 Hello, this is Hans van der Kraas, senior lecturer at IHC Delft Institute for Water Education. In this video, I'm going to show you two tools from the processing toolbox to analyze discrete raster data, such as land cover data. In this video, we'll use the Kareem land cover 2018 raster dataset at 100 meter resolution, which can be downloaded as a geotiff from the Copernicus website. You can download the layer by checking the box and clicking the download button. I've prepared a subcatchment layer, a polygon layer, and I'm going to add the downloaded geotiff with the Kareem land cover data to the map canvas. Let's move the polygons to the top and style the Kareem land cover data with the legend file that came with the download. Find it in the legend folder and it's a QML file that will map the values of the raster to the correct colors. We can make the boundaries a bit more visible by choosing black instead of red. These layers have a different projection. It is good practice for our analysis to have the layers in the same projection, so we are going to reproject the Kareem land cover data and clip it in the same time to a smaller area. I'm going to reproject it to the UTM projection that is used for the subcatchments and clip it to the map canvas. You can do that here in the export tool. Choose the map canvas and it's a 100 meter dataset, so I use a round number of 100 here for the spatial resolution. Then I click OK, it creates the new clipped layer and I can copy the style from the original one to our clipped layer and I can remove the big one which covers the whole of Europe and we see that this one is clipped to the extent that we need. Now I open the processing toolbox to start exploring these processing tools for raster. The first tool I'm going to explore is the raster layer unique values report. You can output different formats but here I'm going to choose a temporary layer to save a table that is derived from our land cover map. If I open the attribute table I find here the field value which is the pixel value so the class value count the number of pixels in that class and square meters the surface area of that class. This covers all the pixels in the whole raster layer. If we want to have information about counts per subcatchment we can use the Zonal Histogram tool. We choose the Corrine land cover map as an input and a vector layer with the zones the subcatchments and this produces a new layer and if I open the attribute table you can see that the features are the subcatchments and that the fields are the land cover classes and the values in the fields are the count of the number of pixels in each land cover class. We can use that for styling using diagrams. If you go to the layer properties there's a tab diagram and we can choose pie chart and we can choose the fields that make up the pie chart. But you need to match the colors with the legend colors that's a lot of work for this dataset you can't read a style file and I didn't find an easy way of doing this so in this case I'm just going to accept the colors and it creates a pie chart for each subcatchment which gives the land cover distribution per subcatchment.