[Moderator] I'd like to introduce Andy Wood
from Vermont Fish and Wildlife.
He's gonna talk today about Connected Forests
for Climate Adaptation:
Updates to Vermont's Conservation Design,
and is everybody online?
I believe they can hear us, so yeah, take it away.
Thanks, thanks so much for coming, everybody.
I was all pumped up from Heather's talk this morning.
Hopefully, you are too,
and the tacos aren't putting you to sleep at this point.
(audience chuckling)
My talk today is about connected forests
for climate adaptation.
I'm gonna give you a little teaser of some work
we've been doing at the Vermont Fish & Wildlife Department
for the last two years to advance our own way
of addressing this issue and thinking about it
from a data science perspective,
and conservation plan perspective.
So briefly, today, I'm just gonna catch folks up
on what is Vermont Conservation Design,
if you're not already familiar with it.
More specifically, I'm gonna talk today about the update
we've been doing to the Habitat Blocks dataset,
which is a critical piece of the overall design.
(voice distorting)
form of over the inputs and output,
and some of the ongoing limitations
and opportunities that go along with this data.
So, but first, just to back up a little bit,
at the Vermont Fish & Wildlife Department,
we believe in the conservation of our fish,
wildlife, plants, and habitat for the people of Vermont.
It's a really big mission.
One of the ways that we deliver on that mission
is through the development and application
of conservation science.
One of our flagship efforts, Vermont Conservation Design,
which I'll say, VCD, from now on,
so that will save myself some breath.
-(audience chuckling) -This is our reflection
of our scientific vision for identifying
the most ecologically functional parts
of the Vermont landscape,
and by that, we mean one that is connected,
intact, and reflecting the diversity
of natural features of the state.
So, it's a spatial product, but it's also a vision
that needs to be put on the ground in various ways.
This is a shift from a more classical species-focused model
of doing conservation that tens of thousands
of species across the state,
there's just no way we can go about working
on them one by one,
so VCD uses a combined coarse filter
and fine filter model for conservation.
The thinking being, if we protect the diversity of habitats,
natural communities, landscapes, endless characteristics,
we can efficiently protect entire fleets of species.
And so it's worked for every species.
We're always gonna need some
of those fine filter conservation approaches
that work on the species and individualized attention.
So how do you actually do that?
How do you put it on the ground?
Well, one of the ways we start is by
identifying an essential unit
of landscape conservation planning.
And we do that by
looking at what we call a habitat block.
Broadly speaking, our habitat blocks are areas
of natural cover surrounded by roads,
development and agriculture.
And this is a data set that's been
around since the early 2010s.
It was originally developed on its own
and now it has become a foundational piece of some
of our other data products in Vermont Conservation Designs.
Largely speaking, as I think we're all aware,
this is mostly forest, but,
bear in mind, it also includes areas of wetlands,
water bodies, cliffs,
and a variety of other natural features.
The data set contains about 4,000 blocks.
They range in size from 20 acres to 150,000 acres.
And sort of spatially speaking, they reflect
what I think many of us already know,
that our largest blocks, here shown the darker red,
are largely along our mountain ranges
and in the northeast kingdom.
So that work on habitat blocks was sort of
a prerequisite for us to work on other data sets
such as interior forests
and connectivity blocks, which may be
familiar terms to many of you.
This is what it looks like on a more local level.
This is here, we're just zoomed into a town,
and you can see, um,
you can see, you know,
we have some towns that have huge blocks,
other towns look really different
where they have small fragmented blocks spread around.
But this data's been used by us and our partners
in a number of conservation planning efforts
over the last decade or so.
So this was a great data set.
It still is, it served us well over time,
but one limitation is that it relied on 30 meter pixel
input of land cover data.
So it's always been very blocky
and it always poorly represents some areas of the landscape,
especially along the edges of our habitat blocks.
The new data that became available to us
for this update is the 2016
LiDAR-derived Forest Canopy data from the
UVM Spatial Analysis Lab,
-say that three times fast. -(audience chuckling)
The big upshot here is we moved from an input data set
of 30 meter pixels to half meter pixel.
So, in my mind, you know,
this is just really a transformational way
to map what's actually out on the ground.
Here's the old data set on on my left.
Here's what we see on the new data set.
Just incredibly much more, you know,
fine grain resolution.
And in fact, I would argue it is a hyper state.
(audience chuckling)
It's not perfect,
just like the Millennium Falcon is not perfect.
So we had to do a little bit of customization to
get the data to reflect, you know,
what we wanted ecologically speaking.
So we kind of split it apart into
some of its component pieces.
We took the water land cover, tree canopy,
the supplemental wetlands and
supplemental shrublands data sets.
And then we added in departments, our department,
Significant Natural Communities data layer,
which helped fill in some gaps for places,
like the top of Mount Mansfield,
that weren't being mapped in the way
that they should have been.
So this is, granted, a gross over simplification
to the GIS folks who really did
the lion's share of the labor.
But, you know, broadly speaking, these are the inputs.
We used this to make a statewide map of natural cover.
And then we removed the developed lands, again,
a gross oversimplification.
We removed certain roads,
houses and other development buffer,
as well as some customization around
ski slope areas that weren't quite matching up
the way that we understood them.
So those were what we consider our fragmented features
that we used to cut the land cover data into
the set of blocks that you'll see it in the final product.
So quickly, natural cover minus
fragmenting features equals habitat blocks.
According to all the GIS folks.
(audience chuckling)
So what I'm gonna do is show you a set of blocks
in the old data set, and then the same shot
in the new data set.
So this is the old habitat blocks.
You can see it is very blocky
and if you compare it to the underlying aerial imagery,
it's just not that accurate.
Here's the same area with our new habitat blocks dataset.
Some of the things that I think are working
really well in this new representation
is our ability to pick up on these connecting lands
that serve as potential pathways between blocks
that just weren't reflected in the old data.
There are some other areas that are getting picked up
that weren't picked up at all in the previous dataset.
And a lot of that comes from our improved wetland data
and other advances in interim tiers as well as just,
you know, better input land cover data.
So going back just to show you that again.
(microphone distorts)
(audience chuckling)
Didn't like that, uh
you know, though there are areas
that aren't shown at all,
and so we're missing these potential pathways for
movement of plants and animals
as we think about a changing climate.
So here's another set of comparison images.
On the bottom in the orange and tan is the old data set.
On the top is the new data set.
What do we really like?
Some things that I think are working great are
better representation of wetland data.
Here we're just,
we have a better understanding of where those features are.
We're better able to detect them.
I think we're getting a much better
and more intuitive display of fragmentation by
this method of buffering houses and other development,
we're calling this like the Swiss cheese effect.
You don't really see it as much
down here in the old dataset, you gotta look close.
And I think the Swiss cheese is something
that people are really gonna understand
and will help us share the message of what's
(voice distorting)
The other big advance, you know, like I talked about
with the connecting lens is our ability to better map
edges of the forest block.
So where these blocks come down and touch the road.
So come down and touch other land uses.
So, you know, you can see in the old image,
we have this kind of blocky cut out here.
In the new image, we have, you know,
a much finer resolution of
where those blocks begin.
And so, you're thinking about something like
wildlife road crosses, this is a really big advancement.
What do we not like?
One of the challenges that came up was
areas that have been managed.
Forests are not always consistently
showing up in the data the way
we would want them to be.
They, you know, they're showing up as holes in our blocks,
which doesn't really reflect our ecological
understanding of those activities.
So that's something we're working on, thinking about,
or talk about that issue and explain the limitations when we
share the data.
Another one that bugs me is the power lines.
If you've been out on a power line, you know,
there are often areas of extensive shrubland, wetland,
those areas should be getting picked up by our data.
But again, because of the inputs,
they are inconsistently picked up sometimes
we get 'em, other times we miss 'em.
But again, I think that one's pretty easy to understand.
It's a power line.
It does require interpretation
when you're using this data
and you know, being aware that that's a limitation.
So, you know, big picture,
we have two data sets now.
I think it's important to understand they are
not apples to apples.
You cannot compare them directly,
'cause they were created in different ways
with different inputs.
But we wanted to understand, you know,
what does this actually look like
when we zoom out on the statewide scale?
Old blocks over here, new blocks over there.
The general pattern is the same.
Our largest blocks are again, along the mountain spines
and the northeast kingdom.
Same is true in the new dataset.
I think where we're, whoops.
There's supposed to be one where we zoomed in on towns.
I think what you'll find is when you look at an area
in greater detail, you know, the individual outlines
of each block have have changed.
And that's largely because we're able to
better detect, you know,
the little squiggles along the edge of the block
where we have actual natural cover
that just simply wasn't mapped in the previous dataset.
We did run some numbers just to see, you know,
how different is this with the caveat that it's not
apples to apples,
and we think it's largely consistent big picture.
And that's important because people have invested a lot
of planning efforts in the previous data set.
You know, we wanna just sort of acknowledge
that that has, you know, gone into a variety
of planning efforts, so,
we're gonna work on this
and figure out some of the other implications.
But largely, we have a product
that can be compared in certain ways.
So I'm getting to the end here.
What does this have to do with climate change?
Big takeaway point I wanna leave you with today is
we have a vastly improved ability to map
pathways of connectivity
for plants and wildlife
as they move throughout the state in a changing climate.
That said, I think there's a lot of other dimensions of this
that we haven't fully explored.
So if you're sitting there
and there's a little light bulb going off in your head
about, you know, what you and your organization
would like to do with this work,
please reach out.
We'd love to hear those ideas
and we're really eager to share this work with you
and see where it goes.
Lastly, I just wanna acknowledge that this is not my work.
This is a team,
lots of very impressive minds here
who help bring this work to fruition
as well as all of our partner organizations and funders.
So, questions?
Yes.
I really like that.
And the idea about edge,
both in terms of buffering the houses
and getting that Swiss cheese effect and also roads.
But I'm curious what buffer distance did you decide was
determined what is and is not?
I think we did 150 feet there, right Ally?
[Ally] I think so.
Yeah, I'll double check the metadata, so.
That person has his hand raised.
Was it the same buffer distance?
So we took for houses, we took the E911
buildings layer and we buffered that out.
So those are the circles we did.
Yeah.
The roads, that's like a much more complicated answer,
but I can share the metadata.
Yeah, okay, great.
Thanks.
Yes, Tim.
I'm curious about mapping young forest.
Is there a way to identify, you know,
early successional young forest conditions on the landscape?
I ask because I, you know,
typically when I'm doing like landscape analyses for
a landowner, I try to report out, you know,
mature forest acreage and young forest acreage,
Looking at the 2016 dataset.
I look at like canopy height values,
just try to visually assess it.
But it would be so awesome for my work
and all of our work to understand like young forest.
And to kind of start answering my own question,
you said that one of the things that bugged you was
that the forest management kind of popped out, right?
You basically, it seemed like you kind of
detected changes in forest cover between what,
2016, 2013, and 2023?
Well, not exactly.
(audience chuckling)
I think, you know,
there are some things you could look into
along that line of questioning.
I think there are other data sets
that are probably gonna be better served,
you know, as you're thinking about things like canopy height
and, you know, detecting certain management practices.
I'm not certain like that this is necessarily the data set
that's gonna tell you that.
I think, you know, like any other data set,
you can use this together with,
you know, all the other tools in your toolkit
and your professional judgment.
Yes.
So you're rolling this out here.
Do you have plans to roll it out with communities,
'cause I'd be really curious to see what the response is
to this Swiss chief effect,
in terms of what people look at as forest cover and,
Yeah.
And moving forward with changes in Act 250
and the idea of lack of housing
and where that housing takes place.
This would be really telling to see
what the response is from the community.
Yeah, that's a great point.
You know, right now we're at the tail end of
kind of the science phase, you know, the analysis phase
and you know, sometime we'll be moving into
kind of the outreach phase of this work.
You know, I will say that, you know, we had,
we tried to bring in a fair number of different points,
viewpoints into our, you know, steering committee
for this work to make sure that we were,
we're headed in a good direction.
But yeah, absolutely the outreach is gonna be a big piece of
how we bring this work to life
in the communities that we live and work in.
Yes.
Is there gonna be an effort to correct
the issue with the recently harvested areas?
It seems like a pretty significant issue
and it would be, I think it'd be unfortunate
to have that in the dataset for a decade
if it takes that long to update.
Yeah.
I think that those conversations about, you know,
what are the implications, what do we do with that,
are happening, so.
Is there a timeline for this getting up
or is it up already?
To be determined.
I think, you know, we're, like I said at the beginning,
for those of you are here, this is kind of a teaser
of what we've been up to for the last two years.
When exactly it's released,
we'll have to keep you posted on that.
Thanks.
Awesome.
All right, thanks.