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[Moderator] I'd like
to introduce Andy Wood

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from Vermont Fish and Wildlife.

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He's gonna talk today
about Connected Forests

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for Climate Adaptation:

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Updates to Vermont's Conservation Design,

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and is everybody online?

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I believe they can hear
us, so yeah, take it away.

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Thanks, thanks so much
for coming, everybody.

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I was all pumped up from
Heather's talk this morning.

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Hopefully, you are too,

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and the tacos aren't putting
you to sleep at this point.

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(audience chuckling)

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My talk today is about connected forests

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for climate adaptation.

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I'm gonna give you a
little teaser of some work

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we've been doing at the Vermont
Fish & Wildlife Department

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for the last two years
to advance our own way

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of addressing this issue
and thinking about it

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from a data science perspective,

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and conservation plan perspective.

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So briefly, today, I'm
just gonna catch folks up

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on what is Vermont Conservation Design,

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if you're not already familiar with it.

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More specifically, I'm gonna
talk today about the update

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we've been doing to the
Habitat Blocks dataset,

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which is a critical piece
of the overall design.

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(voice distorting)

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form of over the inputs and output,

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and some of the ongoing limitations

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and opportunities that
go along with this data.

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So, but first, just to
back up a little bit,

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at the Vermont Fish & Wildlife Department,

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we believe in the
conservation of our fish,

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wildlife, plants, and habitat
for the people of Vermont.

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It's a really big mission.

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One of the ways that we
deliver on that mission

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is through the development and application

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of conservation science.

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One of our flagship efforts,
Vermont Conservation Design,

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which I'll say, VCD, from now on,

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so that will save myself some breath.

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-(audience chuckling)
-This is our reflection

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of our scientific vision for identifying

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the most ecologically functional parts

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of the Vermont landscape,

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and by that, we mean
one that is connected,

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intact, and reflecting the diversity

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of natural features of the state.

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So, it's a spatial product,
but it's also a vision

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that needs to be put on
the ground in various ways.

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This is a shift from a more
classical species-focused model

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of doing conservation
that tens of thousands

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of species across the state,

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there's just no way we
can go about working

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on them one by one,

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so VCD uses a combined coarse filter

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and fine filter model for conservation.

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The thinking being, if we protect
the diversity of habitats,

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natural communities, landscapes,
endless characteristics,

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we can efficiently protect
entire fleets of species.

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And so it's worked for every species.

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We're always gonna need some

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of those fine filter
conservation approaches

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that work on the species and
individualized attention.

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So how do you actually do that?

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How do you put it on the ground?

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Well, one of the ways we start is by

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identifying an essential unit

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of landscape conservation planning.

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And we do that by

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looking at what we call a habitat block.

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Broadly speaking, our
habitat blocks are areas

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of natural cover surrounded by roads,

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development and agriculture.

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And this is a data set that's been

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around since the early 2010s.

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It was originally developed on its own

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and now it has become a
foundational piece of some

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of our other data products in
Vermont Conservation Designs.

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Largely speaking, as I
think we're all aware,

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this is mostly forest, but,

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bear in mind, it also
includes areas of wetlands,

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water bodies, cliffs,

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and a variety of other natural features.

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The data set contains about 4,000 blocks.

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They range in size from
20 acres to 150,000 acres.

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And sort of spatially
speaking, they reflect

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what I think many of us already know,

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that our largest blocks,
here shown the darker red,

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are largely along our mountain ranges

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and in the northeast kingdom.

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So that work on habitat blocks was sort of

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a prerequisite for us to
work on other data sets

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such as interior forests

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and connectivity blocks, which may be

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familiar terms to many of you.

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This is what it looks like
on a more local level.

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This is here, we're
just zoomed into a town,

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and you can see, um,

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you can see, you know,

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we have some towns that have huge blocks,

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other towns look really different

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where they have small
fragmented blocks spread around.

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But this data's been used
by us and our partners

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in a number of conservation
planning efforts

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over the last decade or so.

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So this was a great data set.

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It still is, it served us well over time,

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but one limitation is that
it relied on 30 meter pixel

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input of land cover data.

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So it's always been very blocky

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and it always poorly represents
some areas of the landscape,

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especially along the edges
of our habitat blocks.

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The new data that became available to us

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for this update is the 2016

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LiDAR-derived Forest Canopy data from the

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UVM Spatial Analysis Lab,

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-say that three times fast.
-(audience chuckling)

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The big upshot here is we
moved from an input data set

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of 30 meter pixels to half meter pixel.

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So, in my mind, you know,

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this is just really a transformational way

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to map what's actually out on the ground.

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Here's the old data set on on my left.

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Here's what we see on the new data set.

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Just incredibly much more, you know,

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fine grain resolution.

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And in fact, I would
argue it is a hyper state.

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(audience chuckling)

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It's not perfect,

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just like the Millennium
Falcon is not perfect.

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So we had to do a little
bit of customization to

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get the data to reflect, you know,

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what we wanted ecologically speaking.

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So we kind of split it apart into

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some of its component pieces.

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We took the water land cover, tree canopy,

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the supplemental wetlands and

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supplemental shrublands data sets.

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And then we added in
departments, our department,

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Significant Natural
Communities data layer,

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which helped fill in some gaps for places,

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like the top of Mount Mansfield,

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that weren't being mapped in the way

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that they should have been.

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So this is, granted, a
gross over simplification

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to the GIS folks who really did

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the lion's share of the labor.

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But, you know, broadly
speaking, these are the inputs.

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We used this to make a
statewide map of natural cover.

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And then we removed the
developed lands, again,

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a gross oversimplification.

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We removed certain roads,

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houses and other development buffer,

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as well as some customization around

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ski slope areas that
weren't quite matching up

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the way that we understood them.

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So those were what we consider
our fragmented features

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that we used to cut the
land cover data into

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the set of blocks that you'll
see it in the final product.

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So quickly, natural cover minus

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fragmenting features
equals habitat blocks.

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According to all the GIS folks.

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(audience chuckling)

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So what I'm gonna do is
show you a set of blocks

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in the old data set,
and then the same shot

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in the new data set.

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So this is the old habitat blocks.

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You can see it is very blocky

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and if you compare it to the
underlying aerial imagery,

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it's just not that accurate.

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Here's the same area with our
new habitat blocks dataset.

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Some of the things that
I think are working

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really well in this new representation

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is our ability to pick up
on these connecting lands

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that serve as potential
pathways between blocks

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that just weren't
reflected in the old data.

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There are some other areas
that are getting picked up

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that weren't picked up at
all in the previous dataset.

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And a lot of that comes from
our improved wetland data

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and other advances in interim
tiers as well as just,

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you know, better input land cover data.

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So going back just to show you that again.

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(microphone distorts)

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(audience chuckling)

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Didn't like that, uh

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you know, though there are areas

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that aren't shown at all,

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and so we're missing these
potential pathways for

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movement of plants and animals

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as we think about a changing climate.

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So here's another set
of comparison images.

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On the bottom in the orange
and tan is the old data set.

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On the top is the new data set.

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What do we really like?

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Some things that I think
are working great are

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better representation of wetland data.

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Here we're just,

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we have a better understanding
of where those features are.

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We're better able to detect them.

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I think we're getting a much better

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and more intuitive display
of fragmentation by

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this method of buffering
houses and other development,

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we're calling this like
the Swiss cheese effect.

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You don't really see it as much

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down here in the old dataset,
you gotta look close.

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And I think the Swiss cheese is something

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that people are really gonna understand

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and will help us share
the message of what's

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(voice distorting)

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The other big advance, you
know, like I talked about

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with the connecting lens is
our ability to better map

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edges of the forest block.

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So where these blocks come
down and touch the road.

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So come down and touch other land uses.

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So, you know, you can
see in the old image,

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we have this kind of blocky cut out here.

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In the new image, we have, you know,

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a much finer resolution of

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where those blocks begin.

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And so, you're thinking
about something like

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wildlife road crosses, this
is a really big advancement.

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What do we not like?

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One of the challenges that came up was

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areas that have been managed.

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Forests are not always consistently

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showing up in the data the way

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we would want them to be.

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They, you know, they're showing
up as holes in our blocks,

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which doesn't really
reflect our ecological

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understanding of those activities.

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So that's something we're
working on, thinking about,

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or talk about that issue and
explain the limitations when we

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share the data.

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Another one that bugs
me is the power lines.

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If you've been out on
a power line, you know,

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there are often areas of
extensive shrubland, wetland,

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those areas should be getting
picked up by our data.

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But again, because of the inputs,

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they are inconsistently
picked up sometimes

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we get 'em, other times we miss 'em.

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But again, I think that one's
pretty easy to understand.

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It's a power line.

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It does require interpretation

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when you're using this data

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and you know, being aware
that that's a limitation.

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So, you know, big picture,

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we have two data sets now.

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I think it's important
to understand they are

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not apples to apples.

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You cannot compare them directly,

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'cause they were created in different ways

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with different inputs.

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But we wanted to understand, you know,

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what does this actually look like

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when we zoom out on the statewide scale?

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Old blocks over here,
new blocks over there.

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The general pattern is the same.

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Our largest blocks are again,
along the mountain spines

260
00:12:16,830 --> 00:12:19,020
and the northeast kingdom.

261
00:12:19,020 --> 00:12:20,853
Same is true in the new dataset.

262
00:12:21,990 --> 00:12:23,940
I think where we're, whoops.

263
00:12:23,940 --> 00:12:26,970
There's supposed to be one
where we zoomed in on towns.

264
00:12:26,970 --> 00:12:29,280
I think what you'll find
is when you look at an area

265
00:12:29,280 --> 00:12:32,550
in greater detail, you know,
the individual outlines

266
00:12:32,550 --> 00:12:34,470
of each block have have changed.

267
00:12:34,470 --> 00:12:37,260
And that's largely because we're able to

268
00:12:37,260 --> 00:12:38,850
better detect, you know,

269
00:12:38,850 --> 00:12:40,830
the little squiggles along
the edge of the block

270
00:12:40,830 --> 00:12:44,640
where we have actual natural cover

271
00:12:44,640 --> 00:12:47,493
that just simply wasn't mapped
in the previous dataset.

272
00:12:48,510 --> 00:12:50,640
We did run some numbers
just to see, you know,

273
00:12:50,640 --> 00:12:53,880
how different is this with
the caveat that it's not

274
00:12:53,880 --> 00:12:55,380
apples to apples,

275
00:12:55,380 --> 00:12:59,280
and we think it's largely
consistent big picture.

276
00:12:59,280 --> 00:13:02,280
And that's important because
people have invested a lot

277
00:13:02,280 --> 00:13:05,313
of planning efforts in
the previous data set.

278
00:13:06,300 --> 00:13:08,970
You know, we wanna just
sort of acknowledge

279
00:13:08,970 --> 00:13:11,880
that that has, you know,
gone into a variety

280
00:13:11,880 --> 00:13:13,323
of planning efforts, so,

281
00:13:14,490 --> 00:13:15,450
we're gonna work on this

282
00:13:15,450 --> 00:13:18,000
and figure out some of
the other implications.

283
00:13:18,000 --> 00:13:20,910
But largely, we have a product

284
00:13:20,910 --> 00:13:23,043
that can be compared in certain ways.

285
00:13:24,840 --> 00:13:26,700
So I'm getting to the end here.

286
00:13:26,700 --> 00:13:28,830
What does this have to
do with climate change?

287
00:13:28,830 --> 00:13:32,850
Big takeaway point I wanna
leave you with today is

288
00:13:32,850 --> 00:13:36,000
we have a vastly improved ability to map

289
00:13:36,000 --> 00:13:38,040
pathways of connectivity

290
00:13:38,040 --> 00:13:40,050
for plants and wildlife

291
00:13:40,050 --> 00:13:44,340
as they move throughout the
state in a changing climate.

292
00:13:44,340 --> 00:13:46,950
That said, I think there's a
lot of other dimensions of this

293
00:13:46,950 --> 00:13:48,960
that we haven't fully explored.

294
00:13:48,960 --> 00:13:50,550
So if you're sitting there

295
00:13:50,550 --> 00:13:52,380
and there's a little light
bulb going off in your head

296
00:13:52,380 --> 00:13:54,600
about, you know, what
you and your organization

297
00:13:54,600 --> 00:13:56,280
would like to do with this work,

298
00:13:56,280 --> 00:13:57,236
please reach out.

299
00:13:57,236 --> 00:13:58,830
We'd love to hear those ideas

300
00:13:58,830 --> 00:14:02,250
and we're really eager to
share this work with you

301
00:14:02,250 --> 00:14:03,843
and see where it goes.

302
00:14:13,200 --> 00:14:16,200
Lastly, I just wanna acknowledge
that this is not my work.

303
00:14:16,200 --> 00:14:18,570
This is a team,

304
00:14:18,570 --> 00:14:21,270
lots of very impressive minds here

305
00:14:21,270 --> 00:14:23,610
who help bring this work to fruition

306
00:14:23,610 --> 00:14:26,820
as well as all of our partner
organizations and funders.

307
00:14:26,820 --> 00:14:29,073
So, questions?

308
00:14:32,040 --> 00:14:33,150
Yes.

309
00:14:33,150 --> 00:14:34,255
I really like that.

310
00:14:34,255 --> 00:14:36,063
And the idea about edge,

311
00:14:36,063 --> 00:14:38,340
both in terms of buffering the houses

312
00:14:38,340 --> 00:14:40,710
and getting that Swiss
cheese effect and also roads.

313
00:14:40,710 --> 00:14:44,070
But I'm curious what buffer
distance did you decide was

314
00:14:44,070 --> 00:14:45,743
determined what is and is not?

315
00:14:45,743 --> 00:14:49,410
I think we did 150 feet there, right Ally?

316
00:14:49,410 --> 00:14:50,243
[Ally] I think so.

317
00:14:50,243 --> 00:14:52,140
Yeah, I'll double check the metadata, so.

318
00:14:52,140 --> 00:14:53,057
That person has his hand raised.

319
00:14:53,057 --> 00:14:54,630
Was it the same buffer distance?

320
00:14:54,630 --> 00:14:57,720
So we took for houses, we took the E911

321
00:14:57,720 --> 00:14:59,460
buildings layer and we buffered that out.

322
00:14:59,460 --> 00:15:00,612
So those are the circles we did.

323
00:15:00,612 --> 00:15:01,445
Yeah.

324
00:15:01,445 --> 00:15:04,710
The roads, that's like a
much more complicated answer,

325
00:15:04,710 --> 00:15:05,880
but I can share the metadata.

326
00:15:05,880 --> 00:15:07,020
Yeah, okay, great.

327
00:15:07,020 --> 00:15:07,853
Thanks.

328
00:15:08,940 --> 00:15:09,773
Yes, Tim.

329
00:15:10,650 --> 00:15:14,310
I'm curious about mapping young forest.

330
00:15:14,310 --> 00:15:18,810
Is there a way to identify, you know,

331
00:15:18,810 --> 00:15:22,050
early successional young forest
conditions on the landscape?

332
00:15:22,050 --> 00:15:23,640
I ask because I, you know,

333
00:15:23,640 --> 00:15:26,350
typically when I'm doing
like landscape analyses for

334
00:15:27,330 --> 00:15:30,030
a landowner, I try to
report out, you know,

335
00:15:30,030 --> 00:15:33,360
mature forest acreage
and young forest acreage,

336
00:15:33,360 --> 00:15:35,820
Looking at the 2016 dataset.

337
00:15:35,820 --> 00:15:37,770
I look at like canopy height values,

338
00:15:37,770 --> 00:15:39,180
just try to visually assess it.

339
00:15:39,180 --> 00:15:42,750
But it would be so awesome for my work

340
00:15:42,750 --> 00:15:46,020
and all of our work to
understand like young forest.

341
00:15:46,020 --> 00:15:47,550
And to kind of start
answering my own question,

342
00:15:47,550 --> 00:15:49,380
you said that one of the
things that bugged you was

343
00:15:49,380 --> 00:15:52,200
that the forest management
kind of popped out, right?

344
00:15:52,200 --> 00:15:55,050
You basically, it seemed like you kind of

345
00:15:55,050 --> 00:15:58,357
detected changes in
forest cover between what,

346
00:15:59,257 --> 00:16:02,430
2016, 2013, and 2023?

347
00:16:02,430 --> 00:16:04,017
Well, not exactly.

348
00:16:04,017 --> 00:16:05,610
(audience chuckling)

349
00:16:05,610 --> 00:16:07,143
I think, you know,

350
00:16:08,514 --> 00:16:10,140
there are some things you could look into

351
00:16:10,140 --> 00:16:11,550
along that line of questioning.

352
00:16:11,550 --> 00:16:12,990
I think there are other data sets

353
00:16:12,990 --> 00:16:15,120
that are probably gonna be better served,

354
00:16:15,120 --> 00:16:17,610
you know, as you're thinking
about things like canopy height

355
00:16:17,610 --> 00:16:22,053
and, you know, detecting
certain management practices.

356
00:16:24,090 --> 00:16:28,020
I'm not certain like that this
is necessarily the data set

357
00:16:28,020 --> 00:16:29,220
that's gonna tell you that.

358
00:16:29,220 --> 00:16:31,830
I think, you know, like
any other data set,

359
00:16:31,830 --> 00:16:33,780
you can use this together with,

360
00:16:33,780 --> 00:16:36,120
you know, all the other
tools in your toolkit

361
00:16:36,120 --> 00:16:37,670
and your professional judgment.

362
00:16:39,750 --> 00:16:40,980
Yes.

363
00:16:40,980 --> 00:16:42,660
So you're rolling this out here.

364
00:16:42,660 --> 00:16:45,420
Do you have plans to roll
it out with communities,

365
00:16:45,420 --> 00:16:47,939
'cause I'd be really curious
to see what the response is

366
00:16:47,939 --> 00:16:50,820
to this Swiss chief effect,

367
00:16:50,820 --> 00:16:54,630
in terms of what people
look at as forest cover and,

368
00:16:54,630 --> 00:16:55,463
Yeah.

369
00:16:55,463 --> 00:16:58,650
And moving forward with changes in Act 250

370
00:16:58,650 --> 00:17:01,410
and the idea of lack of housing

371
00:17:01,410 --> 00:17:03,363
and where that housing takes place.

372
00:17:04,560 --> 00:17:05,940
This would be really telling to see

373
00:17:05,940 --> 00:17:08,520
what the response is from the community.

374
00:17:08,520 --> 00:17:09,720
Yeah, that's a great point.

375
00:17:09,720 --> 00:17:12,390
You know, right now
we're at the tail end of

376
00:17:12,390 --> 00:17:15,300
kind of the science phase,
you know, the analysis phase

377
00:17:15,300 --> 00:17:18,750
and you know, sometime
we'll be moving into

378
00:17:18,750 --> 00:17:21,510
kind of the outreach phase of this work.

379
00:17:21,510 --> 00:17:24,600
You know, I will say
that, you know, we had,

380
00:17:24,600 --> 00:17:28,838
we tried to bring in a fair
number of different points,

381
00:17:28,838 --> 00:17:31,140
viewpoints into our, you
know, steering committee

382
00:17:31,140 --> 00:17:33,270
for this work to make sure that we were,

383
00:17:33,270 --> 00:17:34,710
we're headed in a good direction.

384
00:17:34,710 --> 00:17:38,110
But yeah, absolutely the outreach
is gonna be a big piece of

385
00:17:39,221 --> 00:17:41,130
how we bring this work to life

386
00:17:41,130 --> 00:17:43,330
in the communities that
we live and work in.

387
00:17:45,930 --> 00:17:46,763
Yes.

388
00:17:46,763 --> 00:17:48,930
Is there gonna be an effort to correct

389
00:17:48,930 --> 00:17:52,860
the issue with the
recently harvested areas?

390
00:17:52,860 --> 00:17:56,280
It seems like a pretty significant issue

391
00:17:56,280 --> 00:17:58,800
and it would be, I think
it'd be unfortunate

392
00:17:58,800 --> 00:18:02,070
to have that in the dataset for a decade

393
00:18:02,070 --> 00:18:04,500
if it takes that long to update.

394
00:18:04,500 --> 00:18:05,440
Yeah.

395
00:18:05,440 --> 00:18:08,010
I think that those
conversations about, you know,

396
00:18:08,010 --> 00:18:09,870
what are the implications,
what do we do with that,

397
00:18:09,870 --> 00:18:11,553
are happening, so.

398
00:18:15,630 --> 00:18:17,740
Is there a timeline for this getting up

399
00:18:18,780 --> 00:18:20,043
or is it up already?

400
00:18:21,840 --> 00:18:22,860
To be determined.

401
00:18:22,860 --> 00:18:26,070
I think, you know, we're,
like I said at the beginning,

402
00:18:26,070 --> 00:18:28,530
for those of you are here,
this is kind of a teaser

403
00:18:28,530 --> 00:18:31,920
of what we've been up to
for the last two years.

404
00:18:31,920 --> 00:18:33,693
When exactly it's released,

405
00:18:35,250 --> 00:18:37,530
we'll have to keep you posted on that.

406
00:18:37,530 --> 00:18:38,363
Thanks.

407
00:18:41,010 --> 00:18:42,600
Awesome.

408
00:18:42,600 --> 00:18:43,623
All right, thanks.