# Tutorial Zonal Statistics and Area Computations

 Site: OpenCourseWare for GIS Course: QGIS Advanced Tutorials Book: Tutorial Zonal Statistics and Area Computations
 Printed by: Guest user Date: Tuesday, 21 May 2024, 11:15 AM

## 1. Introduction

For many studies it is important to know the statistics of a continuous raster for discrete units (in raster or vector format). For example, the average elevation per subcatchment, the maximum NDVI per land-cover class, etc. In this tutorial you'll explore several tools available in QGIS for calculating zonal statistics and compute surface areas.

## 2. Theory

Watch this video on the GIS Raster Data Model.

## 3. Data exploration

Let's first start QGIS.

1. Start QGIS Desktop with GRASS. We'll need GRASS in Chapter 6 of this tutorial.

2. In the main menu choose Project | Open From | GeoPackage...

3. In the Load project from GeoPackage dialogue use the  button to browse to the provided Data_Zonal_Statistics.gpkg file, choose the ZonalStatics project and click OK.

Now let's explore the data.

4. Click right on subcatchments and choose Properties.

5. Go to the Information tab and check the metadata.

• Does the data contain points, lines or polygons?
• Where is the data stored?
• What is the projection?
• What are the attributes?
6. Click Cancel to close the window.

7. Now click right on DEM and choose Properties.

8. Go to the Information tab and check the metadata.

• Is this a raster or vector?
• Where is the data stored?
• What is the projection?
• What is the minumum, maximum and average values?
• What is the cell size?
9. Now go to the Histogram tab.

• Describe the distribution of the elevation values.
10. Click to save the plot to a PNG file and check the result
11. Click Cancel to close the window.

12. Repeat this for the other layers.

After this quick exploration of the data we'll continue with styling and caculating zonal statistics in the next chapter.

## 4. Zonal statistics with a raster and a vector layer

Let's first look at how to use a polygon vector layer with zones to extract the statistics from a continuous raster layer.

### 4.1. Style a continuous raster

Make sure you see the project with layers subcatchment and DEM visible in the map canvas. In this chapter we'll only work with those layers.

Let's first style the Digital Elevation Model (DEM) so we can clearer see the value range.

The default legends produced by Desktop GIS software are often not intuitive, because the software does not know (1) what information is presented, and (2) the raster data type (e.g. Boolean, discrete or continuous data). To help interpret the results it is good practice to style your layers intuitively.

The DEM is a continuous raster. Continuous rasters represent gradients and can therefore contain real numbers (or floating point). Continuous rasters are styled in QGIS using ramps in the Singleband pseudocolor dialogue.

1. Select the DEM layer and click  to open the Layer Styling panel (or press F7).

2. In the Layer Styling panel choose Singleband pseudocolor from the drop-down list.

3. Right-click on the color ramp and choose Create New Color Ramp.

4. In the popup Color ramp type dialogue choose Catalog: cpt-city from the drop-down list and click OK.

The cpt-city catalogue has a lot of useful preset color ramps.

5. Choose Topography | Elevation. Note that cd-a and sd-a are also nice choices. Click Classify in the Layer Styling panel to apply the ramp.

This gives us more intuitive colors in the DEM where we can clearly distinguish higher and lower areas. Now we will further improve the visualisation.

6. Right-click on the DEM layer in the Layers panel and select Duplicate Layer.

This creates a copy of the DEM layer called DEM copy.

7. Uncheck the DEM layer, check DEM copy and rename the layer to hillshade.

8. In the Layer Styling panel, which should still be open, make sure that the hillshade layer is now selected. In the drop-down list change Singleband pseudocolor to Hillshade.

Which direction is the illumination coming from? Is this possible in reality?

Hillshade gives the best results with an artificial illumination in the northwest, which in reality can not exist in the Northern Hemisphere. If you move the dial in the Layer Styling panel to the southwest, you will see an inverted relief. Also note that there is a Resampling section. The default resampling method for both Zoomed in and Zoomed out is Nearest Neighbor. This method is fine for categorical data however, elevation is considered continuous data. You should therefore choose a Zoomed in resampling method of Bilinear and a Zoomed out resampling method of Cubic.

Next, we're going to blend the DEM with the hillshade layer.

9. Switch on the DEM layer by checking the box.

10. In the Layer Styling panel make sure the dem_fill layer is selected. In the Layer Rendering block of the panel, change the Blending mode to Multiply.

As you can see, blending gives a much nicer effect than transparency. With transparency the colours will fade. Now we can clearly see the elevation differences: the gradient from south to north and the valleys where we expect the streams.

### 4.2. Calculate zonal statistics

In this section we'll use a tool from the Processing Toolbox to calculate the zonal statics from the DEM using the subcatchment polygons. The Processing Toolbox in QGIS provides a lot of tools for processing GIS data. Besides QGIS tools, it also has tools from GDAL, GRASS, and SAGA that are very useful.

1. You can activate the Processing Toolbox by choosing Processing | Toolbox from the main menu.

Now the Processing Toolbox panel shows up.

2. In the Processing Toolbox click on Raster analysis | Zonal statistics (you can use the search box to search for it)

3. In the dialogue choose subcatchments as Input layer and DEM as Raster layer. We leave the Output column prefix as default.

4. Click on to choose the statistics that you want to calculate. Here choose Mean, St dev, Minimum and Maximum and click OK to return.

5. At the Zonal Statistics field use the dropdown menu to choose Save to GeoPackage. There browse to the GeoPackage of this project and indicate in the popup that you want to use subcatchment_elevation as the layer name.

6. Click Run.

You'll get the following error:

This often happens when the polygon layer is derived from raster data, resulting in geometry errors. We can easily fix this.

7. Click Close to close the dialogue.

8. In the Processing Toolbox look for the tool Vector geometry | Fix geometries

9. Save the Fixed geometries to the GeoPackage with the layer name subcatchments_fixed and click Run.

The subcatchments_fixed layer has now been added to the Layers panel.

10. Copy the style from subcatchments to subcatchments_fixed: click right on subcatchments and choose Styles | Copy Style | All Style Categories. Then click right on subcatchments_fixed and choose Styles | Paste Style | All Style Categories

11. Now remove the subcatchments layer from the Layers panel (click right on the name and choose Remove Layer.... Click OK in the popup to confirm. Note that the layer will not be deleted, but is only removed from the Layers panel. Make sure the subcatchments_fixed layer remains and is on the top of the list in the Layers panel:

12. Now repeat steps 2 - 6 with the subcatchments_fixed layer. Also save the output as subcatchments_elevations_fixed, because the other layer already exists.

You'll see the result added to the Layers panel.

13. Copy the style from subcatchments_fixed to subcatchments_elevations_fixed and hide the subcatchments_fixed layer by unchecking the box before the layer name.

Let's check the result in the attribute table.

14. Click right on subcatchments_elevations_fixed and choose Open Attribute Table.

• Which subcatchment has the highest average elevation?
• Which subcatchment has the lowest elevation?
• Which subcatchment has the highest variability in elevation?
In the next chapter we'll calculate zonal statistics using two raster layers.

## 5. Zonal statistics with two raster layers

In this chapter we're going to do the same as in the previous chapter. However, instead of using a polygon layer, we'll use a discrete raster layer with zones to derive the zonal statistics for the DEM.

### 5.1. Style a discrete raster layer

Instead of the subcatchments layer we'll now use the rasterzones layer, which is also provided in the GeoPackage.

The rasterzones layer is a discrete raster where cells have a unique value for the subcatchment they belong to.

1. Make the rasterzones layer visible (check the box) and move it to the top of the Layers panel. You can easily move a layer to the top: click right on the layer name and choose Move to Top.

Let's style the rasterzones layer to make more sense out of it. Because it is a discrete raster, we need to use the Paletted/Unique values renderer, which will give each integer cell value a colour.

2. Select the rasterzones layer in the Layers panel and click to open the Layer Styling panel.

3. Choose Paletted/Unique values as the renderer.

4. To assign random colours to the classes in the raster, click Classify.

Now we're ready to calculate the zonal statistics.

### 5.2. Raster layer zonal statistics

We're going to use another tool from the Processing Toolbox, called the Raster layer zonal statistics tool.

1. Go to the Processing Toolbox and look for Raster analysis | Raster layer zonal statistics.

2. In the Raster Layer Zonal Statistics dialogue choose DEM as Input layer from which we want to calculate the statistics. Choose rasterzones as the Zones layer with the classes that we want the statistics for. Under Advanced you can choose if you want to use the Input layer or the Zones layer as the Reference layer for the centroids of the pixels used for the sampling. In our case we can keep it on Input layer. Note that if either the Input layer or the Zones layer has NODATA pixels, these will not be included in the statistics calculations. Save the Statistics layer to the GeoPackage with the layer name raster_zone_output and click Run.

3. Click Close to close the dialogue.

The result is a non-spatial table that has been added to the Layers panel.

Let's check the contents.

4. Click right on raster_zone_ouput in the Layers panel and choose Open Attribute Table.

• Which statistics are reported by the tool?
• Are the values the same as with the Zonal statistics tool that we have used before?
In the next chapter we'll calculate the areas of the zones.

## 6. Area computations with discrete rasters

For discrete rasters we can calculate the areas using two different tools, which we'll use in the next sections:

• Raster layer unique values report tool
• Zonal histogram
We'll finalise the chapter by visualising the areas as pie charts on the map.

For this chapter we'll use the CORINE 2018 Land Cover raster layer of 100 m resolution that is provided in the GeoPackage for this tutorieal. The original data can be freely downloaded from the Copernicus website. The CORINE land cover map covers Europe. But similar data is provided for the rest of the world through Copernicus. You can find it here.

Note that we have subsetted and reprojected the land cover raster to the study area to match the other layers and to reduce the size. Also we have already applied the styling that was provided with the layer in a .qml file.

### 6.1. Raster layer unique values report

Let's first reorganise our layers so we can work on the ones that we need for this part of the tutorial.

1. Move the subcatchments_fixed layer to the top and move the corine2018_UTM_clipped layer to the second position from the top.

2. Change the line colour of the subcatchments_fixed layer to black to make it more visible. Hide the other layers below.

Your screen should now look like this.

Now we're ready for the area analysis.

3. In the Processing Toolbox choose Raster analysis | Raster layer unique values report.

4. In the dialogue choose corine2018_UTM_clipped as Input layer.

5. We can generate a report in HTML format and/or as a table. Let's try both options. Save the Unique values report to report.html and save the Unique values table to our GeoPackage with the layer name unique_values.

6. Click Run and Close the window after processing.

You can find the HTML results in the Results viewer panel:

7. You can click on the item or open the html file. Check the result.

8. Now open the unique_values table that was added to the Layers panel (click right and choose Open Attribute Table).

You can see that it gives for each class in the raster layer the count (nr. of pixels) and the area in m2.

In the next subsection we're going to calculate the are per subcatchment.

### 6.2. Zonal histogram

In the previous subsection we have derived the areas of all classes in the land cover map for the entire raster layer. However, we are more interested in the class area per subcatchment. We can calculate that with the Zonal histogram tool.

1. In the Processing Toolbox look for Raster analysis | Zonal histogram.

2. In the Zonal Histogram dialogue choose corine2018_UTM_clipped as Raster layer and subcatchments_fixed as Vector layer containing zones. Save the Output zones to the GeoPackage with the name zonal_histogram and click Run.

3. Click Close to close the dialogue.

4. In the Layers panel move the new zonal_histogram layer to the top and copy the style from the subcatchments_fixed layer to zonal_histogram.

5. Now open the attribute table of zonal_histogram and inspect the results.

You can see that the features are the four subcatchments and that in the HISTO_# fields we have the number of pixels in each class.

• How can we calculate the surface area in m2 of each class in each subcatchment?
In the next subsection we're going to visualise this result with pie charts.

### 6.3. Visualise with pie charts

A quick way to visualise the results of the previous subsection is to use diagrams.

1. In the Layers panel click right on the zonal_histogram layer and choose Properties...

2. In the Layer Properties window choose the Diagrams tab.

3. Choose Pie Chart from the drop down menu.

4. Select all HISTO fields and click to add them to Assigned attributes.

There you can change the colours and the text of the legend.

As you see we have a lot of classes. Here we are using CORINE Level 3 classes. In reality we might want to use the more aggregated Level 1 classes. In QGIS you can reclassify rasters with the Reclassify by table tool in the Processing Toolbox.

5. Click OK to see the result on the map

In the next chapter you'll learn another tool to calculate additional univariate statistics.

## 7. Calculate additional univariate statistics

Sometimes we're also interested in percentiles. We can calculate these easilty with the r.univar tool from GRASS which is available in the Processing Toolbox.

For this chapter we're interested in the statistics for the NDVI raster. NDVI is the Normalized Difference Vegetation Index, which is derived from satellite data to detect green vegetation cover. It has values from -1 to 1, where values larger than 0 represent vegetation cover.

NDVI in this tutorial was derived from a Sentinel 2 image of 7 May 2020 and has a spatial resolution of 10 m.

Let's first style the NDVI layer.

1. Move the NDVI_Sentinel_20200507T104558 layer to below the subcatchments_fixed layer and make sure it's visible.

2. Select the NDVI_Sentinel_20200507T104558 layer and click to open the Layer Styling panel.

• What raster data type is NDVI? What renderer should we use for styling?
3. Choose the Singleband Pseudocolor renderer and choose the purple to green (PRGn) color ramp. Click Classify to see the styled layer.

Now let's calculate the statistics of NDVI per subcatchment.

4. In the Processing Toolbox look for GRASS | Raster (r.*) | r.univar.

Note that if you can't use the GRASS tools, you need to start QGIS Desktop with GRASS as mentioned at the start of this tutorial. Only then you can use the GRASS tools in the Processing Toolbox.

5. In the r.univar dialogue make sure to set the following input:

• The NDVI_Sentinel_20200507T104558 layer should be selected as Name of raster map(s)
• Select rasterzones as Raster map used for zoning.  Note that you can only have a raster with zones. It will not work with polygones.
• For Percentile to calculate type 5,99. In this way the 5th and 99th percentile will be calculated. Percentiles need to be separated by comma.
• For field separator type comma. This will be the column separator that will be used in the output text file.
• Because we want to calculate the indicated percentiles we have to check Calculate extended statistics in the Advanced Parameters section.
• Write the Univariate results to a text file with the name univar_NDVI_subcatchments.txt.

6. Click Run. Click Close after processing.

You can open the text file as comma separated file in a spreadsheet programme. Here we'll add it to our QGIS project.

8. In the dialogue browse to the text file. Make sure that CSV (comma separated values) is chosen under File Format. And because our table is non-spatial we need to indicate that under Geometry Definition by selecting No geometry (attribute table only).

9. If the preview under Sample Data looks good, click Add to add the table to the Layers panel and click Close to close the dialogue.

10. Open the attribute table and interpret the results.

11. Repeat the procedure to get the NDVI statistics per land-cover class.

## 8. Conclusion

In this tutorial you have learned to:

• calculate zonal statistics from a continuous raster layer using areas defined by a polygon vector layer
• calculate zonal statistics from a continuous raster layer using zones in a discrete raster layer
• calculate the surface area of classes in a discrete raster
• calculate the surface area of classes in a discrete raster per area defined by a polygon vector layer
• calculate other univariate statistics from raster layers
Here are some videos with results of (similar) exercises with the tools used in this tutorial: