Thanks for the intro.
We'll jump right into it here.
So, a little background for this work,
and really why would we need
fine scale carbon accounting at all.
In New York State, a big legislation was passed,
the Climate Leadership and Community Protection Act of 2019,
which is a bundle of energy, sustainability,
climate legislation.
And in that bundle,
they set really, really aggressive emissions targets,
the state's planning to be net zero by 2050,
and they're looking to offset 15% of the statewide emissions
in some way.
With 60% of the state being forested,
policy makers are really gonna be leaning
on the forest carbon sink,
they're expecting it, or depending on it to more than double
in the next 30 years,
which we know to be a tall, tall order,
very likely impossible.
But to this end,
we're trying to help the state understand the where,
the how, and to what extent forests
are gonna be able to contribute here.
And so, you know, like Sarah and the panel talked about,
like we are right at the peak of this hype cycle,
and hopefully this talk is gonna be about
maybe how we can jump over that gap a little bit,
but really being humble about what we're able to do
in this context at a state scale.
So, stock changes,
you know, what is this accounting method in general, right?
All it really is
is about changes between two points in time.
So as trees grow,
they're pulling carbon outta the atmosphere,
that's assumed to be a removal from the atmosphere.
And if you have a disturbance or a loss
in above ground biomass, that's assumed to be an emission.
And so we're operating at a super coarse level here.
Really, the fate of that woody material
is gonna have a big impact
on the immediacy of those emissions.
Right? Is the wood being transferred to long-lived products,
or is it being burned for fuel right away?
We're above that, right?
For sake of simplicity, and in scale,
we're obscuring those details
and just assuming any losses in aboveground biomass
are gonna be emissions.
So this kind of information, stock change information,
is already implemented at state and national scale
by the United States Forest Service,
with their Forest Inventory and Analysis program.
Folks go out and measure trees on the ground,
and they repeat those measurements
every five to seven years.
That information is absolutely invaluable,
we couldn't do this kind of thing without it,
it's one of the best programs in the world
for that kind of thing,
but it's limited by the density
of that sample spatially, right?
There's only one plot
for every 6,000 acres in New York,
and you're limited temporally by that remeasurement cycle,
five to seven years.
So we're trying to translate these measurements
into a map space, into wall to wall maps,
and we're trying to do it at an annual time-step,
going back in time to 1990, which is our baseline.
The idea being that you could take any parcel of land,
any unit of land,
and get this stock change information
for any slice of time in this 30 year period
from 1990 up until now.
That's the big goal here.
So what are we doing specifically?
We are leveraging the above ground biomass measurements
at FIA plots across New York State,
and we're combining that with Landsat satellite imagery,
which offers that historical perspective, importantly,
and offers that regular, in our case, annual, set of data.
So both of these things,
federally funded, really well supported, widely adopted,
and there's a whole host of open source tooling
and existing applications that have made this possible.
And so we relate these things spatially,
and we train models based on these relationships
to make predictions statewide
where we have Landsat information
but we don't have measurements.
So some nuts and bolts here, quickly.
We used 2000 FIA plots, inventoried since 2002,
that gets at Charlotte's point about, right,
we kind of have this missing piece of historical data
before 2000.
Plots were inventoried,
but the methods are not the same,
and so they don't really match up,
so we're stuck using post-2000 data.
Landsat spectral indices,
which are various combinations of red, green, blue,
and near infrared imagery,
use topographic and climatic data.
Right, this is pretty coarse information,
especially compared to what the last talk was talking about,
right, we don't have any diameter diversity information.
This is what we have wall to wall,
and so that's what we have to use.
We feed all this information into a set of models
that we ensemble together.
The idea being, right, we're taking three individual models
that are all making their own predictions,
and we average them together in a sense,
hopefully getting us
something that's closer to the true value
than any of these individual models produce on their own.
And so, right, we got our models, we can make maps,
but then we compare them back to a set of FIA plots
that were not part of this modeling framework,
that are independent in that regard,
to get a sense of how accurate our predictions are.
So if we do that, we get this kind of scatter plot.
So comparing our predictions at FIA plots
to the measurements at FIA plots.
And, right, the biggest thing that jumps out
is this wall, this like, saturation point,
that 200 metric tons per hectare of above ground biomass,
beyond which our model can't make predictions.
That's that saturation problem that Collin talked about,
and it's problematic, right?
Imagine a forest is continuing to grow,
but our models can't see that because of this problem.
And really it's about the spectral signature,
the color of a forest, is saturating or maxing out
well before the size of that forest is gonna max out.
So it's sort of an inherent problem
to modeling forest structure with spectral information.
And then generally looking at some of our error metrics,
it's quite a bit of error.
40% relative root mean squared error,
that's relative to the mean FIA value.
30% relative mean absolute error.
That is what it is,
that's what we're dealing with here.
The mean error, which is just average plus or minus,
is actually pretty good.
The idea being that if you're pooling
a bunch of these predictions up, right,
you have misses below and misses above,
they're gonna average out
and get something pretty close to the true value, hopefully.
So we can push maps statewide,
like the one I'm showing here,
it's just a single year in time,
and it's pretty hard to gather any meaningful insights
spatially at this scale,
but I just wanted to show that.
You know, we can do this statewide, before I zoom down
way into Huntington Wildlife Forest in Newcomb, New York,
which is in Adirondacks there,
situated within that black box.
So, zoomed in, again,
we have a year's worth of aboveground biomass predictions
for 2015,
we have a LIDAR derived canopy height model there
as just added reference information
to try to get a sense for how well our predictions
are characterizing the structure,
for structure spatially across this landscape.
So, some features to point out
that are that are perhaps interesting.
You can see that pocket of really tall vegetation
in the canopy height model,
in yellow there in the northeast of the map,
sort of missing from from that set
of above ground biomass predictions there on the left,
right, and that gets at that saturation issue.
And we have this snippet of the high peaks,
with high elevation, low stature vegetation,
that is pretty well captured by our model there,
as well as some of these cadastral boundaries
in the southwest there,
especially that boundary running diagonally along the lake.
To the east of that boundary is is a natural area
in ESS property,
it's primary forest that's never been harvested.
And to the west is a more heavily managed working forest
that we know have been harvested in the past 20 years.
And you can see some of those effects of management there,
as well as those property boundaries
in our prediction service.
So I'm gonna zoom into this working forest
and show you a little bit
of our our time series capabilities,
as opposed to just the single snapshot.
And, of course, Collin scooped this for his presentation,
so it's a bit stale at this point,
but, right, we're mapping year over year,
and then we're totalling up the biomass
in that slice of forest
and showing that as a function of time.
So as that red dot moves, the map's gonna change.
And really what this is getting at is,
this slice of forest is accumulating
above ground biomass in the first 20 years,
followed by a large harvest event,
which you can see pretty well,
and then that period of recovery,
which might be too fast to be realistic,
given what we know.
So this is really nice, this year over year mapping,
but it's actually just the fuel for the stock change, right?
Now that we have every year mapped,
we can take any slice in this time window
and do some differencing.
So, back out to Huntington Wildlife Forest,
I've got some decadal differences here.
So on the far left is that first decade,
2000 aboveground biomass minus 1990,
and second decade in the final 2019 aboveground biomass
minus 2010 aboveground biomass.
Each decade, interestingly,
shows a bit of a different pattern.
So the first, the area is characterized by stability
and biomass accumulation.
Then you see some of those harvests
starting to occur in the southern area
that we saw in that time series movie.
And then finally, in the third decade,
some of those harvests that you saw in the second decade
are starting to recover and accumulate biomass again.
And then just a note, right,
some of this pixel level speckling that you're seeing here,
those could be real small scale disturbances
and changes in the forest,
or it could be noise in our model predictions
based on noise in the satellite data,
so it's important to remember.
So again, back to this working forest slice here
that I showed you,
instead of scale differences,
we're gonna see some annual differences.
So, same map, and now this time series plot is showing you
year over year changes.
So current years above ground biomass
minus the previous years above ground biomass,
anything above that line
is gonna be a net accumulation of biomass,
and anything below that dotted line is gonna be an emission.
So the pattern's the same,
but what this tells us
is that the rate of biomass accumulation
is slowing in the first 20 years as we approach 2010.
You have this large harvest event,
right, with that emission,
and then you have that period
of really rapid recovery and biomass accumulation
in the first five years post harvest,
but that rate of recovery slows down
in the second five years after the harvest.
So I showed off quite a bit of what we can do here,
but I'm trying to be humble, right?
That this is far from perfect,
there's a lot that's missing from this,
and I wanna highlight some of those things.
First, that saturation effect that we talked about,
you kind of can't miss it when you're doing this thing.
Right, a forest could be,
there's a question in the last talk that was asked, right,
are the old growth forests still sequestering carbon,
or are they stagnating?
Like we can't really answer that,
right, once you get up to a high enough point of biomass,
our model is sort of imposing an artificial ceiling,
and so that limits what kind of insights we can gather.
Then this problem of subtle changes,
steady growth and decline.
We think our models are pretty good
at predicting big changes,
like those harvests that we showed,
but when it comes to smaller changes,
were more or less unproven,
especially given that the size of our errors
are going to outpace the actual annual changes
that we would expect to occur in terms of biomass.
Right? That's problematic given that the rates,
the rates are really what matter,
maybe there's some wiggle room there
in terms of what we can do.
And lastly,
there's some pretty strong relationships
that we can derive from above ground biomass
to above ground carbon
and below ground live carbon,
but we're really quite limited when it comes to soil
and litter carbon pools.
Information we're using, the spectral information,
doesn't really get at that,
and there's just different drivers behind those two pools.
And then, you know, some of the upshots here,
some of the upsides of why we think
this is a pretty good first approach for this kind of thing.
It's really cheap and efficient, right?
We're using federally funded data that's free to us,
open source tooling, and we're able to turn around some,
we think are pretty nice products in a few short years,
with a pretty small team.
We're pretty confident that our maps and our models
are representing landscape patterns and processes
at a large scale,
that's really nice.
And then we have this flexible capacity here,
we can zoom way down to a landowner or parcel level,
and we can also zoom out to county scale, township scale,
all using the same set of underlying data,
which is powerful.
And then I've showed you the retrospective capacity,
looking back towards 1990,
but this same framework could be turned around
and used in a monitoring sense as we get more satellite data
and take new FIA emissions.
So then just a couple of thoughts
based on the way the panel discussion went.
Right, like, this is far from perfect,
I hope I've sort of described that,
but the state is asking for this, they want this,
and we're doing our best to give them what they need
while also sticking true to our guns
in terms of the rigor and the accuracy here.
So we're sort of in this push-pull position.
Yeah, thank you all for coming out and listening.
It was fun.
Thanks to the support I've gotten from ESF
the DC FIA program,
and yeah, it's a great opportunity, thank you.
(audience members applauding)
Yeah?
[Attendant] Thanks, Lucas,
that was a really great talk.
I'm wondering if you could expand a little bit?
I think what the state
is also probably really interested in
is what should they do with their forest moving forward?
Yes.
[Attendant] And maybe if they are looking for them
for offsets, for it in setting,
it's what can we do with our forests
to make them suck up more carbon?
Yep.
[Attendant] How do you see the work you're doing
in like, hind-casting, translating,
to confident forecasting or future-casting
of different management actions?
So like, you're layering on now multiple uncertainties.
That's a great question,
and I'm gonna do a bit of hand waving,
as I buried my head in the weeds here
on this side of things.
I think that the golden ticket is
you can look at our data set and say,
"Right, this plot of land was owned by these people,
and this is what they did."
They know what they did, you know,
and here is the above ground biomass and carbon implications
of what they did.
So this data alone isn't gonna answer that question,
we need folks to tell us what they did on their land
to match that up with these outcomes before we can say,
you know, "that was good," "that was bad."
[Attendant 2] Just kind of curious,
the tool that might exist
to kind of see through that saturation point,
you kind of indicated was LIDAR?
Yeah.
[Attendant 2] Is that what you were using
to kind of show the areas activity?
That comparison.
[Attendant 2] And that the issue
with kind of using that as tools,
we don't have full data sets for the state,
we only have maybe one or two snapshots in time.
Yep.
[Attendant 2] What's the potential
for like photo AR using,
like aerial imagery data sets
going back decades over time
to show vegetation heights and correlate that?
So, I think you're talking about like,
is the layering of the imagery, right,
you can get a point cloud?
[Attendant 2] Yeah.
The way I've heard it, as digital aerial photogrammetry,
DAP point clouds, I think it's the same thing.
[Attendant 2] Yeah, just using kind of the stereo imagery
to derive height, vegetation height,
you have a good model of ground elevation,
so now you can just kind of go back in time
looking at vegetation height.
So I've seen that kind of of effort done really recently.
You can get at that,
I'm not sure if you're familiar with NIP,
it's the National Imagery Program,
if you can get the data that's behind that, you can do that.
I don't know how far back.
Right, they're changing, there's different resolutions.
Not every year, but they upgrade,
I really don't know about the hind-casting capabilities
and matching those things up.
But you can get a point cloud from aerial imagery,
not as dense a point cloud from like a true LIDAR sensor,
but you can get a vegetation height pretty accurately
from that kind of sensor.
So, I don't know that I fully answered your question.
[Attendant 2] Yeah, I was just kind of wondering
if it could be a good way to see through that saturation
at older time sets.
Yeah.
I mean that kind of information
is the best for this kind of thing,
we just don't,
like, that was the first thing we did, was use LIDAR,
and we covered 60% of the state.
So, it's a patchwork, right,
this was acquired at this time,
and this was acquired at another time, so.
[Attendant 3] Yes, just a question about,
you did a great job kind of describing the limitations
and general error,
was wondering what your thoughts are
with the, you know, applicability of the data,
at what spatial scale.
So as you know, in New York state,
about 50% of the forest
is owned by family forest landowners.
The ownerships are around the average ownership size,
it's quite small.
You know, do you think that some of these technologies
are gonna be appropriate
at some of those kind of stand based scales
or smaller ownerships?
Yeah, that's a great question,
we've been thinking a lot about that.
Right, our error is all at FIA plots,
and that scale is gonna be the hardest comparison
you can make.
(audience members applauding)
The idea is that as you aggregate up,
right, like I said,
that mean error starts to get closer to zero.
We're really trying to work through
aggregating uncertainties
up from these individual pixel level predictions,
and that's an uncracked nut for us.
But in terms, like, that's why we're making this,
is so that, at a landowner level,
you can get this kind of information.
Yeah.
I don't think I nailed it,
but do you have any follow ups based on that?
[Attendant 3] Yeah, I guess,
no, not necessarily,
but I guess that's just the error is coming,
so I'm just trying to think of like,
how the error is generated, or how you're thinking about it,
whether those individual FIA plots represent,
probably like an acre or a point space,
but then how you're relating that to your model,
which is a 30 by 30 meter.
30 meter, yeah, yeah, yeah.
[Attendant 3] Right.
So like, that's what I'm trying to kind of
wrap my head around, yeah,
as a non-like spatial analyst.
Yeah, yeah, yeah.
[Attendant 3] A support practitioner.
No. Yeah.
I think, you know, the more you zoom out,
and the more you aggregate,
the more you're averaging up those misses above and below,
and that's a good thing.
But we haven't quite cracked
the aggregating uncertainties yet to those scales.
[Attendant 3] Thanks.
[Attendant 4] I think that's all the time we have,
at lunch now,
but want catch up with them after?
(audience members applauding)