- [Woman] So our next presenter is Chris Williams
with the Graduate School of Geography at Clark University.
And he will be presenting on
"Plots to Pixels to Policy The Intrepid Journey
"of a Wall-to-Wall Forest Carbon Monitoring
"and Assessment System."
- Fantastic. All right.
I changed it from Intrepid to Foolhardy
because I knew that Chris Woodall would all would be here
and that I expect great hard questions
because what we're trying to do is a really hard thing.
I want to, before I get too far into this,
thank some of the funding sources.
For example, NASA has supported a lot of this work,
so has the Nature Conservancy, a bit of support from NSF.
I'm a professor at Clark University
in geography and environmental science.
And I have a group, a team of individuals working with me
to try and cobble together
a comprehensive wall-to-wall picture
of forest carbon dynamics across the conterminous U.S.,
and that's what I'm going to talk to you about today.
So thanks to Natalia Hasler, Huan Gu, Yu Zhou,
and other members of my group for their contributions.
We all know that carbon is under the microscope
for good reason in forests because we recognize
that forest carbon plays a really significant role
in carbon emissions and carbon uptake
across certainly the U.S. and much of the world,
forests being chief among the natural working lands
that contribute this kind of ecosystem service.
But in order for us to size up policies
and assess the current state,
we need great baseline measurement capabilities,
reporting capabilities, opportunities to verify,
and also just assessment and understanding systems
so that we can detect, monitor,
and assess changes over time,
and project where we think things are going in the future.
So what do we really need?
We need to stand-level estimates of carbon emissions
and removals from the atmosphere;
annually resolved, repeatable, everywhere,
wall-to-wall, and ideally attributed to drivers.
Many of the methods on the market today to do this
have emphasized appropriately live aboveground biomass,
because it's something we can see remotely
and detect spatially, but really much of what we're after
is carbon emissions and removals
or exchange with the atmosphere,
at least with respect to the policy world
concerned with climate change.
And so existing methods
are often ambiguous about mechanisms.
They often lack wall-to-wall coverage
and may not have full forecasting capability.
In my group some time ago, we recognized
a really significant opportunity to take remote sensing
of disturbances, forest disturbances,
remote sensing of biomass at an epoch,
or a moment in time,
combine that with FIA inventory data to assess
that it can help us understand stock accumulation
and change over time in forests of different types
with also a carbon cycle model that helps us extend
beyond the the measured quantities in the field
in plots and also understand full forest carbon dynamics.
And so recognizing this opportunity,
I developed what I'm calling now
the National Forest Carbon Monitoring System
or Carbon Monitoring Reporting and Assessment System,
and it has a flow diagram that looks like this.
We're gonna take it one piece at a time.
First we'll look at how our team has been using
FIA yield curves and plot-based data
served through a validator to generate above-ground biomass
with age curves, and then how we derive carbon stock
and flux assessments at a stand scale with those data.
This is just one example from, happens to be,
from the Pacific Northwest,
a Douglas fir high-productivity stand forest.
And overall, this isn't a singular forest.
This is actually a collection of forest stands
where in-situ plot measurements were made by the FIA,
and they can be arrayed in this fashion
on the lower left-hand curve where we show
above-ground live wood with stand age in age classes.
And we are forcing our cost of carbon cycle
ecosystem process model to match the yield curve
carbon accumulation in a forest of this type in this region,
and of this site productivity class.
When we do that, we then derive curves, for example,
of net ecosystem productivity as a function of stand age.
And this can be produced for a whole array
of forest type groups
and of high and low-productivity classes,
which is a really important additional variable.
We've done that for all of the dominant forest types
and regions across the United States.
We now couple that with observations from remote sensing
of biomass and of disturbance, as well as ancillary data
on forest type distribution and site productivity class.
And we do that so that we can derive information
about the stand age, disturbance legacy,
and site conditions of individual 30-meter pixels
all across the conterminous U.S.
So just to give you a quick glimpse of how we do this,
this is, we're currently using the Kellndorfer NACP
Aboveground Biomass Carbon Baseline Dataset.
And so, for example, this is again our Pacific Northwest
Douglas fir stand.
If it was high-productivity, and we had a measurement
in an individual pixel of about 10 kilograms of carbon
per meter squared, we can use our FIA derived curves
to characterize what the stand age distribution would be
that we would assume is represented
by this 10 kilogram of carbon per meter squared condition.
If it's a low-productivity stand,
we can intersect that observation with some uncertainty
and derive a stand age band shown with the green lines.
So what we're doing here is we're taking
aboveground biomass as a,
to using it as a baseline initialization
in the year of the observations,
this was around the year 2000,
and we're deriving a biomass equivalent stand age
that can then be a marker of the current stocks,
where they were previously
if the stand stayed free of disturbance,
and where it would be going in the next, say,
10, 20, 50 years.
Also, we're relying on a Landsat-derived
disturbance data products.
These originated as the North American Forest Dynamics
Data Products 30-meter mapping of disturbances.
They initially were not attributed to the disturbance type.
But in my group, we intersected those 30-meter
Landsat-derived forest disturbance events
with MTBS fire scars or burn scar information,
as well as aerial detection survey data on bark beetles
and other biotic agents to try to attribute,
in a cursory way, each of those individual 30- meter scale
year-specific disturbance events to a driver
and to map these out in space.
Those two pieces, biomass and disturbance, can be combined
in a way to derive a continuous map of stand age.
And this is continuously mapped for us
across the conterminous U.S. from 1986 to 2010 at present.
And once we have these two pieces,
our yield curve derived carbon sequestration, for example,
or carbon stock dynamics,
as well as information about every pixel's forest type,
site productivity class, biomass, or disturbance
from which we derive stand age,
we can estimate net ecosystem productivity, for example.
And we're gonna zoom in on New England plus New York
in just a second.
So that's the sort of data inputs
and the intermediate variables,
and now I'm going to try to transition
to some applications that give you a sense
of what we can do with this framework.
We will also take a look at some evaluation.
Now we can, we have our annual maps
of carbon stocks and fluxes
at a 30-meter resolution nationally.
And we're also looking at applying them to understand
regional carbon balance and change,
to try to attribute those changes to drivers,
to estimate carbon stock potential of landscapes,
and also to assess carbon emissions risks.
So zooming a little closer to home,
this is a map of forest carbon stocks in the year 2010
according to our data product
across New York plus New England States.
And I just zoomed in on one particular track to show
not just aboveground biomass
but the total live biomass in this one box.
We can report it at a pixel level or over the region.
This happens to hold 68 million metric tons of carbon.
It's mapped here in kilograms of carbon per meter squared.
And our technique also extends
beyond these biomass estimates
to total ecosystem carbon stocks as shown in this lower box.
Now, another thing we can do is, as I said,
zoom in on a singular pixel and ask not just
what it held in terms of its ecosystem carbon stocks
in the year 2010,
but based on its forest type,
for example a maple-beech birch forest type group,
if the stand had about six kilograms of carbon
per meter squared in a year, I don't know, 2000,
how much might it hold in the future?
So we now can take our yield curve information,
for example on the left-hand side
for a Northeast maple-beech birch of high productivity,
the FIA data are sampled with the red pluses.
These curves are our fits through those data
with the curved turf fitting routine that we have.
And let's focus in on just this window
of about 40- to 90-year-old stands.
If it started at about six kilograms of carbon
per meter squared and it was going to acquire
about 10 after another 50 years,
we can see the aboveground biomass expected
in this maple-beech birch forest in the Northeast,
but we also with our framework are able to see
what we expect in terms of soil carbon dynamics,
coarse woody debris dynamics, below ground biomass dynamics,
and so forth.
Furthermore, over that 50-year-period
from a stand age of 40 to 90,
we expect about six kilograms of carbon
to be stored in this ecosystem if it remains
free of disturbance from some initial year
over that 50 years.
And we see that most of it is in biomass
but there's also significant accumulation
in coarse woody debris.
The framework also allows us to estimate
year-specific fluxes of carbon net exchange
with the atmosphere in terms of net ecosystem productivity.
And that's what's shown here with a slight decline
over this 50-year period as the yield curves would suggest
due to a tendency towards saturation.
We've done this for every pixel in our landscape
to just look at the 20-year carbon sequestration opportunity
that we expect at this 30-meter scale across New York
and New England States,
and that's shown in this map illustration.
There are some areas where we see recent
clearcutting having a legacy of carbon emissions
in those sites.
That's why we see some negative values.
And even over this 20-year period,
that can result in a net emission
for that 20-year period as it tends towards regrowth
and carbon sinks following a clearing event.
But much of the landscape is shown to have moderate
to high carbon uptake sequestration potential,
and this can be a really useful tool
for policy makers and state-level accounting
and for assessing opportunities going forward.
So that's a nice prediction,
but of course people would be wondering
how effective we expect, how how successful this is
when we compare it to other data products.
So this now is a time series of aboveground biomass
from 1986 to 2010.
And we're gonna compare this aboveground biomass
to two data products, actually three, excuse me.
Our data product is in the red line,
and this is for the Pacific Northwest,
a place where we've started to look in depth
at what we've seen over the region.
Starting on the left, if we focus in
on recently undisturbed forests,
we see that our estimate is agreeing quite well
with the data set that we train it to agree with,
the NBCD calendar for data set.
So that's no surprise but it is reassuring.
And it also agrees well with Steven Hagen's dataset
with Sussan Saatchi and others,
I think Grant Domke was involved in this as well,
over the Pacific Northwest region
where we saw a tendency towards increased carbon stocks
particularly in the undisturbed forests.
When we include the areas where North American
forest dynamics told us disturbances had occurred,
we find a little bit of a different story where instead
of a tendency for regional accumulation of carbon,
really there's a relatively flat,
relatively constant aboveground biomass.
And that also agrees well with what we were seeing
with our other data products.
Furthermore, eMapR is a data product,
also 30-meter biomass, annually-resolved,
generated by Robert Kennedy at Oregon State University
with his LandTrendr and some Google Earth engine
neural network approaches.
And he has shown, well,
our products show very good agreement.
They're both related to Landsat-derived information
but they're using that in very different ways.
Also on the right, I would show good agreement overall
between bio at a forest-type level
across the Pacific Northwest
between our data product in the red bands
showing that year-to-year variability range
for this 25-year period and what we see
in the NBCD and Hagen et al data sets.
We see that across the dominant forest types.
Another thing I'd like to show you
that we can do is to zoom in on a local assessment.
And this is showing,
sorry for the busy graph,
one particular postage stamp,
and it's showing 1986, 94, 2002, and 2010,
and this period of increased harvest.
Another thing we can do is track
regional scale monitoring and assessment.
So we're looking at carbon stock dynamics,
harvest removals, emissions, fire emissions,
and then net biome productivity and ecosystem productivity
on annual resolution across the entire Pacific Northwest.
I see that we have one minute.
Another thing I'll just tell you
is that we're doing state-level reporting and monitoring
and we're starting to zoom in
on the carbon sequestration opportunities, carbon stocks,
in the '90s, 2000s, 2010s,
and out to 2030 and '50.
For example, here for New Hampshire.
We're excited to be working with states
who are participating in the U.S. Climate Alliance
to do this work.
In future work we're continuing to look
at forest conversions and loss of forests.
And we're also looking at this at a national scale
to come up with some very important conclusions.
For example, that forests are currently holding
only about 50% of their carbon stock potential;
disturbances are being shown to push some regions
toward net carbon emission;
we're seeing that avoided forest carbon loss
could nationwide prevent an emission
of about 20 teragrams of carbon per year;
and that harvest emissions are, as known,
a major offset to carbon uptake in forest lands.
We're doing a lot of innovations.
We intend to work with repeat measures
of FIA plots to test our framework
as well as to perform some scenario testing.
How was the final estimate comparing
with field measure data?
And if this assessment can be used to put arrow bars
in some of the predictions?
Yes. We haven't done that work yet and we should.
We're looking forward to doing it.
So we've been so fixated on generating estimates
at pixel scales where pixel for us is 30 meters
we haven't yet intersected that
with individual plot measurements
either for a given epoch or repeat measurements.
So as I briefly mentioned,
that's one of the, say, innovations or pieces of future work
that we'd really like to do.
Some of our maps are publicly available, James.
So for example, we are, we have posted
with TNC on the Resilient Lands Mapping Tool.
Many of our data sets, and we're,
and we're also generating this for
the Oak Ridge National Lab DAAC,
Data Archive Distribution Center,
and that should be live within weeks.
Who is we?
This is Clark University,
a team of mine in geography and environmental science.
And we are generating state-level results for, well,
like I show here, New Hampshire,
but also all of the New England and York States.
And ultimately we plan to do this nationwide.