Hello everyone, I'm super excited to be here today.
I live in New York now
but I grew up in Vermont, I grew up in St. Johnsbury,
so getting to come to Burlington feels like a homecoming.
This work that I'm gonna talk to you about today
was actually part of my undergrad thesis,
but I'm continuing it as part of my master's thesis now
at SUNY ESF.
So I'm just gonna get started and get
the important thing out of the way.
We're gonna talk about
What is a Disturbance Detection Algorithm.
So there's a whole bunch of
disturbance detection algorithms, or sometimes
they're called change detection algorithms, out there
and they all operate on the same basic principle,
which is you take a big time series of imagery,
could be imagery from anywhere.
You find whatever the baseline condition of that imagery is
and then you look for deviations from that condition,
and we mark those as disturbances or changes.
So all the algorithms that I work with
use satellite imagery.
Satellite imagery is the common input
into these kinds of things.
And specifically a lot of them use Landsat imagery.
So if you're not familiar with the Landsat program,
it's a satellite platform that's been around
since the early eighties,
and it takes images on a 16 day return interval
of the entire globe, which means
there's this absolute wealth of publicly available images
and that's what makes it so popular.
So this here is an example of a Landsat scene
from the Pacific Northwest,
and we're looking at the central pixel of that image
and that row there, that's,
this is our time series of values from that pixel.
And you can see there's a baseline condition.
We've got a pretty steady state.
There's a deviation from that condition.
That's our change that we've marked.
And you can see the recovery back to this steady state.
And you might notice that the,
this is kind of a weird looking image.
This isn't, doesn't look like a photograph.
It doesn't look like what you would expect to see
if you're looking at the forest from space.
And that's because these are composite imagery.
These satellites record information not only
about visual reflectance, visual light reflectance
but also infrared reflectance and vegetation's
a really strong reflector of infrared light.
And we can use that information
to help us differentiate forests
from other things that are green.
And so all of the work that you'll see me talk about today,
or if you see other people talking about this,
they're probably using these composites
that include that imagery.
And we can use this reflectance information
and we can track changes in forest canopy cover
remotely out of wide scale.
But then we can also look historically
because we have this wealth of images in the archive.
And so this becomes a very powerful tool
that we have in our hands.
I have a clicker.
Interest in this remote monitoring technology
is really growing in the forestry sector
in the last couple of years.
We're seeing like a skyrocketing
and exponential growth in interest.
And that's because the demand for the benefits
that forests provide is also growing.
There's regulations being passed
all across the United States and all across the world
that emphasize carbon management
or set net zero emissions goals.
So in New York State we have
the Climate Leadership and Community Protection Act
that requires net zero emissions by 2050.
There's a very similar legislation
with very similar goals here in Vermont.
And these legislations are putting a lot of pressure
on landowners
to conserve their forest lands and sequester
increased amounts of carbon on their forest lands.
And these programs, just like the longer standing
forest certification programs
that have been around for a long time
require some kind of monitoring and verification
to ensure compliance with their goals.
And the monitoring and verification processes
all of you probably know is expensive
and it's very time consuming.
And so remote monitoring becomes a really
appealing alternative in that situation.
And I just wanna give the caveat that like
I understand that remote monitoring technology
is not everybody's favorite thing.
It can be kind of a hot button issue
when you start talking about watching people from space.
Some people don't like that and I understand it.
It's kind of like big brother-ish.
But the point is, is that technology exists,
it's available online, anyone can use it,
and it's gonna be a part of the future of forestry.
It's gonna be a part of your SFI or your FSC audits,
if it's not already.
It's gonna be a part of your conservation easements,
if it's not already.
It's gonna be a part of carbon credit programs
if it isn't already.
And so if these tools are gonna be out there,
if these tools are being used to assess our forests,
it's important and it's essential, I think,
that they're designed from a forestry perspective
with landowners in mind.
And if that alone isn't enough to convince you
that the remote monitoring techniques
and these remote monitoring tools are important,
like Colby mentioned, had a nice slide about,
studies are proving that these disturbance regimes
in our forests are shifting.
They're shifting now.
They're expected to continue to shift.
We're expecting increased insect pest outbreaks,
increased extreme weather,
and that's gonna change the disturbance regimes that we see.
And we need a way to keep track of those things
quickly and accurately,
so we can see how they're affecting our forests.
So putting all of that together
it's clear that we urgently need these tools
and they need to be accurate and efficient.
And so we can monitor how and when disturbance
is taking place in our forests and understand
how it's affecting ecosystems structure and functions
and to inform our stewardship actions in response.
So like I said, oh, okay, there we go.
We do have these monitoring tools and they are powerful,
but one of the things that we're concerned about with them
is they're they're not developed
in a regionally specific way.
So they're often developed
with a national or global data set.
And as we all know, there's tremendous variation
in forest cover types, vegetation species,
and the disturbance regimes in these areas.
And so we don't wanna apply national models to them.
So just to demonstrate that point,
I have two sets of images here.
Perfect, so these images here are from the Adirondack Park,
which is outside of Newcomb, New York.
And we're looking at a shelter wood harvest.
And these images over here are from Oregon,
just outside the Willamette Valley.
And we're looking at a variety of clearcut harvest.
Each set of images are six years apart.
And you could really see that this shelter wood
this signature of the shelter wood disappears very quickly
and it fades back into the landscape.
Whereas the clear cuts, so look specifically
at this clear cut, six years later looks like that.
So they're much more persistent
and they're much more prominent in the landscape.
And remember if we're looking
at this from a satellite perspective,
we're looking at 30 meter pixels.
So this is a higher resolution image
than the satellite sees.
So you're gonna see this variation here
is gonna be less apparent to the satellite.
And so what we wanted to see, what we wanted to look at is
due to these differences in harvesting regimes,
are they affecting the accuracy of these algorithms?
So we looked at three algorithms that are commonly used.
The continuous change detection
and classification algorithm, which is called CCDC,
the Land Trendr algorithm.
And then the landscape change monitoring system
is actually an ensemble data product
of the outputs of CCDC and Land Trendr.
And here's an example of what the outputs look like
from these algorithms.
So each of those red pixels is a 30 meter by 30 meter pixel
where disturbance was detected
over the 30 year time span of our study.
And you can see that there's a significant variation
in the amount of disturbance that's detected
from each algorithm, but also where the algorithms
are detecting the disturbances.
And we compared these, these outputs from these algorithms
to harvest records that were provided very generously
through our landowner partners.
We have a lovely partnership with the people at F&W Forestry
and they gave us all of their harvest records
for 43,000 hectares of forest land in the Adirondack Parks.
That's a huge chunk of land that we were able to look at.
And we had the location and timing of their harvest
as well as the prescription associated with the harvest.
And we were able to compare those to the outputs.
And when we did that, we saw something like this.
So across the board, we saw a higher level of,
with the higher levels of disturbance detection,
we saw higher levels of accuracy.
So I'm just gonna take a second to explain
what you're seeing on the maps here.
So all of this dark blue color on the maps,
those are true positives.
That's where our maps matched our harvest records.
That's really good.
And the rest of the blue, the
the lighter blue color, that's also a match.
Those are true negative detections we call them.
So that's places where there was no harvest
and the algorithm wasn't detecting anything.
But then the things that we really are focusing on
are the errors, the things where we have
a mismatch in the record.
So the orangey color, the darker orange color,
those are areas inside of the harvest polygons
that were not marked by the algorithm.
So we call those false negatives or errors of omission.
That may be a word that some of you are more familiar with.
And then the final one are the false positives.
So that's this yellow color.
And so those are areas that are either
outside of the polygons, we don't have records for that
or the timing doesn't match up.
And so these two error classes, these mismatch classes,
are pretty interesting and I think do a good job
of illustrating the difficulties
with using these programs in our region
and also kind of using them in general.
So with selection harvest and
which is most of what we have here,
there's a lot of variation of what's happening
inside the boundaries of your harvest unit.
Depending on the prescription,
you might have different proportions of leave trees.
You have stream buffers, you have areas that maybe
are inaccessible to your harvesting equipment.
So you really can't expect the algorithms to
produce perfect polygon outlines.
That's just not reasonable.
And then on the other hand,
we've got non harvests related disturbances,
natural disturbances, that happen all of the time
in and outside of our harvest units.
And with this kind of reference data,
you can't assess the accuracy of those things.
So they're false positives, but that doesn't mean
that they don't represent
a real disturbance that happened on the ground.
And that's something that we need to keep in mind
when you're assessing these kind
of products in this way.
So of the three products that we looked at here,
the CCDC algorithm is the one that kind of struggles
in this area because it doesn't detect
a very high proportion of disturbance.
It doesn't fill these polygons very well,
which sort of leaves it behind the other two.
The Land Trendr algorithm especially is the one
that does the best job of filling the polygons.
But you can see that it also has the highest level
of commission error, these false positive errors.
But again, I just wanna emphasize
that being sensitive to disturbance means
that it could be picking up real things that are happening.
We just don't have the records to assess that.
We also looked at detection by harvest prescription
because our, the shelter would harvest,
selective harvest were the things
that were represented the most in our data set.
And we hypothesized that they were likely not available
at that concentration in the data that was used
when validating this algorithm in its development.
And we thought that they would probably
struggle to detect them.
And that is what we saw.
So on the X axis here, you have our harvest categories
and then the Y axis is the mean percent
of the polygon area detected in those categories.
And you can see that across the board,
we're detecting a fairly low level of the harvest.
We're like at the 30% level at best for most things.
The clear cut category we're performing pretty well in
but we expected that
because those were likely represented in the training data,
they're persistent and pervasive, easy to see.
But with these other harvests, we're just not performing up
to the level that we really need to be able to
use them in this region.
The Land Trendr algorithm, which is the green bars
is giving us the best result.
And so that's something we're taking forward
into our future work.
And then the final thing that we looked at is the CCDC
and Land Trendr algorithms give you estimates
of disturbance magnitude with their outputs.
And we were curious to see if you could use that information
to model the amount of timber volume removed
from the harvest.
And we compared, so we compared it to pulpwood removals
and we found that the Land Trendr algorithm actually
does a fairly good job of modeling that relationship
which is something that is useful
in making management decisions,
knowing that you can use those values to look at
the intensity of the harvest.
So just to recap everything that I've said
we need the regional monitoring tool.
Remote monitoring is gonna be playing an important role
in the future of forest management.
So the tools need to suit the region
and of the tools that we looked at
there's significant room for improvement.
But the Land Trendr algorithm performed the best
at detecting partial harvest and estimating magnitudes.
So finally, I would just like to thank
the Climate Applied Forest Research Institute
that provided the funding for this work
and also our landowner partners, F&W Forestry.
(audience applauds)
Yeah, go ahead.
[Audience Member] You mentioned about 30% of the area of,
30% of the harvest area was impacted with.
Did you look at the, which the number of harvests detected?
Yeah, so we are looking polygon to polygon and so
I I don't have that data in these slides
but we did look at the number of like total misses,
like how many polygons were totally missed by the algorithm.
And the Land Trendr ones on the order of 10 or so.
We're looking at like 170 harvests.
The CCDC algorithm were more in the 60 or 70 harvests
totally missed.
And for the purposes of what we looked at,
we were talking about one pixel in the harvest unit.
If there's one pixel in the harvest unit,
that's not a total miss,
but from an operational perspective, one 30 meter pixel
in a harvest unit, that's kind of a miss.
[Audience Member] And how easy would it be
to confuse the false positives that were the false positives
with the kinda incomplete positives?
Yeah, so you're, you're saying how easy would it be
if you're looking at a map like this
to confuse pixels in an area that there might
have been a harvest with the detection?
[Audience Member] that occurred.
There's a, there's a single that doesn't represent
the area accurately, but it, we know cause
of additional information that it's accurate.
But outside of an experiment we, we look at the positive
and we don't, we're asking ourselves was there a disturbance
or treatment here or not?
And is there other information that you can use to
evaluate that?
The one that is in fact positive is more
like the be positive words
Positive
Or is that just
Some of it has to do with user ability,
how much time you spent looking at the maps?
So I spent a lot of time looking at these maps
and I can look at a map that's from not my study area
and I can make pretty educated guesses
about what's a harvest and what's not a harvest
based on the spacial pattern and the way it shows up.
And that's a big piece of my master's work
is doing spatial analysis and pattern analysis
of these things to try to figure out
if we can attribute harvest or non harvest.
But for example, this, so this area right here,
if I flipped back over to this map,
you'll see that it shows up as a false positive.
But because of the density and the way it's grouped,
it's a harvest.
I've been there in person.
The reason it's a false positive on this map
is because it was harvested right before the land was sold.
So it's not in our harvest records, but it is a harvest.
[Audience Member] Could you take a look
at the accuracy in terms of the timing when it,
Yes.
Yep. Yep, so it has to identify it within the harvest window
and we use a one year leniency period on either side.
If it's a three year harvest,
any detection in the three years is good
and then one on either side is good,
but if it, the harvest was in 2017
and the detection is in 2020,
that's outside the window and it's a false positive.
[Audience Member] So I have two questions,
the first is that there seems to be that blue area
in the upper right that all of the different
detection operations found.
Is there something special about that harvest area
that you think made it easier for the,
[Madeline] Are you talking about this one or this one?
[Audience Member] Oh, I didn't even see that top one.
I was talking about the lower one.
[Madeline] This one?
[Audience Member] on the right.
That one.
They all get that.
Is there anything else special about that one?
It's technically a shelter wood.
It's a very heavy shelter wood.
This is on ESF property.
This is an ESF research cut.
So this is another place I've spent a fair amount of time
and I think that my guess for why they pick it up better
is because it's pretty heavy,
but also because of the nature of the property.
Everything around it is left like more alone,
it sees a lot less traffic.
The Huntington ESF property here
is a working research forest,
but the research activities are fairly concentrated.
That's my guess.
[Audience Member] And I guess the second question
is how viable do you think it is to train model
that's specific to data from our region
that might have better prospects of of performance?
So that's what I'm in in the thick of right now,
working on it.
And the answer is,
I think the answer depends on what you consider
to be an improvement and how incremental an improvement is.
One of the things that we're working on is classification.
And the classification stuff I think is gonna be
more regionally specific and more effective regionally
because that's kind of where we're,
I think we're gonna be able to make the gains.
There's this like really careful line you have to walk
when you're doing something like this between
sensitivity and noise and what side of that line
do you wanna end up on? And some of that's application.
So if you are somebody who's doing an audit,
like a carbon credit audit, maybe you have more time
and more resources to chase down and
human validate some of these things on the other end,
like you're using it to pick your spots
where you wanna go out.
You might have more time than somebody
who's a consulting forester working for a private landowner.
Does that kind of thing make sense?
So I think it depends on your application
and what you hope to do with it.
[Audience Member] Cool.
Yeah. Aiden.
(audience member question)
I, I hope so.
So I actually, with FEMC gave me a grant
and I spent some time this summer doing field work
going to places we didn't have harvest records for,
going to the pixels
and trying to identify what happened there.
And I'm hoping to use that information to
help train another tuning of the algorithm.
[Audience Member] I love the idea of going pixel.
just practically, how does somebody even begin
to access the data and explore?
So, the easiest thing for anybody to do is to go
to the LCMS website where they have a data explorer.
The product is created for you and then you can look
at wherever you like for like the northern region,
look at the data and you can compare it to your stuff.
If you want the data for yourself to use,
The Land Trendr program runs on Google Earth Engine.
And so it, you can pretty much run their default script
without any coding knowledge.
That's basically what I do.
I like, my knowledge of Python is minuscule,
but they've got a very helpful user guide
and then you can just run it through Google Earth engine
and it's a roster output that, that you can have
on your computer and do whatever you want with it.
So like it is a publicly available open access tool
for anybody to use.
Anybody who wants to can make these maps,
as long as you're willing to read the instructions.
(audience applauds)