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We've produced a 30-meter global product of forest loss and

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gain on a backdrop of tree cover density.

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The basic product looks like this

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and it is a percent tree cover layer from 2000,

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and then on top of that, forest cover loss and gain.

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We have trees as green scale color, so density

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is saturated. That's 100% tree cover, you go down to Chaco

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and you see darker shades of green, that's 505 tree cover. So this is a percent tree cover layer for 2000.

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Probably the most intensively used forest landscape is found in the southeast United States.

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And in this product you can see all of the

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reds, blues, and magentas that are indicative of forest disturbance

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and recovery. And you see some really intense

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intense land uses.

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Out of this ecozone,

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in the southeast US, 30% of forest land

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either was regrown or lost during this period, which is 12 years, it's incredible.

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Really, trees are as crops here, you might want to re-think a definition of forest

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it's a different thing, it's not really natural.

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In the picture here we have greens, meaning the forest didn't change in the last 12 years

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you can see there's something to do with the watershed protection around a reservoir here.

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Everywhere else, the greens are stable, and the

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blacks are non-forest and then the dynamic is

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red being loss, blue being gain, and these magentas being both, during the 12 year period.

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Brazil, in the last decade, has cut their deforestation rate

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in half. Despite that decrease in Brazil's deforestation rate

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the tropics has a whole have a statistically significant increase

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and that is due to increasing rates of loss in Malaysia, Indonesia, Angola, Peru, Paraguay,

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all the other countries in our study are making up for the loss in Brazil.

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There's three things that changed in the recent past that allowed us to do a global scale Landsat,

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which is 30-meter, characterization of the land surface.

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First is, the last Landsat sensor, ETM+ on the Landsat 7 satellite,

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had a global acquisition strategy.

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So we had observations everywhere. But it had a cost model associated with it,

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so you had to buy data. We always said that we would use the data we could afford,

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not what we really needed. And you were stuck,

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you couldn't do large area, large depth time series with Landsat.

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So what happened in 2008, they opened up the archive for free access.

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So we didn't even have to ask what we needed, we could use it all.

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We started thinking, let's try and mine the archive systematically.

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If we did this project on one CPU, it would have taken 15 years.

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but if we do it in the cloud, it's a matter of days.

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That's the three things: the global acquisition strategy, free data, and cloud computing

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equals the ability to do this.

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And what we like about it is if we're working at 30-meters globally, our history has been to work at global scale,

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and you get a globally consistent product and you can say what's happened

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to the earth in its entirety. But with 30-meter data we can cut out any particular place, and it should be locally relevant.

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So we have a globally consistent and locally relevant product.

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