WEBVTT

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Good afternoon.

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I'm really excited to be introducing Nick
for this talk.

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I was looking, so we,
the geography department started having

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Darwin speakers in 2002,
and at that point we had Doctor Robert

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Gates from Clark who talked about
sustainability science.

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We're back to Clark University again.

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Nick came out of Clark about a year and a
half ago now, something like that.

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So we're kind of coming back full circle
and going back to somebody from Clark.

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So Nick graduated from Clark, as I said,
in 2023 with a PhD in geography,

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doing a dissertation on sort of
mitigating urban heat island effect.

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He's got his master's in education from
Lehman College in New York City,

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and then spent five years actually
teaching high school in the Bronx there.

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So we figured if he could teach in the
Bronx,

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he could probably teach in the Silver
State, but that was too much trouble.

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And then he's got 2 bachelor's degrees,
one in environmental policy and the other

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in French lit.

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And I forgot,
if anybody has questions about French

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Liberty,
you can have to pick up that there.

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While he was at Clark,
he taught full time at Holy Cross as a

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visiting lecturer for a couple of years.

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So we brought Nick in with a lot of
teaching experience,

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which has been really,
really exciting for us.

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He's also been,
I think starting last year,

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managing something called the Hero
program at Clark,

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which is part of the Green in the Gateway
Cities program.

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They've been planting trees in and around
Worcester,

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and I think we're sort of doing,
we've done some of that here on Salem

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States campus also.

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So it's kind of nice that he's been
involved with that.

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So Nick is our tree and our drone guy
there.

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You want to know anything about trees or
anything about drones,

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We're going to talk to Nick there.

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He's been doing tree canopy studies and
swamps cut in Marblehead.

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He helped out.

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We were working with biology that down in
the Greenlawn Cemetery that's become an

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Arboretum.

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He helped out doing some of the work down
there.

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And then he's been teaching our drone
class.

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If anybody's sort of interested in drones
in the fall, we run a drone class.

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We are going to learn a lot about sort of
all about drones and how you get a

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license and you're going to do projects
and those kinds of things.

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And people have learned that Nick was
doing that real quickly as I think he's

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now getting more requests to sort of do
drone things around than he has time to

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do there.

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So without further ado,
I'm going to introduce Nick Drone,

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who's going to be talking about searching
for shade,

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adapting to extreme heat in the Northeast
United States.

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All right.

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Thank you.

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It's really great to be here.

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I have to say the opportunity to speak to
this many people to reflect on prior

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research and future directions at this
time,

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which is an important time both
politically and environmentally is is

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just amazing.

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I really want to thank especially Ryan
Professor Ryan Fischer for helping out

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with the organizing and and coordinating
all of this is also Professor John Hayes

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for establishing the geography speaking
time during 2002, a long time ago,

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but so really grateful for both for that
work.

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My talk is titled searching for shade
extreme heat in the Northeast and behind

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it is a thermal temperature map that is,
and I realized I,

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I was going to pay us more, but I'm,
I'm going to I am plug this in.

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So I'm just going to stay here,
but it's during a heat wave in July 2022.

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So I am going to touch this make sure
this goes.

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So I'm broken this up into kind of three
sections going to go over extreme heat in

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the Northeast,
what the predictions look like,

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what trends are.

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Try to get a little bit into the climate
science for my climate change class

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that's here.

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And then we'll go into my work with
street trees,

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their impacts on temperature and then
potential solution.

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The Milwaukee forest,
and this is me leading a tree talk in the

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Havant Arboretum,
which is a patch of forests in Worcester

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that is very similar to what the types of
green solutions that hopefully we'll be

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able to have more of in our cities.

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So where is the state of the science it
related to extreme heat and global

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warming.

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So in 2023,
the IPCC report released this graphic,

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among others,
talking about different heat or

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temperature thresholds.

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It starts to start at the global level.

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So you'll notice these are global maps
down below.

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And they identified these thresholds of 1.
5°C,

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which I'm going to refer to quite a bit,
as well as 2°C.

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And these are relics or not relics,
but descended from the Paris climate

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agreement.

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So those numbers were identified then as
important for mitigating 1.5°,

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for mitigating any of some of the worst
adverse effects of climate change.

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And then 2°C was sort of the threshold at
which we would begin to see irreversible

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change.

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So maybe no, you know,
ice sheets in the Arctic,

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huge loss of species biodiversity,
you know, no, no coral reefs,

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massive amounts of sea level rise,
changes that we cannot make any

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modifications to.

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So those are important thresholds for us.

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I also want to draw your attention to in
the maps,

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they're looking at the hottest day
temperature.

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So when we're thinking about climate
science and especially when we start to

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think about how climate impacts health,
we're often thinking about the worst case

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scenario.

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So these maps show what the hottest day
would be like that summer and those

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changes that.

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So that was in 2023.

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In 2024 we actually passed for the first
time the global one year average for

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temperature was 1.6°C.

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Don't be too alarmed, the 1.
5 threshold is a 10 year average.

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So in the climate change class we talked
about how 30 years is might be too long

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in order to really understand the rapid
change in our climate.

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So the IPCC or International Panel on
Climate Change is using a 10 year average.

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So we only have broken this threshold
once in this year,

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but it already looks like 2025 is going
to be warmer than 2024.

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In January we as a globe broke
temperature records even though it's very

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cold here,
it's very warm in the Arctic and so

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relatively.

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So we have this about 10,
maybe five year window where our before

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and we reach this 1.
5°C because it's an average,

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we're don't expect to reach it next year
or the year after,

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but we're in a short time period where
you know 5-10 years before that threshold

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will be passed.

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This is what I've been talking about is
global temperature averages.

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But if we think about climate change,
it happens and we experience it locally.

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So looking at this regional map of the
United States.

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Temperatures that we're going to
experience here,

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the changes in temperature are going to
be much more extreme than the not the

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global averages.

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And this is generally true for most
places further north.

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You know, the further north you go,
the more warming that we're seeing or

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further South.

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And with that temperature change comes
heat risks, health risks.

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So this is a study from 2011 and it looks
at just the average temperature.

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And so an increase in 1°F of average
temperature for the Northeast and for the

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Midwest brings an increase of five to 10%
of heat mortality risk.

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So how to look at this map?

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It's basically the big bubbles,
big circles are bad.

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00:08:49.760 --> 00:08:51.000
That's more risk.

a372c480-39ea-4ad1-90f0-38bd87d9856f-0
00:08:51.280 --> 00:08:56.080
And I've kind of put a square around
Philadelphia and New York as cities that

a372c480-39ea-4ad1-90f0-38bd87d9856f-1
00:08:56.080 --> 00:08:57.680
have similar temperatures.

9009b9cd-bba1-470a-b26f-c8f94834a1c5-0
00:08:57.960 --> 00:09:02.080
You'll notice that those cities on the X
axis don't have the highest temperatures.

0cfd90a9-18bb-4a20-b28d-47dabff0aa02-0
00:09:02.360 --> 00:09:07.666
But because of the Northeast is not used
to the extremely hot temperatures,

0cfd90a9-18bb-4a20-b28d-47dabff0aa02-1
00:09:07.666 --> 00:09:10.320
that's why we see a higher risk there.

a8c75440-0843-465e-9689-d7200731702b-0
00:09:11.280 --> 00:09:15.303
And this is true not just in terms of 1°
temperature increase,

a8c75440-0843-465e-9689-d7200731702b-1
00:09:15.303 --> 00:09:18.880
but which is that first column there for
the Northeast.

39d3c646-69f6-43f3-864c-a35c8bca318b-0
00:09:20.000 --> 00:09:23.680
But you can also see that our heat waves
are going to increase in duration.

802e63a4-947d-4462-9631-5e52d6035dc2-0
00:09:23.680 --> 00:09:26.299
So multiple days in a row,
instead of a three day heat wave,

802e63a4-947d-4462-9631-5e52d6035dc2-1
00:09:26.299 --> 00:09:28.360
we might have a four day or five day heat
wave.

2e609771-6810-426c-9455-7d6da9b2801a-0
00:09:28.840 --> 00:09:31.680
And that also has a significant amount of
mortality increase.

863d0483-e3e2-4618-86f9-c310072f2b3e-0
00:09:32.000 --> 00:09:36.435
And then we also see that our heat waves
are happening earlier in the year,

863d0483-e3e2-4618-86f9-c310072f2b3e-1
00:09:36.435 --> 00:09:40.871
so in May instead of June or in even
early May or late April instead of the

863d0483-e3e2-4618-86f9-c310072f2b3e-2
00:09:40.871 --> 00:09:41.280
summer.

3a47fb80-2c98-4ba4-97e8-1f56cf9769d4-0
00:09:41.280 --> 00:09:45.240
So those all accompany significant
mortality increases.

6ce43c97-6f0c-4aed-a765-b7a4e28628c5-0
00:09:45.240 --> 00:09:48.200
And you'll notice that the Northeast is
unique here.

8fd8df98-e224-4ee8-95e0-f456ff1f9a4f-0
00:09:48.200 --> 00:09:48.920
It's not.

4133aad4-3b77-44cf-a5f3-38e610030d83-0
00:09:49.280 --> 00:09:55.760
We don't see the same relationships in
the South or the Midwest and with health.

809d3ea7-2b0d-4f59-a4c8-3d87dd90dd1c-0
00:09:56.520 --> 00:10:01.280
And so with these health risk increases,
we see increases in heat mortality.

ab462a34-5168-4d18-9663-3642f7ebae79-0
00:10:01.280 --> 00:10:06.164
So this is a study ten years later in
2021 and it looks at heat mortality and

ab462a34-5168-4d18-9663-3642f7ebae79-1
00:10:06.164 --> 00:10:11.173
you can see the map of the US looks a
little bit like you would expect for heat

ab462a34-5168-4d18-9663-3642f7ebae79-2
00:10:11.173 --> 00:10:11.800
mortality.

006b4a0b-16bd-4581-a5a9-18f62cb6ebda-0
00:10:11.800 --> 00:10:16.400
The Southwest is extremely high amount of
heat mortality, extreme heat deaths.

82837dcb-3224-4c72-a37b-e2260a563ac4-0
00:10:16.760 --> 00:10:20.699
And there are pockets throughout the
South where we see amounts of large

82837dcb-3224-4c72-a37b-e2260a563ac4-1
00:10:20.699 --> 00:10:22.480
amounts of extreme heat on those.

77696a46-f2fd-450a-a04b-010e83fd9689-0
00:10:22.480 --> 00:10:24.520
You can think of those as a map of cities
almost.

56390854-e88c-450b-9d99-3c58cf069dae-0
00:10:25.320 --> 00:10:31.691
But then there's this corridor from DC to
Boston of extreme heat mortality over the

56390854-e88c-450b-9d99-3c58cf069dae-1
00:10:31.691 --> 00:10:33.360
last like 30-40 years.

89644070-112c-4877-8a47-6c9234063410-0
00:10:33.760 --> 00:10:37.260
And a lot of this is because our
infrastructure and culture is just not

89644070-112c-4877-8a47-6c9234063410-1
00:10:37.260 --> 00:10:39.400
designed around preparing for extreme
heat.

0d04e265-661a-4a2f-a24b-c45b52579c86-0
00:10:39.400 --> 00:10:44.057
We're we're ready to deal with extreme
cold like this week and we have the

0d04e265-661a-4a2f-a24b-c45b52579c86-1
00:10:44.057 --> 00:10:48.093
infrastructure for that,
but we're not prepared in the summer or

0d04e265-661a-4a2f-a24b-c45b52579c86-2
00:10:48.093 --> 00:10:50.640
in early June or late May for heat waves.

164f7a0d-b17b-4bbd-9b90-86f98969d6ff-0
00:10:50.920 --> 00:10:53.800
And a lot of that has to do with some
cultural things.

8aa89502-cdfd-40c6-8d78-815ef4dd12c4-0
00:10:53.800 --> 00:10:57.720
Like I'm very proud last summer we didn't
turn on the AC in our home.

2fcc44bd-2c99-48f9-8337-f9b940b219c3-0
00:10:57.720 --> 00:11:00.040
And you know,
I brag to friends about that.

1aa0ccec-2fc6-4033-a96d-0fe7c875cc34-0
00:11:00.040 --> 00:11:03.292
But if we think about it,
that's not really what we should be

1aa0ccec-2fc6-4033-a96d-0fe7c875cc34-1
00:11:03.292 --> 00:11:04.080
bragging about.

f000c02c-55cf-41ca-9d6a-e3506c4a94a3-0
00:11:04.080 --> 00:11:06.000
Like,
how can we actually live sustainably with

f000c02c-55cf-41ca-9d6a-e3506c4a94a3-1
00:11:06.000 --> 00:11:06.200
this?

eec2860f-2df1-4122-9327-2e84e735d6d4-0
00:11:06.520 --> 00:11:10.990
Now that might not mean turning on the AC,
but how do we build buildings or change

eec2860f-2df1-4122-9327-2e84e735d6d4-1
00:11:10.990 --> 00:11:13.360
our built environment so you don't have
to?

1a100c3e-02f5-4aef-8bf2-f2bebf6047c7-0
00:11:13.840 --> 00:11:15.680
And a lot of that has to do with our
housing stock.

152520ea-9cd3-4f06-bf61-53e6a86b23e2-0
00:11:16.200 --> 00:11:17.680
So these are triple deckers.

ca9742ff-4f0e-4aa4-9d92-aa1f13c8a6f5-0
00:11:17.680 --> 00:11:22.760
You can see one this street in Worcester,
but also we see them in Salem.

6f7ce029-7501-4828-bf16-ddc9610c9669-0
00:11:22.760 --> 00:11:25.880
They're kind of ubiquitous across
Massachusetts landscape.

17027ac5-da57-4b27-8583-fd18967e3cc4-0
00:11:27.400 --> 00:11:32.076
And I was showing this PowerPoint to my
my parents and my dad told me I was born

17027ac5-da57-4b27-8583-fd18967e3cc4-1
00:11:32.076 --> 00:11:34.040
in a triple Decker in, in Chicago.

ffca7590-415a-4ebf-b9a3-b56205dd7c04-0
00:11:34.040 --> 00:11:39.281
So they're not just the Northeast,
but they are not designed for extreme

ffca7590-415a-4ebf-b9a3-b56205dd7c04-1
00:11:39.281 --> 00:11:44.666
heat and they are not well insulated and
especially on third floors can be

ffca7590-415a-4ebf-b9a3-b56205dd7c04-2
00:11:44.666 --> 00:11:48.400
extremely dangerous,
especially for young children.

57da8ff5-306d-4790-959d-387ab05096ec-0
00:11:48.480 --> 00:11:51.926
So there is the health aspect of extreme
heat,

57da8ff5-306d-4790-959d-387ab05096ec-1
00:11:51.926 --> 00:11:54.640
but there's also the economic aspect.

5e475092-95e6-4fd4-ae5b-c88059686d7e-0
00:11:54.640 --> 00:11:57.640
And this study looked at peak energy use.

6338865e-133e-4dc7-af26-ebbd77f4a654-0
00:11:57.720 --> 00:12:01.929
And what's interesting about this is that
for colder temperatures, this line,

6338865e-133e-4dc7-af26-ebbd77f4a654-1
00:12:01.929 --> 00:12:03.440
the slope is a little lower.

e34b8295-e54a-42c6-bf43-451d96e52947-0
00:12:04.080 --> 00:12:08.962
So and the temperatures are the energy
usage doesn't actually get as high as

e34b8295-e54a-42c6-bf43-451d96e52947-1
00:12:08.962 --> 00:12:13.972
during extreme heat where we see that
temperature the the relationship is much

e34b8295-e54a-42c6-bf43-451d96e52947-2
00:12:13.972 --> 00:12:14.480
steeper.

b47948ac-8a3f-4474-9903-35fea3a4d330-0
00:12:14.480 --> 00:12:19.957
And so the increase in energy usage goes
up faster during for every degree of

b47948ac-8a3f-4474-9903-35fea3a4d330-1
00:12:19.957 --> 00:12:23.680
temperature increases and it gets to a
higher level.

1494206b-575f-4139-9c4d-9af0c1a573f3-0
00:12:23.680 --> 00:12:27.917
So often again,
it's those urban the air areas that are

1494206b-575f-4139-9c4d-9af0c1a573f3-1
00:12:27.917 --> 00:12:32.760
relying on energy to deal with heat that
will bear this burden.

0d71e974-ab76-4f1d-9b43-6ff842b00f99-0
00:12:33.840 --> 00:12:37.894
And so when we look and now zooming into
just the Northeast,

0d71e974-ab76-4f1d-9b43-6ff842b00f99-1
00:12:37.894 --> 00:12:40.886
we're looking at where across the
Northeast,

0d71e974-ab76-4f1d-9b43-6ff842b00f99-2
00:12:40.886 --> 00:12:43.080
where will the heat be the worst.

3613ff0e-21b3-491b-8c6a-96ecefda4105-0
00:12:43.080 --> 00:12:46.602
You know,
I showed you that initial map of the

3613ff0e-21b3-491b-8c6a-96ecefda4105-1
00:12:46.602 --> 00:12:52.448
regions of North America across the US,
but if we actually zoom into just the

3613ff0e-21b3-491b-8c6a-96ecefda4105-2
00:12:52.448 --> 00:12:55.971
Northeast,
we see that heat is not distributed

3613ff0e-21b3-491b-8c6a-96ecefda4105-3
00:12:55.971 --> 00:12:57.920
evenly across this region.

6ba2e0b8-a8af-4ac6-9de6-c1f8f9b99bce-0
00:12:57.920 --> 00:13:02.320
So this paper looked at trying to
understand the patterns of heat.

e3966cbe-a2e3-4829-bc2b-73bc1eb4fa18-0
00:13:02.720 --> 00:13:07.517
It uses land surface temperature,
which is a product from the Landsat or

e3966cbe-a2e3-4829-bc2b-73bc1eb4fa18-1
00:13:07.517 --> 00:13:09.160
it's a satellite product.

e9f3951b-4f07-4f74-9c31-7342c99d0e0c-0
00:13:09.240 --> 00:13:14.593
It's an average of temperature,
thermal temperature of the surface of the

e9f3951b-4f07-4f74-9c31-7342c99d0e0c-1
00:13:14.593 --> 00:13:17.560
Earth from May to September 2013 to 2017.

7bde0952-5119-44fe-bb34-dc7655b7284d-0
00:13:18.080 --> 00:13:19.720
So it's an entire summer average.

c902933a-7e96-4a46-8eee-c70f1cebb80e-0
00:13:19.720 --> 00:13:22.120
So it just gives you a sense of where it
could be hot.

e5964a08-39b5-46ec-9e48-0fff2d9c4da3-0
00:13:23.080 --> 00:13:27.987
And they found that heat was concentrated
again in cities and urban areas without

e5964a08-39b5-46ec-9e48-0fff2d9c4da3-1
00:13:27.987 --> 00:13:31.040
access to vegetation,
without a lot of vegetation.

5a811563-046c-4c31-8167-8b22aa786e52-0
00:13:32.160 --> 00:13:35.754
But in these neighborhoods,
there were concentrations of racial and

5a811563-046c-4c31-8167-8b22aa786e52-1
00:13:35.754 --> 00:13:39.771
ethnic populations as well as high
concentrations of young children who are

5a811563-046c-4c31-8167-8b22aa786e52-2
00:13:39.771 --> 00:13:42.520
especially a vulnerable population to
extreme heat.

7bb368ec-899e-46f4-a6d0-83bf85a13fd4-0
00:13:43.000 --> 00:13:46.600
And these trends were strongest for black
and Hispanic or Latin X communities.

d5b9c176-c254-4bf6-b084-2466b307bce8-0
00:13:46.600 --> 00:13:51.085
So we're seeing that these,
the extreme heat is not an equitable

d5b9c176-c254-4bf6-b084-2466b307bce8-1
00:13:51.085 --> 00:13:55.984
distribution at any sense and the people
who are vulnerable to it are,

d5b9c176-c254-4bf6-b084-2466b307bce8-2
00:13:55.984 --> 00:13:58.400
are often the most exposed as well.

d592a4af-7587-4170-8bbe-18e59f73ac43-0
00:13:59.520 --> 00:14:03.560
So I'm going to be showing a lot of land
surface temperature data here.

c5bdeefd-b472-459e-ada4-f2c5feda56f1-0
00:14:03.560 --> 00:14:05.520
So I want to kind of show you where it's
coming from.

8ecc2e02-70dc-4076-a215-8bb89dc37057-0
00:14:06.280 --> 00:14:09.600
This is Landsat 8 or an image of Landsat
8.

3ae5dfac-d84f-409c-8256-38d4bdedd20c-0
00:14:09.600 --> 00:14:16.244
It's a satellite that's a joint product
of NOAA and or NASA and USGS and they

3ae5dfac-d84f-409c-8256-38d4bdedd20c-1
00:14:16.244 --> 00:14:20.247
provide,
it's been continuously providing data

3ae5dfac-d84f-409c-8256-38d4bdedd20c-2
00:14:20.247 --> 00:14:26.040
since 1984 with different Landsat
missions except for Landsat sake.

f2b14edd-c9a2-46ce-9f9b-2008b194ae74-0
00:14:26.040 --> 00:14:32.120
So,
but it provides just surface temperature.

c230927c-b35f-48c6-bb3b-bbf8352c14d6-0
00:14:32.120 --> 00:14:35.280
So it's not telling you the surf,
the temperature that you feel.

e42da1ec-c92f-4f12-892c-87acf3ed1dfd-0
00:14:35.520 --> 00:14:39.160
And it only passes over at 10:30 AM every
two weeks.

8e1d4379-18e2-4ca4-874a-1adcf598c8b5-0
00:14:39.160 --> 00:14:43.040
So it's not like a continuous amount of
coverage.

78b93e49-807e-4ba9-ad28-88aa6307ea42-0
00:14:44.040 --> 00:14:45.760
And critically, it's not air temperature.

0f91baca-9a8f-4fd8-9843-43c24539939d-0
00:14:46.200 --> 00:14:51.545
This is a map of Lynn look at using land
surface temperature by Nate in my class

0f91baca-9a8f-4fd8-9843-43c24539939d-1
00:14:51.545 --> 00:14:54.449
in the fall,
you can see how parts of Lynn,

0f91baca-9a8f-4fd8-9843-43c24539939d-2
00:14:54.449 --> 00:14:59.002
like the Lynn Woods are much cooler where
we see lots of vegetation,

0f91baca-9a8f-4fd8-9843-43c24539939d-3
00:14:59.002 --> 00:15:03.490
whereas downtown Lynn we see pockets of
extreme heat or very, very,

0f91baca-9a8f-4fd8-9843-43c24539939d-4
00:15:03.490 --> 00:15:04.480
very hot areas.

cc6720cf-037d-4548-bb21-73d3de0a7b94-0
00:15:05.360 --> 00:15:10.775
What I like about what Nate did here is
he overlaid it over just like kind of

cc6720cf-037d-4548-bb21-73d3de0a7b94-1
00:15:10.775 --> 00:15:12.720
like a Google imagery layer.

02aea2c2-61c1-40d9-aa5d-bf0bbd82e1fa-0
00:15:12.720 --> 00:15:16.389
And you can actually see the white roofs
in some large buildings in downtown Lynn

02aea2c2-61c1-40d9-aa5d-bf0bbd82e1fa-1
00:15:16.389 --> 00:15:19.298
that are providing some cooling,
even though it's a pretty dense

02aea2c2-61c1-40d9-aa5d-bf0bbd82e1fa-2
00:15:19.298 --> 00:15:19.880
neighborhood.

cdcb7ed4-9894-43ab-a3a4-b56da6dcb88c-0
00:15:21.000 --> 00:15:22.520
But again, it's not air temperature.

4f9b4525-6419-4fc4-a85e-463f8ba7c470-0
00:15:22.520 --> 00:15:28.280
So a lot of my research has been how can
we connect this satellite information?

f9398a2d-b32f-4083-94ec-c1d78d3de1cd-0
00:15:28.280 --> 00:15:32.240
How can we connect that to the lived
experiences of people on the ground?

7948f8be-082a-4a63-bbee-5f693e3ae69f-0
00:15:32.240 --> 00:15:36.920
So my dissertation use this network of
air temperature sensors.

dad02d4d-1a81-45bb-a6c4-1884de307a5b-0
00:15:36.920 --> 00:15:40.048
It's a global network,
but we use just the sensors in New

dad02d4d-1a81-45bb-a6c4-1884de307a5b-1
00:15:40.048 --> 00:15:40.480
England.

d132b732-7039-4fc0-8f9f-84b026e00908-0
00:15:40.880 --> 00:15:43.720
36 of them fell within our study area.

3a6811fc-335c-4f4a-bfed-881542d5a955-0
00:15:43.800 --> 00:15:49.200
So we have this land site Landsat image
during a heat wave that was cloud free.

1a158abf-2fee-40c2-9b4d-d651acb68c13-0
00:15:49.400 --> 00:15:53.293
It's incredibly important when using
satellite data for heat that you have

1a158abf-2fee-40c2-9b4d-d651acb68c13-1
00:15:53.293 --> 00:15:54.280
cloud free imagery.

06637bce-8087-46bf-ac66-e2fb824f15b4-0
00:15:54.280 --> 00:15:58.332
Clouds are extremely cool and so any
clouds could potentially modify the

06637bce-8087-46bf-ac66-e2fb824f15b4-1
00:15:58.332 --> 00:15:59.720
surrounding temperatures.

33dcdbfc-5c62-4f82-8f3e-20c4dc9b3ac9-0
00:16:00.640 --> 00:16:06.056
But this area of Massachusetts is
actually 80% of all census block groups

33dcdbfc-5c62-4f82-8f3e-20c4dc9b3ac9-1
00:16:06.056 --> 00:16:10.960
in Massachusetts and it contained also 36
air temperature sensors.

c49cfd26-9631-41d6-ab01-496d76486179-0
00:16:10.960 --> 00:16:17.792
So using that we had three days of cloud
free imagery that were used to create

c49cfd26-9631-41d6-ab01-496d76486179-1
00:16:17.792 --> 00:16:23.760
this model July 27th 2021, July 22nd,
2022 and September 24th, 2020.

5fb0107a-fc9c-4599-b9f6-ee4796a42a7d-0
00:16:24.000 --> 00:16:25.240
It is hard.

a10884ec-818d-4c11-a23e-a0580856fc38-0
00:16:25.240 --> 00:16:25.560
It is.

916f9f33-2889-44f5-a415-5b74b7c4309f-0
00:16:25.720 --> 00:16:30.880
I spent a lot of my my time looking
trying to find cloud free data.

908e838d-e5cc-49a4-9e10-1f5e3d6bfaf6-0
00:16:31.520 --> 00:16:35.403
Now that I have when we get to the drone
part of this talk,

908e838d-e5cc-49a4-9e10-1f5e3d6bfaf6-1
00:16:35.403 --> 00:16:38.574
that's no longer part of my my my daily
routine,

908e838d-e5cc-49a4-9e10-1f5e3d6bfaf6-2
00:16:38.574 --> 00:16:40.840
but I do spend it's so frustrating.

538d0ade-a490-45c1-a104-fcc19ced8190-0
00:16:40.840 --> 00:16:41.840
Haven't clouds or?

a43795c8-2523-4b90-8cf2-147eea2cd1ae-0
00:18:00.120 --> 00:18:00.880
Say EJ.

b0f79c50-4372-41eb-85f1-5b8ad7a9aa2b-0
00:18:00.880 --> 00:18:06.416
So we talked about the previous study
looked at the distribution or the

b0f79c50-4372-41eb-85f1-5b8ad7a9aa2b-1
00:18:06.416 --> 00:18:09.800
inequities of of heat across the
Northeast.

95b39765-899d-4c4f-8d78-7a577942a714-0
00:18:09.800 --> 00:18:13.360
But in Massachusetts we actually have an
environmental justice definition.

aaa9df02-6735-4096-98f4-c4c04fd229e1-0
00:18:13.360 --> 00:18:17.520
It was passed in 2020 as part of the
climate change bill.

09ee804b-c384-4ffd-9e2b-3e3c913ae6e2-0
00:18:18.440 --> 00:18:23.373
And so it classifieds environmental
justice based on historical inequities

09ee804b-c384-4ffd-9e2b-3e3c913ae6e2-1
00:18:23.373 --> 00:18:26.400
and or vulnerabilities to environmental
harm.

2bf5088c-174a-4aeb-8dba-88c4de6e2405-0
00:18:26.400 --> 00:18:30.760
So median household income is 1 character
of this.

133b9ab0-5721-4eb9-999a-0fa43d0ecc77-0
00:18:30.840 --> 00:18:33.640
We also have minority population,
which is another factor.

a021f00b-1eb2-474f-8cff-0d103066c9b9-0
00:18:33.640 --> 00:18:36.960
So 40% or more of the population being
non white.

37633f2e-12f5-4c2c-b88e-703403f8f9ae-0
00:18:37.880 --> 00:18:39.640
And then English language isolation.

85dcf78b-bc52-4512-bf99-e24efb2afc87-0
00:18:39.960 --> 00:18:43.880
And you can see here on the map of Salem
where those communities are.

bb921d33-37fb-4cc0-b3e5-6cf2a99f4d72-0
00:18:44.760 --> 00:18:48.260
And it's important to note that a block
group or community could have multiple

bb921d33-37fb-4cc0-b3e5-6cf2a99f4d72-1
00:18:48.260 --> 00:18:49.280
characteristics, right?

30923c7e-dd9c-4bab-a3ed-9fc94169c64c-0
00:18:49.280 --> 00:18:50.080
Multiple factors.

0fcde616-cafc-4c4a-80cb-4859aaf67c2d-0
00:18:50.080 --> 00:18:54.000
So it could be low income and minority,
or it could just be minority,

0fcde616-cafc-4c4a-80cb-4859aaf67c2d-1
00:18:54.000 --> 00:18:56.800
or it could just have English language
isolation.

488903bf-1972-4cff-9268-549c9bfc521a-0
00:18:56.800 --> 00:19:01.659
They don't always necessarily stack up,
but so this is what I mean when I'm

488903bf-1972-4cff-9268-549c9bfc521a-1
00:19:01.659 --> 00:19:04.600
talking about the environmental justice
side.

d253b39f-99e2-4c20-998f-a41c3563fb33-0
00:19:05.760 --> 00:19:10.072
And so we combined the Massachusetts
layer of environmental justice with our

d253b39f-99e2-4c20-998f-a41c3563fb33-1
00:19:10.072 --> 00:19:11.080
extreme heat data.

d941b0b6-9601-41eb-9917-deff2650cb2d-0
00:19:11.400 --> 00:19:16.758
I'm able to create 4 typologies or
typical scenarios of extreme heat in

d941b0b6-9601-41eb-9917-deff2650cb2d-1
00:19:16.758 --> 00:19:17.800
Massachusetts.

f29d66c4-1232-42b1-ab98-49d1d0545b36-0
00:19:17.800 --> 00:19:23.040
So number A, or I keep saying number A,
letter A is Chelsea.

04bffa9d-c4c2-483b-bd8e-80f1d97ae2c5-0
00:19:23.560 --> 00:19:26.440
Chelsea is defined as just extremely hot
everywhere.

7bbedede-4051-4db1-98ad-b7f03dafb256-0
00:19:26.440 --> 00:19:30.508
There is nowhere in Chelsea during a heat
wave that is not experiencing the heat

7bbedede-4051-4db1-98ad-b7f03dafb256-1
00:19:30.508 --> 00:19:30.760
wave.

a4c7054b-0826-421b-a7a2-98d3c85cd585-0
00:19:31.520 --> 00:19:35.200
And there's also each block group in
Chelsea is characterized as an

a4c7054b-0826-421b-a7a2-98d3c85cd585-1
00:19:35.200 --> 00:19:37.040
environmental justice block group.

5ce969b6-ed9c-4e00-b9ec-15adae91e40a-0
00:19:37.040 --> 00:19:38.840
So incredible overlap.

b913925b-164e-443b-b0fc-3aa367d28c8d-0
00:19:38.960 --> 00:19:43.562
It's also important to note that Chelsea
is surrounded by other cities that have

b913925b-164e-443b-b0fc-3aa367d28c8d-1
00:19:43.562 --> 00:19:47.426
the same characteristics,
So there's nowhere for someone in Chelsea

b913925b-164e-443b-b0fc-3aa367d28c8d-2
00:19:47.426 --> 00:19:50.040
to go locally to get out of the extreme
heat.

4f668832-86c4-4da2-9f8d-b854c0bcf1d5-0
00:19:52.400 --> 00:19:56.869
For the second typology is Leominster or
or B, which is like Gateway city,

4f668832-86c4-4da2-9f8d-b854c0bcf1d5-1
00:19:56.869 --> 00:20:00.982
like Leominster, Worcester,
Salem I would put under this category as

4f668832-86c4-4da2-9f8d-b854c0bcf1d5-2
00:20:00.982 --> 00:20:01.280
well.

22847ec7-c1c2-4ae0-82b3-c190dce7fd45-0
00:20:01.960 --> 00:20:05.577
They have an urban core with
environmental justice communities,

22847ec7-c1c2-4ae0-82b3-c190dce7fd45-1
00:20:05.577 --> 00:20:09.422
but they're surrounded by forests or
larger amounts of green space,

22847ec7-c1c2-4ae0-82b3-c190dce7fd45-2
00:20:09.422 --> 00:20:13.040
so they have access to typical
temperatures during a heat wave.

d5e2c938-12e4-4930-81b4-c27e4e17e9b7-0
00:20:14.000 --> 00:20:17.480
Then we have small towns like Webster,
which are.

c21f4bc0-d5b3-488c-80c1-4c11eee007de-0
00:20:17.920 --> 00:20:20.294
Have very small environmental justice
communities,

c21f4bc0-d5b3-488c-80c1-4c11eee007de-1
00:20:20.294 --> 00:20:23.368
but also experience extreme heat,
even though they're very, very,

c21f4bc0-d5b3-488c-80c1-4c11eee007de-2
00:20:23.368 --> 00:20:24.160
very small towns.

7a4953dc-96ab-401b-8c1e-dfd7454e0022-0
00:20:24.560 --> 00:20:25.840
It's like 10,000 people.

e17a260e-47a4-4c6f-9377-7176a44ac3d8-0
00:20:26.800 --> 00:20:30.850
And then we have typology D,
which is large cities like Newton,

e17a260e-47a4-4c6f-9377-7176a44ac3d8-1
00:20:30.850 --> 00:20:35.153
which have no environment or few
environmental justice communities,

e17a260e-47a4-4c6f-9377-7176a44ac3d8-2
00:20:35.153 --> 00:20:40.280
but also experience extreme heat and so,
you know, very dense urban environment.

02ec3f92-5588-4748-8b14-b6dfadaa5adc-0
00:20:41.680 --> 00:20:45.786
And what we've found is that when you
layer on multiple factor environmental

02ec3f92-5588-4748-8b14-b6dfadaa5adc-1
00:20:45.786 --> 00:20:46.640
justice factors.

127f0050-a90f-4024-a072-f90a650c6132-0
00:20:46.640 --> 00:20:49.598
So when you add income alongside minority
status,

127f0050-a90f-4024-a072-f90a650c6132-1
00:20:49.598 --> 00:20:54.331
alongside English language isolation,
those are the hottest block groups in the

127f0050-a90f-4024-a072-f90a650c6132-2
00:20:54.331 --> 00:20:57.999
eastern part of the eastern Massachusetts
during a heat wave.

819b3482-1a4b-4c60-8edc-87c681d47211-0
00:20:58.560 --> 00:21:01.800
And so that's what this black arrow is
showing the red line.

d097dd5f-3e47-405c-9fcc-309075bab79b-0
00:21:02.200 --> 00:21:07.284
So you can see the general increase as
you add multiple types of environmental

d097dd5f-3e47-405c-9fcc-309075bab79b-1
00:21:07.284 --> 00:21:09.280
justice in multiple categories.

80f24659-2cf8-4b55-b23a-93d7754e3cd9-0
00:21:09.280 --> 00:21:10.760
You get hotter block groups.

c7766492-1dfe-47f1-973c-eeb9e761d354-0
00:21:11.640 --> 00:21:14.000
The red line is the threshold for extreme
heat.

a85aa286-6827-4ca4-81ef-e60ef2c68981-0
00:21:14.000 --> 00:21:18.110
It's important to know that almost all
environmental justice black groups are

a85aa286-6827-4ca4-81ef-e60ef2c68981-1
00:21:18.110 --> 00:21:19.480
experiencing extreme heat.

a9d5b36c-c4e4-456a-8059-8534bc86d018-0
00:21:19.480 --> 00:21:25.454
So 85% of the EJ communities in our study
area had extreme heat and that would be

a9d5b36c-c4e4-456a-8059-8534bc86d018-1
00:21:25.454 --> 00:21:29.680
places like Chelsea and Webster,
but 15% of them did not.

108a38a8-c75e-4271-a7fa-09039a9cbe71-0
00:21:29.680 --> 00:21:33.951
And that would be the surrounding
communities in Leominster, Worcester,

108a38a8-c75e-4271-a7fa-09039a9cbe71-1
00:21:33.951 --> 00:21:36.798
Salem,
that in these gateway cities is where we

108a38a8-c75e-4271-a7fa-09039a9cbe71-2
00:21:36.798 --> 00:21:41.544
found environmental justice block groups
that were not having the environmental

108a38a8-c75e-4271-a7fa-09039a9cbe71-3
00:21:41.544 --> 00:21:41.959
burden.

e32662bc-7eed-49e6-8dde-a99e31192bdc-0
00:21:42.720 --> 00:21:47.415
And it's important to note that even the
non environmental justice communities in

e32662bc-7eed-49e6-8dde-a99e31192bdc-1
00:21:47.415 --> 00:21:50.965
this box plot over here,
there are still a lot of places like

e32662bc-7eed-49e6-8dde-a99e31192bdc-2
00:21:50.965 --> 00:21:55.260
Newton that experience extreme heat,
even though they're not classified as

e32662bc-7eed-49e6-8dde-a99e31192bdc-3
00:21:55.260 --> 00:21:56.520
environmental justice.

2ec71294-e8a0-46cc-8401-eaf4bce4bc1d-0
00:21:57.400 --> 00:22:02.960
So that is looking at the severity of
extreme heat in specific locations.

5cbc4dd0-c006-48e0-b0b8-aafc5d7a22bb-0
00:22:03.560 --> 00:22:08.366
But I went to a talk in Boston recently
by BU in the School of Public Health and

5cbc4dd0-c006-48e0-b0b8-aafc5d7a22bb-1
00:22:08.366 --> 00:22:12.697
a program called Be Cool where they
actually put air temperature sensors

5cbc4dd0-c006-48e0-b0b8-aafc5d7a22bb-2
00:22:12.697 --> 00:22:14.240
around the city of Boston.

990a2a57-9f01-4562-b69b-0fef797793ba-0
00:22:14.880 --> 00:22:20.019
And this is a study that's looking at the
heat wave or heat advisory from August

990a2a57-9f01-4562-b69b-0fef797793ba-1
00:22:20.019 --> 00:22:22.240
1st to August 3rd from last summer.

566bcfa9-4149-43cf-895d-92c6f4bd50d4-0
00:22:22.640 --> 00:22:28.136
And what they saw is that while Logan
Airport had a heat advisory for three

566bcfa9-4149-43cf-895d-92c6f4bd50d4-1
00:22:28.136 --> 00:22:33.561
days, places like Austin, Brighton,
which is where I've lived in the past,

566bcfa9-4149-43cf-895d-92c6f4bd50d4-2
00:22:33.561 --> 00:22:38.480
had a heat advisory for over 2 days
before that and two days after.

ca0efecb-3429-4657-a37c-0d72368b7269-0
00:22:39.080 --> 00:22:43.311
And then during the actual heat advisory,
there was actually heat emergency,

ca0efecb-3429-4657-a37c-0d72368b7269-1
00:22:43.311 --> 00:22:45.840
which is when the temperature goes above
95°.

252916bd-c8a8-4d61-8808-5610d3549adf-0
00:22:46.320 --> 00:22:50.533
And this was consistent in a lot of the
downtown or or more urban parts of the

252916bd-c8a8-4d61-8808-5610d3549adf-1
00:22:50.533 --> 00:22:50.800
city.

d9646f68-1a31-4f8b-9091-2820dc5b4c91-0
00:22:50.800 --> 00:22:54.240
So we're seeing that in Massachusetts.

52ffb589-2880-474a-8746-687ac40eda78-0
00:22:54.240 --> 00:22:58.653
But in New England broadly,
not only is extreme heat much more spread

52ffb589-2880-474a-8746-687ac40eda78-1
00:22:58.653 --> 00:23:02.941
out and then we might expect,
but we're also seeing that these heat

52ffb589-2880-474a-8746-687ac40eda78-2
00:23:02.941 --> 00:23:08.049
waves are lasting much longer and that
all in companies potential risks for heat

52ffb589-2880-474a-8746-687ac40eda78-3
00:23:08.049 --> 00:23:08.680
mortality.

b3ab6c49-85b5-46b2-a7a7-4fc65399fe4b-0
00:23:09.720 --> 00:23:14.785
So basically taking that,
trying to draw a tie a little bow around

b3ab6c49-85b5-46b2-a7a7-4fc65399fe4b-1
00:23:14.785 --> 00:23:20.985
all of this is we need to start planning
for these communities right now for this

b3ab6c49-85b5-46b2-a7a7-4fc65399fe4b-2
00:23:20.985 --> 00:23:22.120
climate future.

cd6514cd-ccb3-4620-89ba-7d1d64c4800d-0
00:23:22.120 --> 00:23:28.335
We have like a 5 to 10 year window before
we start to see large scale changes to

cd6514cd-ccb3-4620-89ba-7d1d64c4800d-1
00:23:28.335 --> 00:23:32.555
our environment,
and our communities that live in very

cd6514cd-ccb3-4620-89ba-7d1d64c4800d-2
00:23:32.555 --> 00:23:38.080
urbanized neighborhoods are experiencing
that future climate right now.

c6c56abf-6dbc-425c-9689-9a683c3a5c53-0
00:23:38.080 --> 00:23:42.533
So if we can make those modifications,
if we can learn how to adapt our cities

c6c56abf-6dbc-425c-9689-9a683c3a5c53-1
00:23:42.533 --> 00:23:45.240
to this extreme heat,
we'll have a better shot.

71551726-c4e4-4708-abaf-2541d76b03d5-0
00:23:45.640 --> 00:23:48.632
Specifically focusing on vulnerable
populations,

71551726-c4e4-4708-abaf-2541d76b03d5-1
00:23:48.632 --> 00:23:53.335
black groups with lots of young children,
places that have been historically

71551726-c4e4-4708-abaf-2541d76b03d5-2
00:23:53.335 --> 00:23:57.916
marginalized and think about how we can
support those groups individually,

71551726-c4e4-4708-abaf-2541d76b03d5-3
00:23:57.916 --> 00:24:01.519
but also collectively,
like how can we support a community

71551726-c4e4-4708-abaf-2541d76b03d5-4
00:24:01.519 --> 00:24:02.680
during a heat wave?

d133b549-5ed7-4166-8ff9-e453d2404c16-0
00:24:03.720 --> 00:24:04.760
And so an example.

2ae5ba30-5e19-4c2c-a6f7-50881d314d97-0
00:24:04.840 --> 00:24:07.483
And for me,
so as as Professor Radner said, I'm,

2ae5ba30-5e19-4c2c-a6f7-50881d314d97-1
00:24:07.483 --> 00:24:08.400
I'm the tree guy.

ac8ebd35-4a66-4ec2-b586-068a8362023c-0
00:24:08.400 --> 00:24:09.880
I, I like trees a lot.

a84d7c07-da7a-4308-9317-eacdb543be8a-0
00:24:10.120 --> 00:24:14.864
Trees are kind of how I see my role in,
in trying to understand this climate

a84d7c07-da7a-4308-9317-eacdb543be8a-1
00:24:14.864 --> 00:24:16.960
future and our climate adaptation.

a930b8c4-cbd9-4821-9cce-d88d51952433-0
00:24:16.960 --> 00:24:21.104
So I'm always thinking, how can tree,
what role can trees play, if any,

a930b8c4-cbd9-4821-9cce-d88d51952433-1
00:24:21.104 --> 00:24:25.248
in some of these very dense urban
neighborhoods emitting or adapting to

a930b8c4-cbd9-4821-9cce-d88d51952433-2
00:24:25.248 --> 00:24:26.399
this climate future?

1e4cb8f8-34aa-4d6e-a332-a1b4c956d83d-0
00:24:26.400 --> 00:24:29.455
So I'm going to be talking about
Worcester, but an example,

1e4cb8f8-34aa-4d6e-a332-a1b4c956d83d-1
00:24:29.455 --> 00:24:33.173
and I'm going to come back to this,
is this small patch of vegetation in

1e4cb8f8-34aa-4d6e-a332-a1b4c956d83d-2
00:24:33.173 --> 00:24:34.600
Swampscott just on the road.

d4e6a4b2-05d3-4b84-b950-cd588a062531-0
00:24:35.440 --> 00:24:38.280
You can see that some of the this is a
thermal drone image.

f4a49720-18fe-4ef4-8225-5ee7a877af1b-0
00:24:38.880 --> 00:24:44.168
And you can see in this area that it's
been planted with very dense or large

f4a49720-18fe-4ef4-8225-5ee7a877af1b-1
00:24:44.168 --> 00:24:49.113
amounts of native plants and the
temperatures are 20°C cooler than like

f4a49720-18fe-4ef4-8225-5ee7a877af1b-2
00:24:49.113 --> 00:24:53.303
the lawns that or other areas that are
just in the sunshine,

f4a49720-18fe-4ef4-8225-5ee7a877af1b-3
00:24:53.303 --> 00:24:56.600
but right next door that have not been
planted.

b04050a3-b9e7-4d5a-b2c2-872a25a94857-0
00:24:56.600 --> 00:25:02.183
So we're seeing tremendous difference in
the ways that vegetation is responding to

b04050a3-b9e7-4d5a-b2c2-872a25a94857-1
00:25:02.183 --> 00:25:02.520
heat.

f7a70cab-2fcb-43bd-b3a7-6945bb80f997-0
00:25:03.600 --> 00:25:06.792
So I'm looking at,
I'm going to be talking about Worcester

f7a70cab-2fcb-43bd-b3a7-6945bb80f997-1
00:25:06.792 --> 00:25:11.282
as a case study because Worcester has had
a tremendous amount of tree planting and

f7a70cab-2fcb-43bd-b3a7-6945bb80f997-2
00:25:11.282 --> 00:25:13.879
tree turnover in the past 20 years or 10
years.

00b3ddda-8af6-4799-b5e1-14b7eee5bb65-0
00:25:15.200 --> 00:25:19.640
These are some of my students in the HERO
programme measuring some St.

620f36e0-e043-418e-90fa-574e1a63e69c-0
00:25:19.640 --> 00:25:22.080
trees that I'm going to be giving you
examples of.

1da65c4f-f4a1-4405-9767-941f9cee851b-0
00:25:22.080 --> 00:25:24.600
But I wanted you to see what the street
trees look like.

72ccd396-9982-4acd-8248-c3d10e0db843-0
00:25:24.600 --> 00:25:26.120
They're not fully grown yet.

d607c414-f3be-40f2-b6d5-e9c583e210f1-0
00:25:26.400 --> 00:25:30.503
They're still kind of mid sized,
but they're they're much larger than they

d607c414-f3be-40f2-b6d5-e9c583e210f1-1
00:25:30.503 --> 00:25:31.160
were before.

0660f4d2-2fba-40fc-8d05-c037275480e2-0
00:25:32.840 --> 00:25:35.280
I have this longer relationship with
trees.

4d12752b-668a-40ce-9fc4-2a8a5cab9a13-0
00:25:35.280 --> 00:25:39.767
I would say I was a science teacher in
the South Bronx and it was during the

4d12752b-668a-40ce-9fc4-2a8a5cab9a13-1
00:25:39.767 --> 00:25:44.196
Million Trees NYC planting programme
where they were trying to plant 1,000,

4d12752b-668a-40ce-9fc4-2a8a5cab9a13-2
00:25:44.196 --> 00:25:46.120
000 trees over all New York City.

8cb407a1-4a85-47c3-8da5-49bae2a29e58-0
00:25:47.000 --> 00:25:50.900
And so there are some of my students
mulching a tree in in our neighborhood

8cb407a1-4a85-47c3-8da5-49bae2a29e58-1
00:25:50.900 --> 00:25:53.415
and then that the death of a lot of those
trees,

8cb407a1-4a85-47c3-8da5-49bae2a29e58-2
00:25:53.415 --> 00:25:57.521
there was not a lot of stewardship or
understanding led me to want to study the

8cb407a1-4a85-47c3-8da5-49bae2a29e58-3
00:25:57.521 --> 00:26:01.320
tree planting program in Massachusetts,
the Green and the Gateway cities.

1944f076-f93b-46af-a779-8ab6eaa072cd-0
00:26:01.960 --> 00:26:05.026
We often studied these trees when they
were really young,

1944f076-f93b-46af-a779-8ab6eaa072cd-1
00:26:05.026 --> 00:26:06.560
so they've just been planted.

b12bb7b9-577c-4dea-b283-f4c0afa46fa7-0
00:26:06.840 --> 00:26:10.880
So we would use this 10 foot long pole to
measure the tree height.

b13af70c-51c6-486e-af43-b4bec21e3e20-0
00:26:11.160 --> 00:26:13.262
And then if the tree was taller than 10
feet,

b13af70c-51c6-486e-af43-b4bec21e3e20-1
00:26:13.262 --> 00:26:16.920
you had to like put the pole in your head
and hope you kind of like eyeball it.

d7bc49ef-9609-4839-9e30-8b38df41e623-0
00:26:17.800 --> 00:26:20.440
So now that the trees are really large,
we actually use a psychometer.

05bf88c0-26f6-432a-b823-7b9af7d3a4a3-0
00:26:20.440 --> 00:26:22.240
So we don't we don't use this method
anymore.

57705681-9611-4413-9a6b-402017d3d7b8-0
00:26:22.240 --> 00:26:25.938
They've grown too tall,
but spent a lot of time thinking about

57705681-9611-4413-9a6b-402017d3d7b8-1
00:26:25.938 --> 00:26:26.760
tree planting.

c1a1f0b1-fc71-4fc4-b63b-325cccf4cf95-0
00:26:27.280 --> 00:26:31.195
And I'm hoping that we can begin to
understand the impacts of this tree

c1a1f0b1-fc71-4fc4-b63b-325cccf4cf95-1
00:26:31.195 --> 00:26:34.240
planting now that it's been going on for
10 plus years.

02ddbb68-2d1e-4f01-8aaf-343bc29f2634-0
00:26:34.520 --> 00:26:37.000
But it's important to note that this
planting is still happening.

0bffd624-3ef0-4b24-8276-7d34e7c77f51-0
00:26:37.600 --> 00:26:41.413
We've had over 60 trees planted by this
degree in the Gateway Cities program on

0bffd624-3ef0-4b24-8276-7d34e7c77f51-1
00:26:41.413 --> 00:26:43.320
Salem State's campus, with more to come.

38d3df34-2e1d-46b5-90dc-b43f8f91b7f7-0
00:26:44.160 --> 00:26:46.786
So it's really exciting to see how,
you know,

38d3df34-2e1d-46b5-90dc-b43f8f91b7f7-1
00:26:46.786 --> 00:26:51.240
this work has LED it kind of snowballs to
more tree plants, more tree plants.

6991440f-4e03-4fff-9185-7281aee5db6e-0
00:26:51.240 --> 00:26:53.600
And I'm hoping that will continue.

254d0052-e659-4416-a09e-6496d45d50dd-0
00:26:54.280 --> 00:26:58.888
So Worcester is the case study because it
was where the Longhorn beetle was

254d0052-e659-4416-a09e-6496d45d50dd-1
00:26:58.888 --> 00:27:00.040
discovered in 2008.

587bab01-29f3-4a6d-a029-8beee912d4fe-0
00:27:00.200 --> 00:27:04.405
About in 2005 there was a street tree
sentence of Worcester and 80% of the

587bab01-29f3-4a6d-a029-8beee912d4fe-1
00:27:04.405 --> 00:27:06.200
trees were all the same species.

ed8dbc06-36ab-4038-8f63-7ea72240a408-0
00:27:06.520 --> 00:27:07.800
They're all Norway maples.

64bdfaa2-66bb-4ba4-b86f-ef5e049e35b5-0
00:27:08.280 --> 00:27:12.600
And the host species for the Longhorn
beetle is Acer or or genus, host genus.

0e35c86a-d776-4923-8b52-cec04007da0f-0
00:27:12.600 --> 00:27:15.560
So Norway maples were a prime tree.

076a242b-e402-4b8b-ba32-ee38471b0bcf-0
00:27:15.840 --> 00:27:17.280
And so you can see what a typical St.

454295e8-0dbe-48eb-bfa0-0e558dd927f6-0
00:27:17.280 --> 00:27:20.160
in Worcester would look like where that
had lots of St.

37d80157-a205-43b1-b742-400e5c0192ea-0
00:27:20.160 --> 00:27:21.520
trees with normally maples.

184821fc-c900-435f-a7b4-e3b104937224-0
00:27:22.480 --> 00:27:24.760
And this is an example of the beetle.

54adeda6-4d59-4b48-96c3-5961ee4795df-0
00:27:25.760 --> 00:27:31.346
Once the that this beetle was found,
the USDA set up a quarantine area really

54adeda6-4d59-4b48-96c3-5961ee4795df-1
00:27:31.346 --> 00:27:31.920
quickly.

0fa2e754-e1d6-4b9c-b8a9-29fa206f6248-0
00:27:31.920 --> 00:27:34.680
They were worried about the beetle
spreading into the Northeast forest.

c355e4f7-70ca-4905-b25e-17342a9031b5-0
00:27:34.680 --> 00:27:38.363
So Vermont, New Hampshire,
Massachusetts and Worcester was actually

c355e4f7-70ca-4905-b25e-17342a9031b5-1
00:27:38.363 --> 00:27:41.775
referred to many times as the gateway to
the Northeast forest,

c355e4f7-70ca-4905-b25e-17342a9031b5-2
00:27:41.775 --> 00:27:44.862
which I don't think anyone ever thought
of it like that,

c355e4f7-70ca-4905-b25e-17342a9031b5-3
00:27:44.862 --> 00:27:48.600
but we liked it as I live in Worcester,
South of Worcester resident.

d2189c9b-f1de-4691-9de8-388e40e53096-0
00:27:48.600 --> 00:27:52.320
It was great, but 35,
000 trees were removed almost overnight.

b050af91-7565-457c-af99-9296bf5f8b12-0
00:27:52.320 --> 00:27:56.185
So you would go to work and come home and
the federal government would be in your

b050af91-7565-457c-af99-9296bf5f8b12-1
00:27:56.185 --> 00:27:58.920
backyard with a crane airlifting trees
off your property.

f674c89f-c50b-4b10-8ded-4537e12e1445-0
00:27:58.920 --> 00:28:01.160
They did not have to require a permit or
anything.

13e82ecf-a553-4a2b-8a20-6942be092625-0
00:28:01.680 --> 00:28:02.840
It was a national emergency.

bc4221f2-7dd6-49d9-824c-42efda1135d9-0
00:28:02.840 --> 00:28:08.609
So you can imagine the amount of loss and
change in identity that residents

bc4221f2-7dd6-49d9-824c-42efda1135d9-1
00:28:08.609 --> 00:28:09.520
experienced.

e9703cef-7f61-45d2-8453-4cfbbbae87b8-0
00:28:10.080 --> 00:28:13.400
They also talked about how they noticed
their environment change.

4bf1b0ad-7464-4171-b831-651d23c89e12-0
00:28:13.400 --> 00:28:16.841
So it got hotter and colder,
colder in the winters and hotter in the

4bf1b0ad-7464-4171-b831-651d23c89e12-1
00:28:16.841 --> 00:28:17.240
summers.

0d580085-a2fe-4204-a8d1-b5e5da3427c6-0
00:28:19.720 --> 00:28:21.560
Here's another example of some of that
change.

4975a87d-fb6a-41ad-98c9-ea6803df304b-0
00:28:21.880 --> 00:28:25.680
This is in 2009,
right before these trees were removed.

b8cf9d9b-d0af-4aa8-89ff-d35b2083e3d8-0
00:28:25.920 --> 00:28:28.960
And this is the next day into that same
year.

b049e53b-9f09-4501-9650-e351140a6b6d-0
00:28:29.440 --> 00:28:33.671
But they just came through and took
almost every tree on the street that was

b049e53b-9f09-4501-9650-e351140a6b6d-1
00:28:33.671 --> 00:28:34.880
not an Evergreen tree.

56f961ad-038f-4c03-a614-6bfd2f1c84e5-0
00:28:34.880 --> 00:28:36.320
So huge amounts of change.

e77581fb-92a9-4bbf-acba-4f25651da963-0
00:28:36.640 --> 00:28:41.081
This is what this tree,
the street looks like now or not now,

e77581fb-92a9-4bbf-acba-4f25651da963-1
00:28:41.081 --> 00:28:46.240
but 2023 and I'm going to be showing
drone imagery of this same street.

d2e1b2e1-bd18-4a44-8f96-4fe7fd62c17d-0
00:28:46.240 --> 00:28:48.793
So we'll we're seeing it from from the
street level,

d2e1b2e1-bd18-4a44-8f96-4fe7fd62c17d-1
00:28:48.793 --> 00:28:50.720
but we'll look at it as well from above.

e2082462-f888-4c9e-9b0f-2d5ae7b3ce52-0
00:28:51.920 --> 00:28:56.986
And what residents thought they were
experiencing with higher temperatures,

e2082462-f888-4c9e-9b0f-2d5ae7b3ce52-1
00:28:56.986 --> 00:29:00.320
more heat bills was what we were able to
measure.

e565b43d-43e1-474a-9b38-0c12e358d6f6-0
00:29:00.320 --> 00:29:06.663
So this is a study out of Clark in two
2013 that measured that the temperature

e565b43d-43e1-474a-9b38-0c12e358d6f6-1
00:29:06.663 --> 00:29:11.320
increased in these neighborhoods with
tree loss by 4.3°F.

4e8073d3-9304-4ce8-97ae-5b5232bfbc99-0
00:29:11.320 --> 00:29:15.680
So quite a lot and you can see in this
map below what that looks like.

0ba8fb95-664e-4afe-a26b-db2374ead12d-0
00:29:15.680 --> 00:29:22.897
We have a 2007 image with the full canopy
trees and then in 2010 you can see the

0ba8fb95-664e-4afe-a26b-db2374ead12d-1
00:29:22.897 --> 00:29:24.680
amount of tree loss.

978ffd17-b4da-4e26-b656-15664ef1e304-0
00:29:25.320 --> 00:29:28.040
But on previous slide there has been tree
planting.

4b721e15-32b6-4c35-ae01-4156708fd91e-0
00:29:28.040 --> 00:29:31.953
And so you these little dots on this
slide are the public and private trees

4b721e15-32b6-4c35-ae01-4156708fd91e-1
00:29:31.953 --> 00:29:32.880
that were planted.

79590e3d-6274-4f09-b8f9-35c448d0cbc9-0
00:29:33.960 --> 00:29:37.880
And then here you can see the Landsat
imagery showing warming.

bec1a888-5580-4913-b560-cf286342ae00-0
00:29:38.400 --> 00:29:40.800
And this is AZ score of temperature.

c74c8686-7645-4dab-b648-735a79aad6f4-0
00:29:40.800 --> 00:29:42.160
So it's just metric.

82115359-7526-4f79-8c33-877ccb09cbe8-0
00:29:42.160 --> 00:29:45.880
Basically, if it's above 0, it's warmer,
and if it's below 0, it's cooler.

e567a81b-c5b3-4fe9-8760-b0a86bf6a000-0
00:29:46.920 --> 00:29:51.680
But you can see it's generally warmer
where we saw this tree loss.

4c919e37-dabf-47ff-ba79-526d8ac51148-0
00:29:52.960 --> 00:29:54.640
But you can't really see the trees, right?

88b20791-564f-4e9f-9bdf-2b543b9b96b3-0
00:29:54.960 --> 00:29:57.160
Can be in these pixels, these squares.

c4cea73c-a208-4716-978a-1273f4906f15-0
00:29:57.680 --> 00:29:59.960
You can't really see individual trees,
right?

a0c8ba02-1362-4287-8216-4efadfb6ee17-0
00:29:59.960 --> 00:30:03.040
And that's always been kind of the
struggle with using Landsat.

1777e9a5-3e7d-47df-9df7-0f272c62bc15-0
00:30:03.480 --> 00:30:08.440
It flies over at 10:30 AM and also can't
see individual tree.

ae886f4e-938c-41be-bb86-301296dd8f8e-0
00:30:08.440 --> 00:30:11.680
We can't measure the impacts of tree
planting yet.

42de64de-6a1e-4270-ba0a-909ead3864f9-0
00:30:12.000 --> 00:30:16.480
Maybe over long periods of time,
but not at the individual scale.

a6221853-e0dc-4f68-b0c7-94a33f178cbc-0
00:30:17.480 --> 00:30:25.000
So instead of using satellite imagery,
we've been measuring these trees by hand.

47fd4b03-c1db-48e8-8b4a-1c9b54cd3225-0
00:30:25.000 --> 00:30:29.920
So you can see here a pin oak as part of
a repeat survey in 2023.

3360592f-d8f5-4ce0-925e-04001522759e-0
00:30:30.040 --> 00:30:32.880
This is the pin oak in 2016 when we first
measured it.

67d55c27-b844-4512-bfed-21438607d5c9-0
00:30:33.520 --> 00:30:35.084
This was,
that was the year before I started at

67d55c27-b844-4512-bfed-21438607d5c9-1
00:30:35.084 --> 00:30:35.280
Clark.

2fd27ab4-cfde-415a-a8a7-f65f1ac39deb-0
00:30:35.280 --> 00:30:36.960
So I can't claim credit for that tree.

a1fec8de-5264-4df0-9f80-0b5948f41c46-0
00:30:37.600 --> 00:30:40.960
But then you can see the same tree when
we measured it in 2023.

2a295332-71e0-4fc5-979f-264f4d3ade67-0
00:30:41.960 --> 00:30:45.360
And when we did these surveys,
we'd measure the diameter at base height.

aba62d7f-caef-4020-8fb8-43ade1e16c6a-0
00:30:46.080 --> 00:30:50.658
So the diameter is the well,
it's the diameter of the trunk of the

aba62d7f-caef-4020-8fb8-43ade1e16c6a-1
00:30:50.658 --> 00:30:51.000
tree.

87f559be-abd4-40e4-9c0c-043067fe816d-0
00:30:51.000 --> 00:30:53.200
So we're not looking at the canopy,
it's just the trunk.

a394aeae-68e9-49b4-b0c4-70bea32ee831-0
00:30:54.160 --> 00:30:59.770
And then we also look at the crown canopy
width as well as the total height of the

a394aeae-68e9-49b4-b0c4-70bea32ee831-1
00:30:59.770 --> 00:31:02.880
tree,
either using the pole or a psychometer.

e4cfacb0-9230-4364-b72e-af2e39506868-0
00:31:03.520 --> 00:31:05.440
And then we also looked at a bunch of
health metrics.

4ac93de4-1c80-4eaa-b470-e88f9d1a7b01-0
00:31:05.440 --> 00:31:09.360
So that's crown vigor condition and also
if the tree is alive or not.

ec3abdb3-b3f9-4ba5-953d-fc30249aa7a0-0
00:31:10.400 --> 00:31:14.872
And so that survey was in 2023,
which is right before I started at Salem

ec3abdb3-b3f9-4ba5-953d-fc30249aa7a0-1
00:31:14.872 --> 00:31:15.240
State.

5679e7b0-b030-4616-be1b-84e6733164da-0
00:31:16.200 --> 00:31:17.560
And I thought, ah, wouldn't it be great?

62b402ce-c889-445d-a561-67cd8e5a39af-0
00:31:17.960 --> 00:31:21.947
Once we once I started here,
we bought a thermal drone and a multi

62b402ce-c889-445d-a561-67cd8e5a39af-1
00:31:21.947 --> 00:31:22.840
spectral drone.

1925af03-cfeb-416b-87d5-8b20d84d653d-0
00:31:23.040 --> 00:31:27.548
What if we could fly over those same
trees and understand, you know, the,

1925af03-cfeb-416b-87d5-8b20d84d653d-1
00:31:27.548 --> 00:31:32.240
the field metrics, the field survey data,
combine it with the drone imagery.

93b2b14c-17ab-48f8-ba6e-e79783e2935e-0
00:31:32.240 --> 00:31:35.760
And so I asked my classes always to make
methods diagrams.

c7aa2e6c-cb4a-4230-a7c6-1394272ca434-0
00:31:35.760 --> 00:31:37.800
So it's like it has to show a methods
diagram.

5c8c04f0-df6a-4b29-bf54-f9171d76c1eb-0
00:31:38.840 --> 00:31:40.480
So I'll try to go through it pretty
quickly.

f5e3756c-657d-4d27-9a79-2157d9bb3712-0
00:31:40.480 --> 00:31:45.781
But we have two drone flights in 2024 in
the summer and that was Geo referenced

f5e3756c-657d-4d27-9a79-2157d9bb3712-1
00:31:45.781 --> 00:31:49.890
with NAEP imagery and then clipped and
also overlay manually,

f5e3756c-657d-4d27-9a79-2157d9bb3712-2
00:31:49.890 --> 00:31:54.662
which I'll get into more with the tree
points from those drone flights,

f5e3756c-657d-4d27-9a79-2157d9bb3712-3
00:31:54.662 --> 00:31:59.169
we could create a land surface
temperature map as well as NDBI map,

f5e3756c-657d-4d27-9a79-2157d9bb3712-4
00:31:59.169 --> 00:32:00.760
which I'll explain more.

ae4fa4a1-691f-4abe-b572-2322d7178b02-0
00:32:00.760 --> 00:32:06.720
But NDBI is essentially a health metric
using the spectral bands.

24207431-c16c-46e4-a03e-dc3bc2c7c3e0-0
00:32:07.480 --> 00:32:13.076
And so we're able to get health and
temperature in order to isolate the

24207431-c16c-46e4-a03e-dc3bc2c7c3e0-1
00:32:13.076 --> 00:32:17.895
canopy of specific trees,
we use segmentation to find similar

24207431-c16c-46e4-a03e-dc3bc2c7c3e0-2
00:32:17.895 --> 00:32:18.440
pixels.

1cbd7f38-ed71-4c07-8e11-748b2ad5d348-0
00:32:18.440 --> 00:32:22.785
So pixels added similar characteristics
in visible and, and infrared,

1cbd7f38-ed71-4c07-8e11-748b2ad5d348-1
00:32:22.785 --> 00:32:24.400
as well as in the thermal.

06ee5429-9193-47d0-bb92-92add2db1fa2-0
00:32:24.800 --> 00:32:29.040
We classified those pixels,
those patches of pixels that were similar.

2807f863-f21a-4dcb-9d28-5ce712861b86-0
00:32:29.560 --> 00:32:33.080
So they were either trees,
grass or impervious surface.

1bf081c4-938b-454c-999f-13ef04d1afee-0
00:32:33.080 --> 00:32:35.800
So buildings, roads,
anything that wasn't vegetation.

cbef385c-4d51-4e57-adb7-6cf8a889b78a-0
00:32:36.640 --> 00:32:38.440
And then that was manually edited.

77ece9dc-44ce-477b-add8-cde69eff36ee-0
00:32:38.440 --> 00:32:39.800
They, they came out great.

cd6aa715-b37f-4f2f-902b-227c4bef8d5a-0
00:32:39.840 --> 00:32:44.160
I, I love my little patches of pixels,
but they still weren't perfect.

52541eb6-fdca-4263-bdac-2ec66c43dbce-0
00:32:44.160 --> 00:32:48.120
So a lot of these had to be drawn on and
and edited manually.

41424e56-9bf9-41fc-9fa8-a96e937321b2-0
00:32:48.680 --> 00:32:52.240
And so using those polygons,
those or pixel patches,

41424e56-9bf9-41fc-9fa8-a96e937321b2-1
00:32:52.240 --> 00:32:55.600
we were able to calculate descriptive
statistics.

eb26bfa4-fd2f-467c-a860-03f50503815e-0
00:32:55.600 --> 00:32:58.123
So you know, the mean, the median,
the minimum,

eb26bfa4-fd2f-467c-a860-03f50503815e-1
00:32:58.123 --> 00:33:02.277
the maximum land surface temperature for
all these trees and then analyse that

eb26bfa4-fd2f-467c-a860-03f50503815e-2
00:33:02.277 --> 00:33:06.062
with the species information,
with the growth information to understand

eb26bfa4-fd2f-467c-a860-03f50503815e-3
00:33:06.062 --> 00:33:09.479
what are the important factors for
temperature for street trees.

c1a269c3-8235-4b72-b163-c9d5658210e2-0
00:33:10.760 --> 00:33:15.035
So here's more information about the
drone flights as well as this is me with

c1a269c3-8235-4b72-b163-c9d5658210e2-1
00:33:15.035 --> 00:33:17.502
the thermal drone,
my advisor at Clark John,

c1a269c3-8235-4b72-b163-c9d5658210e2-2
00:33:17.502 --> 00:33:21.887
Professor John Rogan and another former
doctoral student who is now a professor

c1a269c3-8235-4b72-b163-c9d5658210e2-3
00:33:21.887 --> 00:33:24.080
at Auburn and has his own drone program.

96ea6204-2145-43b9-a55a-4317c04db29a-0
00:33:24.080 --> 00:33:26.920
So he's been incredibly helpful in kind
of advising me.

16cceb55-e86d-4d0b-bbc7-766267636cea-0
00:33:27.760 --> 00:33:32.551
We have the DJI Maverick 3T and DJI
Maverick 3 M and I always said that that

16cceb55-e86d-4d0b-bbc7-766267636cea-1
00:33:32.551 --> 00:33:34.480
that's a Chinese drone company.

2e13bcbd-e4aa-4f39-9f83-1119620bbd7e-0
00:33:34.880 --> 00:33:39.070
And I always said I wasn't worried about
that drone company as long as TikTok

2e13bcbd-e4aa-4f39-9f83-1119620bbd7e-1
00:33:39.070 --> 00:33:41.702
wasn't banned,
which is kind of gone up and down

2e13bcbd-e4aa-4f39-9f83-1119620bbd7e-2
00:33:41.702 --> 00:33:44.120
recently,
but I think it's still not banned.

1c90b7f6-1db0-48fa-8075-4daa2053257c-0
00:33:44.120 --> 00:33:46.680
So I'm still not worried about our DJI
drones.

0d1829e8-d28e-4a6f-bf68-adfdecfdefaf-0
00:33:48.120 --> 00:33:52.074
And we were looking at a small area where
we had done the field survey of the

0d1829e8-d28e-4a6f-bf68-adfdecfdefaf-1
00:33:52.074 --> 00:33:52.480
streets.

8ff9095e-550f-443a-aab9-724d31764a93-0
00:34:17.200 --> 00:34:17.720
Excellent.

ea4a5066-bfb4-405f-862c-9232ae50b525-0
00:34:18.000 --> 00:34:22.067
We had a high precision GPS unit,
but just with the amount of trees and

ea4a5066-bfb4-405f-862c-9232ae50b525-1
00:34:22.067 --> 00:34:25.739
buildings in the area,
a lot of the points still ended up in the

ea4a5066-bfb4-405f-862c-9232ae50b525-2
00:34:25.739 --> 00:34:28.337
middle of the road or slightly near the
tree,

ea4a5066-bfb4-405f-862c-9232ae50b525-3
00:34:28.337 --> 00:34:30.880
so those points had to be adjusted
manually.

9e937479-777f-4019-b84e-50aa4d84b79f-0
00:34:30.880 --> 00:34:35.360
On top of the drone imagery,
we also noticed the shadow.

dccc8b67-876f-4dc0-9c6e-c6dfc3c51970-0
00:34:35.360 --> 00:34:39.902
So I think this is actually tree shadow
that's appearing as if it is tree in the

dccc8b67-876f-4dc0-9c6e-c6dfc3c51970-1
00:34:39.902 --> 00:34:41.080
multi spectral image.

2d2c7885-d15c-49d0-8611-44eb47f4397e-0
00:34:41.920 --> 00:34:44.623
But overall,
despite in remote sensing we always have

2d2c7885-d15c-49d0-8611-44eb47f4397e-1
00:34:44.623 --> 00:34:48.528
the I try to just be transparent about
issues with the data before I show you

2d2c7885-d15c-49d0-8611-44eb47f4397e-2
00:34:48.528 --> 00:34:49.480
any of the results.

30127739-34c2-448f-96a1-2fbc06071b8f-0
00:34:50.360 --> 00:34:55.200
But we 64 trees were in the study area
with 12 different species.

2dc818d1-49fc-4afb-8ee8-3f8d2a5dc2eb-0
00:34:56.200 --> 00:35:00.200
And so we were able to calculate NDVINDVI
is a health metric.

40fd29bd-9bdb-4278-8822-06d9d883066f-0
00:35:00.200 --> 00:35:03.830
As I said before,
it relies on the near infrared band,

40fd29bd-9bdb-4278-8822-06d9d883066f-1
00:35:03.830 --> 00:35:09.110
so just beyond the visible spectrum and
plants and specifically chlorophyll and

40fd29bd-9bdb-4278-8822-06d9d883066f-2
00:35:09.110 --> 00:35:12.872
leaves reflects really highly,
50% in the near infrared,

40fd29bd-9bdb-4278-8822-06d9d883066f-3
00:35:12.872 --> 00:35:17.360
whereas a healthy tree will actually
absorb all the red wavelength.

4b522c04-2e3a-43ec-b4ff-81e24b9ca93e-0
00:35:17.360 --> 00:35:20.400
So that's why trees don't usually look
red, they look green.

5bf4ad5d-c324-46d1-8bd9-727f3843f795-0
00:35:20.400 --> 00:35:23.240
So there's not,
they're not reflecting red light.

81e75252-17c0-480d-b95e-18fe9ac117c2-0
00:35:24.600 --> 00:35:29.520
And so this ratio, if it's really large,
is a sign of a healthy tree.

a7460c53-c207-4274-b28c-5f286c9edc8a-0
00:35:29.520 --> 00:35:35.335
So NDVI is a value from 1 to -1 whereas
if the tree turns brown or gets those

a7460c53-c207-4274-b28c-5f286c9edc8a-1
00:35:35.335 --> 00:35:40.181
pretty red leaves in the fall,
starts reflecting more red light,

a7460c53-c207-4274-b28c-5f286c9edc8a-2
00:35:40.181 --> 00:35:43.760
and we see that that value of NDVI drops
a lot.

48f94006-3e55-41b9-98b5-c03141767dc1-0
00:35:43.760 --> 00:35:46.640
So it's a good sign or a metric for tree
stress.

145b19bb-58f2-4f1c-b860-a4f5b2a1fa32-0
00:35:46.960 --> 00:35:51.640
And this is a map of where our trees are
as well as with NDVI.

5936ed65-16b4-438f-8878-f4f03ddda960-0
00:35:52.400 --> 00:35:53.640
And this is just a zoom in.

6c233806-ef6c-4bdf-98fd-fcab138fb2d6-0
00:35:53.640 --> 00:35:58.943
So here you can see the tree canopies
that we measured are the ones in with

6c233806-ef6c-4bdf-98fd-fcab138fb2d6-1
00:35:58.943 --> 00:36:00.200
yellow boundaries.

253803e1-d9f8-46c0-862d-c16fc316a822-0
00:36:01.000 --> 00:36:05.139
And you can see what the NDVI kind of
looks like compared to the road or

253803e1-d9f8-46c0-862d-c16fc316a822-1
00:36:05.139 --> 00:36:06.160
compared to grass.

a4226e73-ecd6-4755-a46e-544f6ad4f8ca-0
00:36:06.440 --> 00:36:08.560
It does seem to be a little bit of a
darker green.

5e433709-aa20-4ae3-99ae-a7f44e355668-0
00:36:09.920 --> 00:36:11.840
And then this is our land surface
temperature data.

3c0beb39-73fc-4052-b69b-a8bf64fa3789-0
00:36:11.840 --> 00:36:18.120
So this is imagery that was taken at noon,
so no shade or, you know, mostly at nadir.

f886a5e6-c386-4dcd-b238-618388272f6e-0
00:36:18.120 --> 00:36:20.080
So the the sun is almost directly
overhead.

7d783d09-f1f8-457b-bf78-daddae34cba6-0
00:36:20.920 --> 00:36:24.147
You can see what the tree canopy polygons
or my my patches,

7d783d09-f1f8-457b-bf78-daddae34cba6-1
00:36:24.147 --> 00:36:25.600
my pixel patches look like.

61b2ac50-9ec5-4060-80e4-d320b8091375-0
00:36:26.720 --> 00:36:28.680
And then also the thermal.

c50645f1-cbef-4945-89b9-3f991eb7a82d-0
00:36:28.880 --> 00:36:32.240
What really jumps out here is you know
how hot the roofs are.

e1cee84c-cdd5-4935-bba6-3cbc3e262bb9-0
00:36:33.160 --> 00:36:35.524
It's I,
we didn't have too many solar panels on

e1cee84c-cdd5-4935-bba6-3cbc3e262bb9-1
00:36:35.524 --> 00:36:37.200
roofs, but there is one down here.

b756338b-5347-4b6c-bd90-c821bb977044-0
00:36:37.200 --> 00:36:39.120
They actually cool the roof quite a lot.

e5ed27fd-204b-45f3-8bb2-29450b73a6af-0
00:36:39.120 --> 00:36:41.205
So that,
that'd be another interesting study of

e5ed27fd-204b-45f3-8bb2-29450b73a6af-1
00:36:41.205 --> 00:36:44.160
just like the cooling properties of,
of having solar on your house.

1e58afae-0166-4e82-8bfd-bdc1cb2b512d-0
00:36:45.640 --> 00:36:48.904
But more noticeably for me at least,
is the cool,

1e58afae-0166-4e82-8bfd-bdc1cb2b512d-1
00:36:48.904 --> 00:36:51.320
the cool patches where we have trees.

c6dc8c0b-15a5-4bff-9111-f0c04061326e-0
00:36:51.320 --> 00:36:53.480
So those are some of the coolest areas in
the site.

07a8c0cb-e721-4f64-a9e2-6266b4e1712d-0
00:36:54.280 --> 00:36:56.680
And this is land surface temperature,
not air temperature.

de6b75ab-2954-4c55-a106-3f1430a44f77-0
00:36:56.680 --> 00:36:58.960
So it was not 75°C that day.

3ef21d96-01b8-46bd-82fe-cf415b6facaa-0
00:36:58.960 --> 00:36:59.840
Important to know.

a414262b-58d4-4043-9e30-a83f1129f777-0
00:37:02.280 --> 00:37:05.520
So our research questions where well are
these larger shade trees?

cbb1895a-8c8e-4b4b-9a18-522b8a6979a2-0
00:37:05.520 --> 00:37:08.320
Are they actually cooling more than small
ornamental trees?

802ecbd7-c7fd-49fa-8795-463075ff9824-0
00:37:08.840 --> 00:37:12.160
And so here are all the different species
we have and or genus.

cc94dc62-6fc7-4b3d-aff1-f048f64255e7-0
00:37:12.160 --> 00:37:15.451
If we didn't have high confidence and
identifying the species,

cc94dc62-6fc7-4b3d-aff1-f048f64255e7-1
00:37:15.451 --> 00:37:17.280
we would just stick with the genus.

b9a5088d-c3e1-4d91-89e6-8a0bad34dfb1-0
00:37:18.200 --> 00:37:22.080
And so you can see that the majority of
the species St.

f398ff60-cc6b-4ece-8259-88eb9c69e49d-0
00:37:22.080 --> 00:37:24.200
trees are shade trees.

69b5d8f7-82b3-45e6-9494-05820a1c74b8-0
00:37:24.400 --> 00:37:25.440
They're all quite large.

12182488-a173-44f9-b51c-4366cf825151-0
00:37:26.480 --> 00:37:29.280
This is their average heat height and
feet.

eae427fc-faab-4915-83c1-5940fa978582-0
00:37:29.960 --> 00:37:33.933
And then you can see what the small trees
height is, cherry being the largest,

eae427fc-faab-4915-83c1-5940fa978582-1
00:37:33.933 --> 00:37:36.600
but they're pretty different in terms of
the growth.

9c23cd18-c866-4aa9-9e51-4e9ce2f9afce-0
00:37:36.600 --> 00:37:37.960
So are these shade trees?

92e96a65-9159-46c7-8578-ba17224834c5-0
00:37:37.960 --> 00:37:41.920
Are they cooler than the the smaller
trees, ornamental trees?

31d9a7f3-7e84-4f33-b234-eddbf59ab698-0
00:37:42.160 --> 00:37:45.417
And then also what variables,
what metrics are important for predicting

31d9a7f3-7e84-4f33-b234-eddbf59ab698-1
00:37:45.417 --> 00:37:45.960
temperature?

3dbe69a1-1f41-4612-b2fb-c8433daed81d-0
00:37:46.920 --> 00:37:49.040
So we wanted to choose a temperature
variable.

d441b820-23b9-4332-8c28-74f3b87811e2-0
00:37:49.040 --> 00:37:53.010
We had minimum temperature,
median temperature, median temperature,

d441b820-23b9-4332-8c28-74f3b87811e2-1
00:37:53.010 --> 00:37:57.272
could have used Max temperature,
but I wanted to use just see what these

d441b820-23b9-4332-8c28-74f3b87811e2-2
00:37:57.272 --> 00:37:58.440
look like typically.

6ef05475-a735-441e-bc7a-bc56619173a6-0
00:37:59.400 --> 00:38:02.712
And what we see here is that the minimum
temperature,

6ef05475-a735-441e-bc7a-bc56619173a6-1
00:38:02.712 --> 00:38:06.945
it does look like the pin oak,
which is the largest tree is slightly

6ef05475-a735-441e-bc7a-bc56619173a6-2
00:38:06.945 --> 00:38:11.608
cooler than you know, for example,
the Dogwood or the apple or the Japanese

6ef05475-a735-441e-bc7a-bc56619173a6-3
00:38:11.608 --> 00:38:16.639
lilac cherries are interesting in that
they are, they do seem to be a lot cooler,

6ef05475-a735-441e-bc7a-bc56619173a6-4
00:38:16.639 --> 00:38:20.320
but we just saw much more variation with
the media and LST.

ad5e9856-7e58-4f28-a402-8cda35a1a070-0
00:38:20.320 --> 00:38:23.982
So going forward,
I'm just going to be sticking with

ad5e9856-7e58-4f28-a402-8cda35a1a070-1
00:38:23.982 --> 00:38:29.303
minimum land surface temperature within
that patch of pixels just to kind of

ad5e9856-7e58-4f28-a402-8cda35a1a070-2
00:38:29.303 --> 00:38:34.140
drive home show some other metrics
related to species kind of biology

ad5e9856-7e58-4f28-a402-8cda35a1a070-3
00:38:34.140 --> 00:38:39.600
audience we have looking at NDVI for
different species was really interesting.

b22fb56c-1118-4da6-adcf-ff94a838e6fc-0
00:38:39.880 --> 00:38:44.240
Specifically the Max NDVI seems to be
much higher for the shade trees.

f87fcfc6-8ad4-4532-84ee-4e90f72ee5c5-0
00:38:44.440 --> 00:38:49.581
So the shade trees do seem to be
healthier than the OR less stressed than

f87fcfc6-8ad4-4532-84ee-4e90f72ee5c5-1
00:38:49.581 --> 00:38:53.054
the small ornamental trees,
which is fascinating,

f87fcfc6-8ad4-4532-84ee-4e90f72ee5c5-2
00:38:53.054 --> 00:38:55.000
except for the calorie pair.

d287462c-fea2-4e21-b155-c42a57304129-0
00:38:55.920 --> 00:38:59.880
I don't know how many of you are familiar
with calorie pears, but they're the worst.

bab50dde-c7f5-481b-9d23-d0dd0c6c140c-0
00:39:00.400 --> 00:39:04.065
When I was practicing this with my
parents, my my mother was like,

bab50dde-c7f5-481b-9d23-d0dd0c6c140c-1
00:39:04.065 --> 00:39:05.160
is that the grocery?

ae0a18c2-2716-4bd4-8391-acc908f7bb0b-0
00:39:05.520 --> 00:39:08.040
And then my wife was like,
is that the one that smells like fish?

cfd06ab8-e15e-441a-9644-fd93f9d3dee5-0
00:39:08.880 --> 00:39:09.560
So yes.

25ddb5ee-2e02-4872-ac50-012218b95974-0
00:39:09.600 --> 00:39:10.240
And yes.

423a0064-9108-413b-ba3f-cce6ed7e72ab-0
00:39:10.480 --> 00:39:14.794
These trees are notorious in urban
forestry for being kind of like a

423a0064-9108-413b-ba3f-cce6ed7e72ab-1
00:39:14.794 --> 00:39:15.920
Frankenstein tree.

87ea20a5-3b23-4ac5-bcc3-39a49af233c8-0
00:39:15.920 --> 00:39:18.618
They were designed to not be able to
breed,

87ea20a5-3b23-4ac5-bcc3-39a49af233c8-1
00:39:18.618 --> 00:39:23.218
but then additional cultivars were
introduced and now we have them kind of

87ea20a5-3b23-4ac5-bcc3-39a49af233c8-2
00:39:23.218 --> 00:39:26.040
spreading like wildfire throughout in the
US.

e56ec98e-c7b5-4324-8390-d93783ba6bf8-0
00:39:26.040 --> 00:39:29.280
So they're incredibly invasive and
illegal to plant in many states now.

a52713b0-8b21-4cf0-94cc-79991fb79c7b-0
00:39:30.240 --> 00:39:35.273
And I was surprised, but I was interested,
but not surprised to see that they also

a52713b0-8b21-4cf0-94cc-79991fb79c7b-1
00:39:35.273 --> 00:39:37.760
the lowest NDVI of all these trade trees.

96d45d79-374d-482d-a4ad-fa9e2a467d15-0
00:39:38.440 --> 00:39:42.280
So the calorie pair is just the worst
tree.

5a42e602-f00a-4f03-a0fe-a746afb2a27a-0
00:39:42.880 --> 00:39:48.561
No other way but but so that was the
first question we're thinking about like

5a42e602-f00a-4f03-a0fe-a746afb2a27a-1
00:39:48.561 --> 00:39:49.800
what is the size?

29d5b333-7343-4e37-8387-f1f0b2ea9e09-0
00:39:49.800 --> 00:39:50.880
How does the size impact?

cec7b325-0a69-480f-99af-f9424f425d36-0
00:39:50.880 --> 00:39:55.361
It didn't seem like there was a big trend
there but wanted to understand what are

cec7b325-0a69-480f-99af-f9424f425d36-1
00:39:55.361 --> 00:39:59.024
the important variables for minimum
temperature predicting minimum

cec7b325-0a69-480f-99af-f9424f425d36-2
00:39:59.024 --> 00:39:59.680
temperature.

e2e0ce05-a9ec-4d7b-8f5c-48b26b24ac78-0
00:39:59.680 --> 00:40:03.871
So we use machine learning model called
random forest and this basically

e2e0ce05-a9ec-4d7b-8f5c-48b26b24ac78-1
00:40:03.871 --> 00:40:08.464
organises variables from most important
to least important if based on like the

e2e0ce05-a9ec-4d7b-8f5c-48b26b24ac78-2
00:40:08.464 --> 00:40:12.884
amount of error or the change in a
prediction error if you remove one of the

e2e0ce05-a9ec-4d7b-8f5c-48b26b24ac78-3
00:40:12.884 --> 00:40:14.320
variables from the model.

56819eb3-5ac3-4760-8167-bbdc9f2480e2-0
00:40:14.920 --> 00:40:19.886
So you can see that NDBI Max is and
height growth and DBH growth are all

56819eb3-5ac3-4760-8167-bbdc9f2480e2-1
00:40:19.886 --> 00:40:23.560
clearly more important than the other
variables here.

f63c6758-53f9-43a8-bcb5-96c68822ad62-0
00:40:24.440 --> 00:40:28.955
So taking that information made a multi
linear model, multivariate model,

f63c6758-53f9-43a8-bcb5-96c68822ad62-1
00:40:28.955 --> 00:40:33.105
linear model and you'll notice
immediately that the R-squared is is

f63c6758-53f9-43a8-bcb5-96c68822ad62-2
00:40:33.105 --> 00:40:33.960
extremely low.

271023d9-b1a6-4e77-81ee-fae271aed288-0
00:40:33.960 --> 00:40:38.520
So we only had .
22 or it only explains about 22% of the

271023d9-b1a6-4e77-81ee-fae271aed288-1
00:40:38.520 --> 00:40:39.320
variation.

b0d6473f-921c-4e26-8086-d80fc0ff7e62-0
00:40:40.200 --> 00:40:44.073
So that aside,
Max NDVI did seem to be the most

b0d6473f-921c-4e26-8086-d80fc0ff7e62-1
00:40:44.073 --> 00:40:44.880
important.

f9acce70-2081-4f4d-8603-2896ee6e3dcb-0
00:40:44.880 --> 00:40:49.469
It was significant and they all were
negative relationships,

f9acce70-2081-4f4d-8603-2896ee6e3dcb-1
00:40:49.469 --> 00:40:52.855
but so basically an increase in NDVI of
of .

f9acce70-2081-4f4d-8603-2896ee6e3dcb-2
00:40:52.855 --> 00:40:57.520
1 will actually decrease the land surface
temperature by 1°C.

fc84efc6-6022-4d2a-9abb-f9b8923c29e7-0
00:40:58.800 --> 00:41:02.858
You can see what the scatter plots look
like with the individual R squares in

fc84efc6-6022-4d2a-9abb-f9b8923c29e7-1
00:41:02.858 --> 00:41:04.991
blue,
but none of these were very strong

fc84efc6-6022-4d2a-9abb-f9b8923c29e7-2
00:41:04.991 --> 00:41:05.720
relationships.

34318235-53eb-4ca6-8fdb-8c928709fad8-0
00:41:06.120 --> 00:41:08.957
I did think it was really interesting
that DBH growth,

34318235-53eb-4ca6-8fdb-8c928709fad8-1
00:41:08.957 --> 00:41:13.033
so the diameter of the trunk is more
important than the height of the tree for

34318235-53eb-4ca6-8fdb-8c928709fad8-2
00:41:13.033 --> 00:41:15.200
temperature, which is a little unexpected.

37b95da2-4458-4cea-ba59-c9b4fc801fe7-0
00:41:15.200 --> 00:41:19.795
I would be interested to talk to some
more biology minded people about what

37b95da2-4458-4cea-ba59-c9b4fc801fe7-1
00:41:19.795 --> 00:41:23.000
role the trunk might play in,
in the tree functions.

296f29fc-f354-488b-8a7e-475f0eec0219-0
00:41:23.000 --> 00:41:29.141
And potentially that along with seeing
NDVI being so important might mean that

296f29fc-f354-488b-8a7e-475f0eec0219-1
00:41:29.141 --> 00:41:32.640
like healthier trees have more trunk
growth.

50cdd1c9-ef65-4b29-9bdc-47d7e5ae5d1f-0
00:41:32.720 --> 00:41:36.987
I, I, I don't know so,
but I'm interested to ask to have those

50cdd1c9-ef65-4b29-9bdc-47d7e5ae5d1f-1
00:41:36.987 --> 00:41:37.800
discussions.

a15d740c-ef69-4f8a-b86b-a26c3ef8a175-0
00:41:40.040 --> 00:41:43.360
So some issues with this analysis,
we just need more samples.

9002c5ad-1f99-449a-bbcf-f40cdf6767f3-0
00:41:43.840 --> 00:41:46.680
It was kind of exploratory to see what
this relationship would look like.

18fe471d-3445-4604-9257-2b7a4165e4a8-0
00:41:48.040 --> 00:41:51.040
This is the mean DBH growth by species.

93da90c1-4a98-43a1-99a9-a7b1753eb1fd-0
00:41:51.040 --> 00:41:54.947
And you can see that cherries are
actually have some of the largest DBH,

93da90c1-4a98-43a1-99a9-a7b1753eb1fd-1
00:41:54.947 --> 00:41:58.587
which was quite DBH growth,
which was quite interesting because you

93da90c1-4a98-43a1-99a9-a7b1753eb1fd-2
00:41:58.587 --> 00:42:02.120
remember cherries were also very cool in
the minimum temperature.

5bf99ff5-e09f-44b8-b364-b39a219570c6-0
00:42:02.120 --> 00:42:05.995
So cherries are an interesting genus
there,

5bf99ff5-e09f-44b8-b364-b39a219570c6-1
00:42:05.995 --> 00:42:10.840
but pin oak largest tree height as well
as DBH growth.

899f82da-14b2-4643-9238-a20a3384a864-0
00:42:10.840 --> 00:42:15.969
So in interest in terms of like which
species we might want to plant on street

899f82da-14b2-4643-9238-a20a3384a864-1
00:42:15.969 --> 00:42:20.840
trees, I'm excited to look at this again,
but with more diurnal variation.

f716bfdc-010c-442c-945a-d34a6ab06c06-0
00:42:20.840 --> 00:42:26.523
So what does this relationship stack up
if we fly the drone at 3:00 PM at 7:00 PM

f716bfdc-010c-442c-945a-d34a6ab06c06-1
00:42:26.523 --> 00:42:29.920
in the morning,
this was a drone flight at noon.

5bc8a550-d1fa-475d-8313-e68b47543c63-0
00:42:29.920 --> 00:42:34.176
But are there potentially different times
of the day that we'll see a stronger

5bc8a550-d1fa-475d-8313-e68b47543c63-1
00:42:34.176 --> 00:42:37.840
relationship and then also look at get
more temperature variations?

3753545e-c174-4751-9760-505f60dfd391-0
00:42:37.840 --> 00:42:43.197
So the temperature of this day was 82°,
but what if it was an extreme heat day,

3753545e-c174-4751-9760-505f60dfd391-1
00:42:43.197 --> 00:42:43.800
like 95°?

f85222ac-316c-47ae-a321-939de02858a5-0
00:42:43.800 --> 00:42:45.520
Would we actually see a much clearer
signal?

50f205a6-80cf-4b1e-a3ae-f240d5e5d5f2-0
00:42:45.720 --> 00:42:49.920
Would certain trees do better or worse
species in those conditions?

e879eb10-7dc3-4afe-ad8f-38967f164948-0
00:42:51.440 --> 00:42:54.280
And so there's interesting
recommendations that can come out of this.

bc5b3478-5270-4582-8989-d75fd651a177-0
00:42:54.720 --> 00:43:02.618
Shouts to our staff from the JS class for
modelling how to measure ADBH of a tree

bc5b3478-5270-4582-8989-d75fd651a177-1
00:43:02.618 --> 00:43:04.160
in the JS class.

1ca0c8f7-4781-41bb-a4bf-95921c570ccc-0
00:43:04.320 --> 00:43:07.291
We always do some tree surveying in that
class,

1ca0c8f7-4781-41bb-a4bf-95921c570ccc-1
00:43:07.291 --> 00:43:11.872
but you can see it's incredibly
straightforward for volunteers to measure

1ca0c8f7-4781-41bb-a4bf-95921c570ccc-2
00:43:11.872 --> 00:43:12.120
DBH.

b163ba94-136c-4be2-b495-bb9b75d59453-0
00:43:12.120 --> 00:43:13.520
It's, it's very accessible.

1cbc5597-e8db-4680-9166-a74aed7182a3-0
00:43:13.520 --> 00:43:15.760
Anyone can get up close to a tree like
that.

e1febb3e-dd0e-4684-9c3e-bbc390dae105-0
00:43:16.480 --> 00:43:22.120
And so the fact that DBH growth is really
important is great for urban forestry.

4d614faf-0e18-4be3-95d2-0c30dbe533e1-0
00:43:22.120 --> 00:43:24.240
I think it it's much easier to do that
than height.

dae94868-2828-46a7-8554-d27284626ed1-0
00:43:25.920 --> 00:43:29.614
Our takeaways were that species are not
uniform in their impact,

dae94868-2828-46a7-8554-d27284626ed1-1
00:43:29.614 --> 00:43:34.218
so it does matter what species we plant
in cities in terms of their land surface

dae94868-2828-46a7-8554-d27284626ed1-2
00:43:34.218 --> 00:43:37.004
temperature impact and that NDVI is an
interest,

dae94868-2828-46a7-8554-d27284626ed1-3
00:43:37.004 --> 00:43:39.960
like a metric that could be predictive
for cooling.

77d9a429-4488-4436-849f-b3d568d4139e-0
00:43:41.680 --> 00:43:46.267
I think another take away was that two of
the most important factor or variables

77d9a429-4488-4436-849f-b3d568d4139e-1
00:43:46.267 --> 00:43:47.400
were growth metrics.

4c4ce293-07d5-42fe-a83a-83d06795f34c-0
00:43:47.400 --> 00:43:52.040
So it speaks to the importance of having
repeat studies or repeat surveys.

e8a58357-5c6b-44da-86c1-d56192145b15-0
00:43:52.240 --> 00:43:55.830
So if we just surveyed once,
we wouldn't have been able to capture the

e8a58357-5c6b-44da-86c1-d56192145b15-1
00:43:55.830 --> 00:43:57.600
amount of growth in height and DBH.

a840bb0f-a334-4274-ad2b-e3cec3cbf20a-0
00:43:58.480 --> 00:44:03.040
So now I'm going to pivot to applying
these lessons to Milwaukee Forest.

c912d37b-06b7-435a-93e1-a069123ae15b-0
00:44:03.720 --> 00:44:06.840
And I'm running a little low on time,
so I'll try to speed up a bit.

b30be273-cc66-4c2e-be5f-0acdfa5bcccc-0
00:44:07.120 --> 00:44:09.960
But this is a Swampscott Milwaukee Forest
you saw earlier.

fbd12549-e890-4a51-951b-aac46b4381a0-0
00:44:10.400 --> 00:44:13.200
This is a true color composite or photo
of it.

3057867d-db23-4863-9d83-da71e0d13fce-0
00:44:13.720 --> 00:44:15.720
It's that little patch of green right
there.

4a3811c9-745e-454c-b6c9-90c4f8e3b066-0
00:44:17.200 --> 00:44:20.931
And basically the idea here is that
individual trees just don't aren't large

4a3811c9-745e-454c-b6c9-90c4f8e3b066-1
00:44:20.931 --> 00:44:22.240
enough or not dense enough.

f78ca2a6-28b8-4a87-98b0-4c0284b2cf2a-0
00:44:22.240 --> 00:44:25.600
They can't have the same amount of impact
as a patch of trees.

3cbce8e9-f93e-4ad9-b22d-d42c1d72c3ba-0
00:44:25.600 --> 00:44:28.880
So what could the potential impact of a
patch of vegetation be?

a3cace94-1a1a-46cd-bfc9-549548326316-0
00:44:29.520 --> 00:44:32.920
And then also what does vegetation
quality do?

34308fc4-a419-41d4-80e5-ead9ff19b38d-0
00:44:32.960 --> 00:44:40.556
So if it's just invasive species or kind
of a random mix of, of composition of,

34308fc4-a419-41d4-80e5-ead9ff19b38d-1
00:44:40.556 --> 00:44:45.400
of plants versus chosen native capstone
community.

4ec8a50b-3965-450a-81b4-5383199bbb86-0
00:44:45.400 --> 00:44:49.760
So could those potentially lead to cooler
surfaces?

039ef308-3e05-43b2-952f-1b5379d8a23e-0
00:44:50.080 --> 00:44:52.880
So this is a Milwaukee forest in Danahy
Park in Cambridge.

4147c4f5-b1a8-430a-a355-3ae8098b38a2-0
00:44:53.800 --> 00:44:55.560
These Milwaukee forests are micro forests.

57614c4c-697d-4470-96ed-9e7b3807582d-0
00:44:55.560 --> 00:44:59.471
They're composed of native capstone
communities of species,

57614c4c-697d-4470-96ed-9e7b3807582d-1
00:44:59.471 --> 00:45:00.840
very densely planted.

167c7798-7a18-46b0-bd92-8795a809836d-0
00:45:00.840 --> 00:45:04.640
So there's over 1400 plants within this
small circle.

8e744bf7-a0dd-4fd4-b866-563ab880b2ea-0
00:45:05.720 --> 00:45:07.120
They encourage rapid growth.

b200be89-7b30-4a61-a9cf-e4b249dd5000-0
00:45:07.120 --> 00:45:10.513
So the trees here have grown over 20 feet
in four years,

b200be89-7b30-4a61-a9cf-e4b249dd5000-1
00:45:10.513 --> 00:45:14.800
and they provide a lot of ecological
benefits to biodiversity benefits.

70cd8be2-47ba-4e43-bc1f-f24ca5650bc3-0
00:45:14.800 --> 00:45:17.320
So what's the cooling potential of these
patches?

11b9cfcc-7884-44d8-8807-6b2b946e4343-0
00:45:18.160 --> 00:45:22.690
We flew 3 flights here,
thermal imagery in the morning at 3:00 PM

11b9cfcc-7884-44d8-8807-6b2b946e4343-1
00:45:22.690 --> 00:45:23.720
and at 7:00 PM.

e920f062-e762-4128-b847-aaa3631d9bbe-0
00:45:23.880 --> 00:45:27.160
And we looked at the micro forest and the
surrounding vegetation.

bd699cfb-c5b4-4397-b3b8-0ebd2513b8d5-0
00:45:27.160 --> 00:45:31.776
And the two students who worked on this
project in the fall in the drone

bd699cfb-c5b4-4397-b3b8-0ebd2513b8d5-1
00:45:31.776 --> 00:45:35.760
application class are pictured there with
their drone selfies.

dc0edc3b-03a1-4b90-8ed0-2842e7c27bf6-0
00:45:36.520 --> 00:45:39.240
And I have their permission to share
their final report.

96e5fe42-caea-46be-b0bb-a4a6805d5702-0
00:45:39.240 --> 00:45:44.429
There's a 2 minute video but it'll
explain their work and more information

96e5fe42-caea-46be-b0bb-a4a6805d5702-1
00:45:44.429 --> 00:45:46.920
and footage of the Milwaukee forest.

0e18841a-b77d-4d70-849f-367dff026932-0
00:45:46.920 --> 00:45:54.505
The Danity Park Microphones is planted in
autumn of 2021 through a collaboration

0e18841a-b77d-4d70-849f-367dff026932-1
00:45:54.505 --> 00:46:01.997
with the City of Cambridge and the non
profit organization Biodiversity For the

0e18841a-b77d-4d70-849f-367dff026932-2
00:46:01.997 --> 00:46:09.582
full time forest covers 4310 square feet
in those 1400 trees and shrouds from 32

0e18841a-b77d-4d70-849f-367dff026932-3
00:46:09.582 --> 00:46:10.800
main species.

9fcdf9d1-6de8-4c18-9fca-6b6127904471-0
00:46:11.200 --> 00:46:14.920
2 years following the initial finding the
survival rate was 95%.

6e421ea1-4880-4ad5-aa12-a1737a90fe60-0
00:46:15.160 --> 00:46:18.240
Three largest trees average out at 5.
3 meters tall.

a21b7d18-1f9f-4436-b674-83f811712304-0
00:46:19.360 --> 00:46:22.880
Milwaukee forests and similar ones like
David are carefully designed.

f75b65a5-75c4-4d9d-a023-7e6aa0622164-0
00:46:39.480 --> 00:46:40.920
After the traditional forest.

0b7f46db-a99c-4b57-b9ca-05b8deec3131-0
00:49:05.680 --> 00:49:09.105
Priority,
especially in communities where they have

0b7f46db-a99c-4b57-b9ca-05b8deec3131-1
00:49:09.105 --> 00:49:13.520
been historically marginalized and are
vulnerable to extreme heat.

1f23ac54-04aa-4978-b109-8daddc3b2153-0
00:49:14.560 --> 00:49:17.211
You know,
right now we're kind of removing a lot of

1f23ac54-04aa-4978-b109-8daddc3b2153-1
00:49:17.211 --> 00:49:20.627
the scientific supports,
the data that a lot of the data that I've

1f23ac54-04aa-4978-b109-8daddc3b2153-2
00:49:20.627 --> 00:49:24.399
shown you is being taken off of websites
or no longer publicly available.

a3193b1e-25d0-4f8e-8370-901722ac54e3-0
00:49:24.760 --> 00:49:28.488
But I do think that, you know,
just because we don't have the maps to

a3193b1e-25d0-4f8e-8370-901722ac54e3-1
00:49:28.488 --> 00:49:31.577
show it or we might not be recording
future measurements,

a3193b1e-25d0-4f8e-8370-901722ac54e3-2
00:49:31.577 --> 00:49:35.040
it doesn't mean that it's not hot in
these neighborhoods, right?

33af853a-abcb-4e47-81da-8de042bc049d-0
00:49:35.040 --> 00:49:37.240
That the underlying reality is not the
same.

e3bf4de8-a124-434e-9ef3-89b69c5168ea-0
00:49:38.200 --> 00:49:43.052
And whether we want to plan for it or
whether we want to take action on it does

e3bf4de8-a124-434e-9ef3-89b69c5168ea-1
00:49:43.052 --> 00:49:43.720
still rely.

22d17af5-cd5a-47d9-b483-1ba0e8aae72c-0
00:49:44.320 --> 00:49:48.397
It's something that we can still choose
to do regardless of if, you know,

22d17af5-cd5a-47d9-b483-1ba0e8aae72c-1
00:49:48.397 --> 00:49:49.720
NOAA produces it or not.

70eeabc4-720d-41f8-9119-a9cb1fcf61c7-0
00:49:50.360 --> 00:49:53.043
But I also think this is a moment that as
scientists,

70eeabc4-720d-41f8-9119-a9cb1fcf61c7-1
00:49:53.043 --> 00:49:55.280
we can kind of think about our data
sources.

4094b8fc-5c28-4f5c-b254-68aa56b7f0c6-0
00:49:55.760 --> 00:49:57.808
Obviously,
I've shown these maps of where it's hot,

4094b8fc-5c28-4f5c-b254-68aa56b7f0c6-1
00:49:57.808 --> 00:49:58.400
where it's not.

3d57d130-147f-4bea-9139-69b75c5fdeed-0
00:49:59.280 --> 00:50:02.423
But if you go to the communities,
people who live in these neighborhoods,

3d57d130-147f-4bea-9139-69b75c5fdeed-1
00:50:02.423 --> 00:50:04.760
they'll tell you it's really hot in the
summer, right?

c57d3537-4b5b-4594-8ec4-1e7227ac179a-0
00:50:04.760 --> 00:50:09.087
So increasing the types of knowledge that
we use, the lived experiences,

c57d3537-4b5b-4594-8ec4-1e7227ac179a-1
00:50:09.087 --> 00:50:13.888
I think will allow us to tell a better
narrative around extreme heat and climate

c57d3537-4b5b-4594-8ec4-1e7227ac179a-2
00:50:13.888 --> 00:50:14.600
adaptations.

8a56d2fe-144a-4ef8-9e74-17ba5bd95dd8-0
00:50:15.440 --> 00:50:19.184
This is a Milwaukee forest that's being
planted in Worcester that I'm extremely

8a56d2fe-144a-4ef8-9e74-17ba5bd95dd8-1
00:50:19.184 --> 00:50:19.840
hopeful about.

5650f77d-6c9c-4652-a278-8193999f5356-0
00:50:19.880 --> 00:50:22.440
It's planted where a parking lot used to
be.

84a5e55c-fbc0-40e8-a5b7-8d602f856a05-0
00:50:22.440 --> 00:50:25.160
So the changes in temperature will be
even more extreme.

d68072a2-477e-436d-a070-42f6ac31b241-0
00:50:25.840 --> 00:50:29.012
And this is one of three forests that are
planted in environmental justice

d68072a2-477e-436d-a070-42f6ac31b241-1
00:50:29.012 --> 00:50:29.520
communities.

6d494cca-84c3-419d-bf71-9dcdee288740-0
00:50:30.000 --> 00:50:31.160
So this is downtown Worcester.

11e10dd0-1b0c-4522-8705-82169c173c69-0
00:50:31.160 --> 00:50:32.838
There's another one in the Housing
Authority,

11e10dd0-1b0c-4522-8705-82169c173c69-1
00:50:32.838 --> 00:50:35.720
and then there's another one in Cambridge
right next to the Housing Authority.

efe28e0a-fcb9-48c3-ba21-a5015af159b5-0
00:50:35.720 --> 00:50:39.029
So these are potential solutions because
they're so small,

efe28e0a-fcb9-48c3-ba21-a5015af159b5-1
00:50:39.029 --> 00:50:40.600
they can go almost anywhere.

8e70fc51-2419-4d5e-8630-d4a0a8d1fd26-0
00:50:40.920 --> 00:50:42.160
So with that, thank you.

eade6cdd-9c83-42ca-95e9-c0a4b75b2ca0-0
00:50:42.960 --> 00:50:47.103
Big thanks to the students that helped,
you know, the drone class,

eade6cdd-9c83-42ca-95e9-c0a4b75b2ca0-1
00:50:47.103 --> 00:50:52.050
the jazz class and a lot of the master
students and 942 had to look at a lot of

eade6cdd-9c83-42ca-95e9-c0a4b75b2ca0-2
00:50:52.050 --> 00:50:53.040
joint imageries.

789bd529-dd39-4d2e-8c41-f0e8a0010e20-0
00:50:53.120 --> 00:50:53.640
Thank you a lot.

18b6d80d-57e0-4c45-aa1f-9b5cbd3b1c0a-0
00:50:53.880 --> 00:50:54.240
Yeah.

c84153d7-1797-47da-bc28-01f8f33c215f-0
00:50:54.280 --> 00:50:58.872
So the the question was why do the
Milwaukee forest or the micro forest grow

c84153d7-1797-47da-bc28-01f8f33c215f-1
00:50:58.872 --> 00:51:00.960
so much faster than typical forest?

55d732e0-2e33-4d47-b411-57e302d9b867-0
00:51:00.960 --> 00:51:05.160
So it has to do with the ecological
composition of the the species they plant.

58e10b23-fef0-4c0a-a61c-92d3749a2ea7-0
00:51:05.760 --> 00:51:08.840
But essentially they're planting capstone
communities.

302b561d-8f5b-435b-bfbc-38e667d7b7d8-0
00:51:08.840 --> 00:51:13.559
So in a typical forest,
it might take 3 or 400 years for the most

302b561d-8f5b-435b-bfbc-38e667d7b7d8-1
00:51:13.559 --> 00:51:16.920
dominant tree species to establish
themselves.

8754924c-6a54-4fa6-a01d-afd4d9acf3a3-0
00:51:16.920 --> 00:51:20.000
So like large oaks or the the tallest
trees as well.

d4c78321-a093-4445-81ac-f857bcdc3e91-0
00:51:21.000 --> 00:51:24.370
But what the micro force does,
it tries to turbocharge that process,

d4c78321-a093-4445-81ac-f857bcdc3e91-1
00:51:24.370 --> 00:51:26.520
planting the capstone community right
away.

31110439-3e10-4f63-a0c5-ee8aae38c23a-0
00:51:26.920 --> 00:51:31.141
And then they almost like compete with
each other in such a way that it spurs

31110439-3e10-4f63-a0c5-ee8aae38c23a-1
00:51:31.141 --> 00:51:31.520
growth.

e54db7a4-1aff-4cb3-8f76-2aa6ca5c99bc-0
00:51:32.680 --> 00:51:36.945
So really it's ecological knowledge
that's being applied to create tree

e54db7a4-1aff-4cb3-8f76-2aa6ca5c99bc-1
00:51:36.945 --> 00:51:37.360
growth.

87fd5408-e3e7-4555-93e9-128f051b2c27-0
00:51:40.120 --> 00:51:41.960
And I will say I've looked at St.

e41e14db-d103-4c97-b0e0-d69a862d7de9-0
00:51:41.960 --> 00:51:44.720
Trees 10 years apart,
and they haven't grown at all.

50df56ba-2d2c-453f-b3ef-e0f87652802e-0
00:51:45.120 --> 00:51:46.920
They were like,
the same size of the planting.

7928f154-ce77-4402-abb0-52ca8426aa58-0
00:51:47.200 --> 00:51:51.005
So it does seem to be like the
composition of the vegetation around a

7928f154-ce77-4402-abb0-52ca8426aa58-1
00:51:51.005 --> 00:51:52.800
tree can impact how well it does.

75fd2d9a-50a1-42bb-acd8-5ab3bfc2dbe6-0
00:51:54.760 --> 00:51:55.440
Anecdotally.

b759d797-e2f7-4ca0-b135-5345fa17a9ac-0
00:51:55.680 --> 00:51:59.840
Yeah, we can.

ec9a2493-8f13-480d-b122-f31a615385f6-0
00:51:59.920 --> 00:52:00.280
Oh, yeah.

cf02dbb7-25ac-4c96-950d-3dce56f7384e-0
00:52:00.280 --> 00:52:03.120
I don't know who's doing.

1c788a4c-8d89-49d7-bc10-4f0703f67950-0
00:52:04.840 --> 00:52:09.080
Oh, yeah, we got OK.

c5f4a34a-5c46-48c7-a752-e820d85ee6fe-0
00:52:10.160 --> 00:52:10.400
Yeah.

9b0bdbd5-1890-4a7c-8f22-0136c8bcdb7b-0
00:54:54.560 --> 00:54:55.280
Awesome St.

6ee3e409-1676-4eb3-8463-a51c01b7e80e-0
00:54:55.280 --> 00:54:55.560
trees.

2f98f8f5-1ccf-4f00-91aa-73b6ad184f6d-0
00:54:55.560 --> 00:54:57.560
They are the pin oak and the red oak.

0cc8a68e-0a83-4dff-9ef6-e5d69e0efdc7-0
00:54:57.560 --> 00:55:00.480
So I think they they do really well.

aba0e5ef-8569-4a8a-a8c3-39269277f9b5-0
00:55:01.240 --> 00:55:05.720
They provide a lot of ecosystem services
and they're I've heard them described as

aba0e5ef-8569-4a8a-a8c3-39269277f9b5-1
00:55:05.720 --> 00:55:10.090
like the northeast version of coral reefs,
which makes them sound a lot cooler,

aba0e5ef-8569-4a8a-a8c3-39269277f9b5-2
00:55:10.090 --> 00:55:12.713
right,
because they're they're host to a lot of

aba0e5ef-8569-4a8a-a8c3-39269277f9b5-3
00:55:12.713 --> 00:55:14.680
the ecosystem or the the base level.

9ad9b990-790b-4928-800f-c356d33fd28c-0
00:55:14.680 --> 00:55:16.800
So I would recommend oak trees.

c7cee6aa-5bfe-468e-b650-ef3033a59a8a-0
00:55:16.840 --> 00:55:18.480
My personal preference is the white oak.

b13921f1-63e3-469e-9f83-afa8612037c7-0
00:55:18.520 --> 00:55:25.005
I like the rounded leaves,
but that's more of an aesthetic thing,

b13921f1-63e3-469e-9f83-afa8612037c7-1
00:55:25.005 --> 00:55:27.560
you know, question number.

c1b65912-3231-4e42-96e6-f604bfcde177-0
00:55:27.560 --> 00:55:37.408
I worked for a new base and this is for
our New Bedford and I guess my question

c1b65912-3231-4e42-96e6-f604bfcde177-1
00:55:37.408 --> 00:55:40.240
was just like a lot of.

f8b1e25a-f2e1-4e55-ae1e-8f65664ed1b3-0
00:57:23.160 --> 00:57:23.280
Big.

e9346c32-aea6-4b68-bf5a-cf62f30f0934-0
00:57:23.280 --> 00:57:24.400
It wasn't supposed to be this big.

8e1563d2-1045-4322-b909-1772fb5fd6f2-0
00:57:24.400 --> 00:57:27.762
So there's a certain amount of like
understanding of like what the tree will

8e1563d2-1045-4322-b909-1772fb5fd6f2-1
00:57:27.762 --> 00:57:29.160
look like in the future as well.

9fa30f9c-5a91-4812-94b8-d566038eaf8d-0
00:57:32.800 --> 00:57:40.308
But yeah, Bob,
sorry about the channels of that QR code,

9fa30f9c-5a91-4812-94b8-d566038eaf8d-1
00:57:40.308 --> 00:57:42.680
but still waiting.

608d1988-9978-4fb5-a852-8964c34824aa-0
00:57:43.240 --> 00:57:44.200
Take a QR code.

4c15c8f1-ed6a-49c9-9e37-9be252de8b44-0
00:57:44.520 --> 00:57:45.880
Take a photo.

7c5049b3-604e-41e0-b20a-8e43d3594660-0
00:57:52.320 --> 00:58:03.520
Doctor Gerard,
you have something to give you an apology.

f9893f96-d166-45e3-a0b8-fe5cb76e49bb-0
00:58:05.800 --> 00:58:06.320
Thank you.

6d888e87-7c24-4f0e-bc3e-a9037dd0b713-0
00:58:11.480 --> 00:58:11.880
Should I?

5f526e33-556b-4bb3-b56a-a18337c81eed-0
00:58:12.000 --> 00:58:12.880
No, it's fine.

f559ba48-bfcb-43d4-bee5-b05bb37779b3-0
00:58:12.880 --> 00:58:18.154
And if there's no further questions,
I'll pass it back to Doctor Randy about

f559ba48-bfcb-43d4-bee5-b05bb37779b3-1
00:58:18.154 --> 00:58:18.360
it.

4b2437ee-0433-488c-9500-319cafe7a088-0
00:58:18.400 --> 00:58:19.480
But we're all around pizza.

f447978d-4663-47e8-8548-36e97a99b5a8-0
00:58:22.960 --> 00:58:23.280
I can.