We've produced a 30-meter global product of forest loss and
gain on a backdrop of tree cover density.
The basic product looks like this
and it is a percent tree cover layer from 2000,
and then on top of that, forest cover loss and gain.
We have trees as green scale color, so density
is saturated. That's 100% tree cover, you go down to Chaco
and you see darker shades of green, that's 505 tree cover. So this is a percent tree cover layer for 2000.
Probably the most intensively used forest landscape is found in the southeast United States.
And in this product you can see all of the
reds, blues, and magentas that are indicative of forest disturbance
and recovery. And you see some really intense
intense land uses.
Out of this ecozone,
in the southeast US, 30% of forest land
either was regrown or lost during this period, which is 12 years, it's incredible.
Really, trees are as crops here, you might want to re-think a definition of forest
it's a different thing, it's not really natural.
In the picture here we have greens, meaning the forest didn't change in the last 12 years
you can see there's something to do with the watershed protection around a reservoir here.
Everywhere else, the greens are stable, and the
blacks are non-forest and then the dynamic is
red being loss, blue being gain, and these magentas being both, during the 12 year period.
Brazil, in the last decade, has cut their deforestation rate
in half. Despite that decrease in Brazil's deforestation rate
the tropics has a whole have a statistically significant increase
and that is due to increasing rates of loss in Malaysia, Indonesia, Angola, Peru, Paraguay,
all the other countries in our study are making up for the loss in Brazil.
There's three things that changed in the recent past that allowed us to do a global scale Landsat,
which is 30-meter, characterization of the land surface.
First is, the last Landsat sensor, ETM+ on the Landsat 7 satellite,
had a global acquisition strategy.
So we had observations everywhere. But it had a cost model associated with it,
so you had to buy data. We always said that we would use the data we could afford,
not what we really needed. And you were stuck,
you couldn't do large area, large depth time series with Landsat.
So what happened in 2008, they opened up the archive for free access.
So we didn't even have to ask what we needed, we could use it all.
We started thinking, let's try and mine the archive systematically.
If we did this project on one CPU, it would have taken 15 years.
but if we do it in the cloud, it's a matter of days.
That's the three things: the global acquisition strategy, free data, and cloud computing
equals the ability to do this.
And what we like about it is if we're working at 30-meters globally, our history has been to work at global scale,
and you get a globally consistent product and you can say what's happened
to the earth in its entirety. But with 30-meter data we can cut out any particular place, and it should be locally relevant.
So we have a globally consistent and locally relevant product.
[music]
[beep]