- Our next presenter is Jen Pontius
with the university of Vermont and the USDA forest service.
Her presentation will be on the variability
in forest conversion rates and drivers
across a diverse socio-ecological landscape.
A Vermont regional planning commission case study.
- Hi everybody, I am Jen Pontius,
and I just wanna thank you all
for coming to my first and hopefully
last remote conference presentation.
I realized of course, after I submitted the abstract
that this may be the longest title
for a 20 minute presentation.
So if I were to sum it up,
I'm really here today just to tell you a little bit about,
some of the forest modeling work that we've been doing
specifically in terms of forest fragmentation
and how we might be able to use
some of the products we have developed
to help inform planning.
At the FEMC, we do talk a lot about
the various threats to forested ecosystems in the Northeast.
And there are some common themes that always come up.
Climate change is one, pests and pathogens,
but also fragmentation.
And when we're talking about fragmentation here,
as a part of this project,
we're not talking about civil cultural treatments
or logging activities or even parcelization,
the dividing up of larger parcels into smaller ones.
But really getting at the loss of forest
to developed land use types.
As I mentioned, this project developed from several years
of modeling work that we'd been doing
and this presented several opportunities
that really brought us to
want to work on this project specifically.
And one was just the availability
of these improved forest cover maps
that we were able to create with David (murmurs).
And also the work of Alison Adams,
who had used those forest cover maps
to look at forest conversion on a very broad scale
across the region.
As a part of that study,
she identified some additional questions.
One of which was,
how does forest conversion the drivers of forest conversion
differ across the landscapes?
Does it make more sense if we look at this
at a smaller, more localized scale.
But then what really catalyzed this project
more our collaborators at the Vermont DEC and FPR
as well as at regional planning commissions across Vermont,
who worked with us on a US forest service LSR grant
to both examine those patterns
of forest conversion at the finer scale.
But also, identify ways that we could actually
integrate those products into
land use planning.
So we really had five objectives.
One was just to extend those improved forest mapping
methods across the state,
and then to examine those to see how
forest cover has changed over time
between 1985 and the present,
but then use that to actually identify
what the drivers of those changes were,
and use those relationships
to model forest loss into the future.
But then the application really came
in that fifth objective,
which was to say,
can we overlay this now map of high forest conversion risk
with other maps of high value forest
to help prioritize conservation or planning effort?
And to do all of this on a scale that really was appropriate
for these regional planning commissions to be able to use.
Now in the interest of time
I didn't wanna get too deep into the methods.
So please feel free to ask any questions
and I can share some of the papers
on the background of the modeling that we did,
but essentially we took these historical changes
that we had identified over time from the satellite imagery.
We overlaid those with various spacial maps
of potential forest drivers,
for example distance to roads,
topography, current parcelization or conservation status
and so on and so forth.
And then used Dyamica,
this landscape scale modeling software
to identify locations across the landscape
at a 30 meter scale
where you would have a higher risk of conversion
based on these drivers of historical patterns.
So just jumping into the results,
we were able to find enough cloud free images historically,
to go back through the satellite archive
and be able to
finish the forest cover maps
for all of Vermont.
And again, I can provide information and links to
the original papers that highlight the
improved accuracy and details associated with this method.
And so then once we had those forest cover maps
for 1985, 2000 and 2015 as historical timestamps,
we were able to break down the rates of forest conversion
by each regional RPC.
And there are a couple of interesting patterns
that came out.
So just for perspective,
these are color coded from green being the slowest
rate of forest loss
to red being the fastest rate of forest loss.
And what you can see is a really clear temporal pattern,
where we had much faster forest conversion to development
in the 1985 to 2000 period.
And that really slowed down
although still was a net loss of forest cover,
but did slow down in the 2000 to 2015 era.
But there also is this special pattern.
So that rate of forest loss
was not consistent across all of the RPCs.
Some faced much more
extreme development pressure and forest loss.
These temporal trends are very consistent
with what we saw across the larger region
in the original analysis.
And we really linked to this back to
development pressure which you can capture quite nicely
in this housing price index.
So you can see that we really were in sort of a housing boom
with a lot of incentive for development
as we moved from the early 1990s into around 2000,
2003 when we hit that peak,
and then we had this really precipitous crash.
And this just highlights how it really
is sort of economic conditions
that are driving the rate of conversion.
We still have to investigate
the patterns of those conversions because,
we saw that even when this
housing price index really dropped,
we still had a net forest loss.
It was just much slower.
So, this is when the pattern,
this facial pattern becomes very important.
So our next objective was to take
those spatial patterns that we saw in the historical record
and overlay that with the drivers
to identify some potential correlates we could use,
to quantify forest conversion risk.
And for perspective I wanted to start
with the regional assessment,
that original study,
where you can see the results were highly driven
by population dense areas.
So that's where the majority
of the forest conversion was taking place.
And so the correlations with drivers there was strongest.
So we saw higher risk of forest conversion
near to those high population centers,
near other non-forested areas,
that lower elevations that were flat
and also those parcels that were not
in conservation easement.
When we repeated this now
just for Vermont at the RPC level,
we found that first of all, looking at the state as a whole,
the drivers really were different from
what we had found at the larger region.
So instead of proximity to population density
being one of the primary drivers,
it really was proximity to roads
that were featured much more prominently.
And also we found that the topographic conditions
were not as important.
We also found that this wasn't even consistent
just for the whole state.
That across the RPCs,
those drivers that were weighted most heavily,
really did differ.
While roads were consistently weighted,
the type of road whether it was a distance to an interstate,
the distance to a major road,
or a distance to any road,
did vary depending on the RPC.
And so by doing this at the RPC level,
we were able to get a much more nuanced and localized
set of drivers that really reflected the pressures
in that specific region.
I don't wanna make your eyeballs pop out
but if you are interested,
we do have the report on all of the drivers
and their rank order for their weightings,
in the final risk modeling,
Just to highlight it really was this proximity to roads,
topographic limitations, some,
distance to urban definitely did come up
in some of the RPCs.
But then also land cover characteristics
and economics did show up in a few.
So, there really were differential drivers across the state.
Ultimately this resulted in a much more nuanced
and fine tuned map of forest conversion risk
across the RPCs.
So our next step was to take the current (murmurs)
maps of forest cover, that we had developed
and then link that,
to the rate of conversion that we had
for each of these RPCs.
And we use the average
just to make sure we were hedging our bets
and not falling on either extreme.
And then overlaid that with the probability of conversion
and use this stochastic model,
to be able to project into the future,
which locations were likely to be
converted from forest to developed.
That allows us to model,
the projected forest cover or land cover across the state
at several different timestamps.
And I do wanna note that I've been talking about
our modeling of forest conversion, right?
Flipping from forest to developed land cover types.
But, we also did look at fragmentation metrics
across the state.
And what we see generally over time is,
that not only are we losing forest area,
but the pattern of that forest loss
really is magnifying how fragmented
the forested landscape is.
Both measured in terms of the amount of core forest area,
but also forest edge density.
So we're really seeing our fragmentation metrics
continue to increase even when the rates of change
are relatively low.
And this gets at the importance of the
spatial pattern of forest loss.
Our next step and honestly I think the most interesting part
of this project,
was to figure out how to make it useful.
In terms of informing planning or conservation efforts.
And so we also did some modeling
where we took the risk maps that we had developed
and we overlaid that with other,
forest mapping products,
particularly the really great products
that come out of bio finder and Vermont conservation design.
And so when you overlay these high risk areas
with these high value areas,
you can start to create these more specialized maps
that could identify locations that should be
considered for conservation priority.
Because not everybody wants to download
a bunch of GIS data layers or read this long report.
We've also been working to develop
these fact sheets for each of the RPCs
that really breaks down the key information
such as what the differential rates of change
have been historically
and what that means long-term over the full time period.
What the primary drivers are,
of the forest conversion changes that we see,
and then also, just that map
of what is the current forest cover,
where are the areas of risk?
In this case in the central Vermont
regional planning commission you can see
it really is this sort of proximity to
the I-89 interstate,
and the Montpelier and Waterbury STO town centers.
But when you overlay that
with the high quality habitat blocks,
what you're really seeing are these stands
up in the STO area that really should be considered
as perhaps a high priority
or at least a special consideration
during planning activities.
And again, for perspective
on how that differs across the various RPCs.
Now, here we are in the Northeast kingdom,
and you can see
that these smaller roads become much more important
in terms of the risk.
Again, overlaying that with the high quality blocks
we begin to see some parcels that really are singled out,
as perhaps being
areas that should be looked at more carefully.
And this case of particular concern are,
the locations around (murmurs)
highlighting the importance of tourism in this region
but also the impacts that can have on the forest resource.
One last example I'll give you,
just something that looks quite different
would be Addison County.
So now we're over by Lake Champlain
in a much more highly agricultural area.
Notice that we don't even have that much forest risk at all
because most of this is agricultural.
So while conversion risk is pretty high around Middlebury,
there aren't really a concentration
of high quality forest blocks
that are adjacent to these high-risk areas,
where you have higher risk and high quality is
further up in this sort of Monkton and Bristol area.
And so there always is some danger
when you put up pretty maps.
And so we think it is important
that people are aware of what the limitations are.
First of all, you have to remember that
the methodology took those risk maps
which really just was a probability of conversion
and then applied the stochastic model where,
conversions were just simulated over and over again.
So it's not like you can take an exact pixel and say
I know that by 2040, this is no longer gonna be forest.
It's more looking at the patterns across
the broader landscape.
But not for that pixel level interpretation.
And then also we wanna point out that the rates
of change are really important
and that's primarily driven by economic
and development pressures.
So again, those projections of what's going
to be forest cover and what's not,
or how fast we're gonna lose forest cover
really are dependent upon the economic pressures
that are really driving that conversion across the region.
What can they do?
We do think that these are really useful
in seeing where forests are more likely to disappear
in the future.
So on a relative scale,
which locations are most at risk.
It does help inform us about what the drivers
of historical change have been.
And the assumption is that those drivers will continue
to influence forest conversion patterns.
And then it also shows us that we really are seeing
impacts from forest conversion,
particularly in terms of fragmentation and connectivity.
And, we know that these are incredibly important
from an ecological perspective.
So what this really can do is show us where
we have high risk and again,
integrated with these other products,
high priority locations
where we should focus any of our future efforts.
Most of the work that you see presented today
this is ongoing work.
We are still working to get all of these products up
and discoverable
so that they are easily accessed in various formats
by anybody who might want to use them.
We are currently working with our LSR grant collaborators
on how we might integrate this with other tools,
perhaps like bio finder.
And then also collaborating directly
with our regional planners to help us interpret
the products that we have created.
Does this make sense?
Do the drivers make sense to the patterns that we see
in your RPC makes sense.
And perhaps put together some
used case tests to see
how valid it is and how useful this information is
to inform planning at that regional level.
So if you, or anyone that you work with
might have interest in collaborating
with us to determine if
or how these products are useful,
(background creates inaudible situation)
to your (murmurs),
get in touch with us.
So with that, I will pause there
and try to address any questions you might have.
Thank you everybody for sitting through this
and being a great audience.
Is that I did put a couple of links into the chat.
One of those is a link to the forest mapping products
which are archived in a project at the FEMC.
So if you wanted more details on that methodology,
and then the other is a link to the FEMC fragment tool
which has sort of an aggregation of all sorts
of information on fragmentation across the region.
So, to potentially good sources
if you want more information.
Have we looked at proximity to mills?
We have not,
but that actually is something that could be
interesting if we're talking about forest loss
as a more general, you know, in terms of,
actual logging activities,
but this really was focused on development.
So kind of honing in on where are we seeing
forest conversion that essentially is
not likely to grow back.
But that is something we could do with
the products that we have.
Yeah, so conservation status
was definitely significant in that first model.
In the second model
it did not come up as being as important.
And I think part of it may be
just thought of the range of conservation status data
that we had,
in the database on a spatial scale.
So across the region, and we were able to incorporate places
like national parks
that are clearly conserved or wilderness areas
that are clearly conserved in perpetuity.
So that had a higher conservation status.
Within the state of Vermont we didn't have as much nuance
across the types of different conservation easements.
And I think that's really why it came
out as not being as significant.
So I think that this is where collaborating
with our RPC partners is gonna become
much more interesting because,
they can take the risk maps and they can overlay that
with their own parcel information,
and see what might be explaining some
of those patterns that we came up with
in the historical imagery.
But it's a really great question
and honestly, an important one in terms of policy
and informing different incentives that we use
to minimize fragmentation.
And I have to say Alice Schadler is also on this meeting.
She is our fearless leader who is really working to
integrate all of this work between,
sort of the modeling and the actual planners on the ground.
So Alice, I'm gonna throw you under the bus and say
feel free to chat with Alice as well
if you have other questions
that would make more sense on the planning side.