TANYA: I want to welcome up Michael Evans from Defenders
of Wildlife.
MICHAEL EVAN: Thanks, Tanya.
Excited to be here.
Yeah, so for those of you that aren't
familiar with Defenders of Wildlife,
we are a US-based national nonprofit.
And we focus on the conservation of plants and animals
and the ecosystems that they depend on primarily in North
America and largely in the US.
And so Defenders has kind of typically
done four different things.
The first is that we work with legislators
to try to pass more progressive or protective environmental
laws, although these days, it's mostly
playing defense to stop terrible things from happening.
We also sue government agencies.
Are there any Feds in the room?
[LAUGHTER]
No?
Good.
All right.
We sue government agencies when we
think that they're not fulfilling their obligations
under those laws.
We do have field teams, the third thing,
that work in local landscapes to implement
kind of on-the-ground conservation projects-- a lot
of coding systems work.
And then the fourth area is we work
with those federal agencies to try
to help them improve the ways that they implement
and enforce the conservation laws and policies that
are on the books.
So a caveat-- this might be the least sexy talk
about conservation and technology
that you're going to hear.
But it's in this nitty-gritty policy work
that we really think there is an opportunity to pull some levers
and actually see positive impact for conservation.
And so the group that I'm part of at Defenders
Center for Conservation Innovation
focuses on this fourth part-- working with agencies,
trying to give them better tools,
trying to improve the analyses that they're
using to inform the activities that they're doing.
And so we have this catchy slogan--
working at the intersection of science, tech, and policy
to develop creative and pragmatic solutions.
This is one of those types of mantras
that conservation organizations love to throw up there.
But what does this actually mean?
I'm going to start with an example.
And I'll go through a couple over the course of my time
here.
So we engaged in a partnership with the Nature Conservancy
called the Long Island Solar Roadmap.
And what this is is an effort to fulfill New York state's
commitment to be 50% renewable energy by 2030,
I believe it is.
Long Island is making a huge push
to put utility-scale solar across Long Island.
Where we come in is we want to make sure
that they're doing this in a way that is not
going to disturb a whole bunch of new habitat and native
ecosystems.
So how do we do that?
Well, it turns out that there's a lot of previously
disturbed sites on Long Island.
Here's a little snapshot of Nassau County.
Parking lots being the primary large area
that you could throw a big solar panels on that
aren't going to clear a bunch of new habitat.
So that raises the question, where are all the parking lots?
It actually turns out that open street maps is incomplete.
Nobody has a good map.
So we thought that we could employ
this awesome synchronization between Earth Engine
and TensorFlow to try to find all the parking
lots on Long Island.
And we're going to use this to try
to guide a suite of local collaborators--
Long Island's power authority, municipalities--
to try to show them where they could
put all these solar panels.
And just as to clarify, we're doing all this
with this cloud storage to Colab to TensorFlow workflow.
The majority of these processes are free.
We've actually been able to get quite a lot done
in this without having to use a bunch of paid services, which
has been awesome.
And the reason that we think we needed
TensorFlow and some of the AI approaches
is because if you do a regular classification to pick out
impervious services, roads and parking lotd
look completely identical.
And so through that spatial context, that's important.
So what we did was we trained DeepLab, Version 3, which,
at least when I started talking, was one of the state-of-the-art
computer vision models.
That may have changed in the past five minutes.
Here's a little snippet of the output.
Basically when we combine what that computer vision model was
seeing with our classification approach,
we're able to pull out all the parking lots
and delineate those so that now the decision makers can overlay
this with maps of social acceptance, communities that
are in need of community-based solar,
where existing power is-- all sorts of things
that you need to consider to guide the sustainable
development.
So we're really happy with this and are
excited about future applications
for this TensorFlow machine learning
and Earth Engine marriage.
All right, I promised this talk was going to be unsexy.
And here comes that part.
The least-appealing title slide you're ever going to see.
As I mentioned, the majority of the work we do
is trying to engage with federal agencies.
And the reason for that is, the US and other countries
have a lot of really good conservation laws on the books,
like the Endangered Species Act, for instance.
And in our experience, the single biggest hole,
or thing that's holding conservation back,
is inadequate monitoring and enforcement.
Agencies just don't have the personnel time or resources
to monitor the thousands of voluntary habitat conservation
agreements that are on the books--
the thousands of parcels of critical habitat.
All these things that are great on paper--
we just don't know what's going on there.
So our approach to try to solve this problem
has been fairly simple.
We've developed a couple of algorithms
to do automated change detection, the thought
being that if we can rapidly and repeatedly figure out what's
going on the landscape, we can use that to help monitor
some of these places.
So we're looking at a place in West Texas.
And we just want to be able to do something like this--
see a before and after image and automatically pick out
what's changed.
So a couple examples, just to show
how we've implemented this.
The first is with the dunes sagebrush lizard.
So this is a species that lives, unfortunately,
in the heart of oil and gas country.
All these little dots you see are well pads.
And because of this threat, the lizard
was proposed for listing in 2015.
Ultimately declined, because Texas came up
with a voluntary conservation plan with the oil and gas
companies that they were going to mitigate so much habitat.
Anyways, we were very skeptical of this, as you might imagine.
And so we basically turned change detection
to this area in West Texas where the lizard lives.
And so here is, I think, three months' separate images.
And what we found is that, not only were there oil and gas
well pads, but these ginormous things
called sand mines had started appearing in this region.
So companies that were doing fracking had actually come in
and just started basically wholesale-dredging dune habitat
to produce sand for their fracking operations.
And so we basically presented this information to Texas
in the Fish and Wildlife Service.
We were able to get the Texas conservation
plan rewritten to try to encompass these sand mining
activities.
Ultimately, where this went is the lizard
has been petitioned for listing, and is being considered
by Fish and Wildlife Service to get protections under the ESA
once again.
Another very similar example is with the lesser prairie
chicken.
I believe we officially call them a charming bird.
I might describe them as goofy-looking.
Among their more charming qualities is
they are terrified of tall things, like oil and gas
pumps and wind farms.
And this is another case where the species was listed
on the [INAUDIBLE] and the endangered species list
was removed because a voluntary conservation
plan was put in place by five states out
in the southern Great Plains.
And this was supposed to mitigate habitat loss from some
of these energy industries.
Again, a reason for suspicion.
Running change detection over the prairie chickens' range
out in the Western US, we found basically
from the time it was delisted to the time
that we did this study, there are eight new wind farms,
hundreds and hundreds of oil and gas well pads
that didn't show up in the administrative records
for those states.
And basically hundreds of thousands of acres of habitat
had been removed.
And it's not to say that nobody is monitoring this.
But nobody taking a wholescale look
at how the chickens' habitat was doing
under this range-wide management plan.
And so, again, the story here is we
were able to produce information for this species the Fish
and Wildlife Service used.
And this one is a little bit further along
than the sagebrush lizard.
We got a positive 90-day finding on our listing petition,
which basically means that Fish and Wildlife Service
thinks there's enough information
to seriously consider and analyze
whether the species should be put back on the endangered
species list.
We are really excited and happy with-- well,
happy is the wrong word--
pleased with the outcomes of these analyses.
But we can only monitor so many places at once.
And so we think the real promise, and our priority,
is to democratize these change detection processes.
So most of our attention recently
has been focused on developing tools that
let the public run the same algorithms that we're
using so that other conservation organizations,
or, ideally, the federal agencies themselves, can
do these things.
So the first tool we spun up was just a simple Earth Engine app
that lets a user go in, select an area of interest,
see what habitat type it is.
And then they run our change detection algorithms
in the background and can see what
has changed between whatever date they select.
So here we're looking at more oil and gas development.
There's a theme to who the, quote, "bad guy"
is here in western Wyoming.
And so this was exciting, an exciting first step.
But somebody still has to go in and actually
actively look or know where they're looking and going
and do this again.
So what we're most excited about is a collaboration
with a group called SkyTruth that helped
start Global Fishing Watch.
I think my colleague Christian is in the back
there from SkyTruth.
They have a platform out called SkyTruth Alerts,
which is really fantastic.
It lets users sign up to monitor an area of interest.
And it will email alerts to those users
when some notification happens in that area.
And so typically this has been new permits or plans
that appear in those areas.
But we-- oop--
there's no video.
AUDIENCE: There's no access.
Yeah, you can do it.
MICHAEL EVAN: Oh, well, how about that?
Gotcha.
AUDIENCE: I'm [? switching down ?]
to a [? bit forward. ?]
MICHAEL EVAN: Yeah, let's try switching in here.
[INAUDIBLE]
AUDIENCE: You can take care [INAUDIBLE]
AUDIENCE: [? Where? ?]
MICHAEL EVAN: All right, I'm not going to mess with this.
I can share the video later.
But this somewhat darkened-out screen
here gives you a flavor of what's going on.
So a user can sign up for to monitor an area of interest.
In this case, we're looking at Gulf County
in Florida and the panhandle.
And basically, we run change detection in the background
every month, aggregating all the sentinel to imagery.
And the user is sent an alert passively
whenever something changes within that area
that they signed up to monitor.
So now we have a system where you
don't have to continuously go back and check
an area for changes.
You get alerted when it happens.
And so what we are working with SkyTruth
and have meetings with the Fish and Wildlife Service tomorrow
is to try and get agencies to potentially adopt
this tool as a way to monitor, like I
said, the thousands of conservation agreements,
different mitigation areas--
all the different nuts and bolts of how the Endangered Species
Act and other federal laws are implemented
and be able to basically keep better tabs on the conservation
laws that we have in place.
So we're very excited about this.
We are about to, I think, open it up for beta testing,
where we're looking for feedback from any users that
are interested so we can continuously
improve the algorithms.
Our next step are to potentially incorporate
some of those TensorFlow workflows
that I mentioned at the beginning of the talk
to see if that can improve our precision with change
detection.
And, yeah, we're thinking about ways that we can scale this up.
So I'll be interested to talk to some of the other folks who
have had success building their tools up
to serve to ideally hundreds and thousands of users.
So with that, thank you, everybody.
I guess [INAUDIBLE] questions?
[APPLAUSE]
TANYA: Any questions for Michael?
MICHAEL EVAN: Come on, guys-- have questions.
AUDIENCE: Hey.
Is it on?
TANYA: Mm-hmm.
It is.
AUDIENCE: OK, so you mentioned this--
I think you said thousands of these voluntary conservation
districts.
Is there a master database of all those polygons?
MICHAEL EVAN: [LAUGHS] No.
They exist in a scattering of maybe PDFs
in a bunch of different fish and Wildlife Service offices.
AUDIENCE: PDFs?
MICHAEL EVAN: Yeah.
There's a lot of very basic technical hurdles
to cover before we can use some of these tools.
And I think that's one of the points
that I want to try to communicate when I talk
about this work is, at least, in our world and the groups
that we're working with, they're extremely well-intentioned.
But it's sort of a situation where if you build it,
they won't come.
The data and infrastructure in many of these agencies
is just not there, not centralized, not standardized.
But they want to do the right thing during this.
There's a lot of handholding and prodding that has to happen.
And so simple tools, I think, are really
what we need to focus on [INAUDIBLE]..
AUDIENCE: I have a follow-up question to the
build it and coming.
Can you say what's the handoff from when
you build a tool to some kind of decision being made?
At what point does it turn into legislation or listing
or anything like that?
MICHAEL EVAN: Yeah, that's a really good question.
So the listening decision process
is a little more well-worn.
There's a standard set of procedures
in terms of submitting a petition with information.
So that one's relatively easy to follow.
The relationship but that we're still working out
is how to get agencies to adopt some of these things.
So one model that has worked is, we build something.
We show them that thing over and over again.
And eventually they do it themselves.
[LAUGHS] We've had this happen with a online recovery plan
approach that without going into the details,
they basically copied what we made.
And that's great from our perspective.
Yeah, when you're working with the federal government,
there is an institutional barrier.
We need to get an official agreement
on the books with that agency.
I think that's what we're still trying to figure out.
I don't know if that answers your question, but [INAUDIBLE]..
TANYA: [INAUDIBLE] OK, cool.
Thank you.
Thanks again.
[APPLAUSE]