Tracking Climate Change in Africa with open data
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Video trackingMathematicsSource codeDifferent (Kate Ryan album)Open source
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Chi-squared distributionRippingObservational studyMeasurementAxiom of choiceGreen's functionSatelliteMeasurementFreewareObservational studyInstance (computer science)TwitterEstimatorLink (knot theory)Computer animation
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AreaAdvanced Encryption Standard
Transcript: English(auto-generated)
00:08
Thank you very much, thank you. My second name, which is difficult to pronounce, is Hoopslop, in Dutch. But the French say Hoopslop. And you know, there's lots of different ways.
00:22
But I'll listen to any of them. So I'm going to talk about climate change in Africa. And how to track that with open source tools and open source data, both. Because that's possible, we found out. And you know, the big question is, everybody
00:40
thinks that Africa is drying out, isn't it? I mean, that's the picture you have. This is going to be the future of Africa. This is what 99% of the people think. But why wouldn't it be the other side getting greener? We wanted to find that out. And we have some nice answers, or answers not at least ideas
01:02
for research. My name is Peter Hoopslop. I have 30 years of experience in Africa. I have a company founded in 1996, so quite some time ago. The main work I'm doing at the moment is helping livestock farmers like these in Africa
01:23
with information on where they can find grass and water. And you do that through satellites. For instance, the Sentinel-2 satellites. And we get this information to the farmers by call centers in West Africa and Bamako,
01:42
and in East Africa by the AfriScout app. So you can have an Android app on your phone as a farmer. In East Africa, there's already lots of smartphones. In West Africa, many less. And I do a few drought projects in Netherlands as well. Here.
02:02
Oh, I'm sorry. There you go. OK. So climate change, yes. Everybody must have noticed the talk about climate change. And by now, we are that far that almost nobody denies anymore that climate change is a big thing in the world. There's still a few paid by the big oil companies,
02:23
people who still deny that there's climate change. But we can be sure that there isn't. In the data, let's go back to the data. We see that there is a very clear temperature rise in atmospheres, for instance, but also in oceans. So undeniable. But there's rainfall extremes, things like that.
02:43
And of course, it all originates from the CO2 level rise about twice what we used to have. This is what we can clearly see in data, undeniably. OK. So one of the things that interests me
03:00
is the time scale of climate change. Because we generally say, or generally think, that it's a relatively long time scale. But if you look at this graph, and probably you are familiar with this graph, you see that the global temperature change, this is global, an average, is changing a lot from,
03:20
let's say, 2000 or earlier. You can very clearly see a trend here. So that means that climate change is not only something that works on hundreds of years or something, but also on 10, 15, 20 years, things like that.
03:42
So our hypothesis is that climate change should be technical on a short time scale of just 15 years, for instance, in Africa. That's the assumption we had when we started off. So what does it mean for the African continent?
04:02
Does it mean continent-wide droughts? Does it mean drought everywhere? Or does it mean greener areas in some places and droughts in other places? Well, there has been an IPPC report predicting the effects on Africa. And this IPPC report is quite clear
04:21
that, for instance, in East Africa, they predict droughts, that's Kenya, Ethiopia, Tanzania. In West Africa, the situation is much more diverse. And they predict already that the Sahel region, and we're going to talk about the Sahel region, is greening up.
04:41
That's what predicted in the IPPC project. So that is specifically what I want to do. I'd like to zoom in to the Sahel region. And the Sahel region is the region in orange here, which goes from Senegal right to Eritrea.
05:03
Some people say it's only French-speaking Africa, so it would stop here somewhere. But that is the Sahel region. So that's the region just south of the Sahara and just north of the Savannah area down south. And especially for my generation,
05:23
I'm a bit older than most of you, the Sahel rings a bell because it had always been a troubled region in our minds for a long time. And therefore, of course, there is a lot of focus there. If we want to track climate change, what data are available?
05:41
Well, basically, we have two choices, ground and satellite. Ground data, like, for instance, weather stations and so on, these networks are rapidly deteriorating in Africa. So I've been living, for instance, in Zimbabwe for quite some time.
06:02
And we used to have 5,000 of these weather stations. And now there's just 100 left. So things are going down very rapidly at the moment. So basically, although we would like to use ground data as well, basically, we will get more and more to use satellite data
06:20
until maybe there is some smart engineer who finds out how to measure weather parameters with a smartphone, for instance, and have a community around that. Maybe that's a nice open source project, no? OK, so what we are going to do for this project is use satellite data.
06:43
Then it is important to note that there is two types of satellites. And I always draw this picture because almost everybody in the research community use the low-orbiting satellites. So these satellites are about 800 kilometers from the Earth.
07:03
But if you do so, there is some apparent disadvantages. For instance, these satellites only see a very small part of the globe. So to cover the whole globe, they have to revisit the Earth all the time with tracks
07:21
and so on. And you will see that they only, per 10 days, for instance, come to a certain place once in two days, or once in three days. That means that the amount of data is not as high as we would want.
07:42
The amount of data that come in with these satellites, which are much further from the Earth, and which, in fact, are fixed to a place on the Earth. In fact, if you look at the Meteosat satellite, it always looks at the Gulf of Guinea here. The amount of data that comes out of these satellites,
08:00
in temporal sense, is much higher for a certain area than of these. And still, if you look at parameters like NDVI and so on, almost always these data are used. We try to turn this around because we say, although it's a satellite that is very far from the Earth, we don't need the resolution. We don't need the high resolution of these satellites.
08:25
So these low-orbiting satellites have a high resolution, two or three images per 10 days. And these medium Earth orbit satellites have low resolution, relatively low. But you can produce 200 images for 10 days.
08:42
And so you have lots and lots of data. And that's what we want because we want a high-quality signal to track climate change. So how do we measure climate change trends?
09:00
Well, we could do it in different ways using, for instance, rainfall estimates. There's many rainfall estimates around, like RFE2, ARC2, CHIRPS, and many more. I can give you links if you want. You can download them for free. There's vegetation greenness based both on low and high orbiting satellites.
09:21
We could look at temperatures, for instance. But for this study, we've been using NDVI, which is vegetation greenish, from the METEOSAT satellite. And of course, you could make other choices. This is interesting, especially because we are at the OSG conference here.
09:42
We use a lot of tools. And all of these tools are open source. So we use well-known to you, all of you, of course, the GDAL library. I'm really a fan of GDAL. There's even a special METEOSAT conversion routine inside GDAL. So the METEOSAT images come in an awful format, very, very
10:06
difficult to read, with seven bits, or instead of eight bits, for instance. And GDAL is capable of converting these into normal ones, for instance, and other files. We use QGIS a lot to check results, for instance.
10:24
We use a program called UMDEC, or something. I'm not sure how to pronounce it. To download MSG data, we use Lazarus Pascal to program, Python to program, and scripting in batch files. And this is all open source. The reason why we do all this
10:42
is that it's easy to transfer all our work to Africa. They wouldn't need any licenses there to operate our software or our layers. Well, let's go to work very quickly. What I've done is I registered for free at the UMDESAT, which is an organization that provides METEOSAT data
11:03
for free. You can see my subscription here. I download METEOSAT data from this UMDESAT website using this program, UMDEC, or something like that, which is a simple user interface.
11:21
And I very much like the scriptable user interfaces, because you can easily program with them, and so on. What I do next is create NDVI composites from 100-plus images per 10 days. So I create a 10-day image made up of 100 native images
11:45
from the METEOSAT satellite. And I calculate NDVI for all of those. And what I do is I take the highest NDVI values per pixel to create a sort of composite. And the reason behind that is that the higher the NDVI
12:00
value, the most likely they are not affected by clouds and dust. So that's the maximum composite method, which is well-known probably. But that's what we did. And this is, for instance, the latest image of just eight or nine days ago. And you can see that, of course, from the Gulf up north,
12:25
things are greening and gradually getting drier. This is the normal pattern in West Africa. And then in the time period we use, 2008 to 2023,
12:42
36 images per year, we establish a trend with a z-score. You probably know what a z-score is. And we normalize that on a scale from 0 to 100. And then this is the result. If we do that in the period 2008, 2023, so for 16 years,
13:02
in fact, did I say 16, 15 years, you will see that you get the resulting image like this. And that's interesting, isn't it? Because the Sahel region we were talking about is generally greening up, as you can see. And which is more interesting, also interesting,
13:22
is the region just south. So that's what's called the Savannah region. It's not greening up, but it's getting drier. This has been predicted by IPPC. We have not just found it in the data. And the reason seems to be that rain clouds penetrate
13:40
deeper into the continent. Rain clouds normally come from this area and penetrate in this way into the continent. And they get further than they used to. That's the explanation for this, at least. That's what climate scientists tell me. Just wanted to show you how to use open data to track climate change.
14:02
But there's many, many, many more research necessary and possible. So please go ahead if you want. If there's any questions?
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