GIS Workflows typically involve many steps - with each step generating
intermediate output that is used by the next step. If you change the input data
or want to tweak a parameter, you will need to run through the entire process
again manually. Fortunately, QGIS has a graphical modeler built-in that can
help you define your workflow and run it with a single invocation. You can also
run these workflows as a batch over a large number of inputs.
Our workflow for this exercise will have the following steps.
Apply a MajorityFilter algorithm to the input landcover raster. This
will reduce noise in our output by eliminating isolated pixels.
Convert the resulting raster to a polygon layer.
Query for a class value from the attribute table of the polygon layer and
create a vector layer for that class.
The following steps outline the process to code the above process into a model
and run it on the downloaded datasets.
Launch QGIS and go to Processing ‣ Graphical Modeler….
The Processing modeler dialog contains a left-hand panel and a
main canvas. Select the Inputs tab in the left-hand panel and
drag the + Raster layer to the canvas.
A Parameter definition dialog will pop-up. Enter Input as
the Parameter name and mark Yes to Required.
Click OK.
You will see a box with the name Input appear in the canvas.
This represents the landcover raster that we will use as input. Next step
is to apply a Majorityfilter algorithm. Switch to the
Algorithm tab from the bottom-left corner. Search for the
algorithm and you will find it listed under SAGA provider. Drag
it to the canvas.
Note
If you do not see this algorithm or any of the subsequent algorithms
mentioned in thi tutorial, you may be using the Simplified
Interface of the Processing Toolbox. Switch to the Advanced
Interface by using the dropdown at the bottom of the Processing
Toolbox in the main QGIS window.
A configuration dialog for Majority Filter will be presented.
Leave the values to their default and click OK.
You will note that there is now a new box named Majority Filter
in the canvas and it is connected to the Input box. This is
because the Majority Filter algorithm uses the Input
raster as its input. The next step in our workflow is to convert the output
of majority filter to vector. Find the Polygonize(rastertovector)
algorithm and drag it to the canvas.
Note
The boxes can be moved and arranged by clicking on it and dragging it while
holding the left mouse button. You can also use the scroll-wheel to zoom in
and out in the model canvas.
Select ‘Filtered Grid’ from algorithm ‘Majority Filter’ as the value for
Input layer. Click OK.
The final step in the workflow is to query for a class value and create a
new layer from the matching features. Search for the Extractbyattribute algorithm and drag it the canvas.
Select ‘Vectorized’ from algorithm ‘Polygonize (raster to vector) as the
Input Layer. We want to extract the pixels that represent
Croplands. The corresponding pixel value for this class will be 12. (see
Code Values). Enter DN as the
Selection attribute and 12 as the value. As the
output of this operation will be the final result, we need to name the
output. Enter vectorizedclass as the Output.
Enter the Model name as vectorize and Group
name as raster. Click the Save button.
Name the model vectorize and click Save.
Now it is time to test our model. Close the modeler and switch to the main
QGIS window. Go to Layer ‣ Add Layer ‣ Add Raster
Layer….
Browse to the downloaded LC_hd_global_2001.tif.gz file and click
Open. Once the raster is loaded, go to
Processing ‣ Toolbox.
Find the newly created model under Models ‣ raster ‣
vectorize. Double-click to launch the model.
Select LC_hd_global_2001 as the Input and click
Run.
You will see all the steps being executed without any user input. Once the
processing finishes, a new layer vectorized_class will be added to
QGIS. Let’s improve the model a little bit. Right-click on the
vectorize model and select Edit model.
In Step 12, we hard-coded the value 12 as the class value. Instead, we
can specify it as a input parameter which the user can change. To add this,
switch to the Inputs tab and drag the + String to
the model.
Enter the Parameter Name as Class. Enter 12 as the
Default value.
We will now change the Extractbyattribute algorithm to use this input
instead of the hard-coded value. Click the Edit button next to
the Extract by attribute box.
Click the dropdown arrow for Value and select Class. Click
OK.
You will see from the model diagram that the Extract by
attribute algorithm now uses 2 inputs. The modeler has a shortcut to
launch the model and test it. Click the Run button from the
toolbar.
Notice that the model dialog has a new editable field called
Class. Enter 16 as the Class value and click
Run.
Once the processing finishes, you will see that with just a click of a
button we were able to run a complex workflow and extract the area for
class 16.
Now that our model is ready, we can run it just as easily on a new raster
layer. Load the LC_hd_global_2012.tif.gz file by going to
Layer ‣ Add Layer ‣ Add Raster Layer…. Click the
vectorize` model from the Processing Toolbox panel.
Pick the LC_hd_global_2012 layer as the Input and click
Run.
Once the new output is loaded, you can compare the changes in the Croplands
from 2001 to 2012.
It is always a good idea to add documentation to your model. The modeler
has a built-in Help editor that allows you to embed help
directly in the model. Right-click the vectorize model and select
Edit model.
Click the Edit model help button from the toolbar.
In the Help editor dialog, select any item from the
Select element to edit panel and enter the help text in
Element description. Click OK. This help will be
available in the Help tab when you launch the model to run.
Models can be a great timesaver and allow you to write your workflow once and
run it multiple times. You can even share your model with other users. The
model files are saved in the .qgis2 directory. You can send the .model
file to another user who can copy it to the appropriate directory on their
computer and it will appear in the Processing toolbox. The models
directory location will depend on the platform as follows: (Replace
username with your login name)
