Welcome to the Hydrofiles, the HR Wallingford Podcast. I'm Sally Jackson, and today we're going to be discussing Earth observation and how it's revolutionising the work we do here HR Wallingford. We're also really lucky to be joined by Ray Fielding from the UK Space Agency, who's going to be sitting alongside Gina Tsarouchi, Darren Lumbroso and Quillon Harpham, my colleagues here HR Wallingford. I'll let them introduce themselves.
I'm Quillon Harpham, a technical director in the floods and water group at HR Wallingford. I come at this from an informatics angle. I've spent the last 25 years or so designing and implementing water sector information systems.
Darren?
I'm Darren Lumbroso, I'm a technical director in the floods and water group and I'm a civil engineer by background. And I work in areas of flood risk management, water resources management and disaster risk reduction.
Gina?
I'm Gina Tsarouchi. I'm a Principal Engineer at HR Wallingford. My background is also in civil engineering. But over the last 10 years, I've been working in water resources management and early warning systems and climate change adaptation projects around the world.
Ray?
I'm Ray Fielding, Head of the International Partnership Programme at the UK space agency.
So let's start with the basics. What exactly is Earth observation? Ray, could you give us a quick recap on what Earth observation is and how we might use it?
How I tend to explain this to people is it's using space to look down on the earth. So there's a lot of satellites and infrastructure in space with different parts of different technology on there. And they can give us a picture of the earth that you can't get in any other way. For instance, we can do things like wide area surveillance very, very easily. We can look at what's happening in terms of temperature. What's happening, of course, with optical - people are
very familiar with - but also radar technology. So that gives us a very, very powerful toolset and suite of options that do things from space, which we just can't do on earth and gives us information about the earth which we can't obtain in any other way.
Is it quite new technology or you've been doing this for a long time?
Well, technology itself has been around for a number of years now obviously started off by the need for surveillance in the Cold War. But that now is really expanded into the civilian market. And for least 30 years, various initiatives in Earth observation have been ongoing, led by national governments, but more
these days led by commercial operators. So it's very, very easy now to buy all sorts of data from optical data to radar data - and all sorts of data - which is provided by commercial operators on a sometimes hourly basis, if you can afford it, or by national governments and institutions for the world like greatergood
And HR Wallingford, we're using quite a lot of Earth observation now. So perhaps Darren, you could tell us the kinds of projects that we're using EO Earth observation data at the moment?
Yeah, so we've got a number of projects. So we've got one called DAMSAT, so that's using radar data and other forms of Earth observation data to monitor tailings dams, which hold mining waste, and water retaining dams. And how we use the remote sensing data there is we use the radar data to monitor if there's any abnormal defamations in the structures of the dam, so that might, or that tells an operator if there's a
higher probability of the dam failing. And we also use other remote sensing to see if there's been changes in vegetation downstream of the dam. And that helps operators and regulators see if there's potential pollution incidents from the dam. And why why this particular system is useful is that tailing dams are often in very remote areas. There's often a lot of them. Regulators in many countries, especially low and middle income
countries, are often underfunded. And it allows them that sort of eye in the sky to do remote monitoring of these structures over a very wide area. So that's one project. Another big project is the D-MOSS project, which is a dengue forecasting system project, which we've got operational in Vietnam, Sri Lanka, in Malaysia, and we're using remote sensing data there to basically form a historical series of environmental parameters that we're using to help us predict dengue fever. So that's just two
examples. So there are many others projects as well where we were using Earth observation data.
So both those programmes sound amazing and sound like they're gonna have a really big affect on people's lives on the ground. I should also add that the both D-MOSS and DAMSAT are funded by the Space Agency's IPP project, which is great. That's why it's great to have Ray here as well. Perhaps, you know, you could tell us about how these are being used on the ground, and that sort of difference they are making to people's everyday lives.
So I'll start talking a bit about D-MOSS first, which is the dengue fever forecasting system we have developed, currently been used in Vietnam, Malaysia and Sri Lanka. The objective of the D-MOSS project was to develop an operational system that can give advanced warning of likelihood for a dengue outbreak to happen. And that can be very useful for health officials who working dengue response, dengue control
management. Because if they know that, in the next month or three months, there is a probability of an outbreak happening in this province of Vietnam, for example, they can take early action to prevent that outbreak. And what that early action means is they could, for example, start campaigns to alert people about the outbreak that is happening, they can start spraying insecticides to eliminate mosquito breeding sites. They can advise people to
not store water locally. If the warning comes six months in advance, it can help them with budget planning processes, and they know where to divert their funds in the areas that are more likely to suffer an upcoming outbreak.
And does D-MOSS predict six months in advance?
D-MOSS predicts up to six months in advance. Yes.
And have we seen examples of where that's really made a difference.
It's early days yet, but we have seen examples in Vietnam. There, the users are already reporting that the system has helped them save resources. They have used it to divert funds in areas that are of high risk of an outbreak. So yes, we do hear a lot of positive stories about how the system is is already helping them manage thinking.
And how about for DAMSAT?
So DAMSAT is a system for monitoring tailings dams and tailings dams store the wastes from mining operations. And this waste often contains pretty nasty contaminants. And tailings dams don't have a very good track record in terms of failure. So there's been quite a lot of research that shows the probability in any one year of a tailings dam failure is 1%. So that
might not sound very high. But if you put yourself in the position of someone living downstream of one of these structures, and you think every year there's a 1% chance that your house is going to get engulfed by toxic sludge, and you are potentially going to get killed. Or even if you do survive, it's going to take away your livelihood, because the water that you rely on, and the land that you rely on is going to be contaminated. There's a really significant risk posed by these
structures. So having a system where you can be monitoring, have an eye in the sky, and monitoring them remote is really useful because in Peru, the environmental regulator, like many environmental regulators, not just in low income countries, but also in more wealthy nations, is generally under resourced. And they don't have the physical resources to go out to the sites or all the sites,
especially remote sites, to do inspections. So it's helping to reduce that probability of failure to something that's reasonable.
So we've been running a pilot project in Peru, which has now finished, haven't we? So could you tell us a little bit about that.
So in Peru DAMSAT is being used by the environmental regulator to help them assess whether the deformation of these tailings dams is abnormal, and if it is abnormal, then that gives them the opportunity to go to site to do more detailed inspections. Or the other thing that it can be used for is looking at vegetation and see if there's changes in the remotely monitored vegetation. And that can give an inference that
there may have been a pollution incident. So there might have been leaching from these tailings dams with nasty pollutants, which then affect drinking water supplies. So again, it's giving them that early warning that a dam may potentially be have a higher probability of failure, or if not failure, it's leaching nasty things into the environment.
And Ray it was an IPP project. So perhaps you can tell us a little bit about why IPP supported this project and why it helps you meet your objectives,
The International Partnership Programme was set up using official development assistance funding to help deliver the UK government's aid strategy. So what we're trying to do is look for projects, which provides direct economic or societal benefits to our partner countries. And you just said two projects, which provide exactly that. They provide massive societal benefits they provide, also the follow on economic benefits.
And it's not just far flung countries that actually the technology we're developing for those countries is actually being adopted in this country too. So it provides direct benefit to people in the UK. Perhaps Darren, you could explain
so dams that is being used by Bristol water to help monitor their water retaining dams. And I mean, we're also currently in discussions of looking with a EU partners research partners to see how some of the lessons from D-MOSS and whether D-MOSS can actually be implemented in certain European countries to assess the future risk of the prevalence of dengue and other mosquito borne diseases. So these projects do have applications in in Europe, not just in low and middle income countries.
I guess, Ray in your line of work that you're seeing lots of sectors benefiting from EO; perhaps you could just give us a few examples.
Yeah, the programme is not yet complete. And we've not yet finished all our assessments, we're starting to see some some examples really jumping out to us. For instance, we've got some fisherman safety projects off the coast of South Africa and Madagascar, fairly far
flung regions, Indonesia as well. Those technologies we we've deployed coupled with earth observation have actually save lives with I think, last count, there's over 60 fishermen, fish are people who would have drowned without the technology, which we've deployed in the surveillance systems, which says observation provides.
And when we use Earth observation data, what are the advantages over the conventional on the ground measurements that we've used in the past. Ray, maybe you could pick that
It gives you a capability, which we've never had before, which is wide area one up. surveillance and the ability to detect difference. So satellites, which passes typically in an orbit, low earth orbit, every 90 minutes, can easily look at the same region, time and time again, a very, very large region can be looked at at one time. And you can use the images over each other to detect what differences has happened between the
last time when you looked at that region. And that's very, very difficult to do and expensive to do with terrestrial measurements. And that ability to detect change gives us massive amounts of data. In this project that Darren's has been talking about, the tailings dams project, it allows us to detect minor differences in movements at the dams, which can inform prediction tools to which tells us potential failure of those dams.
And Quillion. You work with data every day. So what have you seen in the history of your work? How has it changed what you do?
My favourite thing about Earth observation data is that there seems to be more scope to get creative about interpreting what you're measuring. So with an in situ measuring device, say a thermometer, for example, you're measuring temperature, okay, so you might have mercury going up and down the tube, but you're measuring temperature, and it's very tied to temperature. With Earth observations, what you're measuring is electromagnetic radiation in different ways, coming back off the Earth, into
the sensor. And so it works a little bit like a human eye. So for example, with a human eye, what your eye is observing is what you're seeing around you every day. But it is your brain that interprets what's going on. And so for example, your eye might see a vehicle coming towards you. And it is your brain that interprets that it's a vehicle coming towards you. And you need to get out of the way. And in the same way, Earth
observations is like an eye on the earth. And what we need to do is apply the brain, which is interpreting all of this amazing data that's coming back in electromagnetic radiation. And we've only just begun to scratch the surface of what's possible with that.
Well, I was going to really just add what the Quillon and Ray said, in the strengths of remote sensing data, we're doing some work in Yemen for the Foreign and Commonwealth and Development office. And there's a real strength there because Yemen is a country where there's
been conflict for many years now. And it's a country where there's something like 24 million people living in poverty and their reliance on their water from groundwater sources and because of the conflict those resources that haven't been very well monitored. And it's not possible to get
in situ measurements because of the conflict. So the questions humanitarian agencies need to, or would like answered regarding groundwater resources and remote sensing data can help to answer some of those questions without having to go and make in situ measurements.
I'm really interested in this concept as well Quillon of this in the eye and the brain. Can you give us a kind of example of how that might work in practice, you know, with an underground measurement that you used to use an Earth observation that you now use?
Yeah, I think there's a little bit more scope to infer other things using Earth observation. So for example, you might be interested in urbanisation. And you want to know how fast cities are expanding and how, how urbanisation is increasing. With Earth observation, yeah. So what does that mean? What does urbanisation mean, and there's all sorts of definitions that you could apply to that. But what you can do with Earth observation is observe
lights at night. And lights at night tells you essentially where the urbanisation is, and, and we were mentioning the long term trends earlier. And so you have the long term trends of lights at night gives you a really good indication of where the urban areas are. Another example is permafrost. So I was talking to somebody about permafrost recently. And permafrost isn't really something that you can see. Most of it is below
the ground, sometimes quite a long way. But the the research question there is, can you see other things on the Earth's surface, which will tell you that there is permafrost? So how the vegetation changes, for example? So there's a huge amount of research questions we can ask ourselves in this as well.
And Ray, there must be some challenges of using Earth observation. It can't all be, you know, good news is there some there's some challenges that come along with this, this new technology.
There are. For instance, the weather, some of the projects that we do involve forestry monitoring, to maintain primal forest in the tropical belt, and also ensure that deforestation isn't happening. Trying to get in a cloud free earth observation image over some of the tropics regions can be extremely difficult.
But there are ways to address that. And so what was traditionally quite a big challenge, which made earth observation of use, but not some of the prime tools, it has now been addressed by taking
multiple data sources. So information from multiple satellites from multiple providers sometimes, which can through clever trickery, and don't ask me how it works, but enables you to create a mosaic of cloud free imagery by taking various images and compiling the parts which are cloud for you to give you an entire clearer picture.
Yes, to add to what Ray was just saying, I think if we look around the world today, the operational use of satellite based technologies and systems is quite limited. And there are a few reasons for that. It's partly because often the costs develop these new technologies can be expensive. It takes time to adapt and convince potential end users of the
benefit to using these new technologies. The fact that sometimes the Earth observation data that are available might not be relevant to the very local context. And there is a bit of a mismatch of what Earth observation data can provide and the need of the end users on the ground. I think maybe one of the key challenges of using Earth observation data is maybe a lack of understanding of what the new technologies are able to offer. And this comes
from two sides. So we have a lack of understanding among end users, government officials in terms of what environmental information can be provided and in what form and I think on the other hand, we have maybe a lack of understanding among the Earth observation scientific community of what kind of information might be useful for the different applications and the different sectors.
That's really interesting because both the D-MOSS project and the DAMSAT that project that we Darren explained to us, you know, to have the huge potential impacts in terms of saving lives and protecting people and how difficult Has it been to persuade people that using earth observation will have that effect for your end users?
It has been difficult at times. So for both projects, the end users were, they really bought into the idea of the technology very early on, because the problems we were trying to solve were very relevant for the local communities. There were a lot of challenges for D-MOSS, for example, which is the dengue fever forecasting system. We had challenges in explaining to the end users about the uncertainty of the forecasts. At the beginning, I remember the they were expecting
the forecasts to be 99% accurate all the time. And that would be the only way they would use a forecast. So we had to build a lot of capacity and understanding and explain to them that, you know, this type of technology can be used as an additional tool to the tools you already use. And there is an added benefit to you using this Earth observation technology, even if it's not 100% accurate all the time.
Yeah, that's a really good point, Gina. And I think that, from what I've seen, with the, the interest of the Partnership Programme, it's kind of twofold. First off is convincing the users normally through use cases, cost effective analysis and even output in business cases, to show that space isn't as expensive as what people think. Traditionally, space has been high cost activity. But now with more and more satellites up there, more and more service providers, the cost
is really, really coming down. And secondly, you mentioned about capacity building. One thing that we found across the programme, including the programmes that HR Wallingford are doing, is being able to enable the end users to interpret the data in a way that works for them. So it's almost taking the space elements out of the end product for them. For instance, when you want to go from A to B, you don't have to be an expert in global navigation, you just have to understand how to use
an app. And I think that's made a vast change in everybody's lives. Same with with weather forecast, you don't disbelieve all weather forecasts, just because it's not a 100% certain. You accept the probability of risks that it that may be wrong in there, because probability most of
time it's right. So putting data in a format, which the end users can easily understand without having to be either be experts in space, or who really understand the detail of the technology behind it, I think is is absolutely key.
It's really interesting, and Quillon I guess your job day to day is making that happen. So how do we take Earth observation data, and turn it into something that people can use?
Let's begin looking at the D-MOSS system, then. As we've been saying, D-MOSS is a dengue fever forecasting system. But you can't see Dengue fever from space, you can't see cases of Dengue fever from space. So D-MOSS is a perfect example of something which infer something else from what you can see. So what we do with D-MOSS is we have a wide variety of social and environmental parameters, such as temperature, wind, speed, soil moisture, urbanisation. And we
track those back using the history. And that's another advantage of Earth observation data: we have decades of history now. And so we can spot trends. So what we do is we plot the trends of dengue fever outbreaks, back to environmental and social parameters. And then if we have forecasts of those environmental and social parameters, we can use those forecasts to extrapolate forwards cases of Dengue fever and outbreaks of that.
Darren, as EO data becomes more usable. Where do you think the biggest opportunities lie in the sectors where HR Wallingford can make a difference?
I think there's I think, as Quillon said, there's there's inferred data and there's data you can use directly. So what I mean by that, so for example, take a continent like Africa. So 53 countries, massive area, but very few African governments know where their buildings are, and the building footprints of every building. So what's happening? Currently, there's companies are using remote sensing data to provide databases of a footprint of every building in
every African country. So that's down to Mud built buildings that might only occupy maybe two or three square metres. And the reason why that information is very important, it's very important for governments and organisations like ours to do risk assessments so you can know what the real flood risk is. You can know where the the critical assets are, how they're going to be affected in
disasters. So that's just one example where it's, it's very powerful and getting that really accurate information of where assets are, and where governments and other key stakeholders actually don't know currently where all those assets are. That that's I think, is a is an example of where you can get directly inferred data from remote sensing, that's, that's really useful for a number of sectors and a number of different users.
So we have a project in India, called Siva, where we're looking at using remote sensing data in real time, in near real time. And that to allow to do something called impact based forecasting of floods. So traditionally, what's happened with floods is that governments and environmental agencies predict water levels and/or flows. And to the person in the street, that doesn't really
necessarily mean that much. What's of use is if you say, well, in three hours time, there's going to be 10, or 100 buildings flooded in this particular area. And so that's an example where we're using remote sensing data to help flood forecasters say what the impact of flooding is, rather than just say, the water level is going to be 10.1 metres, which is pretty esoteric for most users, even even people with a technical training
if they don't know what 10.1 metres means. Whereas saying, as I said, a number of buildings or these roads are going to be flooded in this particular area is much more helpful to people.
So if you're listening, and you think, oh, gosh, I've got something that perhaps EO would be useful for? How can you decide whether EO might be useful in your project?
Yes, I mean, EO like any other resource has its advantages and disadvantages. And I think that there's a big threshold between what you can get for free, and what you have to pay for. So, for example, if your project benefits from wide coverage, then it's a perfect example to use for Earth observation data. You get global
coverage for free, it's not a problem. If you're studying long histories and long trends, particularly if that's combined with wide coverage, then that's a good example of a project where Earth observation data may be able to help. And if you're getting creative, and combining factors together to try and infer other factors, then that's a good example of an Earth observation
data project. And for the free data, if you don't mind 10 metre spatial resolution, if you don't need anything finer than that, if you don't need the data immediately, like you know, in real time or near real time, if you're if you don't mind, the data being a little bit patchy. So we talked about cloud cover is a big problem with optical data. If you don't mind it being a little bit
patchy. If you're happy with one result coming in every few days or every 10 days, depending on where you are in the world, then that's a good Earth observation data project. If you're prepared to pay money for it, then you can solve a lot of those problems, particularly you get more spatial precision in the data.
Yeah, I'd say those are points which do pose challenges for today's Earth observation projects. But one thing that I think it's coming to light in terms of future trend is the amount of data much higher spatial resolution especially from commercial providers, which is now being provided or discussions are ongoing, to be
attained for free. An example of that is the deal the Norwegian government have done with a satellite consortium which provide now free data on a daily basis of all tropics zones, and or forestry zones, reasonably good spatial resolution about three metres. Radar data is also provided. And that's done entirely for free for for the greater good. And as more and more platforms are being launched by more and more competitors, that is going to drive the market price down.
Especially with the new launch systems which are coming on stream now where space launches always were traditionally fairly expensive, and only a few people did them. Now in some cases, you can get your launch for nothing, especially if you're launching a very smaller university type project. So the launch costs are really coming down and
that's only going to go down further. Secondly is the UK space agency and other governments around the world are looking to see how we can stimulate the market further by talking not only to consortia, which to provide systems like Copernicus, and future Copernicus, but also third party data providers, people like planet people like ISI and others, to see what can be done to provide data for free to either stimulate market growth or provide information which can be used
for for the greater world good.
So it seems the possibilities are endless and nd Earth observation is going to become more and more prevalent in the way we use data and how we solve problems on the ground. Does anyone have any, the most exciting thing that's happened in Earth observation, then what they think will will be in the future?
Well, there's a lot of new innovations coming on stream all the time. But still, what excites me most is to explore what we've already got. We have huge libraries of images of the Earth going back decades, and optical images and radar images that you can infer all sorts of things from. Who would have thought that we would be able to talk about Dengue fever in the same sentence as Earth observations, there's so much on offer. And I think we simply just need to dive into what we've already got.
Guessing in the past, maybe up until very recently, the Earth observation field was dominated by big government agencies. But in recent years, I think the game changers are the private companies, which as Ray mentioned earlier, the the they have a lot of small satellite constellations, which offer now daily revisits. And this can potentially, I think, be a revolution in the application of Earth observation data, having access to daily data at medium or high resolution for new applications.
Yeah, I agree with that point. With the prevalence of satellite constellations, plans, and for the future, there's going to be a huge amount of data, an ever decreasing cost going forward. That with new and innovative ways of using that data, I think, combined with the fact that everyone's carrying a very, very powerful portable computer, in the name of their smartphone around with them all times. People will be using Earth observation data in the future and not even
realising it. They'll just be downloading what relevant app is useful for them, whatever their current situation or problem is, Earth observation will be in everyday life. And to me, that's, that's really exciting.
I think that draws us quite nicely to a close. So thanks all of you for a really insightful discussion, and particularly to Ray for coming to join us here today. This has been the Hydrofile. If you're interested in anything we've said today on Earth observation, there's plenty more information on the HR Wallingford website, HR wallingford.com, or on our social channels For Twitter it's at HR Wallingford. Thanks so much for listening.