KIM FISHER: Thanks, Tanya.
And thanks, everybody, for coming to the last session
on the last day.
As always, I'm humbled and inspired
and exhausted after Geo For Good,
and appreciate you sticking with it.
So I'm Kim Fisher.
I am working for the Wildlife Conservation Society.
And probably many of you know we run the four zoos
and the aquarium in New York.
But we also are a globe-spanning organization,
60 different countries around the world,
with a real emphasis on field monitoring and research.
So our geospatial users tend to be not coders.
They tend to do static maps, paper maps
kind of reporting and, yeah, government reports
kinds of things.
But that's changing because of things like Earth Engine,
the still-awesome power of the data and the API,
and particularly these new GUIs apps that
are coming out to make it possible to create
tools for our field staff.
And the other thing I think that's changing is--
I mean, everybody's been referring to the SDGs.
But I think seeing all these data in a common web browser
framework sort of encourages everybody
to think beyond 13, 14, 15, or whatever it is a conservation
organization thinks they work on,
to really realize that they're all kind of--
it's all part of one world, right, and to, yeah,
report on a global rather than a national stage.
So I'm just going to go through a couple of apps and projects
that we have been doing recently,
are doing, within WCS using Earth Engine.
The first one is my colleague Adam Duncan,
by the way, who some of you probably know,
who has created this tool with the URL in the upper right
there.
That's live.
It's focused on Myanmar.
But he just kind of has been doing this in his off time,
like for fun, in the last few years.
It's not part of his remit.
But it's a thin wrapper around Earth Engine
to allow our landscape folks to do the kinds of things
that they very commonly need to do, just clip and ship
kind of functionality.
I want the latest satellite images.
I'm going to do a simple composite classification,
et cetera.
So this is proving very popular.
And you can see how this would be
pretty easy to replicate and scale
across all of our landscapes.
So that's happening.
The second thing is not yet live.
But it's going to be released next week at the UN climate
meeting.
And this is a global forest integrity metric.
And the idea here is--
and again, you can see the UI there
with the buttons on the top.
The idea is to take the UMD forest data
and kind of synthesize that with a bunch
of anthropogenic layers, human footprint layers,
to get a index, or a metric, of forest intactness.
So the areas that we're trying to protect
critters and wild places, how is the forest doing?
So a side-by-side comparison of what
we're trying to add to the story of just where the forest is
losing.
So coming next week.
So then Eric and I are working with Adam
to refine that to finally do a update of the human footprint,
originally from 2002, which probably most of you
are familiar with, trying to measure
the wildness in every place on the terrestrial surface
from most wild to least wild, and is basically
just a simple weighted sum of the various anthropogenic
layers.
Anyway, this was updated in 2016.
And we have a website, wcshumanfootprint.org,
which has some simple web apps to compare the 1993 and 2009
human footprint.
But again, this was done manually,
like three years' worth of work, desktop work,
and very important, showing the way that the human footprint is
growing, but not as fast as population and GDP, and not,
obviously, in the same way everywhere.
So there's some cause for optimism.
But anyway, this is still the old way.
So what we're trying to do is create
what we're calling Human Footprint 3 across the world.
And you could see Adam's tool for Myanmar here
already has basically the parts of a human footprint, which
are part of the forest integrity metric baked in
with, you can see, little sliders
here for the different drivers.
And you can create your own human footprint
based on what you think is important to measure
human impact in your area using this tool.
So what we want to do is make that available
and calculate it for the whole world
as often as possible, i.e., as often
as the underlying data change.
So using a lot of OSM data, take that into Earth Engine,
use other publicly available assets
so we don't maintain them ourselves,
and then automate the calculation of these things,
which I'll get to.
And then the fourth project, which
is going to depend on that, is an update
of the Tiger Conservation Landscape analysis from 2005,
which is itself an update of the TCU analysis in 1995.
Both of these were great.
But they, again, took huge amounts of work.
And that work can't easily be duplicated.
These 2005 scripts are all in old AMLs, if anybody
remembers what those are, the old Esri scripting language.
It's basically useless now.
So we're going to try to update that again
in the cloud, Earth Engine, and, again, make it update
the landscapes themselves-- not the impact on the landscapes,
but actually change where the tiger habitat is
on a semi-automated basis.
So a near real-time monitoring system.
We have this grant from NASA now.
And we're working with partners at WWF, Panthera,
other government organizations, trying
to create this system, which will follow largely
the same sort of conceptual logic
as we're thinking of it now for creating
where tiger habitat is.
So a combination of the observations from our field
staff and that of our partners, the kind of structural habitat,
remotely sensed habitat from the UMD folks, and then
this human footprint stuff.
And we use basically the same rules
that we did before about patch size and connectivity,
and then create maps and reports, not
any individual tiger observations.
Everybody's very sensitive about that.
But summaries by country, protected area, and eco region
that are kind of a web app version of the old maps
from 2005, which should be directly
copy and paste for country reporting on SDG 15.
That's the goal.
So the mission, in order to automate this,
is to take these boxes and arrows
from the original analysis and turn them into this.
And you'll notice that these tasks here
are not anything about tigers.
This is just a screenshot from this extremely helpful
introductory Medium post in the lower right about something
called Apache Airflow, which is a very powerful and complex
workflow manager, a task manager.
And the idea is to instantiate every one
of the computational steps in Airflow
that was required to calculate either human footprint or tiger
habitat and then only run the tasks that
are necessary in any given instance,
based on what has been updated in its inputs.
So if the lights at night data set or whatever has updated,
but none of the structural habitat or tiger observation
stuff is updated, we would run just the branch
that is any calculations downstream
from that calculation, but not all the other things.
And so that's the goal.
There's a Google Airflow as-a-service product,
Cloud Composer, which we're hoping to use to leverage this.
And so the idea is to take all these incredibly powerful
Google Cloud products and talk to Earth Engine,
OSM, et cetera, et cetera, and produce
this on a regular basis.
So if we could do that for tigers,
the idea is that we could do that for other species.
So if we could have how the habitat for any given species
is doing over time, then you can maybe
start to produce a kind of index of species habitat, Google
Nature Index, let's say, and then
report that out the same way we do [INAUDIBLE]..
So that's the idea.
That's what we got.
My email and information is there, so.
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