++ Currently, the examples are being reworked after the latest update because GBIF behaves differently now. Find out more. ++
Welcome to the Dry Land Use Case.
In this example, the GBIF occurrence data of Calopteryx splendens are clipped to the extent of Germany and merged with the land use classification from the Oekosystematlas as well as a time of average temperature provided by the WorldClim dataset.
To begin, we select the Data Catalogue in the top right-hand corner. Here we have several data catalogues to choose from.
In our case we start by searching for Calopteryx splendens in the GBIF data provider. The search function makes it easy to find the species, so we can search for Calopteryx splendens and load the dataset by selecting it.
For the spatial selection we also need the German border, which we found by searching for Germany in the data catalogue.
Next, for the link between the occurrence data and the average temperature, we search for the Average Temperature dataset in the data catalogue.
Caution: The Average Temperature is a spatio-temporal dataset. Always check the spatial and temporal extent in the metadata.
The Average Temperature dataset covers the whole Earth and a time range from 1970/01/01 to 2000/12/31. To do this we need to change the time in the time menu at the top right.
As the dataset does not look very attractive, we will change the colour palette of the raster data. This can be done by right-clicking on the layer and selecting Edit Symbology.
In the symbology menu, scroll down to Create colour table, select a colour map such as VIRIDIS or MAGMA, click the Create colour table button and confirm with the Apply button at the bottom of the symbology menu.
In order to link the occurrence data with the land use classification, it is also necessary to load the Oekosystematlas by searching for it in the personal data catalogue. The personal data catalogue contains all datasets uploaded by the user as well as a section with all datasets, which also contains datasets not listed.
The next step takes place in the Operators section, located in the top right-hand corner.
First we use a Point in Polygon Filter to restrict our occurrence data to Germany. For better readability it is recommended to name the datasets.
Next, we join the raster data to the vector data using the Raster Vector Join Operator, which takes the occurrence data as a vector and the Ecosystem Atlas and Mean Temperature as raster data.
The result is that the vector data is spatially linked to the raster data by position. Therefore, new columns are added to the vector data table containing the information.
The Histogram operator can be used to visualise the distribution of occurrence data as a function of average temperature.
To visualise the classified data, it is recommended to use the Class Histogram operator, which translates the Ecosystem Atlas numbers into class names using the metadata.
The plots then show the distribution of occurrences of Calopteryx splendens as a function, firstly, of the average temperature on 1 January 2000 and, secondly, of the land-use classification of the Ecosystematlas.
Warning: The VAT system is designed primarily for data exploration. Changing the extent of the visual map will recalculate the workflow and could change the results! This must be taken into account when working scientifically with the VAT system. There is also a new window in the bottom left corner. This window must be present when working scientifically with the VAT system, as it allows reproducibility!
Tip: The layers have several options. They can be downloaded to work with the data in other systems. The layers also always have a workflow tree and the workflow_id can be copied to import the workflow directly into Python.