User’s data#

Forest cover change map#

You can use your own forest cover change map with the plugin. To do so, prepare a forest_src.tif raster file and copy it to the data_raw folder in your working directory. This files should have the following characteristics:

  • It must be a multiple band raster file with each band representing the forest cover at one date.

  • Bands must be ordered (first band for t, second band for t+1, etc.).

  • Raster should only have two values: 1 for forest pixels and 0 for non-forest pixels (the raster can thus be of type Byte).

  • No-data value is not important here. It can be set to 0 or 255 for example.

  • The raster file should be projected in the coordinate reference system of the project.

  • The raster must cover at least all the area of the jurisdiction.

Warning

It is much better if the raster is bigger than the jurisdiction (e.g. buffer of 10 km) to reduce edge effects when computing distances to forest edge for example.

While executing the Get variables step, this raster will be used as the forest data source and all the forest variables (forest cover change and distance to forest edge at the different dates) will be computed from this data.

You can create this multiple band raster using the QGIS tool Merge available in Raster > Miscellaneous.

Additional explicative variables#

Preparing the raster files#

To use different or additional explicatives variables for the statistical models, prepare the corresponding raster files in the data folder of the working directory. These additional raster files should have the following characteristics:

  • They should cover at least all the area of the jurisdiction.

  • They should be in the projection of the project.

  • Resolution should be as close as possible to the forest cover raster resolution.

If some of these variables are changing with time, then create several rasters for t1, t2, and t3.

Use these variables in the formula for the statistical models#

If raster variable.tif was added to the list of explicative variables, then add its name variable to the list of variables names for the FAR statistical models, see detail here.