So this would include trees
could include cars or buildings or things like that.
And then the green underneath
is sort of just the ground features that we captured.
And so, if we use those to generate a raster file
here, we have our digital elevation model.
And so, this has removed all the trees
and everything above ground
and we can see sort of down in detail
what the terrain looks like and topography.
You can even see the logging roads in here
and some hiking trails and other different things.
But what's great is when we subtract the DSM and the DEM
this actually just gives us the height
of objects above the ground, right?
And so, this is called a normalized digital surface model.
This can be helpful for doing like tree height analysis
canopy, vegetation analysis and things like that.
And so, again that comparison between July and August
it's pretty interesting to see the regrowth.
If we look at the LiDAR point cloud
and sort of just draw a slice down the middle
and look at it from a profile view
that's what we're looking at here.
And so, this is our profile line from July.
You can see sort of each of these individual trees
and their trunks and a lot of the structure which is great.
From this, you can do things like measure tree heights
measure the width of trunks.
maybe do some mapping of canopy as well.
When we compare this to August
we can sort of go back and forth
between the July and the August.
And just gives you a sense
of the difference in like vegetation density
and leaf density that we see between between the month
as the forest sort of bounced back.
In particular, this highlights
I think a lot of undergrowth
that pops up between July and August
as well as the canopy there too.
If we overlay these two from July and August
on top of each other, it's pretty interesting.
So, the green here is our July data.
The red here is our August data
and you can kind of just see what the difference is.
I know we're gonna get to questions here in a second
but we've developed a web map
and we'd encourage you folks to check this out.
With all these different datasets
you can do some swiping and different things
as well as a web scene showing the 3D model point cloud.
So we can put these links in the chat if that'd be useful
or there's a QR code here that you can pull up
if you're on your phone.
And just our special thanks to the shareholders
of the Little Hogback Community Forest here.
- Great, thank you so much.
Yeah, I think it would be awesome
to put those links in the chat
and we can open it up for questions.
I see a hand raised from Jerry Carlson, Alrighty.
- [Jerry] Yeah, hi, thanks Adam.
Can you hear me?
- Yes.
- [Jerry] Yeah, okay, good.
What would be the average cost per acre
for that kind of data to be collected?
And are there weather extremes
that you wanna try to avoid?
- Yeah, good, two good questions.
So could give a sense of sort of the cost
of this whole drone setup.
We're pretty close to six figures for the LiDAR
the multi-spectral imagery and the drone.
And then it takes some processing software
to actually make this usable.
So, per acre, it's hard to say
but maybe it's say between 10 and $30 per acre
if you're sort of billing out this type of work.
That's fairly common in the industry.
Weather extremes, certainly a factor.
So today it's very windy in Vermont.
We had to cancel one of our flight operations.
Snow doesn't work really well with LiDAR
due to the wavelength of the laser as well.
So you sort of wanna be able to avoid like rain
heavy snow on the ground
or high winds and things of that nature.
Yeah, hopefully, that answers it.
- [Jerry] Yeah, thanks, Adam.
That was great.
- Great, Jim, put a message in the chat here too.
And I think that may go also with Jack's question.
So those are sort of about access road
or what do you need to be able to take off?
And then about line of sight challenges
when monitoring in the forest.
And so, what's interesting about flying under regulations
in the US for drones
is that we're limited to visual line of sight.
So we always have to be able to see the drone
when it's flying.
And we obviously, also need enough open space
that we can take off and land it safely.
And so, in heavily forested areas
yeah, that can be tricky
and it can take some planning in advance, right?
We do a lot of sort of looking at maps
maybe some scouting in the field
to make sure we have that space.
It's easiest in the fall
if you can sort of see through the empty tree canopy
or in this case, when there's no leaves on the trees
in the middle of the summer.
But that is one of the reasons
we sort of weren't able to cover
the entirety of that community forest.
Because due to the sort of terrain and tree canopy
blocking our line sight from the bottom
we couldn't quite see it as it got on top of the hill.
Alison is asking a question
about sort of yeah, a more expensive computer
and equipment and software.
And that's definitely true.
So we're lucky as a geospatial lab
to have some pretty hefty processing power
on our workstations here.
The LiDAR datasets
are gigabytes and gigabytes of data, right?
And you have to be able to sort of visualize that
and analyze that in 3D to make the best use of it.
In the multi-spectral data
for sort of processing all those individual images together
that can take quite a powerful computer as well.
So it can be challenging to sort of do this in the field
on just the laptop.
So there are sorts of all lots of organizational costs
that they may come up
if you're trying to like use UAS in your group.
Greg's asking about sort of using multi-spectral data
for more quantitative analysis
like estimating percent of trees
that might've been defoliated.
And yes, that's definitely something that we can do.
What's great about these datasets
are we're actually planning to use these for more analysis
and hoping to get back in the spring and summer
to just sort of look over time
to see if the defoliation from last year
impacted the growth of the trees come spring with.
With the multi-spectral data
it's a great way to sort of pull out features
that where we have high chlorophyll content versus low ones.
And as undergraduate student in our lab
is gonna do they're undergrad thesis
sort of diving into these datasets
a little bit more for sort of those extra analysis.
So that should be pretty interesting.
Hopefully, maybe by next summer
we can share some of those results.
Yeah, I'm not sure if I missed any questions
please let me know.
Otherwise, happy to answer a few more here
or stick around a little longer
if folks wanna chat about other pieces.
- [Jerry] Yeah, Adam, Jerry again.
I'm wondering about a little bit about the evolution
of drone technology.
You're still using rotary wing.
What about fixed wing and some of the efficiencies
in the larger landscapes that we can detect that way?
- Yep, another great question, Jerry.
So, fixed wings are really great
for covering large areas super efficiently.
A lot of times they're sort of limited
by the payload capacity, right?
And like it's much more difficult
to put a sort of a LiDAR sensor on a smaller fixed wing
than it is on a multirotor.
We use fixed wings quite a bit in our operations for mapping
and monitoring, right?
Either with true color imagery
to make maps and elevation data or multi-spectral imagery.
Those are really good.
Let's say if you're doing hundreds
or thousands of acres, right?
That's where sort of that efficiency of the fixed wing
comes into play.
The downsides are you need even more room
to launch and land, right?
You might need a big field
or something else that's close enough
where you can launch and land
that sort of more like an aircraft, right?
Where it's it's maybe hand launched
and it sort of comes in for a belly landing
compared to the multirotor.
That's where the multirotors
could be really good for forestry applications
is they don't need as much space.
If you have a big enough opening in the canopy
you can set up and take off and fly from there.
But they are limited in terms of flight time
and coverage area, right?
Maybe 50 to 100 acres per flight with the multirotor
where maybe you're doing 500 acres per flight
with the fixed wing, for example.
- [Jerry] Well, aren't they better at automated flight?
- I would say they're pretty equivalent.
So this, we flew fully autonomously for both the mapping
and the LiDAR collection, right?
You can just do flight planning
adjust it for the elevation
and even the multirotors will follow the flight lines.
Yeah, are there other folks with some questions or?
Great, well, if that's it
yeah, we appreciate you coming.
It's obviously hard to fit sort of this much
into just 15 minutes
but hopefully, that was interesting.
And certainly feel free to reach out to me
or certainly anyone at the Spatial Analysis Lab here at UVM
if you're interested in maybe some forestry applications
or some testing of different things come next year.
So great, thanks, everyone.
And hope you have a good Friday and a happy holiday.