 Okay. Right. Let me explain what is the big challenge in terms of forestry. So, this country is basically going to be transformed profoundly in terms of woodland cover. I mean, it's going to be, if the plants go ahead, it will be a massive transformation of the land use in this country. The plants are very ambitious and there is a lot of, we need a lot of data to be sure that we are moving in the right direction. Not only to create an inventory data, a synoptic view of the situation of the country now, but also we should have the capability to project what the situation is likely to be in five, ten, twenty, hundred years time. And we need to monitor continuously. We need additional data to monitor whether we are departing from our expectations in terms of the use of models or we are going into the opposite direction or completely wrong. So, when you are trying to plant a lot of trees in an area or in this country, you need to start by knowing what you've got now. You need a good inventory. We have a national forest inventory. We have very good information about the public land, but very partial information about the private land. And we don't have information about trees outside woodlands, areas that are less than half a hectare. So, there's a lot of trees in there, there's a lot of variability and basically and sadly, it's mostly unmanaged woodlands. So, we need to know the location and its condition. So, forest health is one of the big issues in this country. I don't know why, but Britain seems to be like the meeting point of all the bugs that are occurring in the planet. One way or the other will end up in here. We need to do something about it. One of the things, one of the vital things we need to do is to early detect the spread of this disease, the onset of this disease. Because when we detect symptomatic trees, it's probably too late. Okay? And our containment measures are not going to be very effective. So, as I say, we need to use information to forecast the future and adapt our predictions with new data. Especially, you will see later on if I've got the time to do my presentations, all our models, all the models we've been developing in the forest for many years have for monoculture or even age plantations. Now, there's a big process of transformation of mixed woodlands as natural regeneration continues to cover forestry and those models don't work at all in those situations. As the proportion of the forest in these new contexts is increasing, we need to try to substitute modeling by direct observation and establish some trends in between observations as a temporary shortcut. Okay? So, the question is what kind of data we need, how frequently and how it's going to be processed? What is the level of expertise we have in the country? How rapidly we can produce this data and who is going to use that? So, we're talking about analysis-ready data. I prefer to talk about useful data. What is useful data? Well, data that is reliable, data that is understood and data that can be delivered relatively quickly and it's available. In terms of knowledge about remote sensing, products, cartography derived from spatial cartography derived from remote sensing. Well, you can imagine this is like a big train, okay, with many wagons. We have wagons with people that are no programming, they know remote sensing, they know images very well, they're working with this data and then you've got this level of knowledge is decreasing towards the very end and you have a lot of foresters in this country that don't know anything about remote sensing or know very little and they don't know what to do with this data and they don't know where to get it, okay? So, this is an important consideration. We've got different programs. One is to monitor forest health, one is forest inventory, different levels, tactical, operational, strategic. We're trying to develop a drone program. We are trying to develop our own capability to collect data at will over relatively small areas but they are much bigger than the traditional plots we're measuring the field, okay? I'm going to talk very briefly because of lack of time about what we're doing here with LiDAR. We have different levels of processing. This is done automatically and we are using the National LiDAR surveys in England and Wales. Scotland is data from archive because they don't have a LiDAR program yet. They say it's going to be soon but they don't have it yet. The quality of the data is one, two points per square meter. If we're talking about England, Wales is much better. It's between four to eight, nine, okay? This is a national program. That gives us the possibility to produce estimates almost at the standard level or at a super stand level. We can go below the stand boundaries and show the variability within a forest stand. Some of the data we are capturing in the forest districts now, they are 40 to 90 points per square meter. In areas like fertile forest, we got above 100 points per square meter. So you have the possibility to see absolutely everything at a high level of detail but it's a massive data set that you need to process. Okay? So talking about supercomputers, et cetera, et cetera, and well, automating processes. Right. Well, this is an example of the different cartographic products you can get to describe the terrain. How to describe the properties of the canopy? You see that is a, I mean, for example, you can compare to the right what would be in the circumparment database, a flat polygons associated to a database, spatial database, okay? One of the fields would be the yield class, the productivity of that area. If you look at the same polygons with the yield class generated by LiDAR, you see the spatial variability. And we all know that by visiting the field, there are some areas that are more productive within the same stand and other areas are less productive or have been affected by wind damage, by thinning, by whatever. Okay? So it's not the same. So how to integrate this complexity into something that is already quite simple in the circumparment database in a way that people can understand and take sensible decisions about it is challenging. Okay? It's challenging. Right. In terms of production forecast and how we can use LiDAR to estimate future productions, we know that we know the age, at least in the public land, we know the age and we know the species. So we can estimate from LiDAR a yield class model that we can use to project the characteristics of that crop in a few years' time. Okay? We can add some modifiers by detecting, for example, wind damage. This is what we have been doing with radar imagery. For the private sector, because we don't have any information about age, not even species, we need to classify species using optical data. And then we can use height directly derived from LiDAR as a way of estimating other variables of relevance, okay? Like a basal area or volume. When we have information, very detailed information, we can estimate individual trees. So we can delineate individual tree canopies and locate those canopies in X and Y and classify, well, do the volume estimate and so on and so forth with that. Okay? Also, we can use models to do projections. But what is important, and I would like to conclude with that, I'm not going to extend more, is the possibility that, I mean, using LiDAR is measuring the forest canopy from above. We don't know very much about the stems, okay? We don't know anything, or very little, or we've got a partial information about what is underneath. So by combining LiDAR capabilities, so the kind of point class you can generate from drones with a terrestrial scanner, you can create very accurate models in 3D of every tree in there, or most of the trees in there. It's not perfect yet, okay? So once you've got that, you can estimate all the products, like the stem profiles, which is related to timber quality. And that is a very piece of information, especially for the forest industry. But also, it gives us some feedback about the way we are managing our forest and the way we should be doing that, because we will have a lot of samples in our forest, okay? Just to conclude, well, the kind of what we're doing with LiDAR is to estimate the volume of the hedge rows, and then we can estimate the biomass from there. And finally, finally, this is the end, we are trying to develop our own capabilities to monitor LiDAR in our forest districts. So we are partnering with a company that's trying to fly beyond the visual line of sight, so that will give us the capability to cover large parts of a forest district, if not the whole entire forest district in one go, just by flying these drones at will, okay? That's it, okay? Thank you very much.